An Environmental Scan of Built Environment Data Related to ...
An Environmental Scan of Built Environment Data Related to ...
An Environmental Scan of Built Environment Data Related to ...
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<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
ACKNOWLEDGEMENTS<br />
We are sincerely grateful and recognize the hard work and contributions <strong>of</strong> the project collabora<strong>to</strong>rs<br />
over the past year: the <strong>Built</strong> <strong>Environment</strong> Locally Driven Collaborative Project (LDCP) team and project<br />
consultants who helped make this project a reality.<br />
The <strong>Built</strong> <strong>Environment</strong> LDCP project team would like <strong>to</strong> thank Public Health Ontario (PHO) for its support<br />
<strong>of</strong> this project. The team gratefully acknowledges funding received from PHO through the Locally Driven<br />
Collaborative Projects program.<br />
We acknowledge and thank Sarah Maaten for her contributions <strong>to</strong> the project proposal; as well as<br />
students Caleb Stenzl and Ashley Czerkas for their work on the metadata analysis.<br />
The project team would also like <strong>to</strong> thank the study participants for their important contributions.<br />
This report was prepared through a collaborative effort by the following public health organizations<br />
and consultants:<br />
PUBLIC HEALTH UNITS/AGENCIES<br />
KINGSTON, FRONTENAC AND LENNOX & ADDINGTON PUBLIC HEALTH (PROJECT LEAD)<br />
NIAGARA REGION PUBLIC HEALTH<br />
PUBLIC HEALTH AGENCY OF CANADA<br />
SUDBURY & DISTRICT HEALTH UNIT<br />
YORK REGION PUBLIC HEALTH<br />
CONSULTANTS<br />
Popy Dimoulas-Graham Epidemiologist Consultant;<br />
Project Coordina<strong>to</strong>r (Primary Edi<strong>to</strong>r)<br />
Kim Perrotta <strong><strong>Environment</strong>al</strong> Health Consultant;<br />
Creating Healthy and Sustainable <strong>Environment</strong>s (CHASE)<br />
Kevin Behan <strong><strong>Environment</strong>al</strong> Health Consultant;<br />
Creating Healthy and Sustainable <strong>Environment</strong>s (CHASE)<br />
Amanda Northcott Qualitative <strong>Data</strong> Consultant<br />
© Kings<strong>to</strong>n, Frontenac and Lennox & Adding<strong>to</strong>n (KFL&A) Public Health, 2012
BUILT ENVIRONMENT<br />
LOCALLY DRIVEN COLLABORATIVE<br />
PROJECT TEAM MEMBERS<br />
KINGSTON, FRONTENAC AND LENNOX & ADDINGTON PUBLIC HEALTH (KFL&A)<br />
Paul Belanger (co-principal investiga<strong>to</strong>r), GIS Services Manager<br />
Daphne Mayer (co-principal investiga<strong>to</strong>r), Research Associate<br />
Novella Martinello, Foundational Standard Specialist<br />
NIAGARA REGION PUBLIC HEALTH<br />
Bill Hunter, Manager, <strong><strong>Environment</strong>al</strong> Health<br />
Deborah Moore, Senior Epidemiologist<br />
Ryan Waterhouse, GIS <strong>An</strong>alyst<br />
PUBLIC HEALTH AGENCY OF CANADA<br />
Ahalya Mahendra, Epidemiologist<br />
SUDBURY & DISTRICT HEALTH UNIT<br />
Marc Lefebvre, Manager Resources, Research, Evaluation and Development Division<br />
YORK REGION PUBLIC HEALTH<br />
Asim Qasim, <strong><strong>Environment</strong>al</strong> Research and Policy <strong>An</strong>alyst<br />
Caitlyn Paget, Epidemiologist<br />
Helen Doyle, Manager, Health Protection Division<br />
Jaime Chow, <strong><strong>Environment</strong>al</strong> Epidemiologist<br />
Mira Shnabel, <strong><strong>Environment</strong>al</strong> Health Program Coordina<strong>to</strong>r<br />
Disclaimer<br />
The views expressed in this publication are the views <strong>of</strong> the project team and do not necessarily reflect those <strong>of</strong> Public Health Ontario.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXECUTIVE<br />
SUMMARY<br />
The Ontario Public Health Standards mandate local Public Health<br />
Units <strong>to</strong> address the built environment by increasing public awareness,<br />
supporting healthy public policy, and creating supportive environments.<br />
In order <strong>to</strong> fully understand the relationship between<br />
the built environment and health, public health practitioners and researchers<br />
alike need metrics that are current, reliable, and geographically<br />
commensurate. Objective built environment measures that reflect<br />
the scale <strong>of</strong> walkability and environmental exposures (e.g. air<br />
pollutants and extreme heat) in a community could help public health<br />
practitioners moni<strong>to</strong>r health outcomes, facilitate research, and guide<br />
the development <strong>of</strong> evidenced-informed interventions, programs and<br />
policies. Yet assessments <strong>of</strong> the built environment are hampered by<br />
several inconsistencies, including variations in terminology, computational<br />
methods and data sources.<br />
This report presents the results from an environmental scan used<br />
<strong>to</strong> support the identification <strong>of</strong> walkability and select environmental<br />
exposure (air quality and extreme heat exposure) data for use in the<br />
assessment <strong>of</strong> the urban built environment in Ontario. The environmental<br />
scan consisted <strong>of</strong> a literature review, key informant interviews,<br />
and a survey <strong>of</strong> Ontario Public Health Units (PHUs). The findings were<br />
synthesized through a gap analysis <strong>of</strong> measurement approaches<br />
and data availability, and subsequently applied <strong>to</strong> the development<br />
<strong>of</strong> guiding principles and recommendations <strong>to</strong> influence future directions<br />
for research and policy development in Ontario.<br />
This Locally Driven Collaborative Project (LDCP) was supported by<br />
Public Health Ontario (PHO). Project co-applicants included representation<br />
from Kings<strong>to</strong>n, Frontenac and Lennox & Adding<strong>to</strong>n<br />
(KFL&A) Public Health, York Region Public Health, Niagara Region<br />
Public Health, and the Public Health Agency <strong>of</strong> Canada (PHAC).<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY<br />
The prevalence <strong>of</strong> obesity has more than doubled in Canada over the last twenty years and has become<br />
a leading public health concern. Thus, creating more walkable and less au<strong>to</strong>mobile dependent communities<br />
can contribute <strong>to</strong> decreasing the incidence <strong>of</strong> obese and overweight Canadians and should be<br />
considered as part <strong>of</strong> a comprehensive strategy <strong>to</strong> improve public health. 1<br />
When assessing the degree <strong>of</strong> urban walkability, measurements <strong>of</strong> density, diversity, street connectivity,<br />
and pedestrian-oriented design are commonly used. These types <strong>of</strong> built environment measures are<br />
strongly and consistently associated with walking and health outcomes in the literature. They are <strong>of</strong>ten<br />
correlated with one another, thus making composite indices a compelling measurement approach in<br />
capturing multiple aspects <strong>of</strong> the built environment at once. 2;3<br />
Our findings identified that the most common methods used by Ontario PHUs in assessing urban walkability<br />
included self-administered surveys and systematic observations; a smaller number <strong>of</strong> PHUs assessed<br />
urban walkability using Geographic Information System (GIS) measurements and techniques. GIS<br />
is required <strong>to</strong> operationalize several walkability measures, yet many <strong>of</strong> Ontario’s PHUs identified a lack <strong>of</strong><br />
GIS technical resources as a significant barrier <strong>to</strong> assessing urban walkability. Overall, human resource<br />
capacity, measurement variability between municipalities, and data availability were key challenges identified<br />
by Ontario’s PHUs in the assessment <strong>of</strong> urban walkability.<br />
Organizations with the most established walkability assessment programs in the province were using a<br />
composite index. A walkability index shows the most promise in the application <strong>of</strong> a standardized assessment<br />
<strong>of</strong> urban walkability by PHUs in Ontario.<br />
AIR QUALITY<br />
Several studies have shown that air pollution is associated with a broad range <strong>of</strong> acute and chronic health<br />
effects. With increasing population sizes, industrial emissions, and demand for vehicle transportation,<br />
there is potential for increased concentrations <strong>of</strong> air pollutants as well as a greater risk <strong>of</strong> exposure for<br />
the public.<br />
The environmental scan showed that most Ontario PHUs use indices <strong>to</strong> assess air quality, primarily the Air<br />
Quality Index and the Air Quality Health Index. Individual air pollutant data for these indices are collected<br />
through a network <strong>of</strong> air moni<strong>to</strong>ring stations in Ontario. However, a number <strong>of</strong> stations within this network<br />
do not have adequate spatial resolution <strong>to</strong> evaluate air quality at the neighborhood level. In addition <strong>to</strong><br />
the air moni<strong>to</strong>ring stations, PHUs use emissions estimates data such as the National Pollutant Release<br />
Inven<strong>to</strong>ry, and other built environment data such as major roadways and traffic volumes. While useful<br />
approaches, they remain limited in their ability <strong>to</strong> assess pollutants within a community. To address this<br />
gap, other approaches have been developed <strong>to</strong> predict pollutant levels at a high spatial resolution (e.g.<br />
portable sensors, satellite data, modelling); however, they are not commonly used by Ontario PHUs.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
The relationship between the built environment and air quality is complex and further research is required.<br />
Overall, human resources, financial capacity, and data availability were key challenges identified by PHUs<br />
in the assessment <strong>of</strong> air quality in urban Ontario.<br />
EXTREME HEAT<br />
The built environment plays a significant role in exposure and vulnerability <strong>of</strong> a population <strong>to</strong> extreme heat.<br />
A growing population, increasing urbanization and climate change also impact the risk <strong>of</strong> heat-related<br />
morbidity and mortality.<br />
The environmental scan identified a number <strong>of</strong> measurement approaches and data sources for assessing<br />
extreme heat. This includes composite indices and models that have been developed <strong>to</strong> assess extreme<br />
heat. However, these and other measurement approaches present challenges for operationalization due<br />
<strong>to</strong> data and resource limitations in Ontario. For instance, freely available province-wide meteorological<br />
data do not meet the data requirements for certain indices, nor do these data provide adequate spatial<br />
resolution <strong>to</strong> assess extreme heat within communities.<br />
Alternative measurement approaches using built environment variables have been identified in the literature.<br />
Some Ontario PHUs have access <strong>to</strong> built environment data, but most do not use it in the assessment<br />
<strong>of</strong> extreme heat. A promising measurement approach is land surface temperature from satellite<br />
imagery because <strong>of</strong> its availability and complete coverage across the province. Land surface temperature<br />
has been used by PHUs in composite measures for the assessment <strong>of</strong> community heat vulnerability,<br />
incorporating both individual and community fac<strong>to</strong>rs.<br />
The <strong>to</strong>p challenges identified by Ontario PHUs in the assessment <strong>of</strong> extreme heat included human resources,<br />
financial capacity, and data availability.<br />
COMMON THEMES<br />
Although the <strong>to</strong>pic areas examined in the environmental scan are unique in some respects, many <strong>of</strong> the<br />
challenges related <strong>to</strong> the measures and data were similar. For instance, several organizations were using<br />
similar types <strong>of</strong> measures, but they were using different terminology, computational methods and a variety<br />
<strong>of</strong> data sources. <strong>Data</strong> availability, human resource capacity, financial capacity and variations between<br />
municipalities were reported as prominent challenges in the assessment <strong>of</strong> the urban built environment.<br />
The built environment and health are closely tied <strong>to</strong> equity. Thus, for all <strong>to</strong>pic areas, sociodemographic<br />
characteristics were identified as important fac<strong>to</strong>rs <strong>to</strong> account for in the assessment <strong>of</strong> the urban built<br />
environment.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
RECOMMENDATIONS<br />
The guiding principles and recommendations are closely tied <strong>to</strong> the results <strong>of</strong> the literature review, key<br />
informant interview and survey results, and are applicable <strong>to</strong> all <strong>to</strong>pic areas. Given the cross-disciplinary<br />
nature <strong>of</strong> built environment assessments, the potential audience for the recommendations is broad. The<br />
overarching guiding principles and related recommendations are presented below.<br />
1. Strengthen Multidisciplinary Cooperation<br />
1.1 Engage in multidisciplinary collaboration across all sec<strong>to</strong>rs, including government,<br />
academia and private sec<strong>to</strong>rs<br />
2. Provide Methodological Guidance<br />
2.1 Standardize built environment measures using a multidisciplinary approach across all sec<strong>to</strong>rs<br />
2.2 Put built environment research in<strong>to</strong> practice<br />
3. Improve <strong>Data</strong> Availability and Accessibility<br />
3.1 Increase access <strong>to</strong> high quality data across the Province<br />
3.2 Empower local agencies <strong>to</strong> engage in built environment data initiatives<br />
3.3 Identify and evaluate the use <strong>of</strong> current data sources and sets<br />
3.4 Explore the creation <strong>of</strong> new data sets<br />
3.5 Address the need for data auditing and validation<br />
4. Engage in Systematic Knowledge Transfer and Exchange (KTE)<br />
4.1 Facilitate engagement <strong>of</strong> expertise outside <strong>of</strong> the public health sec<strong>to</strong>r<br />
5. Strengthen Capacity<br />
5.1 Support Public Health Units in a technical capacity <strong>to</strong> assess the built environment<br />
5.2 Enhance education and training for public health pr<strong>of</strong>essionals<br />
6. Strengthen <strong>Built</strong> <strong>Environment</strong> and Health Research<br />
6.1 Increase research funding opportunities for exploring the relationship between the built<br />
environment and health<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
TABLE OF CONTENTS<br />
ACKNOWLEDGEMENTS<br />
EXECUTIVE SUMMARY<br />
LIST OF TABLES<br />
LIST OF FIGURES<br />
ACRONYMS<br />
INTRODUCTION<br />
CHAPTER 1: METHODOLOGY<br />
CHAPTER 2: WALKABILITY<br />
BACKGROUND<br />
LITERATURE REVIEW<br />
KEY INFORMANT INTERVIEWS<br />
SUMMARY OF SURVEY RESULTS<br />
GAP ANALYSIS<br />
CHAPTER 3: AIR QUALITY<br />
BACKGROUND<br />
LITERATURE REVIEW<br />
KEY INFORMANT INTERVIEWS<br />
SUMMARY OF SURVEY<br />
GAP ANALYSIS<br />
CHAPTER 4: EXTREME HEAT<br />
BACKGROUND<br />
LITERATURE REVIEW<br />
KEY INFORMANT INTERVIEWS<br />
SUMMARY OF SURVEY RESULTS<br />
GAP ANALYSIS<br />
CHAPTER 5: DISCUSSION<br />
GUIDING PRINCIPLES & RECOMMENDATIONS<br />
REFERENCES<br />
3<br />
5<br />
12<br />
13<br />
14<br />
17<br />
23<br />
33<br />
35<br />
41<br />
53<br />
64<br />
75<br />
85<br />
87<br />
98<br />
118<br />
123<br />
133<br />
137<br />
139<br />
144<br />
152<br />
159<br />
168<br />
173<br />
185<br />
193<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDICES<br />
APPENDIX A: Walkability and environmental exposures<br />
(air quality and extreme heat) literature review summary tables...................................213<br />
APPENDIX B: Key informant interview letter <strong>of</strong> invitation (LOI)...........................................................214<br />
APPENDIX C: Key informant interview guide – walkability.................................................................216<br />
APPENDIX D: Key informant interview guide – environmental exposures<br />
(air quality and extreme heat).....................................................................................218<br />
APPENDIX E: <strong>Built</strong> environment measures and data sources survey letter <strong>of</strong> invitation (LOI).............226<br />
APPENDIX F: <strong>Built</strong> enviornment measures and data sources survey................................................228<br />
APPENDIX G: GIS meta data – walkability........................................................................................264<br />
APPENDIX H: GIS meta data – air quality.........................................................................................283<br />
APPENDIX I:<br />
GIS meta data – extreme heat...................................................................................285<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
LIST OF TABLES<br />
Table 1: Sampling strategy for identifying key informants...................................................................................... 28<br />
Table 2: <strong>Built</strong> environment measures that comprise Walkability indices in Ontario, 2012....................................... 55<br />
Table 3: Gap analysis for walkability indices, 2012............................................................................................... 78<br />
Table 4: Gap analysis for density measures used <strong>to</strong> assess urban walkability, 2012............................................. 79<br />
Table 5: Gap analysis for connectivity measures used <strong>to</strong> assess urban walkability, 2012...................................... 80<br />
Table 6: Gap analysis for diversity measures used <strong>to</strong> assess urban walkability, 2012............................................ 81<br />
Table 7: Gap analysis for pedestrian oriented design measures used <strong>to</strong> assess urban walkability, 2012............... 82<br />
Table 8: Gap analysis for data sources and sets used in the assessment <strong>of</strong> urban walkability, 2012..................... 83<br />
Table 9: Summary <strong>of</strong> air quality and air pollutant data sources............................................................................. 103<br />
Table 10: Methods <strong>of</strong> modelling air quality and pollution levels noted in the literature review................................... 107<br />
Table 11: Measurement approaches and policy relevant information as identified from the literature<br />
review, key informant interviews, survey and GIS metadata).................................................................... 136<br />
Table 12: <strong>Data</strong> sources for Air Quality and Policy-Relevant Information as Identified in the Literature<br />
Review, Key Informant Interviews, Survey and GIS Metadata Exercise.................................................... 136<br />
Table 13: Select hourly meteorological variables available from <strong>Environment</strong> Canada weather stations................... 145<br />
Table 14: Composite measures <strong>of</strong> heat using meteorological variables, identified in the literature search................ 146<br />
Table 15: Examples <strong>of</strong> built environment measures for the assessment <strong>of</strong> extreme heat by category...................... 147<br />
Table 16: Examples <strong>of</strong> remotely sensed measures <strong>of</strong> the built environment used <strong>to</strong> assess extreme<br />
heat, as identified in the literature search................................................................................................. 148<br />
Table 17: Select satellites and their sensors used in heat-related health studies..................................................... 149<br />
Table 18: Measures <strong>of</strong> community vulnerability <strong>to</strong> extreme heat, as identified in the literature search...................... 151<br />
Table 19: Accessibility <strong>of</strong> data used <strong>to</strong> assess extreme heat in Ontario, 2012......................................................... 161<br />
Table 20: Measurement approaches and policy relevant information as identified from the literature<br />
review, key informant interviews, survey and GIS metadata..................................................................... 170<br />
Table 21: <strong>Data</strong> sources and sets, and policy relevant information as identified from the literature review,<br />
key informant interviews, survey and GIS metadata................................................................................ 171<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
LIST OF FIGURES<br />
Figure 1: <strong>Built</strong> environment LDCP key informant interview process, Ontario, 2012............................................ 30<br />
Figure 2: Number <strong>of</strong> years PHUs assess urban Walkability in Ontario, 2012...................................................... 65<br />
Figure 3: Method(s) used by PHUs <strong>to</strong> assess urban Walkability in Ontario, 2012............................................... 66<br />
Figure 4: Geographic scale most commonly used by PHUs <strong>to</strong> assess urban walkability, in Ontario................... 67<br />
Figure 5: Public Health Unit access <strong>to</strong> data sources in Ontario, 2012............................................................... 70<br />
Figure 6: Major challenges faced by PHUs in assessing urban walkability, 2012............................................... 73<br />
Figure 7: Ontario nitrogen oxides emissions by sec<strong>to</strong>r<br />
(emissions from point/area/transportation sources, 2009 estimates)................................................... 89<br />
Figure 8: Ontario volatile organic compounds emissions by sec<strong>to</strong>r<br />
(emissions from point/area/transportation sources, 2009 estimates)................................................... 90<br />
Figure 9: Ontario PM2.5 emissions by sec<strong>to</strong>r<br />
(emissions from point/area/transportation sources, 2009 estimates)................................................... 91<br />
Figure 10: Operational land use regression predicted surface for Toron<strong>to</strong>........................................................... 105<br />
Figure 11: Distribution <strong>of</strong> risks associated with airborne non-carcinogenic <strong>to</strong>xics for two<br />
neighbourhoods in Toron<strong>to</strong>................................................................................................................ 110<br />
Figure 12: Distribution <strong>of</strong> risks associated with airborne carcinogens for two neighbourhoods in Toron<strong>to</strong>............ 111<br />
Figure 13: Distribution <strong>of</strong> risks associated with criteria air pollutants for two neighbourhoods in Toron<strong>to</strong>............. 112<br />
Figure 14: Airpointer belonging <strong>to</strong> the City <strong>of</strong> Ottawa.......................................................................................... 115<br />
Figure 15: Number <strong>of</strong> years Public Health Units have been assessing urban air quality in Ontario, 2012............. 124<br />
Figure 16: Type <strong>of</strong> index used <strong>to</strong> assess urban air quality in Ontario, 2012.......................................................... 125<br />
Figure 17: Air pollutants measured by type <strong>of</strong> Ministry <strong>of</strong> <strong>Environment</strong> stations in Ontario, 2012......................... 126<br />
Figure 18: <strong>Data</strong> and estimates used <strong>to</strong> assess urban air quality in Ontario, 2012................................................ 128<br />
Figure 19: Access <strong>to</strong> data used <strong>to</strong> assess urban air quality in Ontario, 2012...................................................... 129<br />
Figure 20: Challenges faced by PHUs in assessing air quality in Ontario, 2012................................................... 131<br />
Figure 21: Number <strong>of</strong> years Public Health Units have been assessing extreme heat in Ontario, 2012.................. 160<br />
Figure 22: Demographics used <strong>to</strong> identify populations more vulnerable <strong>to</strong> extreme heat in Ontario, 2012........... 165<br />
Figure 23: Challenges faced by PHUs in assessing extreme heat in Ontario, 2012.............................................. 166<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
ACRONYMS<br />
APHEO<br />
AQI<br />
AQHI<br />
BC<br />
BTEX<br />
CCHS<br />
CO<br />
C<strong>of</strong>A<br />
CMA<br />
DMTI<br />
EC<br />
EHMS<br />
EPA<br />
ESS<br />
FTE<br />
GHG<br />
GIS<br />
GTA<br />
HC<br />
LIDAR<br />
LIO<br />
LDCP<br />
KFLA<br />
MPAC<br />
MODIS<br />
MOE<br />
MOHLTC<br />
MSC<br />
NAPS<br />
NASA<br />
Association <strong>of</strong> Public Health Epidemiologists in Ontario<br />
Air Quality Index<br />
Air Quality Health Index<br />
Black Carbon<br />
Benzene, Toluene, Ethylbenzene, Xylene<br />
Canadian Community Health Survey<br />
Carbon monoxide<br />
Certificate <strong>of</strong> Approval<br />
Census Metropolitan Area<br />
Digital Mapping Technologies Inc.<br />
<strong>Environment</strong> Canada<br />
<strong><strong>Environment</strong>al</strong> Heat Moni<strong>to</strong>ring Systems<br />
U.S.<strong><strong>Environment</strong>al</strong> Protection Agency<br />
Earth Sciences Sec<strong>to</strong>r<br />
Full-Time Equivalent<br />
Greenhouse Gas<br />
Geographic Information System<br />
Greater Toron<strong>to</strong> Area<br />
Health Canada<br />
Light Detection and Ranging<br />
Land Information Ontario<br />
Locally Driven Collaborative Project<br />
Kings<strong>to</strong>n, Frontenac, Lennox & Adding<strong>to</strong>n Health Unit<br />
Municipal Property Assessment Corporation<br />
Moderate Resolution Imaging Spectroradiometer<br />
Ontario Ministry <strong>of</strong> the <strong>Environment</strong><br />
Ontario Ministry <strong>of</strong> Health and Long-Term Care<br />
Meteorological Service <strong>of</strong> Canada<br />
National Air Pollutant Surveillance<br />
National Aeronautics and Space Administration<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
ACRONYMS<br />
NCR<br />
NO x<br />
NO 2<br />
NO<br />
NPRI<br />
NRCan<br />
NRVIS<br />
OMA<br />
OPHS<br />
PAHs<br />
PHIMS<br />
PHO<br />
PHU<br />
PM<br />
PM 10<br />
PM 2.5<br />
RRFSS<br />
SES<br />
SO 2<br />
TEO<br />
TPH<br />
TRI<br />
TRS<br />
TSP<br />
TTS<br />
UHI<br />
UFP<br />
VOCs<br />
WHO<br />
National Capital Region<br />
Nitrogen Oxides<br />
Nitrogen Dioxide<br />
Nitric Oxide<br />
National Pollutant Release Inven<strong>to</strong>ry<br />
Natural Resources Canada<br />
National Resources and Values Information (Ministry <strong>of</strong> Natural Resources)<br />
Ontario Medical Association<br />
Ontario Public Health Standards<br />
Polycyclic Aromatic Hydrocarbons<br />
Public Health Information Management System<br />
Public Health Ontario<br />
Public Health Unit<br />
Particulate Matter<br />
Coarse particulate matter<br />
Fine particulate matter<br />
Rapid Risk Fac<strong>to</strong>r Surveillance System<br />
Socioeconomic status<br />
Sulphur dioxide<br />
Toron<strong>to</strong> <strong><strong>Environment</strong>al</strong> Office<br />
Toron<strong>to</strong> Public Health<br />
Toxic Release Inven<strong>to</strong>ry<br />
Total Reduced Sulphur<br />
Total Suspended Particulates<br />
Transportation Tomorrow Survey<br />
Urban Heat Island<br />
Ultra-Fine Particles<br />
Volatile Organic Compounds<br />
World Health Organization<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
INTRODUCTION<br />
INTRODUCTION<br />
THE BUILT ENVIRONMENT AND HEALTH<br />
The ways in which we design and build our neighbourhoods have substantial implications on our health<br />
and quality <strong>of</strong> life. 3 Although the intention <strong>of</strong> many urban planning schemes implemented in the last fifty<br />
years has been <strong>to</strong> protect the public’s health, by separating industrial and residential areas for example,<br />
emerging research reveals that these built environment policies may have had unintended consequences.<br />
It has been suggested that the land use decisions <strong>of</strong> the past contribute <strong>to</strong> current air pollution concerns<br />
due <strong>to</strong> increased car reliance, and <strong>to</strong> sedentary lifestyles due <strong>to</strong> environments that discourage physical<br />
activity. 4 Rising obesity rates and physical inactivity threaten <strong>to</strong> decrease the longevity <strong>of</strong> future generations<br />
and places a strain on an already fragile healthcare system. 5 Several studies have shown that air pollution<br />
increases the risk <strong>of</strong> death and illness due <strong>to</strong> heart disease, stroke, and respira<strong>to</strong>ry disease through<br />
both short-term and long-term exposures. Many researchers also predict that heat-related illnesses and<br />
deaths are on the rise, worsened by the impacts <strong>of</strong> increased urbanization (e.g. urban health island effects)<br />
and Canada’s aging population 6;7 As a result <strong>of</strong> these concerns, research on the built environment<br />
has proliferated in recent years and there has been a shift in interest <strong>to</strong> designing and re-designing built<br />
environments <strong>to</strong> promote physical activity and minimize environmental exposures. 8-10<br />
BUILT ENVIRONMENT MEASURES AND DATA<br />
Inquiries in<strong>to</strong> the connections between the built environment and health outcomes have revived the need<br />
for comprehensive and detailed measures <strong>to</strong> identify elements <strong>of</strong> the physical and natural environment<br />
that impact environmental exposures and support or detract from walking. 11 However, research in<strong>to</strong> the<br />
relationship between the built environment, walking and environmental exposures is complex and the<br />
diverse nature <strong>of</strong> built environment data presents multiple challenges for public health stakeholders. In<br />
the application <strong>of</strong> these data <strong>to</strong> examine the built environment, it is important <strong>to</strong> recognize that there are<br />
issues around data consistency, the operationalization <strong>of</strong> measures, data resolution, data accuracy and<br />
completeness, and temporality <strong>of</strong> data. 12;13 These built environment data challenges affect most jurisdictions,<br />
including Ontario. Moreover public health researchers and practitioners are increasingly using<br />
objective built environment indica<strong>to</strong>rs, <strong>of</strong>ten measured using geographic information systems (GIS) and<br />
spatial analytic techniques, <strong>to</strong> explore built environment-health relationships. <strong>An</strong> examination <strong>of</strong> these<br />
relationships requires current, relevant, and consistent measures and data.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
INTRODUCTION<br />
THE WALKABILITY PICTURE<br />
Walking is an important piece <strong>of</strong> the built environment puzzle from a public health perspective because it<br />
is the most common physical activity among Canadians. The Canadian Physical Activity Guidelines recommend<br />
that adults accumulate at least 150 minutes <strong>of</strong> moderate <strong>to</strong> vigorous physical activity (e.g. walking)<br />
per week and that children and youth be physically active at least sixty minutes per day. 14 However,<br />
a national survey demonstrated that only 49% <strong>of</strong> Canadians meet these guidelines. 15;16 Many Canadians<br />
would like <strong>to</strong> become more physically active but are deterred largely by concerns about the environment,<br />
distance, and convenience. 17;18 Therefore, identifying the specific characteristics <strong>of</strong> the urban environment<br />
that support or hinder people from living an active lifestyle is important given the inadequate and<br />
declining levels <strong>of</strong> physical activity in both adults and children, increasing sedentary time related <strong>to</strong> electronic<br />
media use and car travel times, and rising levels <strong>of</strong> chronic diseases and obesity. 10<br />
THE AIR QUALITY PICTURE<br />
Air pollution in Ontario is responsible for a significant burden <strong>of</strong> illness and death. In 2004, Canadian researchers<br />
estimated that five common air pollutants (i.e. fine particulate matter, nitrogen dioxide, ground<br />
level ozone, sulphur dioxide and carbon monoxide) contributed <strong>to</strong> approximately 2,900 non-traumatic<br />
deaths each year in four Ontario cities: Windsor, Hamil<strong>to</strong>n, Toron<strong>to</strong>, and Ottawa. They attributed one third<br />
<strong>of</strong> those deaths <strong>to</strong> acute health impacts associated with a mix <strong>of</strong> air pollutants, and two thirds <strong>to</strong> chronic<br />
health impacts associated with fine particulate matter (PM2.5) alone. They concluded that air pollution in<br />
Ontario cities is responsible for 7 percent <strong>to</strong> 10 percent <strong>of</strong> all non-traumatic deaths. 19<br />
Mo<strong>to</strong>r vehicles are a major source <strong>of</strong> air pollutants, and people who spend significant amounts <strong>of</strong> time near<br />
high-traffic roads are <strong>of</strong>ten exposed <strong>to</strong> elevated levels <strong>of</strong> traffic-related pollutants. Studies have shown<br />
that childhood exposures <strong>to</strong> traffic-related pollution are associated with chronic respira<strong>to</strong>ry symp<strong>to</strong>ms,<br />
reduced lung function, impaired lung development, development <strong>of</strong> asthma, increased asthma incidence,<br />
respira<strong>to</strong>ry infections, and middle ear infections. Recent research also shows that traffic-related pollution<br />
exposure is associated with decreased cognitive performance and language abilities in children. 20<br />
THE EXTREME HEAT PICTURE<br />
Episodes <strong>of</strong> extreme heat are increasingly more common in Canada, including Ontario. The Expert Panel<br />
on Climate Change Adaptation 21 advised that heat-related mortality could double in Southern Ontario by<br />
the 2050s, while rates <strong>of</strong> mortality due <strong>to</strong> worsening air pollution compounded by rising temperatures,<br />
could increase by about 15 <strong>to</strong> 25 percent during the same period. It has been predicted that the number<br />
<strong>of</strong> days with average temperatures above 30˚C will increase in cities across Canada, particularly those<br />
located in the Windsor-Quebec corridor. 22 The intensity and duration <strong>of</strong> extreme heat events in Canada<br />
are also expected <strong>to</strong> rise, and have been identified as key climate change risks for human health. 23-25<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
INTRODUCTION<br />
In Canada, over eighty percent <strong>of</strong> the population live in urban areas, and this number continues <strong>to</strong><br />
increase. 26 Canada’s population is aging, with the number <strong>of</strong> people over age sixty-five expected <strong>to</strong><br />
double within the next twenty-five years. 27;28 Senior citizens are at an elevated risk from extreme heat<br />
events. 7;29;30<br />
THE BUILT ENVIRONMENT LOCALLY DRIVEN COLLABORATIVE PROJECT<br />
This project was guided by the following central research question:<br />
What objective<br />
measures <strong>of</strong> urban<br />
walkability and<br />
select environmental<br />
exposures (air<br />
pollutants and<br />
extreme heat) are<br />
in current use,<br />
and what data<br />
are available, and<br />
necessary, <strong>to</strong> assess<br />
the urban built<br />
environment<br />
in Ontario?<br />
The Ontario Public Health Standards mandate public health units in the province<br />
<strong>to</strong> address the built environment by increasing public awareness, supporting<br />
healthy public policy, and creating supportive environments. 31 To this<br />
end, Ontario’s public health practitioners must be guided by the best available<br />
evidence from population heath assessment, surveillance, research and knowledge<br />
exchange, and program evaluation <strong>to</strong> inform public program, services<br />
and policies related <strong>to</strong> the built environment.<br />
Using an intensive collaborative priority setting process, Public Health Ontario<br />
(PHO) identified the built environment as one <strong>of</strong> the priority funding areas in<br />
their 2011 Locally Driven Collaborative Project (LDCP) initiative. Given that a<br />
comprehensive examination <strong>of</strong> all the built environment fac<strong>to</strong>rs associated with<br />
health is beyond the scope <strong>of</strong> the LDCP initiative, the focus <strong>of</strong> this collaborative<br />
project is on walkability and environmental exposures, namely air quality<br />
and extreme heat. Since eighty-five percent <strong>of</strong> Ontario residents lived in urban<br />
areas in 2011 and that urban migration is projected <strong>to</strong> continue <strong>to</strong> grow, this<br />
project focuses on urban areas. 26;32<br />
More specifically, through an environmental scan, this collaborative project aimed <strong>to</strong>:<br />
(i)<br />
(ii)<br />
Identify existing objective measures and indices <strong>of</strong> urban walkability and environmental exposures<br />
(air quality and extreme heat);<br />
Identify built environment data necessary for the construction <strong>of</strong> those measures and indices;<br />
(iii) Identify data currently collected by municipalities, the private sec<strong>to</strong>r, partners, and other sources<br />
that could contribute <strong>to</strong> and enhance the assessment <strong>of</strong> urban walkability and environmental<br />
exposure in Ontario;<br />
(iv) Identify gaps between necessary and available data collected in Ontario related <strong>to</strong> walkability<br />
and select environmental exposures; and,<br />
(v)<br />
Develop policy recommendations that would promote the development and use <strong>of</strong> such data<br />
and measures.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
INTRODUCTION<br />
These project objectives were achieved using a collaborative approach, including core representation<br />
from the following project co-applicants: Kings<strong>to</strong>n, Frontenac and Lennox & Adding<strong>to</strong>n (KFL&A) Public<br />
Health (Study Lead), York Region Public Health, Niagara Region Public Health, and the Public Health<br />
Agency <strong>of</strong> Canada (PHAC).<br />
This report provides an overview <strong>of</strong> the evidence and foundational information for built environment<br />
measures and relevant data sources, as well as future directions for research and policy development in<br />
Ontario.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
INTRODUCTION<br />
KEY DEFINITIONS<br />
The key definitions that were applied in this study for the built environment, walkability and environmental<br />
exposures (air quality and extreme heat) are presented below.<br />
THE BUILT ENVIRONMENT<br />
The built environment has been defined in different<br />
ways by researchers in diverse fields <strong>of</strong> study<br />
including public health, urban design and transportation.<br />
For the purposes <strong>of</strong> this study, the built<br />
environment encompasses all buildings, spaces<br />
and products that are created, or modified, by<br />
people. Features <strong>of</strong> the built environment are<br />
numerous and include homes, schools, workplaces,<br />
parks/recreation areas, greenways, business<br />
areas and transportation systems. It extends<br />
overhead in the form <strong>of</strong> electric transmission lines,<br />
underground in the form <strong>of</strong> waste disposal sites<br />
and subway trains, and across the country in<br />
the form <strong>of</strong> highways. It includes land-use planning<br />
and policies that impact our communities in<br />
urban, rural and suburban areas. 33<br />
WALKABILITY<br />
<strong>Environment</strong>s that make walking feasible and<br />
appealing have been labelled as “pedestrianoriented”<br />
or “walkable”. 34 Walkability is a concept<br />
<strong>to</strong> describe the extent <strong>to</strong> which characteristics <strong>of</strong><br />
the built environment and land use mix encourage<br />
leisure activities, exercise, recreation, and<br />
ease <strong>of</strong> travel for residents in a neighbourhood. 35<br />
Researchers have focused on different types <strong>of</strong><br />
walking, whether walking for recreation or exercise,<br />
or walking <strong>to</strong> reach a destination. This latter<br />
category <strong>of</strong> walking has a variety <strong>of</strong> labels, including<br />
active travel, non-mo<strong>to</strong>rized travel, transportrelated<br />
physical activity, destination-oriented<br />
walking, and utilitarian walking. 8<br />
ENVIRONMENTAL EXPOSURES:<br />
AIR QUALITY & EXTREME HEAT<br />
Exposure <strong>to</strong> contaminants and environmental<br />
conditions is both directly and indirectly impacted<br />
by the built environment, including, but not limited<br />
<strong>to</strong> air quality, water quality, vec<strong>to</strong>r-borne disease,<br />
climate change and extreme heat. For the<br />
purpose <strong>of</strong> this study, environmental exposure is<br />
defined as air quality and extreme heat in the built<br />
environment that can impact human health.<br />
Air quality is a term used <strong>to</strong> describe ambient<br />
atmospheric conditions based on the presence<br />
or absence <strong>of</strong> chemical, biological or physical<br />
compounds that impact human and/or environmental<br />
health. Air quality is affected by hundreds<br />
<strong>of</strong> pollutants associated with a wide range <strong>of</strong> activities<br />
and processes.<br />
Extreme heat is a term used <strong>to</strong> describe conditions<br />
<strong>of</strong> high temperature and/or humidity that<br />
can put people at risk for heat-related illnesses. 36<br />
URBAN ENVIRONMENT<br />
Urban environment in the context <strong>of</strong> this study refers<br />
<strong>to</strong> an area with a population <strong>of</strong> at least 1,000<br />
and no fewer than 400 people per square kilometre.<br />
26<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
1METHODOLOGY
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
CHAPTER 1:<br />
METHODOLOGY<br />
<strong>An</strong> environmental scan was conducted using a mixed method approach <strong>to</strong> achieve the specific project<br />
objectives. A literature review was performed <strong>to</strong> understand the relationship between the built environment<br />
and the three <strong>to</strong>pic areas (walkability, air quality, extreme heat) within the scope <strong>of</strong> the project. Key<br />
informant interviews provided the Canadian and Ontario context <strong>of</strong> built environment research and<br />
initiatives. A survey <strong>of</strong> public health units in Ontario gathered information on measures and data sources<br />
currently in use <strong>to</strong> assess the built environment, as well as the challenges faced by these organizations.<br />
The results from all three research activities were synthesized <strong>to</strong>gether in the gap analysis <strong>to</strong> form the<br />
basis for discussion and the development <strong>of</strong> guiding principles and recommendations.<br />
As the scope <strong>of</strong> this project addressed three distinct subject areas, the methodology was developed <strong>to</strong> be<br />
consistent across the whole project. In all cases, the primary focus remained the built environment. The<br />
study received ethics approval from Queen’s University Health Science and Affiliated Teaching Hospitals<br />
Research Ethics Board.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
26<br />
METHODOLOGY<br />
LITERATURE REVIEW<br />
A comprehensive literature review was performed <strong>to</strong> find and report on studies that identified measures<br />
and explored the relationship between the urban built environment and walkability, air quality and extreme<br />
heat.<br />
Search Strategy<br />
The literature search focused on peer-reviewed academic research articles but also included grey literature<br />
sources. Online resources were searched for relevant content, specifically PubMed, Medline, Google<br />
Scholar, Google, and Academic Search Premier. In addition, <strong>to</strong> capture articles not indexed in these<br />
databases, journals recognized for examining associations between the built environment or community<br />
planning and health were also searched. These included the Journal <strong>of</strong> Physical Activity & Health, International<br />
Journal <strong>of</strong> Health Geographics and American Journal <strong>of</strong> Preventive Medicine.<br />
A Boolean search strategy was employed with keywords developed for each <strong>to</strong>pic area. The walkability<br />
search terms were: (“Indica<strong>to</strong>r” OR “Measure” OR “GIS” OR “Geospatial” OR “Health Outcome”) AND<br />
(“<strong>Built</strong> <strong>Environment</strong>”) AND (“Physical Activity” OR “Walking” OR “Walkability). For the air quality literature<br />
review, the initial search terms were: (“Air Quality” OR “Air Pollution” OR “Adverse Effect”); however,<br />
due <strong>to</strong> the limited studies found pertaining <strong>to</strong> air quality and the built environment, a second phase <strong>of</strong><br />
the literature review was conducted with additional search terms, as follows: “Modelling” OR “Land<br />
Use Regression” OR “Dispersion Modelling” OR “Kriging” OR “Spatial Distribution” OR “Micro-environments”<br />
OR “Neighbourhood Level”) AND (“<strong>Built</strong> <strong>Environment</strong>” OR “Land Use”) AND (“Pollution” OR “Air<br />
Quality” OR “Air Emissions” OR “Traffic <strong>Related</strong> Air Pollutants” OR “Point Sources”). The extreme heat<br />
literature review used the search terms: (Extreme) Heat” OR “Heat Index” OR “Heat Wave” OR “Humidex”<br />
or “Urban Heat Island” OR “Adverse Effect”.<br />
Article Selection and <strong>Data</strong> Extraction<br />
Fifty articles for walkability and seventy articles for environmental exposures were selected for formal<br />
review mainly from the disciplines <strong>of</strong> public health, environmental science, urban design, and planning.<br />
For inclusion in the literature review, a study had <strong>to</strong> come from a reputable source and include some discussion<br />
<strong>of</strong> indica<strong>to</strong>rs, measures, <strong>to</strong>ols, data sources, or databases in keeping with the project objectives.<br />
In order <strong>to</strong> focus on outdoor air quality, studies related <strong>to</strong> the following <strong>to</strong>pics were excluded: indoor/<br />
household air pollution, <strong>to</strong>bacco smoke/exposure, occupational studies, and soil quality.<br />
Evidence from Ontario was prioritized, but where it was absent or incomplete, Canadian studies were<br />
also drawn upon, followed by international studies. Recently published studies were given preference for<br />
inclusion in the literature review (last 5-7 years).<br />
Information was extracted from each article using a standardized format <strong>to</strong> describe the country, objective,<br />
population studied, public health measure, indica<strong>to</strong>r(s) measured, methods/data components,<br />
source <strong>of</strong> data, results, and conclusions (See Appendix A)<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
METHODOLOGY 27<br />
KEY INFORMANT INTERVIEWS<br />
The purpose <strong>of</strong> the key informant interviews was <strong>to</strong> help identify both areas <strong>of</strong> strength and weakness in<br />
the collection, availability, and comparability <strong>of</strong> built environment data in Ontario. Key informant interviews<br />
were conducted from April <strong>to</strong> June 2012 with a <strong>to</strong>tal <strong>of</strong> 12 key informants interviewed for walkability, 6<br />
for air quality and 5 for extreme heat. Key informants represented a diverse cross-section <strong>of</strong> disciplines<br />
(e.g. public health, land use planning, academia) as well as local, provincial, and federal agencies. For the<br />
most part, key informants were experts in their respective fields and/or represented the most established<br />
organizations (from the project team’s perspective) as it related <strong>to</strong> the assessment <strong>of</strong> urban walkability, air<br />
quality and extreme heat exposure in Ontario.<br />
Interview Questionnaire<br />
The questionnaire for the key informant interviews was developed by the project team and consultants<br />
(see Appendices A-C). The completed literature review helped inform questionnaire development. Questions<br />
focused on issues <strong>of</strong> data availability, data quality, data gaps, internal capacity, data acquisition, and<br />
other challenges. Consensus on the number <strong>of</strong> questions and types <strong>of</strong> questions was reached among<br />
the project team before implementation <strong>of</strong> the survey. The questionnaire was piloted with the first key<br />
informants; no significant changes <strong>to</strong> the questions were required.<br />
Sampling<br />
The key informants for walkability, air quality, and extreme heat differed; therefore the criteria for<br />
inclusion also differed. In general, key informants were interviewed if they:<br />
(i)<br />
Had expertise in the urban built environment, specific <strong>to</strong> walkability and/or environmental<br />
exposures (air quality and extreme heat); and,<br />
(ii) Were familiar with measures and respective data sources used <strong>to</strong> assess the built environment<br />
in Ontario.<br />
Key informants were identified by the project team using the sampling criteria outlined below (Table 1).<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
28<br />
METHODOLOGY<br />
Table 1: Sampling strategy for identifying key informants<br />
Walkability Key Informants<br />
<strong><strong>Environment</strong>al</strong> Exposure Key Informants<br />
(air quality and extreme heat)<br />
Criteria<br />
Those with expertise related <strong>to</strong> the<br />
urban built environment, specifically<br />
walkability (may include physical<br />
activity) measures and data sources.<br />
Those who have operationalized<br />
walkability measures and/or indices<br />
in Ontario.<br />
Those who have expertise or responsibility for<br />
modelling and/or moni<strong>to</strong>ring air quality and/or<br />
extreme heat at the provincial and federal levels <strong>of</strong><br />
government;<br />
Those who have conducted research on air quality<br />
as it is impacted by traffic corridors and local point<br />
sources;<br />
Those who have conducted air modelling and/or<br />
air moni<strong>to</strong>ring at a municipal level; and<br />
Those with expertise in extreme heat.<br />
Interviews<br />
Figure 1 outlines the key informant interview process. Potential key informants were invited <strong>to</strong> participate<br />
in the interviews by email and/or telephone. A meeting was then scheduled based on availability <strong>of</strong> both<br />
the interviewer and key informant.<br />
The questionnaire and consent form were shared with the key informant prior <strong>to</strong> the meeting for review in<br />
order <strong>to</strong> provide adequate notice and time <strong>to</strong> prepare for the interview. Prior <strong>to</strong> the interview commencing,<br />
verbal consent was provided by the key informant. Semi-structured interviews that were comprised <strong>of</strong><br />
open-ended questions, were conducted either face-<strong>to</strong>-face or by telephone (preference was given <strong>to</strong><br />
face-<strong>to</strong>-face interviews where possible). Interviews were conducted for approximately one hour and were<br />
audio recorded for transcription purposes. All transcriptions were sent <strong>to</strong> interviewees for approval and<br />
confirmation <strong>of</strong> content after the interview.<br />
One <strong>of</strong> the initial air quality key informant interviews <strong>to</strong>ok place using a questionnaire intended for a different<br />
interviewee. Due <strong>to</strong> the small number <strong>of</strong> interviews, this change in interview content would have<br />
had a large impact on the evidence collected. To correct this error, the interview was repeated with the<br />
appropriate questionnaire. However, as the repeated interview <strong>to</strong>ok place after the rest <strong>of</strong> the interviews,<br />
it was not included in the NVivo transcription and qualitative analysis process. Instead, the air quality<br />
consultant integrated the results in<strong>to</strong> the air quality key informant analysis.<br />
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METHODOLOGY 29<br />
Qualitative <strong>An</strong>alysis<br />
Directed qualitative content analysis <strong>of</strong> the transcribed interviews was performed using NVivo s<strong>of</strong>tware.<br />
Thematic analysis guided the built environment data needs identified by the project team (data availability,<br />
data gaps, internal capacity, data acquisition, and other challenges). A start list <strong>of</strong> parent nodes (codes)<br />
was first created in NVivo 10 <strong>to</strong> mirror the initial start list <strong>of</strong> themes. Child nodes (sub-codes) were created<br />
as warranted <strong>to</strong> reflect emerging concepts. Ill-fitting codes were discarded, and similar codes were combined.<br />
Thus, the directed content analysis approach allowed for the identification <strong>of</strong> emerging themes.<br />
For consistency, one qualitative coder completed the thematic analysis.<br />
Trustworthiness <strong>of</strong> the qualitative interview data and analysis for the current study was established by<br />
addressing the dependability, credibility, and transferability <strong>of</strong> findings.<br />
The dependability <strong>of</strong> study results was ensured by:<br />
• The development <strong>of</strong> an interview guide;<br />
• The audio recording and the verbatim transcription <strong>of</strong> the interviews; and,<br />
• The detailing <strong>of</strong> data collection and analysis, including an audit trail <strong>of</strong> the analyst’s<br />
interaction with the data.<br />
The credibility <strong>of</strong> the findings (internal validity) was enhanced by the following actions:<br />
• The interviewers summarized respondent’s answers during the interview;<br />
• Participants were given opportunities <strong>to</strong> provide additional thoughts as well as <strong>to</strong> clarify any<br />
misunderstandings during the interview;<br />
• All interviews were audio recorded and transcribed verbatim, enabling sufficient time <strong>to</strong> review<br />
and check data;<br />
• Transcribed interviews were returned <strong>to</strong> interview participants for review;<br />
• The qualitative analyst sought alternative explanations and opinions <strong>of</strong> interviewees during<br />
analysis; and,<br />
• Triangulation <strong>of</strong> interview data with other project component findings enhanced the credibility <strong>of</strong><br />
the qualitative findings.<br />
The transferability <strong>of</strong> findings helps <strong>to</strong> determine a study’s external validity. The purpose <strong>of</strong> this project<br />
was <strong>to</strong> support the identification <strong>of</strong> walkability and environmental exposure measures and data specific<br />
<strong>to</strong> urban Ontario. Sufficient detail <strong>of</strong> the study pro<strong>to</strong>col has been provided in this report <strong>to</strong> enable outside<br />
researchers and practitioners <strong>to</strong> judge if the findings can be applied <strong>to</strong> other settings through transparency<br />
and reflexivity.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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METHODOLOGY<br />
Figure 1: <strong>Built</strong> environment LDCP key informant interview process, Ontario, 2012<br />
Submit for and receive REB approval<br />
Create list <strong>of</strong> nominated walkability & environmental exposure stakeholders<br />
Send the invitation letter <strong>to</strong> 15-20 <strong>of</strong> nominated stakeholders<br />
Recruit key informants that represent the criteria identified in Table 1<br />
Schedule interview time<br />
Email questions and consent form <strong>to</strong> interviewee<br />
Conduct and record interview via telephone or in-person<br />
Transcribe key informant interview data<br />
<strong>An</strong>alyze key informant notes using NVivo s<strong>of</strong>tware<br />
Summarize results<br />
SURVEY<br />
The project team developed and administered an electronic survey <strong>to</strong> all Ontario Public Health Units (36<br />
PHUs) in July 2012 (see Appendices D and E for survey questions and documentation). The purpose <strong>of</strong><br />
the survey was <strong>to</strong> inquire about built environment measures and data used by Ontario PHUs <strong>to</strong> assess<br />
urban walkability, air quality and extreme heat.<br />
The survey included three distinct sections, one for each subject area: walkability, air quality and extreme<br />
heat. Only one completed survey was accepted from each Public Health Unit jurisdiction; however, each<br />
section could be completed separately.<br />
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METHODOLOGY 31<br />
Survey Development<br />
The survey was developed in consultation with the project team and consultants. The literature review<br />
and key informant interviews were used <strong>to</strong> inform survey question development. Consensus on number<br />
<strong>of</strong> questions and types <strong>of</strong> questions asked was reached among the project team before surveying PHUs.<br />
The survey was pilot tested with project team organizations and was modified based on the feedback<br />
received. This survey included complex skip patterns <strong>to</strong> reduce the number <strong>of</strong> questions asked <strong>of</strong> health<br />
units that were not currently assessing the built environment.<br />
Administration <strong>of</strong> Survey<br />
The survey invitation was addressed <strong>to</strong> PHUs across Ontario; more specifically, <strong>to</strong> the Medical Officer <strong>of</strong><br />
Health (MOH), Direc<strong>to</strong>r <strong>of</strong> Chronic Diseases, and Direc<strong>to</strong>r <strong>of</strong> <strong><strong>Environment</strong>al</strong> Health. In some cases, an<br />
Epidemiologist and/or Planner also received the initial invitation <strong>to</strong> participate. <strong>An</strong> electronic copy <strong>of</strong> the<br />
survey and a copy <strong>of</strong> the consent form (Micros<strong>of</strong>t Word attachment) were also shared with each participating<br />
Public Health Unit. Given the cross-disciplinary nature <strong>of</strong> the <strong>to</strong>pics and detailed information<br />
being sought from respondents, Public Health Units were encouraged <strong>to</strong> include other departments (e.g.<br />
Geographic Information System (GIS); Transportation Planning; Land Use Planning) and/or municipalities,<br />
as well as employee(s) with knowledge <strong>of</strong> GIS in order <strong>to</strong> accurately complete the survey.<br />
On July 11, 2012, the survey was administered <strong>to</strong> Public Health Units via email and electronically using<br />
Fluid Surveys (2012), an online survey s<strong>of</strong>tware program. Reminders for survey completion were sent at<br />
the end <strong>of</strong> weeks one and two, and two days prior <strong>to</strong> the survey deadline. If the survey was not completed<br />
by the end <strong>of</strong> the second week, the project coordina<strong>to</strong>r and designated project team members<br />
followed up with PHUs by email or telephone.<br />
Survey <strong>An</strong>alysis<br />
Survey data were exported from Fluid Surveys (2012) <strong>to</strong> perform qualitative and quantitative analyses for<br />
all three <strong>to</strong>pic-areas using Micros<strong>of</strong>t Excel (2007) and IBM SPSS (2012). Descriptive reports exported<br />
from Fluid Surveys were also reviewed. Questions in a fixed-response format were analyzed using descriptive<br />
analyses and cross-tabulations. Qualitative analysis was used <strong>to</strong> analyze open-ended questions<br />
<strong>to</strong> determine common themes. All data were aggregated for the purposes <strong>of</strong> confidentiality.<br />
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METHODOLOGY<br />
GAP ANALYSIS AND RECOMMENDATIONS<br />
The results from the literature review, key informant interviews, and survey were synthesised for the<br />
assessment <strong>of</strong> gaps between data requirements and availability. This was achieved by organizing and<br />
describing measures using the following categories:<br />
• A general description <strong>of</strong> the measurement category;<br />
• Inputs required <strong>to</strong> operationalize the measures;<br />
• Current use <strong>of</strong> measures in Ontario, including the proportion <strong>of</strong> measures in current use in Ontario<br />
(where possible);<br />
• Theoretical operation <strong>of</strong> the measures in Ontario;<br />
• Desirability, usefulness and benefits <strong>of</strong> using the measures;<br />
• Challenges identified in using the measures; and,<br />
• Linkages with other built environment measures.<br />
<strong>Data</strong> sources and sets were organized and described using the following categories:<br />
• <strong>Data</strong> source/set;<br />
• Topic area covered by data source/set;<br />
• Current use <strong>of</strong> data source/set in Ontario PHUs;<br />
• Desirability, usefulness and benefits <strong>of</strong> using the data source/set;<br />
• Access and availability <strong>of</strong> the data source/set in Ontario; and,<br />
• Barriers, challenges and limitations <strong>of</strong> data source/set.<br />
Following completion <strong>of</strong> the gap analysis, a set <strong>of</strong> guiding principles and recommendations were developed<br />
by the project team. After a series <strong>of</strong> meetings and feedback shared via electronic correspondence<br />
from project team members, consensus on the final principles and recommendations was<br />
achieved.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY<br />
BUILT ENVIRONMENT MEASURES & DATA USED IN THE ASSESSMENT OF URBAN WALKABILITY<br />
BACKGROUND LITERATURE REVIEW KEY INFORMANT INTERVIEWS SUMMARY OF SURVEY RESULTS GAP ANALYSIS<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario2
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35<br />
BUILT ENVIRONMENT MEASURES & DATA USED IN THE ASSESSMENT OF URBAN WALKABILITY<br />
CHAPTER 2: WALKABILITY<br />
BACKGROUND<br />
WALKABILITY AND HEALTH<br />
Walking is an acceptable form <strong>of</strong> physical activity among the broadest cross section <strong>of</strong> society regardless<br />
<strong>of</strong> age, sex, ethnic group, education or income level. 37 It involves little expense, does not require<br />
learning new skills and can be used for transportation purposes as well as exercise. Additionally, walking<br />
is an activity that can be done close <strong>to</strong> home, making it a practical option for people who do not<br />
have time <strong>to</strong> commute <strong>to</strong> a gym or are unable <strong>to</strong> afford the expense <strong>of</strong> a car, gym membership or home<br />
equipment. 37;38<br />
Walking represents a more sustainable form <strong>of</strong> transportation than the au<strong>to</strong>mobile. By reducing reliance<br />
on the au<strong>to</strong>mobile, walking contributes <strong>to</strong> reductions in air pollution and has the potential <strong>to</strong> reduce the<br />
rates <strong>of</strong> respira<strong>to</strong>ry diseases associated with air pollution. 39;40 A study linking land use, transportation, air<br />
quality and health in the Atlanta Region, Georgia, found that walkable neighbourhoods were linked <strong>to</strong><br />
fewer per capita air pollutants and greenhouse gases. Travel patterns <strong>of</strong> residents in the least walkable<br />
neighbourhoods generated about twenty percent higher carbon dioxide emissions than travel by those<br />
who lived in the most walkable neighbourhoods. 41<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
36<br />
WALKABILITY<br />
Walking is about more than simply getting from one place <strong>to</strong> another. The public health benefits <strong>of</strong> walkfriendly<br />
environments are extensive and far reaching. For example:<br />
• In Canada, the prevalence <strong>of</strong> obesity has more than doubled over the last twenty years. Thus,<br />
creating more walkable, less au<strong>to</strong>mobile dependent communities can contribute <strong>to</strong> decreasing<br />
the incidence <strong>of</strong> obese and overweight Canadians. 1<br />
• Physical inactivity is a leading risk fac<strong>to</strong>r attributable <strong>to</strong> a number <strong>of</strong> health problems such<br />
as heart attacks, strokes, hypertension and diabetes. Thus, increasing the rate <strong>of</strong> active<br />
transportation has potential <strong>to</strong> lead <strong>to</strong> broader public health benefits. 1;42;43<br />
• A study by the Heart and Stroke Foundation found that Canadians living in compact, mixed use<br />
neighbourhoods were 2.4 times more likely <strong>to</strong> meet physical activity guidelines. 44<br />
• Walking promotes the goals <strong>of</strong> civic life by providing opportunities for face-<strong>to</strong>-face contact,<br />
casual interaction and public participation, all <strong>of</strong> which are closely tied <strong>to</strong> improved mental health<br />
and well-being. People on streets and in public places are an essential ingredient for vibrant,<br />
economically viable and safe communities as well. 45;46<br />
• Specific groups such as children and older people who are <strong>of</strong>ten more reliant on their local<br />
neighbourhoods can gain significant health benefits and independence through walking. 9;47<br />
• Increased physical activity among youth is likely <strong>to</strong> produce improvements <strong>to</strong> health and quality <strong>of</strong><br />
life, such as preventing and reducing the prevalence <strong>of</strong> overweight and obese youth, increasing<br />
self-esteem, and enhancing scholastic success, in addition <strong>to</strong> the longer term health gains <strong>of</strong><br />
preventing chronic diseases such as diabetes and cardiovascular disease. 48<br />
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WALKABILITY 37<br />
WALKABILITY AND THE BUILT<br />
ENVIRONMENT<br />
NEIGHBOURHOOD DESIGN<br />
Using a socio-ecological lens, physical activity and walking behaviour<br />
can be examined at multiple levels; by individual fac<strong>to</strong>rs<br />
(such as attitudes <strong>to</strong> physical activity), the microenvironment<br />
(conduciveness <strong>of</strong> the places where people live, learn and work<br />
for physical activity) and the macroenvironment (general socioeconomic,<br />
cultural and environmental conditions). Interventions<br />
that aim <strong>to</strong> create built environments that support physical activity<br />
are therefore suitable population-based approaches for<br />
tackling the rising population levels <strong>of</strong> physical inactivity and<br />
obesity. 43<br />
Neighbourhood design relates <strong>to</strong> travel patterns primarily by impacting<br />
proximity between destinations and directness <strong>of</strong> travel<br />
between these destinations. The design <strong>of</strong> neighbourhoods can<br />
be described in terms <strong>of</strong>:<br />
• Density and land use mix characteristics that work in<br />
tandem <strong>to</strong> determine how many activities are within a<br />
convenient distance.<br />
• Proximity as a function <strong>of</strong> both density (compactness) <strong>of</strong><br />
development and the level <strong>of</strong> land use mix.<br />
• Connectivity which determines how directly one can travel<br />
between activities on a street or path network. 49<br />
There is a significant body <strong>of</strong> research related <strong>to</strong> the design<br />
and quality <strong>of</strong> the built environment and its effects on levels <strong>of</strong><br />
physical activity. Studies have demonstrated that aspects <strong>of</strong> the<br />
built environment can both promote and discourage walking.<br />
People walk more in environments characterised by compact<br />
land development, interconnected city streets and blocks, and<br />
mixed land-uses (e.g. parks, commercial, residential). These<br />
characteristics are thought <strong>to</strong> promote pedestrian activity by<br />
making it more practical <strong>to</strong> reach destinations on foot. Features<br />
<strong>of</strong> the neighbourhood environment such as sidewalks and bike<br />
paths are related <strong>to</strong> increased utilitarian and leisure non-mo<strong>to</strong>rized<br />
trips. The importance <strong>of</strong> having access <strong>to</strong> suitable recreational<br />
facilities (e.g. parks and recreation centers), has also been<br />
established. 43;48;50;51<br />
Neighbourhood design can significantly<br />
influence the fac<strong>to</strong>rs<br />
that contribute <strong>to</strong> peoples’ decision<br />
<strong>to</strong> walk. For instance, when<br />
everyday destinations such as<br />
grocery s<strong>to</strong>res, schools, post <strong>of</strong>fices<br />
and daycares are placed<br />
near or within residential areas,<br />
people are encouraged <strong>to</strong> adopt<br />
active transportation options<br />
(walking or cycling), rather than<br />
drive or be driven <strong>to</strong> their destination.<br />
Higher population densities<br />
can support shops and services<br />
in the neighbourhood, and build<br />
ridership for better quality transit<br />
which in turn encourages walking<br />
<strong>to</strong> and from transit s<strong>to</strong>ps. Likewise,<br />
when streets are designed<br />
<strong>to</strong> reduce traffic speeds and<br />
make routes pleasant for pedestrians<br />
(e.g. shorter blocks, sidewalks,<br />
and a grid street pattern),<br />
many people will adopt active<br />
forms <strong>of</strong> transportation. Together,<br />
these and other design features<br />
can contribute <strong>to</strong> the walkability<br />
<strong>of</strong> a neighbourhood. 1<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
38<br />
WALKABILITY<br />
Several other urban design characteristics facilitate active transport.<br />
Residential density, a greater diversity <strong>of</strong> businesses and greater proximity<br />
<strong>to</strong> shopping centres in a neighbourhood are associated with increased<br />
walking. 15 Greater residential densities and street connectivity<br />
<strong>of</strong> walkable neighbourhoods also support higher levels <strong>of</strong> public<br />
transit service and ridership, including walking <strong>to</strong> and from transit. 3;52<br />
Research indicates that perceptions about neighbourhood safety,<br />
aesthetics and the location <strong>of</strong> recreational facilities influence walking<br />
activity. 15;50;53 Thus, an inviting pedestrian environment with access<br />
<strong>to</strong> commercial, leisure and other destinations is recognized as a key<br />
component <strong>of</strong> walkability. 9;54<br />
WALKABILITY & VULNERABLE POPULATIONS<br />
It is important <strong>to</strong> highlight that individuals may respond differently <strong>to</strong> their environment depending on their<br />
age, gender, education, income, ethnicity, physical health, attitudes or preferences. 34;46;55 Furthermore,<br />
there are important equity implications when contextualizing the walkability <strong>of</strong> neighbourhoods. For example,<br />
it is vital <strong>to</strong> understand the difference between an individual who chooses <strong>to</strong> walk as a result <strong>of</strong><br />
living in a walkable neighbourhood and someone who, for financial constraints or other reasons, has<br />
no choice but <strong>to</strong> walk in a neighbourhood that may or may not be conducive <strong>to</strong> walking. 54 Research<br />
that focuses solely on built environment and land use characteristics has potential <strong>to</strong> misrepresent the<br />
neighbourhood-individual interaction. In Ontario, walking conditions vary greatly by socioeconomic status<br />
across neighbourhoods. For example:<br />
• Recent research conducted in the City <strong>of</strong> Ottawa found that lower socioeconomic status (SES)<br />
neighbourhoods suffer a greater burden <strong>of</strong> traffic, have more pedestrian vehicle collisions and<br />
have less green space than their higher SES counterparts. 46;53;56;57<br />
• In Toron<strong>to</strong>, rates <strong>of</strong> diabetes were higher in neighbourhoods with low walkability. These high risk<br />
neighbourhoods were comprised <strong>of</strong> lower average household incomes and higher concentrations<br />
<strong>of</strong> visible minority residents and immigrants; lower levels <strong>of</strong> walking and cycling; poor access or<br />
longer distances <strong>to</strong> healthy resources such as s<strong>to</strong>res selling fresh fruits and vegetables, and fewer<br />
parks and recreation centres. 53;58 Furthermore, lower SES neighbourhoods had less access <strong>to</strong><br />
public transit compared <strong>to</strong> higher SES neighbourhoods.<br />
<strong>An</strong>other Canadian study demonstrated that when opportunities exist <strong>to</strong> walk and cycle, low-income<br />
Canadians were more likely <strong>to</strong> make use <strong>of</strong> them. 59 Consequently, creating walkable environments has<br />
significant potential for addressing health disparities between socially advantaged and disadvantaged<br />
groups by providing supportive environments <strong>to</strong> those who rely on walking for transport, are unable <strong>to</strong><br />
access other forms <strong>of</strong> physical activity, and who are most exposed <strong>to</strong> the health hazards linked <strong>to</strong> au<strong>to</strong>mobile<br />
emissions and other pollutants. 57<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 39<br />
ASSESSING WALKABILITY<br />
Measuring the physical environment characteristics <strong>of</strong> neighbourhoods is the first step <strong>to</strong> understanding<br />
the walkability <strong>of</strong> an area. 3 Interest in the connections between land use and health has introduced new<br />
methods and <strong>to</strong>ols for describing health-related aspects <strong>of</strong> the built environment. 60 Yet <strong>to</strong> understand the<br />
impact <strong>of</strong> the built environment on physical activity, the development <strong>of</strong> high quality measures is essential.<br />
Several measures <strong>of</strong> the built environment have been developed over the years <strong>to</strong> examine multiple elements<br />
<strong>of</strong> the environment in relation <strong>to</strong> multiple modes and purposes <strong>of</strong> physical activity.<br />
Early research in the area <strong>of</strong> built environments and health relied on subjective measures <strong>of</strong> the environment<br />
collected through self-report instruments. Examinations <strong>of</strong> this complex relationship have been<br />
increasingly augmented by objective measures. Generally, three methods are being used in the assessment<br />
<strong>of</strong> walkability 61 :<br />
(i)<br />
(ii)<br />
(iii)<br />
Perceived measures obtained by telephone interview or self-administered questionnaires;<br />
Observational measures obtained using systematic observational methods (audits); and,<br />
Archival data sets that are <strong>of</strong>ten layered and analyzed with GIS.<br />
Common objective measures <strong>of</strong> walkability in an urban environment include measurements <strong>of</strong> density<br />
(e.g. residential density), diversity (e.g. land use mix as measured by retail floor area ratio, proximity<br />
measures), street connectivity (e.g. intersection density), and pedestrian oriented design (e.g. presence<br />
<strong>of</strong> cross-walks). These types <strong>of</strong> built environment measures, along with considerations <strong>of</strong> neighbourhood<br />
design and demographics, represent built environment fac<strong>to</strong>rs that are strongly and consistently associated<br />
with physical activity, walking and health outcomes in the literature. These built environment metrics<br />
are also <strong>of</strong>ten correlated with one another which has motivated the use <strong>of</strong> composite indices <strong>to</strong> capture<br />
multiple aspects <strong>of</strong> the built environment at once. 2;3 By measuring both form and content <strong>of</strong> neighbourhoods,<br />
walkability indices are expected <strong>to</strong> measure the degree <strong>to</strong> which an area provides opportunities<br />
<strong>to</strong> walk <strong>to</strong> various destinations. 54 Such indices are thought <strong>to</strong> capture the inter-relatedness <strong>of</strong> many built<br />
environment characteristics, minimize the effect <strong>of</strong> spatial collinearity, and ease the communication <strong>of</strong><br />
results. 60;61<br />
Increasingly, objective measures <strong>of</strong> the built environment are obtained using GIS technology. GIS integrates,<br />
analyzes, and maps data linked <strong>to</strong> time and location. GIS has become an invaluable <strong>to</strong>ol for<br />
studying the relationship between the built environment and physical activity. It has been used <strong>to</strong> test for<br />
associations between a number <strong>of</strong> built environment features and physical activity. However, despite the<br />
advances in GIS measurements, there is wide variability in how GIS variables are constructed <strong>to</strong> represent<br />
built environment concepts. Overcoming this and other measurement challenges will require a concerted<br />
effort by all relevant disciplines. 2;5;61<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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WALKABILITY<br />
A 2011 report reviewing the evidence establishing<br />
connections between walking, municipal environments<br />
and health in Ontario <strong>of</strong>fered that creating<br />
walkable places must be considered as part <strong>of</strong><br />
a comprehensive strategy <strong>to</strong> improve conditions<br />
for human health, promote population levels <strong>of</strong><br />
physical activity and reduce environmental damage<br />
caused by widespread au<strong>to</strong>mobile use. 57<br />
Until now, public health advocates, community<br />
leaders, and researchers across Ontario lack<br />
an empirically-based, actionable set <strong>of</strong> community<br />
level indica<strong>to</strong>rs that can be used <strong>to</strong> measure<br />
walkability in their jurisdictions. In the absence <strong>of</strong><br />
consensus, municipalities and local communities<br />
are examining various indica<strong>to</strong>rs in silos. Thus,<br />
current approaches are disparate and limit the<br />
comparability <strong>of</strong> outcomes between communities<br />
across the province.<br />
Objectively measured, province-wide built environment<br />
indica<strong>to</strong>rs that clearly illustrate the scale<br />
<strong>of</strong> walkability in a community could help public<br />
health practitioners identify their needs, moni<strong>to</strong>r<br />
health outcomes, facilitate research, guide possible<br />
interventions and inform planning and development<br />
decision making. Providing communities<br />
with the <strong>to</strong>ols <strong>to</strong> gather reliable and valid information<br />
about their own physical environments<br />
would allow them <strong>to</strong> make more effective assertions<br />
and better advocate their needs <strong>to</strong> decision<br />
makers. With indica<strong>to</strong>rs in place <strong>to</strong> measure<br />
the quality <strong>of</strong> the built environment as it relates<br />
<strong>to</strong> walking, both decision-makers and the general<br />
public can make informed decisions about<br />
policy and priorities. High quality indica<strong>to</strong>rs can<br />
help convey complex built environment and land<br />
use information in a user-friendly approach <strong>to</strong> engage<br />
and empower the wider public in promoting<br />
and advocating for their quality <strong>of</strong> life. 38<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 41<br />
LITERATURE REVIEW<br />
Understanding the impact <strong>of</strong> the built environment<br />
on walkability requires relevant, easy-<strong>to</strong>understand,<br />
and reliable measures <strong>to</strong> assess<br />
built environment features. 61-63 These measures<br />
assist in determining the status and progress <strong>of</strong><br />
interventions and provide evidence <strong>of</strong> the state <strong>of</strong><br />
population health within the context <strong>of</strong> the built<br />
environment. 63;64<br />
There has been considerable progress over the<br />
past decade in the use <strong>of</strong> multiple modes <strong>of</strong> assessment<br />
and diverse built environmental measures.<br />
61 Although measures used <strong>to</strong> assess walkability<br />
vary considerably, there are three main approaches<br />
used <strong>to</strong> operationalize environmental<br />
indica<strong>to</strong>rs related <strong>to</strong> walkability. The first method,<br />
obtained by interview or self-administered questionnaires,<br />
examines the extent <strong>to</strong> which individuals<br />
perceive various elements <strong>of</strong> the built environment.<br />
The second approach uses systematic<br />
observations, or audits, <strong>to</strong> quantify attributes<br />
<strong>of</strong> built environment characteristics. Finally, the<br />
third method uses geospatial databases and<br />
geographic information system (GIS) data in order<br />
<strong>to</strong> assess or develop indica<strong>to</strong>rs. 61;65;66<br />
PERCEPTIONS AND OBSERVATIONS<br />
OF THE BUILT ENVIRONMENT<br />
Numerous studies have examined physical activity<br />
behavior in relation <strong>to</strong> perceptions <strong>of</strong> the<br />
environment. Because <strong>of</strong> the slow speed and<br />
nature <strong>of</strong> walking, a pedestrian is typically much<br />
more aware <strong>of</strong> and exposed <strong>to</strong> the environment<br />
than a driver. The micro-features in the environment<br />
largely shape how accommodating an area<br />
is for pedestrian travel. Individuals’ perceptions<br />
<strong>of</strong> the built environment provide information on<br />
how a person experiences a neighbourhood and<br />
are measured by subjective indica<strong>to</strong>rs derived<br />
mostly by self-report data methods. These perceived<br />
measures are considered <strong>to</strong> be ‘subjective’<br />
because two unique individuals in the same<br />
environment may perceive it differently. Generally,<br />
identifying perceptions <strong>of</strong> the built environment<br />
is an important approach in determining<br />
behavioral patterns. According <strong>to</strong> the literature,<br />
the most commonly assessed measures <strong>of</strong> perception<br />
include land use, traffic, aesthetics, and<br />
safety from crime at neighborhood or community<br />
levels. 1;11;34;61<br />
In addition <strong>to</strong> perceived measures <strong>of</strong> the built environment,<br />
researchers have developed methods<br />
<strong>to</strong> assess the actual physical environment as it is<br />
being directly observed. 61 Audit <strong>to</strong>ols allow systematic<br />
observation <strong>of</strong> the physical environment,<br />
including the presence or absence <strong>of</strong> features hypothesized<br />
<strong>to</strong> affect physical activity. For example,<br />
the quality <strong>of</strong> existing sidewalks and the cleanliness<br />
<strong>of</strong> the walking environment. Audit <strong>to</strong>ols are<br />
used for measuring physical features that are<br />
best assessed through direct observation (e.g.<br />
architectural character, landscape maintenance).<br />
These <strong>to</strong>ols most commonly include one or more<br />
measures <strong>of</strong>: land use (e.g. commercial space);<br />
streets and traffic (e.g. pedestrian crosswalk);<br />
sidewalks (e.g. presence, width, and continuity<br />
<strong>of</strong> sidewalks); cycling features (e.g. presence <strong>of</strong><br />
bike lanes); public space/amenities (e.g. presence<br />
<strong>of</strong> benches); architecture or building characteristics<br />
(e.g. building height); parking/driveways<br />
(e.g. presence <strong>of</strong> parking lot(s)); maintenance<br />
(e.g. presence <strong>of</strong> litter); and indica<strong>to</strong>rs related <strong>to</strong><br />
safety (e.g. presence <strong>of</strong> graffiti). 13;61;67;68 Several<br />
audit <strong>to</strong>ols have been developed over the years<br />
including: Pedestrian <strong><strong>Environment</strong>al</strong> <strong>Data</strong> <strong>Scan</strong><br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
42<br />
WALKABILITY<br />
(PEDS) which was designed <strong>to</strong> capture a range <strong>of</strong><br />
elements <strong>of</strong> the built and natural environment efficiently<br />
and reliably 11;13;67 ; Systematic Pedestrian<br />
and Cycling <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> (SPACES), an<br />
audit instrument that subjectively evaluates the<br />
attractiveness and the degree <strong>of</strong> physical difficulty<br />
related <strong>to</strong> mobility and reflected in the pedestrian<br />
and cycling environment 11;69 ; and, both the Irvine-<br />
Minnesota inven<strong>to</strong>ry (I-M) and St. Louis University<br />
<strong>An</strong>alytic Audit Tool (SLU) have 150 <strong>to</strong> 200 measures<br />
and include questions with extensive detail<br />
about land uses. 11;62;67;70<br />
Audit <strong>to</strong>ols have also been used <strong>to</strong> engage people<br />
in better understanding their neighbourhoods and<br />
communities, as well as <strong>to</strong> empower community<br />
members in shaping local political decisions related<br />
<strong>to</strong> their environment. In the Canadian context,<br />
the Spryfield community in Nova Scotia recognized<br />
that one <strong>of</strong> the ways people could make<br />
improvements <strong>to</strong> their community was by taking<br />
inven<strong>to</strong>ry. They developed an audit <strong>to</strong>ol (“Walkabout<br />
Tool”) that would help bring their community<br />
<strong>to</strong>gether <strong>to</strong> assess the built environment, discover<br />
what improvements were desired and help<br />
people <strong>to</strong> work <strong>to</strong>gether <strong>to</strong> achieve them. The<br />
<strong>to</strong>ol was based on “Placecheck”, a checklist <strong>to</strong>ol<br />
widely and successfully used in many areas <strong>of</strong><br />
the United Kingdom <strong>to</strong> assess built environment<br />
features related <strong>to</strong> physical activity. 63;71 Researchers<br />
also developed a neighbourhood level audit<br />
<strong>to</strong>ol <strong>to</strong> measure and compare the active living potential<br />
<strong>of</strong> 112 Montreal neighbourhoods 55;67;68<br />
GEOGRAPHIC INFORMATION<br />
SYSTEMS (GIS)<br />
Early research on the relationship between built<br />
environments and health relied on subjective<br />
measures <strong>of</strong> the built environment collected<br />
through self-report instruments. 5;15 Today, GIS has<br />
become an invaluable <strong>to</strong>ol that integrates, analyzes,<br />
and maps data linked <strong>to</strong> time and location.<br />
GIS integrates hardware, s<strong>of</strong>tware, and data for<br />
capturing, managing, analyzing, and displaying<br />
all forms <strong>of</strong> geographically referenced information.<br />
72 It is rapidly becoming an essential part <strong>of</strong><br />
health research and GIS techniques are increasingly<br />
being utilized by the public health sec<strong>to</strong>r. 58<br />
Combined with measures <strong>of</strong> individual physical<br />
activity behavior collected by accelerometry and<br />
global positioning system (GPS) devices, observation,<br />
or self-report instruments, GIS has been<br />
used <strong>to</strong> test for associations between a number<br />
<strong>of</strong> built environment features and physical activity<br />
or walking. 5 These spatial approaches provide<br />
the ability <strong>to</strong> create maps, measure distances<br />
and travel times, and define the extent and nature<br />
<strong>of</strong> spatial relationships. 58 They take in<strong>to</strong> account<br />
the physical location <strong>of</strong> areas, boundaries,<br />
people, and services, as well as types <strong>of</strong> land<br />
use and natural features. Increased availability <strong>of</strong><br />
high-resolution spatial data combined with the<br />
computational power <strong>of</strong> GIS translates in<strong>to</strong> many<br />
options available <strong>to</strong> measure aspects <strong>of</strong> the built<br />
environment that influence walking. The use <strong>of</strong><br />
such objective measures <strong>of</strong> the built environment<br />
are important <strong>to</strong> better understanding how land<br />
use may influence walking behaviours and also<br />
<strong>to</strong> identify specific dimensions that could be used<br />
by public health and land use planners and policy<br />
makers <strong>to</strong> increase walking. 15<br />
BUILT ENVIRONMENT<br />
MEASURES OF WALKABILITY<br />
Guiding research on built environments and<br />
physical activity propose that different domains<br />
<strong>of</strong> physical activity (e.g. leisure, transportation)<br />
are affected by different environmental attributes.<br />
Leisure physical activity may be most affected by<br />
access <strong>to</strong>, and characteristics <strong>of</strong>, public and pri-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 43<br />
vate recreation facilities. Transportation physical<br />
activity may be most related <strong>to</strong> the proximity and<br />
directness <strong>of</strong> routes from home <strong>to</strong> destinations<br />
as well as characteristics <strong>of</strong> the walking and cycling<br />
infrastructure, including sidewalks, bicycle<br />
lanes, and trails. Therefore, <strong>to</strong> understand the influences<br />
<strong>of</strong> the built environment on walkability, a<br />
wide range <strong>of</strong> built environmental measures are<br />
required. 61<br />
Urban form refers <strong>to</strong> the layout <strong>of</strong> metropolitan<br />
areas and consists <strong>of</strong> many different components.<br />
Common components or metrics <strong>of</strong> walkability in<br />
an urban environment include density, land use<br />
mix (diversity), and street connectivity. These built<br />
environment measures, along with considerations<br />
<strong>of</strong> neighbourhood design and demographics, represent<br />
built environment fac<strong>to</strong>rs that are strongly<br />
and consistently associated with physical activity,<br />
walking and health outcomes in the literature.<br />
DENSITY AND DIVERSITY<br />
Density (compactness) is a measure <strong>of</strong> the amount<br />
<strong>of</strong> activity found in an area and can be defined in<br />
terms <strong>of</strong> population, housing unit, or employment<br />
density. High density represents compact land development,<br />
reduced travel distances between trip<br />
origin and destination, and reduced dependence<br />
on mo<strong>to</strong>rized transportation. 2 Therefore, density<br />
is an important correlate <strong>of</strong> walking. Population<br />
density is one <strong>of</strong> the most common measures<br />
included in studies <strong>of</strong> the built environment and<br />
transportation-based physical activity, primarily<br />
because the data are readily available (e.g. census<br />
data), it is easy <strong>to</strong> compute, and it has been<br />
consistently associated with walking for transportation.<br />
Gross population density (population<br />
per <strong>to</strong>tal land area) and net residential density<br />
(e.g. residential units per residential acre) are also<br />
commonly used measures <strong>of</strong> density from the<br />
reviewed literature. 61 It has been recommended<br />
that where possible, density measures should be<br />
measures <strong>of</strong> net density (as opposed <strong>to</strong> gross<br />
density) because it excludes other land uses.<br />
That being stated, residential density is important<br />
because it serves as a proxy for other urban form<br />
fac<strong>to</strong>rs, and is <strong>of</strong> particular importance at larger<br />
geographic scales <strong>of</strong> measurement or in cases<br />
where there is insufficient data. 49<br />
The retail floor area ratio (FAR), also known as<br />
commercial density, is a diverse measure that<br />
can be applied not only as a density indica<strong>to</strong>r but<br />
also as an indica<strong>to</strong>r <strong>of</strong> pedestrian-oriented design<br />
and used in conjunction with land use mix (LUM).<br />
When applying FAR (ratio <strong>of</strong> building square footage<br />
<strong>to</strong> land square footage or <strong>to</strong>tal parcel area),<br />
higher numbers indicate that the building is using<br />
most or all <strong>of</strong> the land, and lower ratios suggest<br />
much <strong>of</strong> the land is used for parking; fac<strong>to</strong>rs<br />
thought <strong>to</strong> impact pedestrian access. It is <strong>of</strong>ten<br />
used in composite measures assessing walkability<br />
and is a standard planning measure that is frequently<br />
used in development regulations. 10;49;61;73<br />
Diversity refers <strong>to</strong> the spatial arrangement <strong>of</strong><br />
land use that influences the distance and mode<br />
<strong>of</strong> travel. Mixed land use brings different and<br />
necessary uses in<strong>to</strong> relative proximity, thereby<br />
shortening trip distances and encouraging active<br />
modes <strong>of</strong> transport. 2 Mixed use is also thought<br />
<strong>to</strong> provide more visual variety and interest for<br />
pedestrians. 70 Although land use mix is a rather<br />
abstract measure, it is considered <strong>to</strong> be a crucial<br />
fac<strong>to</strong>r <strong>to</strong> include when assessing physical activity<br />
and walkability. It is theorized that multifunctional<br />
environments can improve proximity and reduce<br />
travel times between departure sites (e.g. home)<br />
and destination sites (e.g. work) and consequently<br />
encourages active transportation and physical<br />
activity. 65 Mixed land uses also have been associated<br />
with lower au<strong>to</strong>mobile ownership, use and<br />
emissions. 74<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
44<br />
WALKABILITY<br />
Measures <strong>of</strong> land use mix (LUM) can be categorized<br />
as accessibility (degree <strong>to</strong> which mixedland<br />
activities are easy <strong>to</strong> reach by residents),<br />
intensity (volume or magnitude <strong>of</strong> mixed-land<br />
uses present in an area), and pattern measures<br />
(degree <strong>of</strong> evenness <strong>of</strong> various land-use types in<br />
an area). 61;74 Given the appropriate parcel data,<br />
the quantitative calculation <strong>of</strong> land use mix is<br />
possible through a GIS or database interface. 49<br />
The most frequently used LUM measure is the<br />
entropy index representing how land is divided<br />
between different uses, most frequently residential,<br />
commercial (retail, entertainment), <strong>of</strong>fice, and<br />
institutional uses. Since the entropy index does<br />
not consider the type or intensity <strong>of</strong> mixing, the<br />
dissimilarity index provides a useful complement.<br />
The index measures dissimilarity based on<br />
predominant use <strong>of</strong> neighbouring squares (areas<br />
are divided in<strong>to</strong> one hectare squares and a predominant<br />
land use is assigned <strong>to</strong> each square). 38<br />
However, data availability is <strong>of</strong>ten a limiting fac<strong>to</strong>r<br />
in using these measures since parcel-level<br />
data are required <strong>to</strong> compute many land-use mix<br />
measures. These data are typically obtained from<br />
land ownership records but parcel-level data may<br />
be unavailable in some locations and in others<br />
may lack detail about land use. For business locations,<br />
alternative sources <strong>of</strong> data include the<br />
Yellow/White Pages or employment records. 61<br />
Several studies have opted <strong>to</strong> use survey items <strong>to</strong><br />
approximate land use mix (e.g. ‘‘Are there shops<br />
where you live?’’) but such approaches do not<br />
allow between-study comparisons because <strong>of</strong><br />
unspecified definitions <strong>of</strong> place. 2 Song and colleagues<br />
74 proposed the following considerations<br />
for choosing which land use measures <strong>to</strong> implement:<br />
(i) the extent <strong>to</strong> which a measure captures<br />
the presence or configuration <strong>of</strong> land uses; (ii)<br />
practical considerations including data collection,<br />
amount <strong>of</strong> computation and ease <strong>of</strong> communicability;<br />
and (iii) connection between the measures<br />
and the purpose <strong>of</strong> the investigation.<br />
Proximity describes the number and variety <strong>of</strong><br />
destinations within a specified distance <strong>of</strong> any location.<br />
It is a function <strong>of</strong> both density <strong>of</strong> development<br />
and the level <strong>of</strong> land use mix. As proximity<br />
and directness between destinations increases,<br />
distance between destinations decreases. As<br />
the distance between destinations decreases, so<br />
does travel by car. When distances between destinations<br />
are sufficiently short (1km or less), walking<br />
trips will more likely substitute for some driving<br />
trips. 49;60 Close proximity <strong>to</strong> parks (e.g. hectares<br />
<strong>of</strong> parks and playgrounds per/capita; proportion<br />
<strong>of</strong> 1-km buffer covered by parks), trails (e.g. distance<br />
<strong>to</strong> closest trail; km <strong>of</strong> trail per 1,000 residents;<br />
number <strong>of</strong> people in a 2.5 km radius <strong>of</strong> a<br />
trailhead; number <strong>of</strong> trail/hiking clubs per 1,000<br />
residents.), recreational facilities (e.g. <strong>to</strong>tal public<br />
community activity centres per/capita), pathways<br />
(e.g. walking path length (km)), and schools have<br />
been consistently correlated with physical activity,<br />
particularly in children. 34;38;48;49;65;75 One study<br />
found that residing in a neighbourhood with nearby<br />
parks and open spaces doubled the chances<br />
<strong>of</strong> an adult walking for a home-based discretionary<br />
trip (e.g. shopping, recreation, etc.). 73 Additionally,<br />
Tucker and colleagues 48 showed that<br />
greater access <strong>to</strong> recreational opportunities were<br />
essential <strong>to</strong> facilitating healthy levels <strong>of</strong> physical<br />
activity in youth. This was likely the first Canadian<br />
study <strong>of</strong> its kind and one <strong>of</strong> the only studies set<br />
in a mid-sized North American city (London, Ontario),<br />
as the literature is dominated by studies set<br />
in larger U.S. cities.<br />
Distance <strong>to</strong> retail activity is important in creating<br />
inviting pedestrian environments and in predicting<br />
levels <strong>of</strong> walking in cities or metropolitan<br />
areas, and small distances matter. 76 . Krizek and<br />
Johnson 76 defined neighborhood retail establishments<br />
as those having 200 or fewer workers in<br />
the following North American Industrial Classification<br />
System (NAICS) categories:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 45<br />
• Food and beverage s<strong>to</strong>res (e.g. grocery,<br />
convenience, meat, fish, specialty, alcohol);<br />
• Health and personal care s<strong>to</strong>res (e.g.<br />
pharmacy);<br />
• Clothing and clothing accessory s<strong>to</strong>res (e.g.<br />
shoes, jewelry, luggage);<br />
• Sporting goods, hobby, book, and music<br />
s<strong>to</strong>res; general merchandise s<strong>to</strong>res (includes<br />
department s<strong>to</strong>res);<br />
• Miscellaneous s<strong>to</strong>res (e.g. florists, novelty,<br />
used merchandise, pet, art, <strong>to</strong>bacco); and,<br />
• Food services and drinking places (e.g.<br />
restaurants).<br />
Distance from home <strong>to</strong> the nearest neighborhood<br />
retail establishment is a common proximity measure,<br />
and distances commonly used <strong>to</strong> analyze<br />
walking behavior include: less than 200 meters,<br />
200 <strong>to</strong> 399 meters, 400 <strong>to</strong> 599 meters, and 600<br />
meters or more. 76 Other research proposed the<br />
use <strong>of</strong> the following proximity indica<strong>to</strong>rs <strong>to</strong> measure<br />
diversity: percent <strong>of</strong> residential dwellings within<br />
2.5 km (walking distance) <strong>of</strong> retail sales, food and<br />
beverage, business/<strong>of</strong>fice, and neighbourhood<br />
grocery s<strong>to</strong>res. 38 Using metrics such as mean<br />
distance <strong>to</strong> supermarket (metres) and number <strong>of</strong><br />
supermarkets within 1,000 metres, Larsen and<br />
Gilliland 77 used network-based GIS accessibility<br />
measures <strong>to</strong> determine the extent <strong>to</strong> which food<br />
deserts exist in London, Ontario, between 1961<br />
and 2005. They concluded that residents living in<br />
inner-city neighbourhoods <strong>of</strong> low socioeconomic<br />
status had the poorest access <strong>to</strong> supermarkets<br />
and that spatial inequalities in access <strong>to</strong> supermarkets<br />
have increased over time, particularly<br />
in certain neighbourhoods where distinct urban<br />
food deserts now exist. Other studies have determined<br />
the number <strong>of</strong> retailers within a certain distance,<br />
using either circular buffers or road network<br />
buffers. When Seliske 51 examined the relationship<br />
between the built environment and obesityrelated<br />
behaviours in Ontario youth, circular and<br />
road network buffers were used <strong>to</strong> identify that<br />
the presence <strong>of</strong> food retailers near schools was<br />
strongly associated with students regularly eating<br />
their lunch at snack-bars, fast-food restaurants<br />
or cafés. At 100 metres, students with 3 or more<br />
food retailers near their schools had 3.42 greater<br />
odds <strong>of</strong> eating their lunchtime meal at a food retailer<br />
compared <strong>to</strong> students with no food retailers<br />
near their schools. In order <strong>to</strong> minimize fast-food<br />
consumption among Ontario youth, this same<br />
study also determined that 1,000 metres was the<br />
optimal buffer size <strong>of</strong> the food retail environment<br />
surrounding schools. 51<br />
Additionally, built environments that are more walkable<br />
tend <strong>to</strong> support higher levels <strong>of</strong> public transit<br />
service and ridership. 3;78;79 The provision <strong>of</strong> public<br />
transit use in turn reduces au<strong>to</strong> dependence and<br />
plays a major role in meeting energy consumption,<br />
greenhouse gas emission, and air pollution<br />
reductions. 3;52 Lachapelle and colleagues 52 found<br />
a positive association between the frequency <strong>of</strong><br />
commuting by transit and physical activity when<br />
controlling for sociodemographic characteristics,<br />
car availability, and neighborhood income and<br />
walkability. Other research found that adults living<br />
in the <strong>to</strong>p twenty-five percent <strong>of</strong> the most walkable<br />
areas in Vancouver, were between 2 and 3<br />
times more likely <strong>to</strong> walk or take transit for any<br />
home-based trip compared <strong>to</strong> those in the least<br />
walkable neighbourhoods. 3 Measures <strong>of</strong> access<br />
<strong>to</strong> public transit s<strong>to</strong>ps (e.g. distance <strong>to</strong> nearest<br />
transit s<strong>to</strong>p; transit s<strong>to</strong>p density; number <strong>of</strong> transit<br />
s<strong>to</strong>ps; service areas and routes; percent population<br />
with bus access) are commonly used in testing<br />
for associations with transportation physical<br />
activity. 1;5;60;70;72;73;77;80 Not only are a higher number<br />
<strong>of</strong> transit s<strong>to</strong>ps associated with higher levels<br />
<strong>of</strong> physical activity, 5 Ryan and Frank’s 81 research<br />
identified that higher land-use density near sta-<br />
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46<br />
WALKABILITY<br />
tion areas placed greater opportunities near the<br />
transit system, which increased the propensity<br />
<strong>to</strong> use transit for accessing those opportunities.<br />
In this study, land-use density near station areas<br />
was measured in terms <strong>of</strong> net residential density<br />
in the station area buffer, and average retail floorarea-ratio<br />
(FAR) in the station area buffer. 81<br />
STREET PATTERN CONNECTIVITY<br />
Connectivity affects the ease <strong>of</strong> travel between<br />
places and represents the degree <strong>to</strong> which roads,<br />
pedestrian walkways, trails and cycling paths are<br />
connected so that moving from point A <strong>to</strong> point B<br />
is relatively easy. 82 Street networks that are more<br />
connected are thought <strong>to</strong> increase walkability by<br />
<strong>of</strong>fering shorter and many alternate routes. The<br />
grid pattern, where streets cross at right angles<br />
and form small blocks and numerous intersections,<br />
is the archetypal high connectivity network;<br />
shorter blocks in a street grid system result in<br />
more intersections and better connectivity. On<br />
the other hand, neighbourhoods that include fewer<br />
intersections, blocks or sidewalks, as well as<br />
more dead-ends and cul-de-sacs, are argued <strong>to</strong><br />
be less conducive <strong>to</strong> walking. 2;83 Diverse studies<br />
have examined the association between various<br />
measures <strong>of</strong> street connectivity. Many, but not all<br />
<strong>of</strong> these studies find positive associations between<br />
measures <strong>of</strong> connectivity and walkability.<br />
Measures <strong>of</strong> connectivity are related <strong>to</strong> the physical<br />
design and the layout <strong>of</strong> transportation infrastructure.<br />
Connectivity measures quantify the network<br />
connections between trips and describe directness<br />
<strong>of</strong> possible paths and the number <strong>of</strong> mobility<br />
options available. 60 The most common measures<br />
<strong>of</strong> connectivity include: block size and length<br />
(e.g. block density; average or median block area),<br />
intersection density (e.g. number <strong>of</strong> intersections<br />
per unit <strong>of</strong> area), street density, connected node<br />
ratio (e.g. percentage <strong>of</strong> 4-way intersections),<br />
segment/intersections ratio, and the number<br />
<strong>of</strong> intersections per length <strong>of</strong> street network.<br />
The easiest way <strong>to</strong> operationalize street network<br />
connectivity is by measuring the number <strong>of</strong> intersections.<br />
This is an important measure because a<br />
higher density <strong>of</strong> intersections corresponds with a<br />
more direct path between destinations. It is <strong>of</strong>ten<br />
more straight forward <strong>to</strong> measure the number <strong>of</strong><br />
intersections than block size or block length because<br />
the latter measures are difficult <strong>to</strong> assess in<br />
suburban areas where the street layout may not<br />
match the traditional definition <strong>of</strong> a block. Derived<br />
indices such as the alpha and gamma index are<br />
also measures that are reported and analyzed in<br />
relation <strong>to</strong> pedestrian behavior and mode choice.<br />
The alpha index uses the concept <strong>of</strong> a circuit (a<br />
finite, closed path starting and ending at a single<br />
node) and is the ratio <strong>of</strong> the number <strong>of</strong> actual<br />
circuits <strong>to</strong> the maximum number <strong>of</strong> circuits. The<br />
gamma index represents the ratio <strong>of</strong> the number<br />
<strong>of</strong> links in the network <strong>to</strong> the maximum possible<br />
number <strong>of</strong> links between nodes. 2;3;49;61;65;83;83;83<br />
PEDESTRIAN ORIENTED DESIGN<br />
In addition <strong>to</strong> the characteristics <strong>of</strong> dwelling<br />
density, mixed land use and street pattern<br />
connectivity, several studies have documented the<br />
importance <strong>of</strong> street design and physical activity<br />
in relation <strong>to</strong> safety, pleasant surroundings, sidewalks<br />
and accessible recreational facilities such<br />
as parks and walking trails. 53 For instance, higher<br />
levels <strong>of</strong> objectively measured safety and comfort<br />
are positively associated with <strong>to</strong>tal physical activity<br />
behavior. Street design refers <strong>to</strong> the scale and<br />
design <strong>of</strong> sidewalks and roads and how they are<br />
managed for various uses (e.g. narrower streets,<br />
traffic signalling and calming designs that regulate<br />
speed and volume); street networks that support<br />
and balance a variety <strong>of</strong> transport modes<br />
(e.g. public transit, walking, cycling and mo<strong>to</strong>r-<br />
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WALKABILITY 47<br />
ized vehicles); street-specific bicycle-friendly design;<br />
and street lights that reduce night-time glare,<br />
uplight and light trespass (e.g. reduce night-light<br />
pollution in rural and urban areas). 82<br />
With respect <strong>to</strong> leisure-time physical activity, higher<br />
neighborhood safety, higher traffic safety, and<br />
aesthetically appealing communities are positively<br />
associated with physical activity engagement. 5;70<br />
Aesthetics are <strong>of</strong>ten assessed by observation and<br />
include the presence <strong>of</strong> grass, flowers, and trees<br />
(shade); presence <strong>of</strong> public art, interesting natural,<br />
architectural, and his<strong>to</strong>rical features. 50 Measures<br />
<strong>of</strong> neighborhood comfort, cleanliness and safety<br />
include the following (noting that some measures<br />
could appear in multiple categories):<br />
• Comfort: presence <strong>of</strong> cross-walks, sidewalk<br />
buffers; number <strong>of</strong> traffic lanes; street width;<br />
block length; sidewalk width; traffic circles;<br />
curb bulb-outs; speed bumps/humps;<br />
pavement treatments; posted speed limits.<br />
• Cleanliness: percentage <strong>of</strong> street segments<br />
with visible litter, graffiti or dumpsters.<br />
• Safety: crime rates; presence <strong>of</strong> graffiti,<br />
windows facing street, street lighting,<br />
abandoned or vacant buildings, and rundown<br />
buildings; indica<strong>to</strong>rs <strong>of</strong> loitering, alcohol or<br />
drugs, and gang activity. 5;50;58;70;84;85<br />
In a California research study evaluating indices <strong>of</strong><br />
accessibility, safety and comfort, safety emerged<br />
as the most important built environment characteristic<br />
related <strong>to</strong> both destination and recreational<br />
walking. 84 Areas with a higher percentage <strong>of</strong><br />
design elements related <strong>to</strong> perceived crime safety<br />
had higher adult walking rates compared <strong>to</strong> areas<br />
in which these features were absent. In another<br />
study measuring walkability <strong>to</strong> schools among<br />
elementary-aged children across Ontario, nearly<br />
seventy-five percent <strong>of</strong> students preferred <strong>to</strong> walk<br />
or cycle <strong>to</strong> school. However, schools and parents<br />
were less supportive <strong>of</strong> these modes <strong>of</strong> transport,<br />
identifying security (e.g. vandalism and theft <strong>of</strong> bicycles<br />
on school property) and safety (e.g. unsafe<br />
routes) concerns as barriers. 86<br />
It is important <strong>to</strong> mention the normalized difference<br />
vegetation index (NDVI), the most widely<br />
used vegetation health index. It has been used<br />
in numerous studies <strong>to</strong> estimate vegetation biomass,<br />
greenness, and dominant species. NDVI<br />
calculations are based on the principle that green<br />
plants absorb radiation in the visible region <strong>of</strong> the<br />
spectrum and reflect radiation in the near-infrared<br />
region. Values close <strong>to</strong> 1 represent strong vegetation<br />
cover, while low values indicate rock and<br />
bare soil. 65;75;87 A recent study investigated the<br />
impact <strong>of</strong> neighborhood greenness on physical<br />
activity levels, using satellite images <strong>to</strong> calculate<br />
the NDVI. 75 Among pre-school children, higher<br />
levels <strong>of</strong> neighborhood vegetation were associated<br />
with higher levels <strong>of</strong> physical activity after<br />
controlling for race/ethnicity, gender, parental<br />
education and body mass index (BMI), as well<br />
as parental support for physical activity. <strong>An</strong>other<br />
study showed that higher neighbourhood greenness,<br />
as measured by the NDVI, was associated<br />
with lower odds <strong>of</strong> children increasing their BMI<br />
scores over two years. 87<br />
DEMOGRAPHICS<br />
When assessing walkability, one must consider<br />
the people that live, work, and play in the built<br />
environment. People react in different ways <strong>to</strong><br />
their surroundings depending on demographic<br />
characteristics such as age, gender, education,<br />
and income. Ross and colleagues 88 investigated<br />
the influence <strong>of</strong> neighborhood characteristics<br />
on BMI in urban Canada. Using measures <strong>of</strong><br />
dwelling density, urban sprawl, and social characteristics<br />
(e.g. recent immigrant (≤ 5 years),<br />
educational attainment and income), the study<br />
concluded that BMI was strongly patterned by<br />
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48<br />
WALKABILITY<br />
an individual’s social position in urban Canada,<br />
and the magnitude <strong>of</strong> the effect differed for men<br />
and women. For instance, living in a neighborhood<br />
with a high proportion <strong>of</strong> recent immigrants<br />
was associated with lower BMI for men, but not<br />
for women. 88 Using an activity-friendly index, another<br />
study found that high diabetes neighbourhoods<br />
in Toron<strong>to</strong> had lower average household<br />
incomes and higher concentrations <strong>of</strong> visible<br />
minority residents and immigrants. Meanwhile,<br />
higher income neighbourhoods had low rates<br />
<strong>of</strong> diabetes, even in parts <strong>of</strong> Toron<strong>to</strong> that scored<br />
low on activity-friendliness or had poor access <strong>to</strong><br />
health care resources. The researchers hypothesized<br />
that a higher income may provide people<br />
with more options for exercise and healthy eating<br />
and make them less dependent on their immediate<br />
environment. 58 Butler and colleagues 59<br />
also examined socio-demographic fac<strong>to</strong>rs (i.e.<br />
age, marital status, labour force status, student<br />
status, education level, yearly household income,<br />
immigrant status, region, urban/rural status,<br />
smoking status), geography, and physical activity<br />
(i.e. typical daily activity, leisure-time physical<br />
activity as measured by a Physical Activity Index)<br />
correlates <strong>of</strong> walking and cycling for non-leisure<br />
purposes (i.e. <strong>to</strong> work, school, or errands) across<br />
Canada. This study found that for both genders,<br />
and all models, lower household incomes were<br />
more highly associated with walking and cycling.<br />
They concluded that where opportunities exist <strong>to</strong><br />
walk and cycle, low-income Canadians are more<br />
likely <strong>to</strong> make use <strong>of</strong> them. 59 Furthermore, money<br />
spent on pedestrian and cycling infrastructure<br />
was particularly important in supporting the mobility<br />
and participation <strong>of</strong> lower-income individuals<br />
in work and community life. 59 <strong>An</strong>other study by<br />
Larsen and colleagues 77 found that residents <strong>of</strong><br />
inner-city neighbourhoods <strong>of</strong> low socioeconomic<br />
status had the poorest access <strong>to</strong> supermarkets.<br />
They concluded that policies aimed at improving<br />
public health must recognize not only the spatial,<br />
but also socioeconomic inequities with respect <strong>to</strong><br />
access <strong>to</strong> healthy and affordable food.<br />
COMPOSITE MEASURES<br />
<strong>Built</strong> environment metrics are <strong>of</strong>ten correlated<br />
with one another. For example, grid patterns,<br />
sidewalks, and public transit are usually found<br />
<strong>to</strong>gether in old parts <strong>of</strong> cities or traditional pre-<br />
World War II neighborhoods which also have high<br />
density and diversity. This, along with the multitude<br />
<strong>of</strong> built environment fac<strong>to</strong>rs that influence<br />
transportation mode choice, has motivated the<br />
use <strong>of</strong> composite indices <strong>to</strong> capture many aspects<br />
<strong>of</strong> the built environment at once. 2;73 By measuring<br />
both form and content <strong>of</strong> neighbourhoods, walkability<br />
indices are expected <strong>to</strong> measure the degree<br />
<strong>to</strong> which an area provides opportunities <strong>to</strong><br />
walk <strong>to</strong> various destinations. 54 Such indices are<br />
thought <strong>to</strong> capture the inter-relatedness <strong>of</strong> many<br />
built environment characteristics thereby minimizing<br />
the effect <strong>of</strong> spatial collinearity, and ease<br />
the communication <strong>of</strong> results. 60;61 It has also been<br />
suggested that composite measures <strong>of</strong> walkability<br />
are more consistent predic<strong>to</strong>rs <strong>of</strong> walking behavior<br />
than single component measures. 60<br />
The earliest work undertaken by Frank and<br />
colleagues, 89 used a walkability index with three<br />
sub-components (residential density; connectivity;<br />
and land use mix) and found significant associations<br />
with physical activity. In attempting <strong>to</strong><br />
replicate this original walkability index, modifications<br />
are <strong>of</strong>ten required due <strong>to</strong> differences in both<br />
the structure and availability <strong>of</strong> secondary data for<br />
a study area. For instance, subsequent versions<br />
<strong>of</strong> indices have modified the land use mix (LUM)<br />
computation by varying or adding new categories<br />
such as retail floor area ratio (FAR) as an indica<strong>to</strong>r<br />
<strong>of</strong> pedestrian-oriented design. 10;61 In 2010, Frank<br />
and colleagues 73 modified the index <strong>to</strong> account for<br />
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WALKABILITY 49<br />
FAR in a composite measure that also included:<br />
net residential density (ratio <strong>of</strong> residential units <strong>to</strong><br />
the land area devoted <strong>to</strong> residential use per block<br />
group); retail floor area (retail building floor area<br />
footprint divided by retail land floor area footprint);<br />
intersection density (ratio between the number <strong>of</strong><br />
true intersections (three or more legs) <strong>to</strong> the land<br />
area <strong>of</strong> the block group in acres); and land use<br />
mix or entropy score (residential, retail, entertainment,<br />
<strong>of</strong>fice and institutions). Composite indices<br />
vary by the components they include, the scale at<br />
which they are measured, and the methods used<br />
in computation but the literature suggests that<br />
they are suitable measures <strong>of</strong> walkability. 10;73<br />
Both the Vancouver study and the Atlanta-based<br />
SMARTRAQ study found that walkable neighbourhoods<br />
were associated with increased<br />
physical activity when measured using an index<br />
that included land use mix, residential/commercial<br />
density and street connectivity (intersection<br />
density). In Vancouver, the walkability index<br />
turned out <strong>to</strong> be a highly significant predic<strong>to</strong>r<br />
<strong>of</strong> travel patterns as well as obesity. 3;78 Toron<strong>to</strong><br />
researchers assessed relationships between<br />
neighbourhood design and diabetes using an<br />
Activity-Friendly Index (AFI) that measured how<br />
conducive individual neighbourhoods were <strong>to</strong><br />
walking, bicycling and other types <strong>of</strong> physical<br />
activity. The index included measurements such<br />
as population density, density <strong>of</strong> and access <strong>to</strong><br />
retail services, car ownership rates, and rates <strong>of</strong><br />
drug-related and violent crime. They found that<br />
people living in “activity-friendly” neighbourhoods<br />
reported walking and bicycling more <strong>of</strong>ten and<br />
were less dependent on cars for travel. Activityfriendly<br />
neighbourhoods also had lower rates <strong>of</strong><br />
diabetes. 58<br />
<strong>An</strong>other study conducted in Montreal by Manaugh<br />
and colleagues 54 examined four existing walkability<br />
measures and indices at multiple geographic<br />
scales in order <strong>to</strong> understand how these measures<br />
were related <strong>to</strong> actual observed travel behavior.<br />
All <strong>of</strong> the examined walkability indices and<br />
individual measures performed well in describing<br />
pedestrian behavior. It was also recognized that<br />
socio-demographic characteristics played a key<br />
role in walkability outcomes. The study results include<br />
the following:<br />
• Indices were all highly correlated with<br />
walking trips for most non-work trip<br />
purposes;<br />
• The highest level <strong>of</strong> correlation was<br />
observed for home-based shopping trips.<br />
For example, the free online Walk Score<br />
index explained as much, if not more, <strong>of</strong><br />
the variation in walking trips <strong>to</strong> shopping<br />
compared <strong>to</strong> the other walkability indices<br />
being studied; and,<br />
90<br />
• The simple Pedshed method was found<br />
<strong>to</strong> be the best walkability index when it<br />
came <strong>to</strong> explaining the odds <strong>of</strong> walking <strong>to</strong><br />
school. 54<br />
Web-based geospatial technologies that estimate<br />
neighborhood walkability have emerged as<br />
potential <strong>to</strong>ols <strong>to</strong> be used in public health. The<br />
Walk Score <strong>to</strong>ol has become increasingly recognized<br />
in the study <strong>of</strong> walkability due <strong>to</strong> its accessibility,<br />
international scale and use <strong>of</strong> up-<strong>to</strong>-date<br />
data. This popular <strong>to</strong>ol allows a user <strong>to</strong> enter any<br />
query location in<strong>to</strong> the online interface on the<br />
Walk Score website (publicly available at www.<br />
walkscore.com) and receive the Walk Score (0<br />
<strong>to</strong> 100) assigned <strong>to</strong> that location, free <strong>of</strong> charge.<br />
Duncan and colleagues 66 evaluated the validity <strong>of</strong><br />
Walk Score for assessing neighborhood walkability<br />
based on several GIS indica<strong>to</strong>rs <strong>of</strong> neighborhood<br />
walkability in four U.S. metropolitan areas<br />
with several street network buffer distances (i.e.<br />
400-, 800-, and 1600-meter buffers). This study<br />
confirmed previous findings demonstrating that<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
50<br />
WALKABILITY<br />
Walk Score is a valid measure for estimating<br />
neighborhood walkability in multiple geographic<br />
locations and at multiple spatial scales. 66 Similarly,<br />
other researchers have used free, publicly<br />
available data <strong>to</strong> develop composite measures<br />
<strong>of</strong> walkability. Vargo and colleagues 60 used data<br />
that was readily available from Google and found<br />
these measures <strong>to</strong> be nearly as effective in predicting<br />
walking outcomes as walkability measures<br />
derived without such publicly and nationally<br />
available data.<br />
Other researchers and practitioners have applied<br />
composite indices <strong>to</strong> evaluating urban sprawl. Indicative<br />
features <strong>of</strong> sprawl are ‘‘leapfrog’’ development<br />
pattern, low density, homogeneous and segregated<br />
land uses, and an extensive disconnected,<br />
hierarchical road network, making mo<strong>to</strong>rized<br />
travel a necessity and active transport unsafe and<br />
impractical. 2 Areas <strong>of</strong> urban sprawl are associated<br />
with higher levels <strong>of</strong> au<strong>to</strong>mobile dependence<br />
and overweight populations compared <strong>to</strong> more<br />
compact neighbourhoods. 57 For instance, one<br />
Canadian study showed that metropolitan sprawl<br />
was associated with higher BMI for men, but the<br />
effect was not significant for women. 88 Based on<br />
an index developed by Razin and Rosentraub in<br />
2000, the urban sprawl index used in this study<br />
was composed <strong>of</strong> three equally weighted dimensions:<br />
(i) proportion <strong>of</strong> Census Metropolitan Area<br />
(CMA) dwellings that are single or detached units,<br />
(ii) dwelling density, and (iii) percentage <strong>of</strong> CMA<br />
population living in the urban core (an urban area<br />
around which a CMA is delineated and contains<br />
a minimum <strong>of</strong> 100,000 residents). 88;91 <strong>An</strong>other<br />
urban sprawl index created in collaboration with<br />
Smart Growth America was developed <strong>to</strong> assess<br />
urban development patterns based on 4 fac<strong>to</strong>rs<br />
(comprised <strong>of</strong> twenty-two measures): residential<br />
density; neighborhood mix <strong>of</strong> homes, jobs, and<br />
services; strength <strong>of</strong> activity centers and down<strong>to</strong>wns;<br />
and, accessibility <strong>of</strong> the street network.<br />
They applied this index <strong>to</strong> 83 metropolitan areas<br />
across the United States (representing nearly half<br />
<strong>of</strong> the nation’s population) and found that people<br />
living in areas with more sprawl tended <strong>to</strong> drive<br />
greater distances, own more cars, breathe more<br />
polluted air, face a greater risk <strong>of</strong> traffic fatalities<br />
as well as walk and use transit less. Although<br />
this study was not designed <strong>to</strong> prove that landuse<br />
patterns cause these mentioned conditions,<br />
sprawl and its component fac<strong>to</strong>rs were found<br />
<strong>to</strong> be a greater predic<strong>to</strong>r than numerous demographic<br />
control variables that were also tested. 92<br />
LIMITATIONS OF MEASURES<br />
AND METHODOLOGIES<br />
Metrics <strong>of</strong> the built environment can be created<br />
with a variety <strong>of</strong> data sources, computational<br />
methods, and characterizations <strong>of</strong> the environment.<br />
In 2006, Handy and colleagues 93 found<br />
little consistency in measures <strong>of</strong> the built environment<br />
and walking, making it difficult <strong>to</strong> compare<br />
results from one research study <strong>to</strong> another. Four<br />
years later, Feng and colleagues 2 concluded that<br />
the most striking feature <strong>of</strong> their research was the<br />
absence <strong>of</strong> agreement on how the built environment<br />
should be measured and modeled. Spatial<br />
extent, source <strong>of</strong> data, and the number and<br />
range <strong>of</strong> places compared across studies were<br />
so variable, that virtually no two studies evaluated<br />
built environment metrics regarding these three<br />
features the same way. 2 Choice <strong>of</strong> methodology<br />
is further complicated by variations in terminology.<br />
In 2011, Butler and colleagues 5 stated that the<br />
lack <strong>of</strong> standardization among built environment<br />
definitions creates a challenge for public health<br />
researchers and practitioners and hampers their<br />
ability <strong>to</strong> synthesize evidence and identify environmental<br />
features that may explain physical activity<br />
behavior.<br />
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WALKABILITY 51<br />
GIS data is increasingly being used <strong>to</strong> evaluate<br />
neighborhood walkability. 66 Although GIS measurements<br />
have evolved and improved over the<br />
years, there is still wide variability in how GIS<br />
variables are constructed <strong>to</strong> represent built environment<br />
concepts. 2;5 Typically one <strong>of</strong> three<br />
GIS methods is used <strong>to</strong> define spatial units <strong>to</strong><br />
measure aspects <strong>of</strong> the built environment. The<br />
first method is <strong>to</strong> use pre-defined spatial units<br />
such as census tracts <strong>to</strong> construct measures <strong>of</strong><br />
the built environment. However,<br />
<strong>to</strong> enhance accuracy,<br />
some studies have begun <strong>to</strong><br />
use location information (e.g.<br />
addresses, postal codes) <strong>to</strong><br />
define unique areas for each<br />
individual. <strong>An</strong>other common<br />
method <strong>to</strong> define spatial units<br />
has been <strong>to</strong> establish a circular<br />
buffer around individuals’<br />
geocoded location at a given<br />
radius. While likely providing a<br />
more representative assessment<br />
<strong>of</strong> the built environment<br />
that may influence walking, a<br />
circle may not accurately represent<br />
the spatial area that<br />
influences walking. 15 Barriers<br />
(e.g. fences, walls, buildings,<br />
large or busy roadways, rivers,<br />
cliffs, railways) frequently exist<br />
on the landscape and prevent<br />
walking along the shortest<br />
path. Information on these types <strong>of</strong> barriers is<br />
not normally included in network databases and<br />
the time and expense required <strong>to</strong> collect such information<br />
is prohibitive. Given that these barriers<br />
are not included in network databases and the<br />
time and expense required <strong>to</strong> collect such information<br />
is prohibitive, some way <strong>of</strong> recognizing<br />
the increased walking distances created by such<br />
barriers must be included in a model designed <strong>to</strong><br />
Increasingly,<br />
objective<br />
measures<br />
<strong>of</strong> the built<br />
environment<br />
are obtained<br />
using GIS.<br />
measure accessibility. 80 Thus, a polygon-based<br />
road network buffer can be used <strong>to</strong> define one<br />
kilometre areas around respondent locations, accounting<br />
for the increased walking distances created<br />
by these barriers. 80 While this method may<br />
provide a more accurate assessment <strong>of</strong> the actual<br />
land area that influences walking, joining road<br />
vertices using straight lines may lead <strong>to</strong> inaccuracies<br />
in areas that do not have dense, regular<br />
grid street patterns 15 It is therefore apparent that<br />
researchers and practitioners<br />
need <strong>to</strong> carefully consider the<br />
most appropriate measurements<br />
with which <strong>to</strong> calculate<br />
built environment characteristics<br />
and the associated<br />
methodological limitations.<br />
While GIS data can be useful<br />
for measuring neighborhood<br />
walkability, there are challenges<br />
in the measurement,<br />
analysis and interpretation<br />
<strong>of</strong> data collected using GIS<br />
methods and <strong>to</strong>ols that may<br />
require specialized expertise.<br />
Furthermore, GIS data layers<br />
might not be readily accessible<br />
for certain geographic regions<br />
and can be expensive <strong>to</strong><br />
acquire. 61;66<br />
As mentioned earlier, metrics<br />
used <strong>to</strong> assess neighborhood<br />
walkability include use <strong>of</strong> self-reported information<br />
and use <strong>of</strong> systematic field observation (audits).<br />
Yet self-reported measures <strong>of</strong> neighborhood<br />
walkability can be implicated in same-source bias,<br />
and there are other noteworthy problems including<br />
issues with reliability, validity, low response<br />
rates and a biased sample <strong>of</strong> respondents. In relation<br />
<strong>to</strong> systematic field observations, they can be<br />
very laborious (i.e. time-intensive and have mul-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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WALKABILITY<br />
tiple logistical constraints), <strong>of</strong>ten require significant<br />
specialized training and are no<strong>to</strong>rious for being<br />
very costly. 66 Plus, the relationships observed<br />
cannot be interpreted as definitely causal (due <strong>to</strong><br />
a lack <strong>of</strong> longitudinal studies for example). 43 Available<br />
evidence establishes correlations between<br />
the built environment and walking, but correlation<br />
does not mean causality. This has led researchers<br />
<strong>to</strong> debate whether self-selection explains the<br />
observed correlations; that is, do residents who<br />
prefer <strong>to</strong> walk choose <strong>to</strong> live in more walkable<br />
neighborhoods? 93 Therefore, the results from built<br />
environment and walking studies with cross-sectional<br />
designs have been criticized on the issue<br />
<strong>of</strong> self-selection and the bias it introduces in<strong>to</strong><br />
studies. 8 Additional criticisms highlight that built<br />
environment and walking studies do not account<br />
for the possibility that walking, particularly transportation<br />
walking, substitutes for other forms <strong>of</strong><br />
physical activity. Several research reviews stress<br />
the importance <strong>of</strong> matching specific measures <strong>of</strong><br />
the built environment <strong>to</strong> specific types <strong>of</strong> physical<br />
activity. 8<br />
in the development <strong>of</strong> walkability indices) and<br />
their association with walking behaviour. Different<br />
computations <strong>of</strong> LUM were found <strong>to</strong> be relevant<br />
for different types and amounts <strong>of</strong> walking, suggesting<br />
the need <strong>to</strong> explore alternative or complimentary<br />
measures <strong>of</strong> the environment. 10<br />
Although limitations have been identified for all<br />
types <strong>of</strong> built environment measures, existing<br />
measures have stimulated rapid advancements<br />
in understanding environmental correlates <strong>of</strong><br />
physical activity in a variety <strong>of</strong> populations and<br />
settings. Most measures <strong>of</strong> the built environment<br />
are considered <strong>to</strong> be first-generation measures,<br />
so further development is needed. In particular,<br />
further work is required <strong>to</strong> standardize measures<br />
and terminology, improve the technical quality <strong>of</strong><br />
measures, ensure relevance for diverse populations,<br />
and integrate built environment measures<br />
in<strong>to</strong> public health and planning systems. 5;61 Moving<br />
forward, cross- and inter-disciplinary collaborations<br />
will be critical <strong>to</strong> making these measurement<br />
improvements.<br />
A composite measure <strong>of</strong> walkability is useful <strong>to</strong><br />
avoid problems <strong>of</strong> multi-collinearity between<br />
variables that are highly correlated with one another.<br />
For example, areas with higher residential<br />
densities <strong>of</strong>ten have more mixed use and connected<br />
streets. However, the use <strong>of</strong> composite<br />
measures introduces methodological concerns<br />
regarding validity, reliability, and generalizability,<br />
as many <strong>of</strong> these indices are developed for a<br />
specific setting. 2;73 In addition, composite indices<br />
may be less useful for assessing the impact <strong>of</strong> an<br />
intervention, as one cannot identify the specific<br />
component that should be the highest priority for<br />
change. 2 One particular area where increased<br />
transparency is needed is in the measurement<br />
and computation <strong>of</strong> land use mix (LUM) variables.<br />
Christian and colleagues 10 examined different<br />
entropy based computations <strong>of</strong> LUM (used<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 53<br />
KEY INFORMANT INTERVIEWS SUMMARY:<br />
WALKABILITY<br />
Twelve key informant interviews were conducted between April and June 2012 <strong>to</strong> explore built environment<br />
data availability, quality and gaps as well as capacity for the assessment <strong>of</strong> urban walkability in Ontario.<br />
Key informants represented a diverse cross-section <strong>of</strong> disciplines including public health, land use<br />
planning, academia, as well as local, provincial, and federal agencies. For the most part, key informants<br />
represented the most established organizations in terms <strong>of</strong> the assessment <strong>of</strong> urban walkability in Ontario.<br />
The results <strong>of</strong> the key informant interviews were subsequently used <strong>to</strong> inform the development <strong>of</strong> a survey<br />
that was administered <strong>to</strong> Public Health Units across Ontario.<br />
HIGHLIGHTS<br />
• Walkability indices have been applied in major urban areas across Ontario.<br />
• Public health organizations with the most established walkability assessment programs were<br />
using a walkability index:<br />
o There was, however, limited collaboration between organizations in the development and<br />
implementation <strong>of</strong> indices. That said, the indices in current use had similar key components:<br />
density, land use mix, and connectivity.<br />
• Similar data sources were being used <strong>to</strong> assess walkability among organizations, with a<br />
predominant use <strong>of</strong> secondary data sources.<br />
• GIS was commonly used in the assessment <strong>of</strong> urban walkability.<br />
• Several challenges were identified, including human resource and financial capacity challenges<br />
as well as numerous data limitations (lack <strong>of</strong> data-sharing agreements, lack <strong>of</strong> standardization,<br />
availability, accessibility, cost, and quality <strong>of</strong> data).<br />
• Recommendations for data infrastructure improvements included the need for data sharing<br />
agreements, improvements <strong>to</strong> core built environment data sets, collaboration between<br />
public health and land use planning departments, and standardization <strong>of</strong> terminology and<br />
methodologies used in the assessment <strong>of</strong> urban walkability.<br />
BUILT ENVIRONMENT MEASURES<br />
The most commonly used measures in the assessment <strong>of</strong> urban walkability as identified by the key<br />
informants, included land-use mix (measure <strong>of</strong> diversity), density (population, residential, employment),<br />
retail floor area ratio (commercial density), street connectivity, and proximity (<strong>to</strong> community focal points).<br />
Less <strong>of</strong>ten, streetscape fac<strong>to</strong>rs (such as aesthetics) were included in the assessment <strong>of</strong> walkability.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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WALKABILITY<br />
For the most part, organizations with the most established walkability assessment programs in Ontario<br />
were using an index. Table 2 represents five walkability indices used across the province <strong>of</strong> Ontario;<br />
check marks indicate the individual components that comprise each index. Several <strong>of</strong> these walkability<br />
indices incorporated similar measures, namely: measures <strong>of</strong> density, diversity (e.g. land use mix), and<br />
connectivity (e.g. intersection density).<br />
For most organizations, land use planning and public health departments worked closely <strong>to</strong>gether <strong>to</strong><br />
operationalize these indices. Five organizations were assessing walkability using an index with similar<br />
components, however there was limited collaboration with one another in relation <strong>to</strong> the development<br />
and implementation <strong>of</strong> these indices. One organization was able <strong>to</strong> operationalize their walkability index<br />
in multiple jurisdictions across Ontario using core data sets that are widely accessible. However, this was<br />
accomplished independently, with limited collaboration and input from the jurisdictions studied. <strong>An</strong>other<br />
organization relied on the built environment indica<strong>to</strong>rs established by APHEO <strong>to</strong> help operationalize their<br />
index.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 55<br />
Table 2: <strong>Built</strong> environment measures that comprise walkability indices in Ontario, 2012 (n=5)<br />
Measures<br />
Organization<br />
1<br />
Organization<br />
2<br />
Index<br />
Organization<br />
3<br />
Organization<br />
4<br />
Organization<br />
5<br />
Density<br />
Residential density • • • • •<br />
Population density •<br />
Diversity<br />
Mixed land use • • •<br />
Retail floor area<br />
ration (FAR) or<br />
Commercial density<br />
Employment<br />
density<br />
Proximity<br />
(e.g. <strong>to</strong> services,<br />
employment)<br />
Connectivity<br />
Street connectivity<br />
(e.g. Intersection<br />
density)<br />
• • •<br />
•<br />
• •<br />
• • • • •<br />
Trail availability •<br />
Bicycle path<br />
availability<br />
Parking •<br />
•<br />
Paedestrian oriented design<br />
Aesthetics •<br />
Sidewalk<br />
characteristics<br />
•<br />
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56<br />
WALKABILITY<br />
DATA INFRASTRUCTURE<br />
The methodologies used for the computation <strong>of</strong> walkability measures (e.g. formulas, individual components),<br />
including the level <strong>of</strong> granularity (i.e. geographic scale) for analysis, varied by organization. However,<br />
most organizations were accessing secondary data sources in order <strong>to</strong> assess urban walkability in<br />
Ontario and GIS was the most commonly used <strong>to</strong>ol in these assessments. Primary data collection was<br />
uncommon, with only one organization using the Pedestrian <strong><strong>Environment</strong>al</strong> <strong>Data</strong> <strong>Scan</strong> (PEDS) audit <strong>to</strong>ol<br />
<strong>to</strong> assess urban walkability.<br />
Key informants identified commonly used data sources in the assessment <strong>of</strong> urban walkability,<br />
as follows:<br />
• Canadian Community Health Survey (CCHS; Statistics Canada)<br />
• Census (Statistics Canada)<br />
• DMTI Spatial<br />
• Google Maps<br />
• Land Information Ontario (LIO; Ministry <strong>of</strong> Natural Resources)<br />
• Municipal Property Assessment Corporation (MPAC)<br />
• National Resources and Values Information (NRVIS; Ministry <strong>of</strong> Natural Resources)<br />
• Street Network File (Statistics Canada)<br />
• Rapid Risk Fac<strong>to</strong>r Surveillance System (RRFSS)<br />
• Transportation Tomorrow Survey (TTS; Ministry <strong>of</strong> Transportation)<br />
When asked for suggestions in relation <strong>to</strong> where <strong>to</strong> get the most appropriate sources <strong>of</strong> built environment<br />
data <strong>to</strong> measure walkability across Ontario, key informants identified the larger core data sets (presented<br />
above). Most notably, the Census, Street Network files from Statistics Canada, LIO, the Transportation<br />
Tomorrow Survey (TTS) from the Ministry <strong>of</strong> Transportation, DMTI and MPAC were recommended as<br />
core data sources and sets for the assessment <strong>of</strong> urban walkability. Several key informants highlighted<br />
the need for improvement and enhancement <strong>of</strong> these core data sets in order <strong>to</strong> capture walkability<br />
more completely and accurately. For example, it was suggested that TTS could potentially incorporate a<br />
specific section on walkability.<br />
DATA AVAILABILITY AND ACCESSIBILITY<br />
The core data sets are readily available, but many <strong>of</strong> the data sets that are considered <strong>of</strong> high value <strong>to</strong> key<br />
informants are financially expensive (e.g. MPAC, DMTI, RRFSS). The cost <strong>of</strong> purchasing data from these<br />
existing sources was identified by many key informants as a barrier <strong>to</strong> undertaking walkability studies.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 57<br />
One key informant stated the following:<br />
“Certainly cost is a barrier <strong>to</strong> purchasing some <strong>of</strong> the retail data sets<br />
such as the Dunn and Bradstreet as well as MPAC. It would be fantastic<br />
if MPAC data was available for a reasonable cost.”<br />
Moreover, some municipalities may not be willing or able (based on existing legal agreements) <strong>to</strong> share<br />
data already purchased or local parcel-level zoning files, and thus, data availability across jurisdictions<br />
<strong>of</strong>ten depends on existing sharing agreements and relationships. Creating and fostering sharing agreements<br />
across jurisdictions was identified by many key informants as a prominent data need.<br />
In addition, key informants expressed that existing data is <strong>of</strong>ten created for other land use and planning<br />
functions, so data specific <strong>to</strong> walkability is not <strong>of</strong>ten accessible. Some zoning files from smaller municipalities<br />
or cities are not always created, or the quality <strong>of</strong> files are questionable given the GIS capacity<br />
and resources <strong>of</strong> smaller communities, and thus data for these locales was considered inaccessible by<br />
several key informants.<br />
The availability, quality and usability <strong>of</strong> metadata was also a concern for some key informants. Often<br />
metadata that describes who created and/or edited the file, when and how the file was created, and how<br />
data were collected was missing or incomplete.<br />
One key informant indicated that one <strong>of</strong> the greatest challenges was related <strong>to</strong> knowing what data are<br />
available.<br />
“...the challenge is knowing what’s there. Because we don’t hold the<br />
data, we’re not the data cus<strong>to</strong>dians, it’s difficult for a program area<br />
<strong>to</strong> obviously know all the files that belong <strong>to</strong> another agency...it’s<br />
impossible for me <strong>to</strong> be aware <strong>of</strong> everything that the City has unless I<br />
go out and ask for a catalogue...a catalogue <strong>to</strong> me is calling colleagues<br />
in the Planning Department, the Engineering Department that I happen<br />
<strong>to</strong> know”<br />
Two key informants indicated that they were fortunate <strong>to</strong> not have overly prohibitive data access and<br />
availability challenges. One <strong>of</strong> these key informants came from a well-resourced organization (i.e. in terms<br />
<strong>of</strong> human resource and financial capacity) where for the most part, appropriate data sharing agreements<br />
were in place. <strong>An</strong>other specified the benefits <strong>of</strong> being part <strong>of</strong> an upper-tier municipality as it relates <strong>to</strong><br />
data accessibility:<br />
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WALKABILITY<br />
“We’re in a lucky position being an upper-tier municipality that actually<br />
holds this data and partners well with our local municipality so we<br />
used existing data sets that are used for other land use and planning<br />
functions”<br />
DATA QUALITY<br />
Key informants indicated that data quality varies depending on the data source and its intended use. In<br />
general, they were confident in the data quality <strong>of</strong> existing data sources. Key informants were most confident<br />
in data that were collected at the city or regional level. Key informants felt quality control mechanisms<br />
are most rigorous at the city or regional level compared <strong>to</strong> larger-level data sets that produce provincial<br />
or national data. The larger the city, the more resources and capacity <strong>to</strong> collect data and conduct quality<br />
control, and thus the more confident some key informants were in the data submission for larger cities<br />
and regions. Key informants <strong>of</strong>ten voiced that smaller more rural areas do not have the human resource<br />
and financial capacity <strong>to</strong> implement the necessary quality control processes for local data sources.<br />
Crude land-use, population, and retail data is widely available across Ontario using such sources as the<br />
Statistics Canada, DMTI, and LIO. However, key informants voiced concern and suspicion about the<br />
quality and coverage <strong>of</strong> land-use data from these data sources for rural or smaller communities, most<br />
notably from the Road Network and Trail Network Files (LIO) and Street Network File (Statistics Canada).<br />
There was concern that smaller cities and communities do not have the GIS capacity or data <strong>to</strong> accurately<br />
feed these files, and that existing data may not be properly validated given large geographic areas<br />
<strong>of</strong> more rural areas. Parcel-level or planning files either do not exist for some smaller communities, or are<br />
not readily available <strong>to</strong> researchers across jurisdictions <strong>to</strong> supplement existing data sets. Most inconsistencies<br />
in data coverage affected the capacity <strong>of</strong> smaller municipalities <strong>to</strong> obtain detailed GIS data <strong>to</strong><br />
supplement existing standardized data sources.<br />
By using existing data sets such as the census and MPAC, the quality <strong>of</strong> an organization’s built environment<br />
data was described as “really good” by one key informant. However, most key informants commented<br />
on the lack <strong>of</strong> consistency between municipalities, from the way data is collected <strong>to</strong> the way it is<br />
applied in the assessment <strong>of</strong> walkability. One key informant stated:<br />
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WALKABILITY 59<br />
“the biggest issues with data quality are probably around consistency<br />
between municipalities in terms <strong>of</strong> the way that they are collecting<br />
data and then within the data sets that are available at the provincial<br />
level, the classification (e.g. in the retail-type data) is <strong>of</strong>ten not suited<br />
<strong>to</strong> the types <strong>of</strong> businesses that we are looking for...or there are <strong>of</strong>ten<br />
many duplicates if you’re trying <strong>to</strong> go through and clean records which<br />
can be quite time consuming as well.”<br />
The geographic scale <strong>of</strong> data for existing sources was also a concern. It is difficult <strong>to</strong> disentangle data<br />
or conduct analyses for smaller, more defined geographic scales such as communities/neighbourhoods<br />
within larger municipalities. Key informants noted RRFSS and CCHS as particularly problematic in this<br />
regard.<br />
In relation <strong>to</strong> the frequency <strong>of</strong> data submission, key informants reported that it is <strong>of</strong>ten difficult <strong>to</strong> track<br />
trends because <strong>of</strong> the frequency <strong>of</strong> data submission for existing sources and the temporality <strong>of</strong> data<br />
that is needed. Concern was voiced about the temporality <strong>of</strong> data even within newer versions <strong>of</strong> existing<br />
data sources.<br />
<strong>An</strong>other data gap identified by some key informants was the lack <strong>of</strong> complete and/or usable sidewalk<br />
data. Sidewalk data from existing sources were difficult <strong>to</strong> integrate in<strong>to</strong> broader databases, such as in<strong>to</strong><br />
road network databases. Key informants voiced that sidewalk data also required a lot <strong>of</strong> cleaning and<br />
was subsequently unusable and impractical in several instances.<br />
“integrating that [sidewalks] in<strong>to</strong> the road network is still a challenge<br />
technically and time wise.”<br />
“it would be nice <strong>to</strong> have...sidewalk data which we don’t have and nobody<br />
really collects that information as far as we know in the format<br />
that would be useful for us.”<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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WALKABILITY<br />
STANDARDIZATION<br />
Key informants identified the need for standardization, from standard terminology <strong>to</strong> standards for operationalizing<br />
built environment measures. They noted inconsistencies in operationalization and variations<br />
in the pro<strong>to</strong>cols and <strong>to</strong>ols used <strong>to</strong> collect, manage, and analyze walkability data.<br />
“the data is collected differently, the explanations are different”<br />
Standardized data collection and analysis pro<strong>to</strong>cols and s<strong>of</strong>tware would enable cross-jurisdiction comparability<br />
<strong>of</strong> walkability findings. It must be noted, however, that some key informants voiced their concern<br />
about a standardized set <strong>of</strong> walkability indica<strong>to</strong>rs that would be applied regardless <strong>of</strong> context. Because<br />
<strong>of</strong> the differences between the size and the level <strong>of</strong> development <strong>of</strong> communities, it was suggested by<br />
one key informant that it may be necessary <strong>to</strong> have a scoring system based on different types <strong>of</strong> urban<br />
contexts.<br />
One key informant pointed out how context matters especially as it relates <strong>to</strong> the dissemination <strong>of</strong> results<br />
for their walkability assessment:<br />
“I think we have <strong>to</strong> be ready for the conversation that’s going <strong>to</strong> come<br />
up. The interesting thing about the built environment is that context<br />
matters immensely and so once it’s about me and where I live, I have<br />
things <strong>to</strong> say about that. You know, if you’re talking about the walkability<br />
in my Region or you’re talking about my neighbourhood, well yeah, but<br />
what about this path. Yeah okay, I know you can walk there, but it’s<br />
a scary place <strong>to</strong> walk. You know there’s a discussion and a visceral<br />
response which we expect [in discussing the results <strong>of</strong> a walkability<br />
assessment].”<br />
DATA SHARING AGREEMENTS<br />
A major challenge identified with the current data infrastructure was the data licensing and sharing agreements<br />
that prohibit sharing across municipalities or jurisdictions. Many key informants reported that lack<br />
<strong>of</strong> cross-municipal data sharing agreements and collaboration was a major data infrastructure challenge<br />
as it relates <strong>to</strong> walkability assessments.<br />
A recommendation was <strong>to</strong> set up a small unit that is responsible for collecting and updating information<br />
that is available for all municipalities across the province, even suggesting a central and open data site<br />
that is easily accessible by all municipalities. This was identified as particularly important for smaller municipalities<br />
that may not have the expertise <strong>to</strong> collect and maintain appropriate data. Similarly, key inform-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 61<br />
ants also recommended better means <strong>to</strong> collaborate with urban planners, whom many believed have a<br />
greater understanding <strong>of</strong> appropriate data sources for walkability analyses. They also felt that planners<br />
would benefit from such partnerships, as public health practitioners have a better understanding <strong>of</strong> how<br />
<strong>to</strong> translate walkability findings in<strong>to</strong> practical health intervention practices.<br />
CAPACITY<br />
HUMAN RESOURCE CAPACITY<br />
Staff shortages and competing priorities were identified as internal organizational capacity challenges<br />
with regards <strong>to</strong> walkability assessments. It was noted that smaller municipalities have even greater capacity<br />
challenges as they <strong>of</strong>ten lacked the financial and human resources <strong>to</strong> conduct analyses or hire<br />
independent consultants.<br />
“There is a huge learning curve around this [assessing walkability]. So,<br />
the concepts are simple in terms <strong>of</strong> mixed use, residential density,<br />
connectivity. The ‘floor-area retail ones’ take a little bit <strong>to</strong> get your head<br />
around, but the concepts are simple. The application <strong>of</strong> them, the<br />
techniques that are involved, the specific questions that come up and<br />
how <strong>to</strong> handle them were difficult even for our experienced group”<br />
Additionally, the translation <strong>of</strong> evidence for practical policy or intervention purposes was identified as a<br />
prominent internal capacity challenge. Collaboration between public health units and urban planning departments<br />
was recommended <strong>to</strong> conduct scientifically sound walkability studies that can be translated<br />
in<strong>to</strong> policy and have practical use.<br />
GIS EXPERTISE<br />
The majority <strong>of</strong> key informants interviewed work with existing sources <strong>of</strong> data <strong>to</strong> evaluate and describe<br />
walkability, as opposed <strong>to</strong> primary data collection in the field. The most prominent internal capacity challenge<br />
public health practitioners experienced when using objective walkability data was the lack <strong>of</strong> technical<br />
GIS expertise <strong>to</strong> interpret, clean, and enhance data, as well as conduct geographic analyses. It was<br />
noted that Public Health Units generally do not have an in-house GIS expert, and <strong>of</strong>ten Epidemiologists<br />
do not have the GIS expertise <strong>to</strong> conduct the appropriate analyses. Some public health units also lacked<br />
the appropriate GIS programs and licenses. One key informant stated the following:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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“What I know is that as in many public health units, the Epidemiologist,<br />
because he or she is good with computers and good with numbers, is<br />
given a GIS bundle...[but] it is it’s own discipline and what happens is<br />
that people from other disciplines are attempting or trying <strong>to</strong> take on<br />
this role that requires a great deal <strong>of</strong> attention.”<br />
Even a well-resourced organization (i.e. in terms <strong>of</strong> human resource and financial capacity) commented on<br />
the lack <strong>of</strong> GIS expertise available <strong>to</strong> meet their needs in relation <strong>to</strong> the assessment <strong>of</strong> urban walkability:<br />
“we have issues with having funded technical staff <strong>to</strong> do GIS analysis. I<br />
have some staff who have bits and pieces <strong>of</strong> that strength, but there’s<br />
not a GIS expert per se.”<br />
CHALLENGES<br />
Several challenges were identified by key informants in relation <strong>to</strong> the current walkability data infrastructure,<br />
many <strong>of</strong> which have already been mentioned in the sections above. Lack <strong>of</strong> data standardization<br />
was identified as a challenge, with reference <strong>to</strong> walkability indica<strong>to</strong>rs that are operationalized in multiple<br />
ways and various pro<strong>to</strong>cols and s<strong>of</strong>tware programs that are being used <strong>to</strong> collect, manage, and analyze<br />
walkability data.<br />
Other data limitations included data coverage (for smaller communities), scale, frequency <strong>of</strong> submission<br />
(temporality), accessibility (cost, sharing), and overall quality <strong>of</strong> data including lack <strong>of</strong> quality control<br />
mechanisms. Lack <strong>of</strong> complete metadata was identified as a significant data gap in conducting walkability<br />
studies.<br />
Capacity challenges included lack <strong>of</strong> technical GIS expertise, staff shortages, competing priorities as well<br />
as limited capacity in relation <strong>to</strong> translating walkability evidence.<br />
RECOMMENDATIONS<br />
Collaboration between public health and urban planning was recommended, including the need for education<br />
and a greater understanding <strong>of</strong> respective roles and responsibilities. One key informant stated:<br />
“we’ve been trying <strong>to</strong> build those bridges between Planning and Engineering<br />
[and Public Health] and really helping everybody understand,<br />
but they are really different languages...Public Health needs <strong>to</strong> understand<br />
that world <strong>to</strong> be able <strong>to</strong> work with that world and integrate best<br />
in<strong>to</strong> that world as possible.”<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 63<br />
Several key informants suggested a provincial approach such as a Public Health Ontario providing a reposi<strong>to</strong>ry<br />
<strong>of</strong> key indica<strong>to</strong>rs and a core data set <strong>to</strong> be used as a building block. Creating and fostering data<br />
sharing agreements across Ontario jurisdictions was identified by many key informants as a prominent<br />
data need as well. One recommendation was <strong>to</strong> set up a small unit that is responsible for collecting and<br />
updating information that is available for all municipalities across the province, even suggesting a central<br />
and open data site that is easily accessible by all municipalities.<br />
Further collaboration among municipalities was recognized as a necessary and important next step. One<br />
key informant specifically stated that the Ontario Federation <strong>of</strong> Municipalities should be on board for any<br />
provincial approach. While another suggested that municipalities will be an important linkage for data<br />
sources moving forward.<br />
“if municipalities were able <strong>to</strong> have some sort <strong>of</strong> a round table where<br />
they got <strong>to</strong>gether and discussed how they were collecting their data<br />
and tried <strong>to</strong> come up with consistent pro<strong>to</strong>cols for data collection for<br />
these types <strong>of</strong> measures and then implement those pro<strong>to</strong>cols, I think<br />
that would be very useful”<br />
Several key informants mentioned that some <strong>of</strong> the core built environment data sets should be improved<br />
and enhanced <strong>to</strong> capture walkability more completely and accurately (e.g. TTS survey, as mentioned<br />
earlier).<br />
Several key informants recognized the need for a reduced list <strong>of</strong> core standardized walkability measures<br />
and/or a standardized walkability measurement <strong>to</strong>ol that can be applied or manipulated for various contexts.<br />
However, they recognized that a “one size fits all” approach was not necessarily the solution and<br />
that ultimately, context matters.<br />
Finally, one key informant emphatically stated that there needs <strong>to</strong> be political will (it’s “not on public or<br />
political radar”) in order <strong>to</strong> thrust assessments <strong>of</strong> urban walkability in Ontario forward.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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SUMMARY OF SURVEY RESULTS:<br />
WALKABILITY<br />
This section represents a summary <strong>of</strong> the built environment survey results specific <strong>to</strong> walkability measures<br />
and data sources. The survey was administered <strong>to</strong> Ontario Public Health Units (PHU) in July 2012.<br />
The number <strong>of</strong> organizations responding <strong>to</strong> each question varies due <strong>to</strong> the structure (skip pattern) <strong>of</strong><br />
the survey. For instance, if a PHU indicated that they assess walkabiltiy using an index, then they were<br />
prompted <strong>to</strong> provide further details about the specific components <strong>of</strong> the index. Whereas a PHU that did<br />
not report using an index was prompted <strong>to</strong> skip forward <strong>to</strong> other survey questions. Overall, this impacted<br />
the number <strong>of</strong> PHUs (i.e. denomina<strong>to</strong>r) that responded <strong>to</strong> each survey question.<br />
HIGHLIGHTS<br />
• 25 Ontario PHUs completed the walkability section <strong>of</strong> the survey<br />
• 72% <strong>of</strong> PHUs surveyed assess walkability in urban environments in Ontario<br />
• Most PHUs (65%) have been assessing urban walkability for 1 <strong>to</strong> 5 years<br />
• The most common methods <strong>of</strong> assessing urban walkability in Ontario include self-administered<br />
survey (66.7%) and systematic observation (61.1%)<br />
• 38.9% <strong>of</strong> PHUs use Geographic Information Systems (spatial analytics) <strong>to</strong> assess walkability<br />
• Street network (44%) is the most common geographic data used <strong>to</strong> assess urban walkability<br />
• 44% (8 PHUs) assess urban walkability using an index (or composite measure)<br />
• Of the 3 PHUs that have been assessing urban walkability for the longest period <strong>of</strong> time (6 <strong>to</strong> 10<br />
years), all three were using an index <strong>to</strong> assess urban walkability.<br />
• 44% (8 PHUs) assess urban walkability using individual measures<br />
• Most PHUs have access <strong>to</strong> the following data: location <strong>of</strong> schools (92%), parks (92%), trails<br />
(92%), and recreation centres (88%)<br />
• The most common challenges identified by PHUs included (i) human resource capacity (84%), (ii)<br />
variations between municipalities (64%), and (iii) data availability (60%)<br />
The walkability survey response rate was 69% (25/36), with 25 Ontario PHUs completing the survey.<br />
Overall, 72% (18/25) <strong>of</strong> PHUs that responded <strong>to</strong> the survey, assess walkability in urban environments in<br />
Ontario.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 65<br />
Overall, 18% have been assessing walkability for less than one year, 65% for 1 <strong>to</strong> 5 years, and 18% for<br />
6 <strong>to</strong> 10 years (Figure 2).<br />
Figure 2: Number <strong>of</strong> years PHUs assessed urban walkability in Ontario, 2012 (n=17)<br />
18%<br />
18%<br />
66<br />
WALKABILITY<br />
Ontario PHUs were asked <strong>to</strong> identify which method(s) they use <strong>to</strong> assess urban walkability – the following<br />
methods were identified: self-administered survey (assessing perceptions) (66.7%), systematic observation<br />
(i.e. audit <strong>to</strong>ol) (61.1%), Geographic Information System (spatial analytics) (38.9%), interview (e.g.<br />
RRFSS) (27.8%), and accelerometers (5.6%) (Figure 3). PHUs also identified other methods including<br />
focus groups, public consultation, and desk<strong>to</strong>p mapping/visualization.<br />
Figure 3: Method(s) used by PHUs <strong>to</strong> assess urban walkability in Ontario, 2012 (n=18)<br />
Method<br />
Self-administered survey<br />
Systematic observation<br />
Geographic Information System<br />
Interview<br />
Other<br />
22%<br />
28%<br />
39%<br />
61%<br />
67%<br />
Accelerometers<br />
6%<br />
0 2 4 6 8 10 12 14<br />
No. <strong>of</strong> Public Health Units<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 67<br />
Of the 3 PHUs that have been assessing urban walkability for the longest time (6 <strong>to</strong> 10 years), all three<br />
were using self-administered surveys and systematic observation <strong>to</strong> assess urban walkability. Two <strong>of</strong><br />
these PHUs were also using GIS <strong>to</strong> assess urban walkability.<br />
Of the 11 PHUs that have been assessing urban walkability for 1 <strong>to</strong> 5 years, 73% (8 PHUs) use selfadministered<br />
surveys. A small portion (36%; 4 PHUs) use GIS <strong>to</strong> assess walkability.<br />
When asked about geographic scale most commonly used <strong>to</strong> assess urban walkability, street network,<br />
dissemination area, census track, dissemination block, census subdivision, and census division were<br />
identified (Figure 4). Other geographic scales were identified including postal code, neighbourhood, city,<br />
and ward level as well as school catchment area.<br />
Figure 4:<br />
Geographic scale most commonly used by PHUs <strong>to</strong> assess urban walkability in<br />
Ontario, 2012 (n=18)<br />
Geographic Scale<br />
Street Network<br />
Dissemination Area<br />
Census Tract<br />
Dissemination Block<br />
Census Subdivision<br />
Census Division<br />
Other<br />
6%<br />
11%<br />
22%<br />
28%<br />
33%<br />
44%<br />
50%<br />
0 1 2 3 4 5 6 7 8 9 10<br />
No. <strong>of</strong> Public Health Units<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
68<br />
WALKABILITY<br />
WALKABILITY INDEX<br />
Of the PHUs that assess urban walkability, 44% (8 public health units) assess urban walkability using<br />
an index (or composite measure). Of the 3 PHUs that have been assessing urban walkability for the<br />
longest period <strong>of</strong> time (6 <strong>to</strong> 10 years), all three were using an index <strong>to</strong> assess urban walkability.<br />
Of the 8 PHUs that use a walkability index, 3 (38%) PHUs identified using density and retail floor<br />
area as part <strong>of</strong> the index. For density, the census was identified as the primary data source. One PHU<br />
indicated that density is integrated in<strong>to</strong> the model but it is not significant because <strong>of</strong> high correlation with<br />
connectivity. For retail floor area, MPAC and commercial land use (just the footprint not the height <strong>of</strong> the<br />
building) were identified as data sources.<br />
Five (63%) PHUs use land use mix (LUM) as part <strong>of</strong> their walkability index. <strong>Data</strong> sources and<br />
methodologies <strong>to</strong> calculate LUM include MPAC, commercial land use as a diversity measure, community<br />
walkabout, surveys, focus groups, GIS inven<strong>to</strong>ry, and indirect measures (observation) for a ‘Canadian<br />
Master Walking Class’ and ‘Stepping It Up’ pilot.<br />
Seven (88%) PHUs use connectivity measures as part <strong>of</strong> their walkability index. <strong>Data</strong> sources used<br />
<strong>to</strong> assess connectivity included the following: community walkabout, surveys, focus groups and a GIS<br />
inven<strong>to</strong>ry for one PHU; streets, sidewalks, multiuse paths and sometimes parks for another PHU; number<br />
and connectivity <strong>of</strong> sidewalks surrounding school areas through observation; road network GIS layer;<br />
finally, sidewalks, recreation trails, routes, and perceived risk at one PHU.<br />
When PHUs were asked about other elements included in their walkability indices, they reported the<br />
following: pathways and trails network map, and quality <strong>of</strong> urban environment (e.g. street trees).<br />
One PHU indicated that their team has been utilizing a GIS-based <strong>to</strong>ol developed as part <strong>of</strong> the Coalitions<br />
Linking Action & Science for Prevention (CLASP) research project <strong>to</strong> assess walkability, utilizing MPAC<br />
parcel data, census data, Canadian Community Health Survey (CCHS) measures and other health<br />
data that is accessible <strong>to</strong> the PHU. <strong>An</strong>other PHU noted that they do not specifically do the work but<br />
have contracted universities or consultants <strong>to</strong> assist in the assessment <strong>of</strong> walkability in their health<br />
jurisdiction.<br />
INDIVIDUAL MEASURES<br />
Of the PHUs that assess urban walkability, 44% (8 PHUs) assess individual measures.<br />
None <strong>of</strong> the 3 PHUs that have been assessing urban walkability for the longest time (6 <strong>to</strong> 10 years)<br />
indicated using individual measures <strong>to</strong> assess urban walkability.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 69<br />
The following individual connectivity measures are used by PHUs <strong>to</strong> assess urban walkability:<br />
• Block size and length (4 PHUs)<br />
• Intersection density (3)<br />
• Street density (2)<br />
• Connected node ratio (1)<br />
One health unit uses the ‘I can walk’ <strong>to</strong>ol <strong>to</strong> assess connectivity at the neighbourhood level (www.icanwalk.ca).<br />
The following individual density measures are used by PHUs <strong>to</strong> assess urban walkability:<br />
• Population density (6 PHUs)<br />
• Residential density (3)<br />
The following individual diversity measures are used by PHUs <strong>to</strong> assess urban walkability:<br />
• Proximity (6 PHUs), including <strong>to</strong>:<br />
o Schools<br />
o Food outlets<br />
o Parks<br />
o Trails<br />
• Land use mix (LUM) (3)<br />
• Retail Floor Area (FAR) (1)<br />
The following are individual pedestrian oriented design measures used by PHUs <strong>to</strong> assess urban walkability:<br />
• Crime rates (4 PHUs)<br />
• Street lighting (4)<br />
• Canopy coverage (trees) (4)<br />
• Posted speed limits (3)<br />
• Presence <strong>of</strong> street furniture (3)<br />
• Sidewalk width (2)<br />
• Proximity <strong>to</strong> sources <strong>of</strong> air pollution (2)<br />
• Presence <strong>of</strong> traffic circles (1)<br />
• No. <strong>of</strong> traffic lanes (1)<br />
• Presence <strong>of</strong> graffiti (1)<br />
• Pedestrian and cyclist collision rates (Police Dept.)<br />
• Accessible transit s<strong>to</strong>ps (YRT/Viva)<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
70<br />
WALKABILITY<br />
Public health units reported having access <strong>to</strong> several data (sources) related <strong>to</strong> walkability, most commonly<br />
having access <strong>to</strong> location <strong>of</strong> schools (92%), parks (92%), trails (92%), and recreation centres (88%)<br />
(Figure 5). Even PHUs that do not assess urban walkability, have access <strong>to</strong> these data (schools, parks,<br />
trails, recreation centres).<br />
Figure 5: Public Health Unit access <strong>to</strong> data sources in Ontario, 2012 (n=25)<br />
<strong>Data</strong> Source<br />
Location <strong>of</strong> schools<br />
Location <strong>of</strong> parks<br />
Location <strong>of</strong> trails<br />
Location <strong>of</strong> recreation centres<br />
Census boundary files<br />
Presence <strong>of</strong> sidewalks<br />
Public transit data<br />
Street network files<br />
Speed limits per street<br />
Rapid Risk Fac<strong>to</strong>r Surveillance System (RRFSS)<br />
Traffic counts<br />
Municipal Property Assessment Corporation (MPAC)<br />
Crime statistics<br />
Street lighting standards<br />
Digital elevation model (<strong>to</strong>pography)<br />
Location <strong>of</strong> controlled cross walks<br />
Location <strong>of</strong> traffic controls (e.g. speed bumps)<br />
Other<br />
DMTI Spatial<br />
Business registrations<br />
76%<br />
72%<br />
68%<br />
64%<br />
60%<br />
60%<br />
56%<br />
56%<br />
52%<br />
48%<br />
48%<br />
44%<br />
40%<br />
36%<br />
32%<br />
32%<br />
92%<br />
92%<br />
92%<br />
88%<br />
0 5 10 15 20 25<br />
No. <strong>of</strong> Public Health Units<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 71<br />
Public Health Units also identified having access <strong>to</strong> data for collisions, urban forests, litter containers,<br />
walking <strong>to</strong> work, and transit use. Other data sources include Teranet; Transportation Tomorrow Survey<br />
(TTS); Metrolinx; and the Vulnerable Persons Registry.<br />
The results for data access presented in Figure 5 were reviewed based on the number <strong>of</strong> years PHUs<br />
have been assessing urban walkability, The majority <strong>of</strong> PHUs had access <strong>to</strong> the following data regardless<br />
<strong>of</strong> the number <strong>of</strong> years they had been assessing walkability or whether or not they were assessing urban<br />
walkability at all: MPAC, RRFSS, public transit data, and presence <strong>of</strong> sidewalks.<br />
Most PHUs that had been assessing urban walkability for 1 <strong>to</strong> 5 years or 6 <strong>to</strong> 10 years, had access <strong>to</strong><br />
census boundary files, street network files, speed limits per street, street lighting standards, and traffic<br />
counts.<br />
All or most PHUs who had been assessing urban walkability for the longest period <strong>of</strong> time (6 <strong>to</strong> 10 years)<br />
had access <strong>to</strong> DMTI Spatial, location <strong>of</strong> traffic controls and location <strong>of</strong> controlled cross walks. For those<br />
PHUs assessing walkability for less than 6 years, a small proportion had access <strong>to</strong> these data.<br />
More PHUs with 1 <strong>to</strong> 5 years <strong>of</strong> experience had access <strong>to</strong> digital elevation model (<strong>to</strong>pography) compared<br />
<strong>to</strong> PHUs that had been assessing walkability for less than one year or more than 6 years.<br />
MEASURES CURRENTLY OF MOST VALUE<br />
When PHUs were asked which measures (in current use) are <strong>of</strong> most value in assessing urban walkability,<br />
responses varied as follows:<br />
• 3 PHUs reported connectivity (bike paths, multi-use paths, trails, sidewalks, and streets)<br />
• 2 PHUs mentioned proximity<br />
(residential areas <strong>to</strong> parks and amenities; public transit access points)<br />
• 2 PHUs reported sidewalk information, including condition affected by seasonal variances<br />
• 1 PHUs reported their walkability index<br />
• 1 PHU reported land use mix<br />
One PHU also listed the following measures <strong>of</strong> importance: shade availability; location <strong>of</strong> parks, schools,<br />
recreation centres, farmers markets, businesses etc.; availability <strong>of</strong> crosswalks/safe crossing for<br />
pedestrians; accessibility <strong>of</strong> neighbourhoods; and availability <strong>of</strong> benches/seating areas.<br />
The following methods were identified as being <strong>of</strong> most value in assessing urban walkability: self<br />
administered surveys (1 PHU) and Rapid Risk Fac<strong>to</strong>r Surveillance System (RRFSS) (1 PHU).<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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WALKABILITY<br />
MEASURES THAT WOULD BE OF MOST VALUE<br />
PHUs (that do not currently assess walkability) were asked about measures that would be <strong>of</strong> most value<br />
in assessing walkability. Although several PHUs indicated that they were not prepared <strong>to</strong> comment on<br />
this question (e.g. “has not been assessed at this time.”), three PHUs identified the following measures:<br />
• Land use<br />
• Connectivity<br />
• Sidewalk data<br />
• Rates <strong>of</strong> school aged children walking <strong>to</strong> and from school<br />
• Barriers for residents walking (<strong>to</strong> work, school, retail, transit)<br />
• Enablers for walking<br />
• Census section that goes beyond or adds <strong>to</strong> the “Mode <strong>of</strong> transportation <strong>to</strong> work”<br />
One PHU suggested that a standardized assessment <strong>to</strong>ol that could communicate the same information<br />
throughout the province would be <strong>of</strong> most value in assessing urban walkability.<br />
CHALLENGES<br />
Public Health Units identified the major challenges their organization’s face in assessing urban walkability.<br />
The most common challenges identified by PHUs included human resource capacity (84%), variations<br />
between municipalities (64%), and data availability (60%) (Figure 6).<br />
In relation <strong>to</strong> PHUs that have been assessing urban walkability for less than one year, all three PHUs<br />
reported lack <strong>of</strong> human resource capacity as a challenge.<br />
For PHUs assessing urban walkability for 1 <strong>to</strong> 5 years, most PHUs (9) reported lack <strong>of</strong> human resource<br />
capacity as a challenge. Most (8 PHUs) also reported data availability and variations between municipalities<br />
as challenges.<br />
Of the 3 PHUs that have been assessing urban walkability for the longest period <strong>of</strong> time (6 <strong>to</strong> 10 years),<br />
2 cited human resource capacity and lack <strong>of</strong> GIS technical support as challenges in assessing urban<br />
walkability. The third PHU indicated that there were no major challenges <strong>to</strong> report.<br />
Most <strong>of</strong> the 8 PHUs that do not assess urban walkability, cited human resource capacity (88%; 7 PHUs)<br />
and variations between municipalities (75%; 6 PHUs) as the most common challenges in assessing<br />
urban walkability.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 73<br />
Figure 6: Major challenges faced by Ontario PHUs in assessing urban walkability, 2012 (n=25)<br />
Challenge<br />
Human resource capacity<br />
Variations between municipalities<br />
<strong>Data</strong> availability<br />
Financial capacity<br />
<strong>Data</strong> accessibility<br />
Lack <strong>of</strong> GIS technical support<br />
<strong>Data</strong> quality<br />
Other<br />
No challenges <strong>to</strong> report<br />
4%<br />
20%<br />
48%<br />
44%<br />
40%<br />
40%<br />
64%<br />
60%<br />
84%<br />
0 5 10 15 20 25<br />
No. <strong>of</strong> Public Health Units<br />
Several PHUs commented further on the capacity challenges they face:<br />
“All <strong>of</strong> this work is being done at the University at the moment. We do<br />
not have the resources <strong>to</strong> do the work in-house.”<br />
“Capacity is a major challenge for our health unit. We are very interested<br />
in learning more from this report. We look forward <strong>to</strong> implementing<br />
more practices in the area <strong>of</strong> walkability and the built environment.”<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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WALKABILITY<br />
“Currently we do not have a dedicated person or department that<br />
focus specifically on walkability or the built environment. We have a<br />
limited capacity within our agency and municipality <strong>to</strong> do this work<br />
but are currently working on bridging the gap between civic work and<br />
public health <strong>to</strong> advise departments on considering health including<br />
walkability when creating plans for the built environment.”<br />
One PHU commented on GIS capacity as follows:<br />
“We do not have GIS internally and our municipalities all have various<br />
levels <strong>of</strong> GIS capabilities. The urban centre has GIS but not all the files<br />
we would like, and they do not have the time <strong>to</strong> create reports based<br />
on multiple fac<strong>to</strong>rs (i.e. walkability).”<br />
One PHU commented on lack <strong>of</strong> political interest, while another stated that it is not a priority issue. Lack<br />
<strong>of</strong> standards, related <strong>to</strong> methodologies and analyses, was also identified as a challenge.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 75<br />
GAP ANALYSIS: WALKABILITY<br />
Insights gathered from the comprehensive literature review, key informant interviews and survey administered<br />
<strong>to</strong> Ontario Public Health Units were compiled <strong>to</strong> identify gaps between the necessary and available<br />
built environment data for the assessment <strong>of</strong> urban walkability in Ontario. The gap analysis for the assessment<br />
<strong>of</strong> urban walkability in Ontario was conducted for the following measures and data sources:<br />
• Measures <strong>of</strong> density, connectivity, diversity, pedestrian oriented design and indices.<br />
• <strong>Data</strong> sources from MPAC / Teranet / OMNR; DMTI, Environics <strong>An</strong>alytics; GeoBase; and, Ministry<br />
<strong>of</strong> Natural Resources.<br />
OVERALL FINDINGS<br />
The overall findings from the walkability gap analysis, including gaps identified for measures, methodologies<br />
and data sources, are presented below (Tables 3 – 8).<br />
MEASURES<br />
• Several organizations are using similar types <strong>of</strong> measures (e.g. density, diversity, connectivity,<br />
pedestrian oriented design), but they are using different computational methods and a variety <strong>of</strong><br />
data sources. It is challenging <strong>to</strong> draw comparisons between health jurisdictions, especially for<br />
organizations that are predominantly using local data sources.<br />
• Lack <strong>of</strong> standard built environment terminology, definitions, and measures creates a challenge for<br />
public health researchers and practitioners and hampers their ability <strong>to</strong> synthesize evidence and<br />
identify environmental features that may explain walkability.<br />
• Public health organizations with the most established walkability assessment programs in the<br />
province were using a walkability index. However, there was limited collaboration with one another<br />
in the development and implementation <strong>of</strong> these indices.<br />
o Walkability indices may perform well in describing pedestrian behavior, but they may be less<br />
useful for intervention, as one cannot identify the specific component that should be the<br />
highest priority for change.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
76<br />
WALKABILITY<br />
METHODOLOGY<br />
• According <strong>to</strong> the survey, PHUs were predominantly using self-administered survey (assessing<br />
perceptions) and systematic observation (i.e. audit <strong>to</strong>ol) <strong>to</strong> assess urban walkability. Yet selfreported<br />
measures <strong>of</strong> neighborhood walkability can be implicated in same-source bias, and there<br />
are other noteworthy limitations including issues with reliability, validity, low response rates and a<br />
biased sample <strong>of</strong> respondents.<br />
• GIS methods are commonly used in the assessment <strong>of</strong> urban walkability and are required <strong>to</strong><br />
operationalize several measures, however 40% <strong>of</strong> PHUs indicated that lack <strong>of</strong> GIS technical<br />
support was a challenge for their organization.<br />
• Although GIS measurements have evolved and improved over the years, there is still wide<br />
variability in how GIS variables are constructed <strong>to</strong> represent built environment concepts (e.g.<br />
circular buffers versus polygon-based road network buffers).<br />
• GIS data layers might not be readily accessible for certain geographic regions and can be<br />
expensive <strong>to</strong> acquire.<br />
• Walkability is being assessed at different scales (namely street network and dissemination area)<br />
depending on the needs and requirements <strong>of</strong> an organization.<br />
• Simplicity <strong>of</strong> computation, readily available data, and ease <strong>of</strong> interpretation make density a<br />
commonly used metric; however there are many ways <strong>to</strong> calculate densities using different units<br />
<strong>of</strong> measurement.<br />
• Web-based geospatial technologies that estimate neighborhood walkability have emerged<br />
as potential <strong>to</strong>ols <strong>to</strong> be used in public health, however their utility in rural environments and<br />
specifically in the Ontario context has not been adequately assessed.<br />
• People react in different ways <strong>to</strong> their surroundings depending on demographic characteristics<br />
such as age, gender, education, and income yet many assessments <strong>of</strong> urban walkability in<br />
Ontario do not account for these fac<strong>to</strong>rs.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 77<br />
DATA SOURCES AND SETS<br />
• Core data sets were readily available, but many <strong>of</strong> the data sets that were considered <strong>of</strong><br />
high value by key informants were cost prohibitive (e.g. MPAC, DMTI, RRFSS). The cost <strong>of</strong><br />
purchasing data from these existing sources was identified by many key informants as a barrier <strong>to</strong><br />
undertaking walkability studies.<br />
• Creating and fostering sharing agreements across health jurisdictions was identified by many key<br />
informants as a prominent data need.<br />
• Existing data is <strong>of</strong>ten created for other land use and planning functions, so data specific <strong>to</strong><br />
walkability is not <strong>of</strong>ten accessible.<br />
• <strong>Data</strong> quality varies depending on the data source and its intended use.<br />
• The larger the city/region, the more resources and capacity <strong>to</strong> collect data and implement quality<br />
control mechanisms; thus the more confident some key informants were in the data submission<br />
for larger cities and regions.<br />
• Key informants voiced concern and suspicion about the quality and coverage <strong>of</strong> land-use data<br />
for rural or smaller communities, most notably from the Road Network and Trail Network Files<br />
(LIO) and Street Network File (StatsCan). There was concern that smaller cities and communities<br />
do not have the GIS capacity or data <strong>to</strong> accurately feed these files, resulting in unreliable data for<br />
these areas.<br />
• It is difficult <strong>to</strong> track trends because <strong>of</strong> the frequency <strong>of</strong> data submission for existing sources and<br />
the temporality <strong>of</strong> data that is needed.<br />
• Lack <strong>of</strong> cross-municipal data sharing agreements and collaboration was identified as a major<br />
data infrastructure challenge.<br />
OTHER GAPS<br />
• One <strong>of</strong> the most common challenges identified by PHUs was the lack <strong>of</strong> human resource capacity.<br />
Even organizations with the most experience in the assessment <strong>of</strong> urban walkability identified<br />
human resource capacity as a challenge.<br />
• Variations between municipalities were identified as a major challenge among PHUs. For instance,<br />
a region may be comprised <strong>of</strong> several municipalities, each operating at varying levels <strong>of</strong> human<br />
resource and financial capacity. The methods <strong>of</strong> collection and application as well as data<br />
sources being applied also varied by municipality (even within the same region).<br />
• For some more rural public health units, the assessment <strong>of</strong> urban walkability is not<br />
considered a priority.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
78<br />
WALKABILITY<br />
Table 3: Gap analysis for walkability indices, 2012<br />
AIR QUALITY<br />
Table 3: Gap analysis for walkability indices, 2012<br />
Measure Description Inputs †‡ Current Use in<br />
Ontario<br />
Measures<br />
Count ¥<br />
Theoretical Op.<br />
Ontario<br />
Desirability<br />
Challenges<br />
Link bw<br />
Measurement<br />
Approaches ¥<br />
Walkability<br />
Indices<br />
(Also known<br />
as Composite<br />
Measures)<br />
Walkability indices<br />
are derived from<br />
a combination<br />
<strong>of</strong> individual built<br />
environment<br />
measures<br />
(mostly using GIS<br />
methods). †‡¥<br />
Indirect measure <strong>of</strong><br />
walkability ¥<br />
Urban sprawl<br />
indices: features<br />
<strong>of</strong> sprawl include<br />
‘‘leapfrog’’<br />
development<br />
pattern, low density,<br />
homogeneous<br />
and segregated<br />
land uses, and<br />
an extensive<br />
disconnected,<br />
hierarchical road<br />
network, making<br />
mo<strong>to</strong>rized travel a<br />
necessity and active<br />
transport unsafe<br />
and impractical.<br />
Indices vary by the<br />
components they<br />
include, the scale<br />
at which they are<br />
measured, and the<br />
methods used in<br />
computation.<br />
Indices <strong>of</strong>ten<br />
include measures <strong>of</strong><br />
density, diversity &<br />
connectivity.<br />
The relative<br />
importance <strong>of</strong> each<br />
measure depends<br />
on the specific<br />
formula employed.<br />
All indices require<br />
a reference<br />
geographic area.<br />
<strong>Data</strong> sources<br />
include: Census,<br />
MPAC, DMTI,<br />
Environic <strong>An</strong>alytics,<br />
Ontario Road<br />
Network or National<br />
Road Network .†‡¥<br />
10 PHUs reported<br />
using an index,<br />
out <strong>of</strong> the 19 that<br />
currently assess<br />
walkability. †‡<br />
4 PHUs are more<br />
advanced in<br />
implementation;<br />
modifications have<br />
been made <strong>to</strong><br />
make the index<br />
specific <strong>to</strong> their<br />
jurisdiction .†‡<br />
1 research<br />
institution has<br />
implemented an<br />
index in multiple<br />
jurisdictions across<br />
Ontario (generalized<br />
index) ‡<br />
Some PHUs work<br />
in conjunction with<br />
external consultants<br />
<strong>to</strong> implement<br />
and calculate the<br />
index. †‡<br />
2 PHUs are using<br />
a GIS based <strong>to</strong>ol<br />
developed by Larry<br />
Frank. †‡<br />
All PHUs use spatial<br />
measures and<br />
methods (i.e. GIS). †‡<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary by health<br />
jurisdiction. †‡¥<br />
13 different<br />
walkability indices<br />
were identified<br />
in the literature<br />
review (50 articles<br />
reviewed), created<br />
by various<br />
academics and<br />
using a variety<br />
<strong>of</strong> data sources;<br />
5 applied in the<br />
Canadian context<br />
(ON, BC, QC), 1<br />
in Australia and all<br />
others applied in<br />
the USA.<br />
> 15 different<br />
walkability indices<br />
identified in the<br />
literature review.<br />
Several PHUs have<br />
implemented a<br />
walkability index<br />
but they differ in the<br />
components used<br />
and application,<br />
with some<br />
overlap. †‡<br />
1 Ontario research<br />
institute has<br />
implemented a<br />
walkability index in<br />
multiple jurisdictions<br />
across Ontario<br />
(generalized index )‡<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary, making<br />
comparisons<br />
between health<br />
jurisdictions<br />
challenging .†‡¥<br />
Walkability indices<br />
have been successful<br />
in describing the<br />
walking environment<br />
in several<br />
jurisdictions. †‡¥<br />
Strongly and<br />
consistently<br />
associated with<br />
physical activity,<br />
walking and health<br />
outcomes in the<br />
literature. ¥<br />
<strong>Built</strong> environment<br />
metrics are <strong>of</strong>ten<br />
correlated with<br />
one another & thus<br />
has motivated the<br />
use <strong>of</strong> composite<br />
indices <strong>to</strong> capture<br />
many aspects <strong>of</strong> the<br />
built environment at<br />
once.¥<br />
Research has<br />
suggested that<br />
composite measures<br />
<strong>of</strong> walkability are<br />
more consistent<br />
predic<strong>to</strong>rs <strong>of</strong> walking<br />
behavior than<br />
single component<br />
measures. ¥<br />
Urban sprawl is<br />
associated with<br />
higher levels <strong>of</strong><br />
car dependence<br />
& overweight<br />
populations than<br />
cities with more<br />
compact buildings. ¥<br />
Indices vary in<br />
both structure<br />
& availability <strong>of</strong><br />
data. Thus, lack<br />
<strong>of</strong> consistency in<br />
measurements<br />
(including bw<br />
municipalities within<br />
same jurisdiction). †‡¥<br />
Methodological<br />
concerns regarding<br />
validity, reliability,<br />
and generalizability. ¥<br />
Less useful for<br />
intervention, as one<br />
cannot identify the<br />
specific component<br />
that should be the<br />
highest priority for<br />
change. ¥<br />
Human resource<br />
capacity, data<br />
availability, financial<br />
capacity & lack <strong>of</strong><br />
GIS expertise .†<br />
4/5 <strong>of</strong> most<br />
advanced orgs in<br />
Ontario are working<br />
independently from<br />
one another; limited<br />
collaboration. ‡<br />
Captures interrelatedness<br />
<strong>of</strong> many built<br />
environment<br />
characteristics.<br />
Minimize the<br />
effect <strong>of</strong> spatial<br />
collinearity.<br />
Eases the<br />
communication <strong>of</strong><br />
results.<br />
†Survey ‡Key informant interview ¥Literature review<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario<br />
Click <strong>to</strong> open the full table.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 79<br />
Table 4: Gap analysis for density measures used <strong>to</strong> assess urban walkability, 2012<br />
AIR QUALITY<br />
Table 4: Gap analysis for density measures used <strong>to</strong> assess urban walkability, 2012<br />
Measure Description ¥ Inputs †‡¥ Current Use in<br />
Ontario<br />
Measures<br />
Count ¥<br />
Theoretical Op.<br />
Ontario<br />
Desirability ¥<br />
Challenges<br />
Link bw<br />
Measurement<br />
Approaches ¥<br />
Density<br />
(Population and<br />
Land-Use)<br />
Density is a measure<br />
<strong>of</strong> the amount <strong>of</strong><br />
activity found in an<br />
area and can be<br />
defined in terms <strong>of</strong><br />
population, housing<br />
unit, or employment<br />
density.<br />
Important correlate<br />
<strong>of</strong> walking.<br />
High density<br />
represents compact<br />
land development<br />
and reduces travel<br />
distances between<br />
trip origin and<br />
destination; reduces<br />
dependence<br />
on mo<strong>to</strong>rized<br />
transportation;<br />
supports higher<br />
levels <strong>of</strong> public<br />
transit service and<br />
ridership, including<br />
walking <strong>to</strong> and from<br />
transit.<br />
Density is a ratio in<br />
which a measure <strong>of</strong><br />
population or built<br />
form serves as the<br />
numera<strong>to</strong>r and a<br />
measure <strong>of</strong> land area<br />
(e.g. per unit area) as<br />
the denomina<strong>to</strong>r .†‡¥<br />
The denomina<strong>to</strong>r can<br />
be either <strong>to</strong>tal land<br />
area (as in “gross<br />
density”), or a pared<br />
down measure <strong>of</strong><br />
usable land area (as in<br />
“net density”).<br />
Density measures<br />
include: ¥<br />
• Population density<br />
• Residential<br />
(household)<br />
density (e.g. ratio<br />
<strong>of</strong> residential<br />
units <strong>to</strong> the land<br />
area devoted <strong>to</strong><br />
residential use per<br />
block group)<br />
• Employment<br />
density<br />
Recommend<br />
measures <strong>of</strong> net<br />
density (as opposed<br />
<strong>to</strong> gross density)<br />
because it excludes<br />
other land uses. ¥<br />
<strong>Data</strong> sources include:<br />
census, MPAC, DMTI,<br />
Environic <strong>An</strong>alytics,<br />
Ontario Road<br />
Network or National<br />
Road Network †‡¥<br />
PHUs use<br />
these individual<br />
measures: †<br />
• Population<br />
density (6 PHUs)<br />
• Residential<br />
density (3)<br />
5 (<strong>of</strong> 10) PHUs<br />
identified using<br />
density as part <strong>of</strong><br />
their index. †‡<br />
1 Ontario research<br />
institute uses<br />
population density<br />
(per square<br />
kilometre <strong>of</strong><br />
residential area)<br />
as part <strong>of</strong> their<br />
walkability index. ‡¥<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary by health<br />
jurisdiction. †‡¥<br />
40 (80%) studies<br />
identified using<br />
density measures<br />
in the literature<br />
review (50 articles<br />
reviewed); 24<br />
applied in the<br />
Canadian context<br />
(ON, BC, QC), 1<br />
in Australia and all<br />
others applied in the<br />
USA.<br />
~ 25 different<br />
density measures<br />
identified in the<br />
literature review.<br />
Several PHUs have<br />
Simplemented<br />
density measures,<br />
namely population<br />
and residential<br />
density. †‡<br />
1 Ontario<br />
research institute<br />
implemented<br />
density measures<br />
in several health<br />
jurisdictions<br />
across Ontario,<br />
thus applicability<br />
in more than one<br />
health jurisdicion<br />
is possible as part<br />
<strong>of</strong> a generalizable<br />
index. ‡<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary, making<br />
comparisons<br />
challenging. †‡¥<br />
Simplicity <strong>of</strong><br />
computation,<br />
readily available<br />
data, and ease <strong>of</strong><br />
interpretation make<br />
density a commonly<br />
used metric. ¥<br />
Strongly and<br />
consistently<br />
associated with<br />
physical activity,<br />
walking and health<br />
outcomes in the<br />
literature. ¥<br />
Consistency in<br />
data sources<br />
for calculating<br />
population<br />
density in multiple<br />
jurisdictions in<br />
Ontario census<br />
and parcel-level<br />
data available<br />
from government<br />
sources) .†‡¥<br />
Census data<br />
(Statistics Canada)<br />
is comprehensive<br />
for the entire<br />
population and is<br />
produced every<br />
five years, allowing<br />
changes in density<br />
<strong>to</strong> be moni<strong>to</strong>red<br />
over time. ¥<br />
Many ways <strong>to</strong><br />
calculate densities<br />
using different units<br />
<strong>of</strong> measurement.<br />
Scale <strong>of</strong> density<br />
measurement is<br />
another challenge <strong>to</strong><br />
measuring density<br />
(e.g. calculations<br />
<strong>of</strong> parcel density,<br />
block density,<br />
neighbourhood<br />
density, and gross<br />
density for the<br />
same area will each<br />
produce distinct<br />
results).<br />
Census data<br />
has limitations<br />
including it’s focus<br />
on residential<br />
population counts<br />
thereby being less<br />
useful for examining<br />
employment<br />
density. It also<br />
uses predefined<br />
geographic units for<br />
measurement that<br />
may not capture the<br />
types <strong>of</strong> changes<br />
that are <strong>of</strong> most<br />
interest.<br />
To measure change<br />
before 2001, only<br />
Census Tracts (CTs)<br />
are available.<br />
Integrated in<strong>to</strong><br />
most walkability<br />
and sprawl indices<br />
(composite<br />
measures); namely<br />
residential density<br />
measures. †‡¥<br />
Proximity is<br />
a function <strong>of</strong><br />
both density<br />
(compactness) <strong>of</strong><br />
development and<br />
the level <strong>of</strong> land<br />
use mix. ¥<br />
Density and land<br />
use mix work<br />
in tandem <strong>to</strong><br />
determine how<br />
many activities are<br />
within a convenient<br />
distance. ¥<br />
Residential density<br />
is important<br />
because it serves<br />
as a proxy for other<br />
urban form fac<strong>to</strong>rs,<br />
and is <strong>of</strong> particular<br />
importance at<br />
larger geographic<br />
scales <strong>of</strong><br />
measurement or in<br />
cases where data<br />
is missing. ¥<br />
†Survey ‡Key informant interview ¥Literature review<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario<br />
Click <strong>to</strong> open the full table.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
80<br />
WALKABILITY<br />
Table 5: Gap analysis for connectivity measures used <strong>to</strong> assess urban walkability, 2012<br />
AIR QUALITY<br />
Table 5: Gap analysis for connectivity measures used <strong>to</strong> assess urban walkability, 2012<br />
Measure Description ¥ Inputs †‡¥ Current Use in<br />
Ontario<br />
Measures<br />
Count ¥<br />
Theoretical Op.<br />
Ontario<br />
Desirability ¥<br />
Challenges<br />
Link bw<br />
Measurement<br />
Approaches ¥<br />
Connectivity<br />
(related <strong>to</strong> street<br />
pattern)<br />
Connectivity affects<br />
the ease <strong>of</strong> travel<br />
between places<br />
& represents the<br />
degree <strong>to</strong> which<br />
roads, pedestrian<br />
walkways, trails,<br />
etc. are connected<br />
so that moving from<br />
point A <strong>to</strong> point B is<br />
relatively easy.<br />
Measures quantify<br />
the network<br />
connections<br />
between trips <strong>to</strong><br />
describe directness<br />
<strong>of</strong> possible paths<br />
& no. <strong>of</strong> mobility<br />
options available.<br />
The denser the<br />
street network<br />
is in terms <strong>of</strong><br />
intersections and<br />
blocks, the higher its<br />
connectivity will be.<br />
Indirect measure <strong>of</strong><br />
walkability.<br />
Measures are related<br />
<strong>to</strong> the physical<br />
design & layout<br />
<strong>of</strong> transportation<br />
infrastructure.<br />
Measures <strong>of</strong><br />
connectivity include:<br />
block size and length;<br />
intersection density;<br />
street density;<br />
connected node ratio;<br />
segment/intersections<br />
ratio; number <strong>of</strong><br />
intersections per<br />
length <strong>of</strong> street<br />
network; alpha index;<br />
gamma index.<br />
Easiest way <strong>to</strong><br />
operationalize street<br />
network connectivity<br />
in a GIS environment<br />
is by measuring<br />
the number <strong>of</strong><br />
intersections.<br />
<strong>Data</strong> sources include:<br />
Local database,<br />
DMTI, Ontario Road<br />
Network or National<br />
Road Network.<br />
PHUs use these<br />
individual measures † :<br />
• Block size and<br />
length (4 PHUs)<br />
• Intersection<br />
density (3)<br />
• Street density (2)<br />
• Connected node<br />
ratio (1)<br />
9 (<strong>of</strong> 10) PHUs<br />
use connectivity<br />
measures as part<br />
<strong>of</strong> their walkability<br />
index; 1 research<br />
institution uses<br />
connectivity as<br />
well .†‡<br />
1 PHU uses<br />
the ‘I can walk’<br />
<strong>to</strong>ol <strong>to</strong> assess<br />
connectivity at the<br />
neighbourhood level<br />
(icanwalk.ca). †<br />
Most PHUs use<br />
spatial methods for<br />
measurement (i.e.<br />
GIS). †‡<br />
When asked which<br />
measures (in current<br />
use) are <strong>of</strong> most<br />
value in assessing<br />
urban walkability,<br />
3 PHUs reported<br />
connectivity (bike<br />
paths, multi-use<br />
paths, trails,<br />
sidewalks, and<br />
streets). †<br />
36 (72%) studies<br />
identified using<br />
connectivity<br />
measures in the<br />
literature review (50<br />
articles reviewed)<br />
using a variety<br />
<strong>of</strong> data sources;<br />
17 applied in the<br />
Canadian context<br />
(ON, BC, QC), 1<br />
in Australia and all<br />
others applied in the<br />
USA.<br />
> 30 connectivity<br />
measures identified<br />
in the literature<br />
review.<br />
Several PHUs<br />
have implemented<br />
connectivity<br />
measures, namely<br />
the intersection<br />
density measure. †‡<br />
1 Ontario<br />
research institute<br />
implemented<br />
connectivity<br />
measures in several<br />
health jurisdictions<br />
across Ontario<br />
(as part <strong>of</strong> a<br />
generalized index )‡<br />
<strong>Data</strong> sources vary<br />
considerable by<br />
PHU: community<br />
walkabout; surveys;<br />
focus groups and<br />
GIS inven<strong>to</strong>ry for<br />
one PHU; includes<br />
streets, sidewalks,<br />
multiuse paths and<br />
sometimes parks;<br />
Road Network GIS<br />
Layer .†<br />
Street networks<br />
that are more<br />
connected<br />
are thought <strong>to</strong><br />
increase walkability<br />
by <strong>of</strong>fering<br />
shorter and many<br />
alternate routes. ¥<br />
Several<br />
studies have<br />
found positive<br />
associations<br />
between measures<br />
<strong>of</strong> connectivity and<br />
walkability. ¥<br />
Greater street<br />
connectivity<br />
supports higher<br />
levels <strong>of</strong> public<br />
transit service and<br />
ridership, including<br />
walking <strong>to</strong> and<br />
from transit. ¥<br />
<strong>Data</strong> sources vary<br />
considerably by<br />
health jurisdiction. †¥<br />
No standard<br />
methods for<br />
operationalizing<br />
measures. †‡¥<br />
Different <strong>to</strong>ols are<br />
used <strong>to</strong> characterize<br />
road network<br />
configuration in<br />
relation <strong>to</strong> physical<br />
activity. †‡¥<br />
Not all studies<br />
have found positive<br />
associations<br />
between measures<br />
<strong>of</strong> connectivity and<br />
walkability. ¥<br />
Most measures<br />
use data from the<br />
street network,<br />
but omitting<br />
pedestrian networks<br />
(e.g., sidewalks,<br />
park paths) may<br />
appreciably<br />
underestimate<br />
connectivity.¥<br />
Determining how <strong>to</strong><br />
handle freeways or<br />
other limited-access<br />
roads is another<br />
methodological<br />
issue. ¥<br />
Integrated in<strong>to</strong><br />
most walkability<br />
indices (composite<br />
measures); namely<br />
the intersection<br />
density measure.<br />
†Survey ‡Key informant interview ¥Literature review<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario<br />
Click <strong>to</strong> open the full table.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 81<br />
Table 6: Gap analysis for diversity measures used <strong>to</strong> assess urban walkability, 2012<br />
AIR QUALITY<br />
Table 6: Gap analysis for diversity measures used <strong>to</strong> assess urban walkability, 2012<br />
Measure Description ¥ Inputs †‡¥ Current Use in<br />
Ontario<br />
Measures<br />
Count ¥<br />
Theoretical Op.<br />
Ontario<br />
Desirability ¥<br />
Challenges<br />
Link bw<br />
Measurement<br />
Approaches ¥<br />
Diversity<br />
(Land Use Mix<br />
and Proximity)<br />
Diversity refers<br />
<strong>to</strong> the spatial<br />
arrangement <strong>of</strong><br />
land use that<br />
influences the<br />
distance and mode<br />
<strong>of</strong> travel.<br />
Mixed land use<br />
brings different and<br />
necessary uses in<strong>to</strong><br />
relative proximity,<br />
thereby shortening<br />
trip distances and<br />
encouraging active<br />
modes <strong>of</strong> transport.<br />
Proximity describes<br />
the no. & variety<br />
<strong>of</strong> destinations<br />
within a specified<br />
distance <strong>of</strong> any<br />
location; function<br />
<strong>of</strong> both density <strong>of</strong><br />
development &<br />
level <strong>of</strong> land use<br />
mix.<br />
As proximity and<br />
directness between<br />
destinations<br />
increases,<br />
distance between<br />
destinations<br />
decreases.<br />
The entropy index<br />
(entropy-based<br />
measure) is frequently<br />
used; land use types<br />
include: residential,<br />
retail, entertainment,<br />
<strong>of</strong>fice and institutional. ¥<br />
Dissimilarity index<br />
measures dissimilarity<br />
based on predominant<br />
use <strong>of</strong> neighbouring<br />
squares. ¥<br />
With appropriate parcel<br />
data, the calculation<br />
<strong>of</strong> land use mix is<br />
possible through a GIS<br />
or database interface. ¥<br />
LUM data are typically<br />
obtained from land<br />
ownership records. ¥<br />
Proximity is <strong>of</strong>ten<br />
calculated using<br />
circular or road<br />
network buffers. ¥<br />
Distances (e.g. 400m,<br />
800m) commonly used<br />
<strong>to</strong> analyze walking<br />
distance vary. ¥<br />
<strong>Data</strong> sources include:<br />
Census, MPAC, DMTI,<br />
Environic <strong>An</strong>alytics,<br />
Ontario Road Network<br />
or National Road<br />
Network .†‡¥<br />
PHUs use these<br />
individual measures † :<br />
• Land Use Mix (3<br />
PHUs)<br />
• Proximity<br />
(schools, food<br />
outlets, parks,<br />
trails) (6)<br />
studies identified<br />
using diversity<br />
measures in the<br />
literature review<br />
(50 articles<br />
reviewed);<br />
21 applied in<br />
the Canadian<br />
context (ON,<br />
BC, QC), 1 in<br />
Australia and all<br />
others applied in<br />
the USA.<br />
43 (86%)<br />
7 (<strong>of</strong> 10) PHUs<br />
identified using<br />
LUM as part <strong>of</strong> their<br />
index. †‡<br />
1 Ontario research<br />
institute uses LUM<br />
as part <strong>of</strong> their<br />
walkability index. ‡¥<br />
<strong>Data</strong> sources and<br />
methodologies <strong>to</strong><br />
calculate LUM vary<br />
considerably. †‡¥<br />
PHUs did not identify<br />
using dissimilarity<br />
indices. †‡<br />
When asked which<br />
measures (in current<br />
use) are <strong>of</strong> most<br />
value in assessing<br />
urban walkability,<br />
3 PHUs reported<br />
proximity (2 PHUs)<br />
and LUM (1). †<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary by health<br />
jurisdiction. †‡¥<br />
> 80 diversity<br />
measures<br />
identified in the<br />
literature review.<br />
Most PHUs are<br />
using diversity<br />
measures (either<br />
individually or as<br />
part <strong>of</strong> an index)<br />
<strong>to</strong> assess urban<br />
walkability. †‡<br />
1 Ontario<br />
research institute<br />
implemented<br />
diveristy measures<br />
in several health<br />
jurisdictions<br />
across Ontario,<br />
thus applicability<br />
in more than one<br />
health jurisdiction<br />
is possible as part<br />
<strong>of</strong> a generalizable<br />
index. ‡<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary, making<br />
comparisons<br />
between health<br />
jurisdictions<br />
challenging. †‡¥<br />
Mixed land use<br />
brings different and<br />
necessary uses in<strong>to</strong><br />
relative proximity,<br />
thereby shortening<br />
trip distances and<br />
encouraging active<br />
modes <strong>of</strong> transport.<br />
Strongly and<br />
consistently<br />
associated with<br />
physical activity,<br />
walking and health<br />
outcomes in the<br />
literature.<br />
Parks, trails,<br />
recreational<br />
facilities, pathways,<br />
and schools<br />
within walking<br />
distance have<br />
been consistently<br />
correlated with<br />
physical activity,<br />
particularly in<br />
children.<br />
Distance <strong>to</strong> retail<br />
activity is important<br />
in creating inviting<br />
pedestrian<br />
environments and in<br />
predicting levels <strong>of</strong><br />
walking in cities.<br />
Distances used <strong>to</strong><br />
analyze walking<br />
distance vary,<br />
making comparisons<br />
between jurisdictions<br />
challenging.<br />
The entropy index<br />
does not consider<br />
the type or intensity<br />
<strong>of</strong> mixing. ¥<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary, making<br />
comparisons<br />
challenging. †‡¥<br />
<strong>Data</strong> availability<br />
is <strong>of</strong>ten a limiting<br />
fac<strong>to</strong>r since parcellevel<br />
data are<br />
required <strong>to</strong> compute<br />
many land-use mix<br />
measures. Parcellevel<br />
data may be<br />
unavailable in some<br />
locations and in<br />
others may lack<br />
detail about land<br />
use. ¥<br />
Many studies<br />
have opted <strong>to</strong> use<br />
survey items <strong>to</strong><br />
approximate land<br />
use mix (e.g. ‘‘Are<br />
there shops where<br />
you live?’’) but such<br />
approaches do not<br />
allow betweenstudy<br />
comparisons<br />
because <strong>of</strong><br />
unspecified<br />
definitions <strong>of</strong> place. ¥<br />
The retail floor area<br />
ratio (FAR) is <strong>of</strong>ten<br />
used in conjunction<br />
with LUM.<br />
Proximity is<br />
a function <strong>of</strong><br />
both density <strong>of</strong><br />
development and<br />
the level <strong>of</strong> land<br />
use mix.<br />
†Survey ‡Key informant interview ¥Literature review<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario<br />
Click <strong>to</strong> open the full table.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
82<br />
WALKABILITY<br />
Table 7: Gap analysis for pedestrian oriented design measures used <strong>to</strong> assess urban walkability, AIR QUALITY2012<br />
Table 7: Gap analysis for pedestrian oriented design measures used <strong>to</strong> assess urban walkability, 2012<br />
Measure Description ¥ Inputs †‡¥ Current Use<br />
in Ontario<br />
Measures<br />
Count ¥<br />
Theoretical Op.<br />
Ontario<br />
Desirability ¥<br />
Challenges<br />
Link bw<br />
Measurement<br />
Approaches ¥<br />
Street design refers<br />
<strong>to</strong> the scale &<br />
design <strong>of</strong> sidewalks<br />
and roads, and how<br />
they are managed<br />
for various uses.<br />
POD includes<br />
measures <strong>of</strong><br />
neighborhood<br />
comfort (including<br />
aesthetics),<br />
cleanliness and<br />
safety.<br />
Retail floor area<br />
ratio (FAR) is used<br />
as an indica<strong>to</strong>r <strong>of</strong><br />
POD measuring<br />
retail density and<br />
site design. Low<br />
ratio indicates a<br />
low density retail<br />
development likely<br />
surrounded by<br />
substantial parking;<br />
high ratio indicates<br />
smaller setbacks<br />
& less surface<br />
parking; two fac<strong>to</strong>rs<br />
thought <strong>to</strong> facilitate<br />
pedestrian access.<br />
The normalized<br />
difference<br />
vegetation index<br />
(NDVI) is used<br />
<strong>to</strong> estimate<br />
vegetation biomass,<br />
greenness, and<br />
dominant species.<br />
Various methods<br />
can be used <strong>to</strong><br />
assess street design<br />
(audit, survey <strong>of</strong><br />
perceptions, GIS);<br />
method depends on<br />
specific measures<br />
being assessed.<br />
Aesthetics and<br />
cleanliness are<br />
<strong>of</strong>ten assessed by<br />
observation (e.g.<br />
audit) or survey (<strong>of</strong><br />
perceptions).<br />
FAR = retail building<br />
floor area footprint<br />
divided by retail land<br />
floor area footprint;<br />
included in walkability<br />
indices. <strong>Data</strong><br />
source: DMTI (Route<br />
Logistics);<br />
Environic <strong>An</strong>alytics<br />
(Direc<strong>to</strong>ry <strong>of</strong><br />
Shopping Centres).<br />
NDVI: ratio between<br />
measured reflectivity<br />
in the red and near<br />
infrared band, in<br />
satellite images<br />
(DEM/SRTM).<br />
Safety and<br />
crime statistics<br />
obtained from<br />
police department;<br />
otherwise, through<br />
observation (audit) or<br />
survey.<br />
PHUs use individual<br />
measures <strong>of</strong><br />
comfort and safety,<br />
including: †<br />
• Crime rates (4<br />
PHUs)<br />
• Street lighting (4)<br />
• Canopy coverage<br />
(trees) (4)<br />
• Posted speed<br />
limits (3)<br />
• Presence <strong>of</strong> street<br />
furniture (3)<br />
5 (<strong>of</strong> 10) PHUs<br />
identified using<br />
FAR as part <strong>of</strong> their<br />
index. †‡<br />
1 Ontario research<br />
institute uses LUM<br />
as part <strong>of</strong> their<br />
walkability index. ‡¥<br />
PHUs did not identify<br />
using NDVI indices. †‡<br />
When asked which<br />
measures (in current<br />
use) are <strong>of</strong> most<br />
value in assessing<br />
urban walkability,<br />
2 PHUs reported<br />
sidewalk information<br />
(including condition<br />
affected by seasonal<br />
variances). †<br />
34 (68%) studies<br />
identified using<br />
POD measures<br />
in the literature<br />
review (50<br />
articles reviewed);<br />
14 applied in the<br />
Canadian context<br />
(ON, BC, QC)<br />
and all others<br />
applied in the<br />
USA.<br />
> 100 diversity<br />
measures<br />
identified in the<br />
literature review.<br />
Several PHUs<br />
are using POD<br />
measures (either<br />
individually or as<br />
part <strong>of</strong> an index)<br />
<strong>to</strong> assess urban<br />
walkability. †‡<br />
1 Ontario<br />
research institute<br />
implemented safety<br />
measures in several<br />
health jurisdictions<br />
across Ontario,<br />
thus applicability<br />
in more than one<br />
health jurisdicion<br />
is possible as part<br />
<strong>of</strong> a generalizable<br />
index. ‡<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary considerably,<br />
making<br />
comparisons<br />
between health<br />
jurisdictions<br />
extremely<br />
challenging. †‡¥<br />
Higher levels<br />
<strong>of</strong> objectively<br />
measured safety<br />
(e.g. traffic safetly)<br />
and comfort<br />
(e.g. aesthetically<br />
appealing<br />
communities) are<br />
positively associated<br />
with physical activity<br />
engagement.<br />
Since building<br />
setbacks are<br />
important predic<strong>to</strong>rs<br />
<strong>of</strong> walking and POD,<br />
FAR is introduced<br />
<strong>to</strong> increase the<br />
sensitivity <strong>to</strong> retail<br />
use believed <strong>to</strong><br />
stimulate pedestrian<br />
activity.<br />
Higher levels <strong>of</strong><br />
neighborhood<br />
vegetation have<br />
been associated<br />
with higher levels <strong>of</strong><br />
physical activity.<br />
FAR is a standard<br />
planning<br />
measure and is<br />
frequently used<br />
in development<br />
regulations - and<br />
therefore is useful<br />
<strong>to</strong> apply <strong>to</strong> policy or<br />
existing regulations.<br />
Higher levels<br />
<strong>of</strong> objectively<br />
measured safety<br />
(e.g. traffic safetly)<br />
and comfort<br />
(e.g. aesthetically<br />
appealing<br />
communities)<br />
are positively<br />
associated with<br />
physical activity<br />
engagement.<br />
Since building<br />
setbacks are<br />
important<br />
predic<strong>to</strong>rs <strong>of</strong><br />
walking and POD,<br />
FAR is introduced<br />
<strong>to</strong> increase the<br />
sensitivity <strong>to</strong> retail<br />
use believed <strong>to</strong><br />
stimulate pedestrian<br />
activity.<br />
Higher levels <strong>of</strong><br />
neighborhood<br />
vegetation have<br />
been associated<br />
with higher levels <strong>of</strong><br />
physical activity.<br />
FAR is a standard<br />
planning<br />
measure and is<br />
frequently used<br />
in development<br />
regulations - and<br />
therefore is useful<br />
<strong>to</strong> apply <strong>to</strong> policy or<br />
existing regulations.<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary (especially<br />
for comfort,<br />
aesthetics and<br />
safety measures),<br />
making comparisons<br />
challenging. †‡¥<br />
Self-reported<br />
measures implicated<br />
in same-source<br />
bias and issues with<br />
reliability, validity, low<br />
response rates and<br />
a biased sample <strong>of</strong><br />
respondents. ¥<br />
Systematic field<br />
observations can<br />
be very laborious<br />
(i.e. time-intensive<br />
and have multiple<br />
logistical constraints),<br />
<strong>of</strong>ten require<br />
significant specialized<br />
training and are<br />
no<strong>to</strong>rious for being<br />
very costly. ¥<br />
Retail floor area<br />
ratio (FAR),<br />
also known as<br />
commercial<br />
density, is a<br />
diverse measure<br />
that can be<br />
applied not only<br />
as a density<br />
indica<strong>to</strong>r but also<br />
as an indica<strong>to</strong>r<br />
<strong>of</strong> pedestrianoriented<br />
design<br />
and used in<br />
conjunction with<br />
land use mix<br />
(LUM).<br />
†Survey ‡Key informant interview ¥Literature review<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario<br />
Click <strong>to</strong> open the full table.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY 83<br />
Table 8: Gap analysis for data sources and sets used in the assessment <strong>of</strong> urban walkability, 2012<br />
WALKABILITY<br />
Table 8: Gap analysis for data sources and sets used in the assessment <strong>of</strong> urban walkability, 2012<br />
<strong>Data</strong> Source <strong>Data</strong>set Topic Area Current Use in Ontario PHUs Desirability Access/Availability Barriers/Challenges/Limitations<br />
MPAC /<br />
Teranet /<br />
OMNR<br />
Ontario Parcel<br />
database<br />
3 components:<br />
• Digital Assessment<br />
Parcel Fabric<br />
(MPAC)<br />
• Digital Ownership<br />
Parcel Fabric<br />
(Teranet)<br />
• Digital Crown Parcel<br />
Fabric (OMNR)<br />
LUM<br />
Retail Density<br />
56% <strong>of</strong> 25 PHUs that responded <strong>to</strong> the survey<br />
report having access <strong>to</strong> MPAC data †<br />
7 (<strong>of</strong> 10) PHUs identified using LUM as part <strong>of</strong> their<br />
walkability index. †‡<br />
5 (<strong>of</strong> 10) PHUs identified using retail floor area as<br />
part <strong>of</strong> their walkability index †‡<br />
PHUs identified using the following as individual<br />
measures † :<br />
• Land Use Mix (3 PHUs)<br />
• Retai floor area (1)<br />
Long list <strong>of</strong> Property Codes including detailed Residential, Commercial and<br />
Industrial codes<br />
Updated quarterly<br />
3 mapping specifications dictate supporting spatial data— POLARIS (Province<br />
<strong>of</strong> Ontario Land Registration Information System) mapping, Basic Index<br />
Mapping (BIM) and Pre-Basic Index Mapping (Pre-BIM):<br />
• Where POLARIS, features related <strong>to</strong> survey plans (including reference &<br />
subdivision), roads, major easements, <strong>to</strong>wnship fabric, railways and major<br />
water bodies are captured.<br />
• Where BIM, features relate <strong>to</strong> survey plan text, roads, major easements, and<br />
geographic <strong>to</strong>wnship fabric, railways and major water bodies.<br />
• Where Pre-BIM much less extensive. Features relate primarily <strong>to</strong> road text,<br />
geographic <strong>to</strong>wnship fabric and major water bodies.<br />
Available at no cost through MNR’s LIO Warehouse <strong>to</strong> Ontario<br />
municipalities, conservation authorities, and provincial ministries. Eligible<br />
organizations must enter in<strong>to</strong> the relevant license agreement(s) and<br />
become members <strong>of</strong> the Ontario Geospatial <strong>Data</strong> Exchange (ODGE) <strong>to</strong><br />
access.<br />
Municipalities wishing <strong>to</strong> access the ownership data must be licensed<br />
through Teranet.<br />
Through Teranet the only costs incurred by municipalities for standard<br />
deliveries is a modest delivery and support charge.<br />
Variable licensing fees depending on derived products.<br />
Internal use for walkability does not appear <strong>to</strong> be subject <strong>to</strong> fee/royalty<br />
Some municipalities report quality issues with the spatial component<br />
<strong>of</strong> Ontario Parcel database. They use their own digital files and merge<br />
with MPAC data.<br />
Accuracy variable from location <strong>to</strong> location and depends on the source<br />
data available and the build procedure employed.<br />
• Where POLARIS standards were used <strong>to</strong> assemble the ownership<br />
mapping and where good control and legal or cadastral surveys<br />
were available, the data has better accuracy than other types <strong>of</strong><br />
Ontario Parcel mapping.<br />
• Where the product is assembled upon 1:10,000 (or smaller) scale<br />
<strong>to</strong>pographic mapping, and where control and surveys are not<br />
available, or not used, the data is much less accurate. http://www.<br />
ontarioparcel.ca/<br />
http://www.ontarioparcel.ca/<br />
http://www.ontarioparcel.ca/<br />
Good coverage <strong>of</strong> the entire province. ‡<br />
DMTI<br />
CanMap Route<br />
Logistics<br />
Network <strong>An</strong>alysis (<strong>to</strong><br />
measure proximity or <strong>to</strong><br />
model population affected)<br />
Connectivity<br />
Posted Speed Limits<br />
DMTI data used <strong>to</strong> assess connectivity measures. †‡<br />
Excellent level <strong>of</strong> detail, appropriate for assessing walkability. £<br />
The dataset is GIS-ready and requires minimal, if any, massaging prior <strong>to</strong><br />
analysis.<br />
Vendor <strong>of</strong>fers dataset in multiple formats.<br />
The dataset is designed for location-based service (LBS) applications and so is<br />
already <strong>to</strong>pologically clean.<br />
Fee ‡£<br />
Preferential pricing may be available <strong>to</strong> public sec<strong>to</strong>r clients.<br />
Presents a continuing expense <strong>to</strong> acquire annual updates (subscription<br />
model).<br />
Depending on new residential development activity, point-in-time<br />
extracts may weaken the representativeness <strong>of</strong> local calculations. Note<br />
also that not all streets are bordered by sidewalks.<br />
Enhanced Points <strong>of</strong><br />
Interest (EPOI)<br />
Business Facilities &<br />
Amenities<br />
(for measures that capture<br />
proximity and accessibility <strong>to</strong><br />
destinations)<br />
DMTI data used <strong>to</strong> assess connectivity measures. †‡<br />
Offers provincial coverage and enhanced locational precision. Includes SIC and<br />
NAICS codes <strong>to</strong> identify business types (e.g., restaurants, retail, etc.).<br />
Vendor <strong>of</strong>fers dataset in multiple formats.<br />
Fee £<br />
Preferential pricing may be available <strong>to</strong> public sec<strong>to</strong>r clients.<br />
Presents a continuing expense <strong>to</strong> acquire annual updates (subscription<br />
model).<br />
Depending on business churn, point-in-time extracts may weaken the<br />
representativeness <strong>of</strong> local calculations.<br />
Environics<br />
<strong>An</strong>alytics<br />
BusinessWhere<br />
Business Facilities &<br />
Amenities<br />
(for measures that capture<br />
proximity and accessibility <strong>to</strong><br />
destinations)<br />
DMTI data used <strong>to</strong> assess connectivity measures. †‡<br />
Offers provincial coverage and enhanced locational precision. Includes SIC and<br />
NAICS codes <strong>to</strong> identify business types (e.g. restaurants, retail, etc.).<br />
Businesses are verified annually.<br />
Vendor <strong>of</strong>fers dataset in multiple formats.<br />
Fee £<br />
Preferential pricing may be available <strong>to</strong> public sec<strong>to</strong>r clients.<br />
Presents a continuing expense <strong>to</strong> acquire annual updates (subscription<br />
model).<br />
Depending on business churn, point-in-time extracts may weaken the<br />
representativeness <strong>of</strong> local calculations.<br />
GeoBase<br />
National Road Network<br />
(NRN)<br />
Network <strong>An</strong>alysis (<strong>to</strong><br />
measure proximity or <strong>to</strong><br />
model population affected)<br />
Connectivity<br />
Walkability Indices<br />
Density<br />
Diversity<br />
Unknown use across Public Health Units.<br />
No specific example <strong>of</strong> NRN usage.<br />
68% <strong>of</strong> PHUs indicated having access <strong>to</strong> street<br />
network files. †<br />
44% <strong>of</strong> PHUs indicated that Street networks (44%)<br />
are the most common geographic scale used <strong>to</strong><br />
assess urban walkability. †<br />
Good coverage at both national and provincial scales. The Ontario Road<br />
Network (ORN) is used <strong>to</strong> populate the NRN’s Ontario level file. £<br />
Excellent level <strong>of</strong> detail, appropriate for assessing walkability. £<br />
The dataset is GIS-ready and requires minimal, if any, manipulation prior <strong>to</strong><br />
analysis.<br />
Vendor <strong>of</strong>fers dataset in multiple spatial formats. £<br />
Detailed Metadata records with online access and retrieval.<br />
Free with no cost user registration. £<br />
Excludes select street attributes that could be required <strong>to</strong> compute<br />
specific calculations. £<br />
Depending on new residential development activity, point-in-time<br />
extracts may weaken the representativeness <strong>of</strong> local calculations.<br />
Public Health unit road authority or “Local” Road Network file may<br />
override NRN usage due <strong>to</strong> technical specifications (i.e. Accuracy,<br />
Update Frequency, Maintenance etc.).<br />
Ministry<br />
<strong>of</strong> Natural<br />
Resources<br />
Ontario Road Network<br />
(ORN)<br />
Network <strong>An</strong>alysis (<strong>to</strong><br />
measure proximity or <strong>to</strong><br />
model population affected)<br />
Connectivity<br />
Walkability Indices<br />
Density<br />
Diversity<br />
Unknown use across Public Health Units.<br />
No specific example <strong>of</strong> ORN usage.<br />
68% <strong>of</strong> PHUs indicate having access <strong>to</strong> street<br />
network files. †<br />
44% <strong>of</strong> PHUs indicated that street networks (44%)<br />
are the most common geographic scale used <strong>to</strong><br />
assess urban walkability. †<br />
Standardized provincial dataset with detailed documentation and regular<br />
update frequency. ORN is the authoritative source <strong>of</strong> roads data for the Ontario<br />
Government. £<br />
Excellent level <strong>of</strong> detail, appropriate for assessing walkability. £<br />
The dataset is GIS-ready and requires minimal, if any, manipulation prior <strong>to</strong><br />
analysis.<br />
Detailed Metadata records with online access and retrieval.<br />
Free with no cost user registration. £<br />
Depending on new residential development activity, point-in-time<br />
extracts may weaken the representativeness <strong>of</strong> local calculations.<br />
Public Health unit road authority or “Local” Road Network file may<br />
override NRN usage due <strong>to</strong> technical specifications (i.e. Accuracy,<br />
Update Frequency, Maintenance etc.).<br />
† Survey ‡ Key Informant Interview ¥ Literature Review £ GIS Metadata<br />
Click <strong>to</strong> open the full table.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
3AIR QUALITY<br />
MEASURES & DATA USED IN THE ASSESSMENT OF AIR QUALITY<br />
BACKGROUND LITERATURE REVIEW KEY INFORMANT INTERVIEWS SUMMARY OF SURVEY RESULTS GAP ANALYSIS
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
87<br />
MEASURES & DATA USED IN THE ASSESSMENT OF AIR QUALITY<br />
CHAPTER 3: AIR QUALITY<br />
BACKGROUND<br />
AIR QUALITY AND HEALTH<br />
Hundreds <strong>of</strong> studies conducted in communities around the world have consistently demonstrated that<br />
short-term increases in levels <strong>of</strong> the five common air pollutants are: ground-level ozone (O 3<br />
), fine and<br />
coarse particulate matter (PM 2.5/10<br />
), sulphur dioxide (SO 2<br />
), nitrogen dioxide (NO 2<br />
) and carbon monoxide<br />
(CO) - are associated with a broad range <strong>of</strong> acute health effects including 94-98 :<br />
• Reduced lung function;<br />
• Increased frequency and severity <strong>of</strong> asthmatic symp<strong>to</strong>ms;<br />
• Increased emergency room visits and hospital admissions for respira<strong>to</strong>ry and cardiovascular<br />
conditions including respira<strong>to</strong>ry infections, asthma; and<br />
• Increased non-traumatic deaths for respira<strong>to</strong>ry and cardiovascular conditions.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
88<br />
AIR QUALITY<br />
Several studies have also demonstrated that long-term exposures <strong>to</strong> elevated levels <strong>of</strong> PM 2.5<br />
/ 10<br />
are<br />
associated with the development <strong>of</strong> chronic heart and lung diseases, including lung cancer, among<br />
adults. 97;99;100 <strong>An</strong> American Heart Association comprehensive review <strong>of</strong> the relationship between health<br />
and fine particulate matter (PM 2.5<br />
), the air pollutant most strongly linked <strong>to</strong> chronic health effects concluded<br />
that:<br />
• There is a causal relationship between exposure <strong>to</strong> PM , cardiovascular disease and death;<br />
2.5<br />
• Long term exposure (i.e. a few years) <strong>to</strong> elevated levels <strong>of</strong> PM increases the risk for<br />
2.5<br />
cardiovascular mortality and reduces life expectancy; and<br />
• Reductions in air levels <strong>of</strong> PM can decrease cardiovascular mortality within a few years.98<br />
2.5<br />
AIR POLLUTION BURDEN OF ILLNESS IN ONTARIO<br />
Several studies suggest that air pollution has a significant impact on health in Ontario. In 2005, the Ontario<br />
Medical Association (OMA) used risk coefficients from epidemiological studies, health statistics from<br />
Ontario communities, and air quality data from air moni<strong>to</strong>ring stations across Ontario, <strong>to</strong> estimate the<br />
burden <strong>of</strong> illness from air pollution in Ontario. The OMA estimated that air pollution is responsible for approximately<br />
5,900 non-traumatic deaths, 16,800 hospital admissions, and 59,700 emergency room visits<br />
each year in Ontario. 94 Researchers from Health Canada and <strong>Environment</strong> Canada conducted a study<br />
<strong>to</strong> estimate the burden <strong>of</strong> illness associated with air pollution in several cities across the country. They<br />
concluded that the five common air pollutants – ozone, PM 2.5<br />
/ PM 10<br />
, SO 2<br />
, NO 2<br />
and CO, were responsible<br />
for approximately 2,900 non-traumatic deaths each year in Windsor, Hamil<strong>to</strong>n, Toron<strong>to</strong> and Ottawa. They<br />
attributed one third <strong>of</strong> those deaths <strong>to</strong> acute health impacts associated with the mix <strong>of</strong> the five air pollutants<br />
and two thirds <strong>to</strong> the chronic health impacts associated with PM 2.5<br />
alone. They concluded that air<br />
pollution is responsible for between 7% and 10% <strong>of</strong> all non-traumatic deaths in cities across Ontario. 19<br />
AIR QUALITY & VULNERABLE POPULATIONS<br />
Many studies have shown that air pollution increases the risk <strong>of</strong> death and illness due <strong>to</strong> heart disease,<br />
stroke, and respira<strong>to</strong>ry disease through both short term and long term exposures. While everyone<br />
faces increased health risks due <strong>to</strong> air pollution, the risk is greater for:<br />
• People with cardiovascular conditions such as angina, congestive heart failure, heart rhythm<br />
problems; those who have suffered a previous heart attack;<br />
• People with respira<strong>to</strong>ry conditions such as asthma and chronic obstructive lung disease;<br />
• People with diabetes and/or those who are obese;<br />
94;98;101;102<br />
• The elderly, women (especially those that are pregnant), and young children.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 89<br />
AIR QUALITY AND THE BUILT ENVIRONMENT<br />
The five common air pollutants measured in Ontario are ground-level ozone (O 3<br />
), fine particular matter<br />
(PM 2.5<br />
), nitrogen dioxide (NO 2<br />
), carbon monoxide (CO) and sulphur dioxide (SO 2<br />
).* Many <strong>of</strong> those activities<br />
involve the combustion <strong>of</strong> fossil fuels (e.g. natural gas, coal, oil, gasoline, diesel fuel, and jet fuel) in<br />
industrial processes, utilities and transportation. These five air pollutants have also been associated with<br />
the greatest burden <strong>of</strong> illness in modern society.<br />
Ground-level ozone is a secondary air pollutant that is formed in the atmosphere from reactions between<br />
other air pollutants; primarily nitrogen oxides (NO x<br />
) and volatile organic compounds (VOCs) in the presence<br />
<strong>of</strong> sunlight. 103 Because these reactions are affected by meteorological conditions, elevated concentrations<br />
<strong>of</strong> ground-level ozone are typically recorded on hot and sunny days from May <strong>to</strong> September<br />
between noon and early evening. 103 The transportation sec<strong>to</strong>r is the largest source <strong>of</strong> both NO x<br />
(73%)<br />
and VOCs (37%) in Ontario (Figures 7 and 8).<br />
Whenever a fuel (e.g. gasoline, diesel, natural gas, wood or coal) is combusted, nitrogen oxides are<br />
emitted. Major sources <strong>of</strong> NO x<br />
emissions include the transportation sec<strong>to</strong>r, industrial processes and<br />
utilities. 103<br />
Figure 7: Ontario nitrogen oxides emissions by sec<strong>to</strong>r (emissions from point/area/transportation sources,<br />
2009 estimates)<br />
7% 4% 6% 8%<br />
2%<br />
Smelters/Primary Metals<br />
Other Transportation<br />
Road Vehicles<br />
Other Industrial Processes<br />
46%<br />
Cement and Concrete<br />
Utilities<br />
27%<br />
Miscellaneous<br />
Source: Ministry <strong>of</strong> <strong>Environment</strong> 103 . Reproduced with permission<br />
*These air pollutants are similar <strong>to</strong> the criteria air contaminants assessed by the MOE, which include the 5 common air pollutants and <strong>to</strong>tal reduced sulphur (TRS).<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
90<br />
AIR QUALITY<br />
Figure 8: Ontario volatile organic compounds emissions by sec<strong>to</strong>r (emissions from point/area/<br />
transportation sources, 2009 estimates)<br />
12%<br />
3% 8%<br />
General Solvent Use<br />
Other Transportation<br />
13%<br />
Road Vehicles<br />
14%<br />
Other Industrial Processes<br />
Miscellaneous<br />
24%<br />
26%<br />
Residential<br />
Printing/Surface Coating<br />
Source: Ministry <strong>of</strong> <strong>Environment</strong> 103 . Reproduced with permission<br />
Airborne particulate matter (PM) is the general term used <strong>to</strong> describe a mixture <strong>of</strong> microscopic solid particles<br />
and liquid droplets that are suspended in air. Particulate matter is classified according <strong>to</strong> its size because<br />
<strong>of</strong> the different health effects associated with particles <strong>of</strong> different diameters. Fine particulate matter<br />
(PM 2.5<br />
) is less than 2.5 microns in diameter and can penetrate deep in<strong>to</strong> the respira<strong>to</strong>ry system. 103<br />
PM can be emitted directly from a source or formed in the atmosphere by the transformation <strong>of</strong> gaseous<br />
emissions. It includes aerosols, smoke, fumes, dust, fly ash and pollen. 103 The three major sources <strong>of</strong><br />
directly emitted PM 2.5<br />
are residential (40%), the transportation sec<strong>to</strong>r (25%) and industrial sources (28%)<br />
(Figure 9). Under certain meteorological conditions, emissions from US based coal-fired power plants<br />
account for a significant share <strong>of</strong> the PM 2.5<br />
in southern Ontario. 103<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 91<br />
Figure 9: Ontario PM2.5 emissions by sec<strong>to</strong>r (emissions from point/area/transportation sources,<br />
2009 estimates)<br />
22%<br />
3%<br />
Smelters/Primary Metals<br />
9%<br />
Other Transportation<br />
Road Vehicles<br />
15%<br />
7%<br />
4%<br />
Other Industrial Processes<br />
Cement and Concrete<br />
Residential<br />
40%<br />
26%<br />
Miscellaneous<br />
Source: Ministry <strong>of</strong> <strong>Environment</strong> 103 . Reproduced with permission<br />
The transportation sec<strong>to</strong>r accounted for 88% <strong>of</strong> all CO emissions. Electric utilities and smelters are the<br />
major contribu<strong>to</strong>rs <strong>to</strong> SO 2<br />
emissions in Ontario, accounting for approximately 54 per cent <strong>of</strong> the provincial<br />
SO 2<br />
emissions.(MOE, 2010). 104<br />
URBAN FORM, AIR QUALITY AND EXPOSURE<br />
While there are a number <strong>of</strong> articles which suggest that compact or walkable communities are associated<br />
with lower vehicle-related emissions <strong>of</strong> air pollutants, it is much more complicated <strong>to</strong> say how urban<br />
form is related <strong>to</strong> air quality and exposure <strong>to</strong> air pollutants. For example:<br />
• One study suggested that, while compact neighbourhoods can reduce per capita vehicle-related<br />
emissions, they can also concentrate the emissions particularly if the neighbourhoods do not<br />
have a strong mix <strong>of</strong> land uses and an efficient public transit system 105 ;<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
92<br />
AIR QUALITY<br />
• <strong>An</strong>other study found that population centrality and population density were the strongest urban<br />
form predic<strong>to</strong>rs for air quality. Population centrality correlated with lower concentrations <strong>of</strong> ozone,<br />
PM 2.5<br />
and aggregate pollutant levels, and population density correlated with higher concentrations<br />
<strong>of</strong> PM 2.5<br />
. This study also found that transit supply was correlated with lower concentrations <strong>of</strong><br />
PM 2.5<br />
. The researchers noted that the impact <strong>of</strong> urban form on air quality, particularly PM 2.5<br />
, was<br />
comparable <strong>to</strong> the impact <strong>of</strong> climate, and therefore significant from a public health perspective 106 ;<br />
• A third study conducted in southern California used population density, intersection density and<br />
land use mix as indica<strong>to</strong>rs <strong>of</strong> neighbourhood walkability. They found that concentrations <strong>of</strong> NO x<br />
and PM 2.5<br />
were highest near the city centre and major roadways, while the concentrations <strong>of</strong><br />
ozone were higher in the outer-lying areas. This study found that when comparing estimated<br />
ischemic heart disease mortality rates among neighbourhoods, differences attributable <strong>to</strong> physical<br />
inactivity were comparable <strong>to</strong> differences attributable <strong>to</strong> individual air pollutants. The researchers<br />
suggest that the study demonstrates the importance <strong>of</strong> considering the impact <strong>of</strong> urban form on<br />
both physical activity and air pollution exposures. 107<br />
AIR QUALITY AND INCOMPATIBLE LAND USES<br />
Air quality can vary substantially across a community as local emission sources such as highways, industrial<br />
facilities, and truck depots add <strong>to</strong> background levels <strong>of</strong> air pollution that includes transboundary air<br />
pollution. 108 In Ontario, the Ministry <strong>of</strong> the <strong>Environment</strong> (MOE) has responsibility for permitting industrial<br />
facilities and other emission sources with certificates <strong>of</strong> approval (C<strong>of</strong>As) based on the emissions from<br />
a single facility or operation and, sometimes, on a single source within a facility. This approach does<br />
not take in<strong>to</strong> consideration background levels <strong>of</strong> air pollution or the cumulative impacts <strong>of</strong> a variety <strong>of</strong><br />
emission sources in a local area. Consequently, while the C<strong>of</strong>A process ensures that individual emission<br />
sources do not exceed air standards, it does not ensure that air levels within a community stay within<br />
health-based air standards. 109<br />
His<strong>to</strong>rically, these shortcomings in regula<strong>to</strong>ry control have been mitigated, <strong>to</strong> some extent, by recommending<br />
separation distances <strong>to</strong> keep industrial facilities separate from sensitive land uses such as<br />
homes, daycares, schools and hospitals. 109 However, separation distances between sensitive land uses<br />
and industrial point sources have not always been preserved as development pressure on communities<br />
grows. In addition, in Ontario, high volume traffic corridors have not been included in the Land Use Compatibility<br />
Guidelines developed by the MOE. 109<br />
URBAN FORM AND VEHICLE-RELATED EMISSIONS<br />
The relationships between land use patterns, vehicle emissions, air quality, and human health are complex.<br />
A number <strong>of</strong> studies have demonstrated that variations in air quality within a community can be<br />
as great as the variation between communities. Millar and colleagues as discussed by Hankey 107 , and<br />
other researchers 110 , suggest that the built environment, including neighbourhood location, urban design<br />
and proximity <strong>to</strong> roads, can impact exposures. Air pollution is made up <strong>of</strong> a variety <strong>of</strong> substances,<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 93<br />
each with different sources, patterns <strong>of</strong> distribution,<br />
chemical reactions and health impacts.<br />
Each pollutant therefore has a different association<br />
with land use patterns and transportation,<br />
making it difficult <strong>to</strong> determine how a particular<br />
land use policy will affect levels <strong>of</strong> air pollution<br />
and exposures <strong>to</strong> air pollution.<br />
Several studies have demonstrated that the walkability<br />
<strong>of</strong> communities can have a significant impact<br />
on emissions from the transportation sec<strong>to</strong>r<br />
by influencing the extent <strong>to</strong> which people depend<br />
upon au<strong>to</strong>mobiles and other modes <strong>of</strong> transportation.<br />
For example:<br />
• The California Air Resources Board found that<br />
“complete” neighbourhoods (i.e. compact<br />
neighbourhoods built around public transit<br />
with a variety <strong>of</strong> services within a five minute<br />
walk) can reduce vehicle-related air emissions<br />
by up <strong>to</strong> 20% relative <strong>to</strong> more typical<br />
suburban neighbourhoods 108 ;<br />
• A study conducted by Frank and Chapman,<br />
as described by Frank and colleagues, 49 in<br />
Atlanta found that people who lived in the<br />
most au<strong>to</strong>-oriented neighbourhoods drove<br />
30% more than those who lived in the most<br />
walkable neighbourhoods. It found that with<br />
each step up a five-part walkability scale,<br />
vehicle related emissions <strong>of</strong> NO x<br />
and VOCs<br />
decreased by 6% and 3.6% respectively, and,<br />
• In King County, Washing<strong>to</strong>n, a 5% increase in<br />
the walkability <strong>of</strong> a neighbourhood, using land<br />
use mix, street connectivity, net residential<br />
density and retail floor area ratios as the<br />
indica<strong>to</strong>rs <strong>of</strong> walkability, was associated with<br />
6.5% reduction in vehicle miles travelled and<br />
a 5.6% and 5.5% reduction in vehicle-related<br />
emissions <strong>of</strong> NO x<br />
and VOCs respectively. 111<br />
ALTERNATIVE MODES OF<br />
TRANSPORTATION AND AIR QUALITY<br />
Several studies have suggested that public transit and<br />
active transportation can have a significant impact on<br />
local air quality and/or human health. Using air modelling<br />
and road count data, a Toron<strong>to</strong> study estimated<br />
that 190 premature deaths could be avoided, and $900<br />
million in health benefits could be realized, each year,<br />
if vehicle emissions in Toron<strong>to</strong> were reduced by 30 per<br />
cent by shifting <strong>to</strong> other modes <strong>of</strong> travel. 112<br />
A US study modelled the impact <strong>of</strong> eliminating all short<br />
au<strong>to</strong>mobile trips (≤ 8km) and replacing 50% <strong>of</strong> them<br />
with cycling trips in 11 metropolitan areas. Assuming a<br />
20% reduction in vehicle use they found that:<br />
• Air levels <strong>of</strong> PM in most <strong>of</strong> the metropolitan<br />
2.5<br />
urban centres would be reduced by 0.08 <strong>to</strong> 0.15<br />
g/m³ (1 <strong>to</strong> 2%) and in upwind states by about<br />
0.05 μg/m³;<br />
• Exceedances <strong>of</strong> the annual air standard for PM2.5<br />
would be reduced by 5 <strong>to</strong> 25% in the urban grids;<br />
• Air levels <strong>of</strong> ozone in the metropolitan urban<br />
centres would increase while the air levels <strong>of</strong><br />
ozone in the suburbs, downwind states, and small<br />
urban centres would be reduced;<br />
• 433 deaths, 2,000 asthma attacks, 75 chronic<br />
obstructive pulmonary disease cases, 93,607<br />
emergency room visits or hospital admissions,<br />
and 660 heart attacks and related hospital<br />
admissions could be avoided each year; and<br />
• The net health benefits were valued at $3.6 billion<br />
per year. 113<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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AIR QUALITY AND TRAFFIC CORRIDORS<br />
The principal source <strong>of</strong> variation in air quality within many communities is vehicle-related air pollution associated<br />
with high volume traffic corridors. 114 A review <strong>of</strong> 15 different studies conducted by the World<br />
Health Organization (WHO) found that concentrations <strong>of</strong> air pollutants along traffic corridors were 1.2 <strong>to</strong><br />
2.3 times higher than background levels in those urban areas. 102 In 2010, the Health Effects Institute (HEI)<br />
released a special report on traffic-related air pollution which concluded that:<br />
• Traffic emissions are the principal source <strong>of</strong> variation in the levels <strong>of</strong> air pollutants within many<br />
cities;<br />
• Traffic emissions have an impact on air quality at an urban and regional scale as well as a local<br />
scale; and<br />
• Air pollution associated with traffic corridors can extend up <strong>to</strong> 300 <strong>to</strong> 500 metres from a highway<br />
depending upon fac<strong>to</strong>rs such as traffic volume, wind direction and wind speed.<br />
Furthermore, Brugge and colleagues 115 reviewed the air quality and health studies directed at high volume<br />
traffic corridors and they concluded that:<br />
• Air levels <strong>of</strong> ultra-fine particles (UFP), black carbon (BC), CO and NO are all elevated along high<br />
x<br />
volume traffic corridors with greater than 30,000 vehicles per day;<br />
• People living beside these highways are likely <strong>to</strong> receive much higher exposures <strong>to</strong> traffic-related<br />
air pollutants than people living more than 200 metres from highways;<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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• Evidence from a variety <strong>of</strong> sources suggests that vehicle-related air pollutants have adverse<br />
effects on cardiovascular systems; and<br />
• There is strong evidence linking traffic corridors <strong>to</strong> the development <strong>of</strong> asthma and reduced lung<br />
function among children. 115<br />
Other researchers found statistically significant associations between residents who live in close proximity<br />
<strong>to</strong> traffic corridors and increases in asthma and other respira<strong>to</strong>ry diseases, reduced lung function,<br />
adverse birth outcomes, childhood cancer, and premature death. 116 Although there are a number <strong>of</strong><br />
uncertainties making it difficult <strong>to</strong> prove that traffic corridors are causing these health effects, the study<br />
authors concluded that enhanced precautionary land use, smart growth and transportation polices could<br />
better protect the public’s health. 116<br />
Recent studies have also linked air pollution <strong>to</strong> other health concerns. A Montreal study’s findings<br />
suggested that traffic-related air pollution might be associated with an increased incidence <strong>of</strong> postmenopausal<br />
breast cancer, 117 and a Danish study based on 53,000 participants in the Danish Diet Cancer<br />
and Health cohort, identified that traffic-related air pollution might increase the risks <strong>of</strong> cervical and<br />
brain cancer. 118<br />
SEPARATION DISTANCES FROM TRAFFIC CORRIDORS<br />
Proximity <strong>to</strong> high traffic areas has been a key area <strong>of</strong> academic research as well as a focus for public<br />
health units. Kim and colleagues 119 found traffic density and annual averages <strong>of</strong> daily traffic were significantly<br />
correlated with NO x<br />
and NO, accounting for 35 <strong>to</strong> 60% <strong>of</strong> the variation. Elemental carbon, VOCs,<br />
CO, and fine particulate matter are also expected <strong>to</strong> be higher in close proximity <strong>to</strong> traffic buffers. 110;120<br />
Approximately 32% <strong>of</strong> Canadians reside within 500 metres (m) from a highway or within 100 m from a<br />
major road. 121 Amram and colleagues 122 found that for 10 <strong>of</strong> Canada’s largest cities, 16.3% <strong>of</strong> schools<br />
were within 75 metres and 36.1% were within 200 metres <strong>of</strong> a major road.<br />
Brauer and colleagues 121 evaluated research in Canada on traffic related air pollutants, finding similar results<br />
<strong>to</strong> other international studies. Air pollutants flow <strong>to</strong>wards adjacent land areas, with higher pollutant<br />
levels noted approximately 50 <strong>to</strong> 100 m from major roads, and approximately 150 m from highways. 114;121<br />
Pollutant gradients could be as high as 500 m <strong>to</strong> 800 m from the road. Determining distribution becomes<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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more complex when meteorological conditions<br />
are considered. The impact <strong>of</strong> wind speed and<br />
direction has been noted <strong>to</strong> increase the distance<br />
downwind from roadways by two or three<br />
fold, and <strong>to</strong> be dependent on the reactivity <strong>of</strong> the<br />
pollutant. 114;121<br />
Traffic buffers with a specific distance from residences<br />
have previously been used as a measure<br />
<strong>of</strong> exposure <strong>to</strong> air pollutants in the Hamil<strong>to</strong>n<br />
area 110 and in Vancouver. 120 A Vancouver-based<br />
study found a higher risk for new born infants being<br />
small for gestational age (SGA) and having a<br />
low birth weight (LBW) in pregnant women residing<br />
within 50 m from a major highway. 120 However,<br />
there was no significant effect for pregnant<br />
women within 150 m from a major highway or<br />
within 50 m from a major roadway. 120 Morgenstern<br />
and colleagues 123 noted a dose-response<br />
relationship with distances <strong>of</strong> 50 m or less from<br />
major roads having the highest effect on allergen<br />
sensitization in children.<br />
While road distance is a simple and easy measure<br />
<strong>of</strong> potential pollutant exposure, it does not<br />
accurately estimate pollutant levels or sources<br />
<strong>of</strong> pollution other than traffic. Furthermore simple<br />
road distance measures might not consider other<br />
fac<strong>to</strong>rs influencing exposure such as <strong>to</strong>pography,<br />
land use, wind patterns, and traffic levels. 124<br />
AIR QUALITY AND EXTREME HEAT<br />
While a better understanding is required for air<br />
quality in the built environment, it is also important<br />
<strong>to</strong> consider how air quality relates <strong>to</strong> other environmental<br />
health issues such as extreme heat.<br />
The relationship between extreme heat and air<br />
quality is complex with various influencing fac<strong>to</strong>rs,<br />
in addition <strong>to</strong> the interaction between the two.<br />
Solar radiation is one meteorological variable <strong>of</strong><br />
potential interest <strong>to</strong> extreme heat which also impacts<br />
the distribution <strong>of</strong> air pollutants. 121<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 97<br />
The built environment also plays an important role in linking heat with pollutant exposure, as temperature<br />
is positively correlated with ozone levels. 106 One study noted that temperature differences within urban<br />
areas were greater than or equal <strong>to</strong> urban-rural differences and contributed <strong>to</strong> the intra-urban differences<br />
<strong>of</strong> ozone. 125 Ren and colleagues 126 noted how ambient air pollution, in particular ozone, can interact with<br />
temperature and impact heart rate variability. <strong>An</strong>other study using regression modelling highlighted the<br />
impact <strong>of</strong> vegetation and pavements on explaining spatial and temporal differences in surface temperature,<br />
which in turn contributed <strong>to</strong> ozone levels. 125<br />
ASSESSING AIR QUALITY<br />
The <strong><strong>Environment</strong>al</strong> Moni<strong>to</strong>ring and Reporting Branch (EMBR) <strong>of</strong> the MOE measures ambient air quality<br />
across the province manages the Air Quality Index (AQI), issues smog advisories, and prepares the province’s<br />
annual air quality report. The EMBR is responsible for air quality moni<strong>to</strong>ring at the regional scale.<br />
It conducts ambient air moni<strong>to</strong>ring <strong>to</strong> assess background conditions representative <strong>of</strong> rural and urban<br />
settings, including transboundary assessments.<br />
The Air Quality Index (AQI) is a single indica<strong>to</strong>r <strong>of</strong> air quality that is used <strong>to</strong> communicate the quality <strong>of</strong><br />
the air <strong>to</strong> the public. It is based on the six criteria air pollutants, which the MOE describes as air pollutants<br />
that have adverse effects on human health and the environment. They include ground-level ozone, PM 2.5<br />
,<br />
NO 2<br />
, SO 2<br />
, CO and <strong>to</strong>tal reduced sulphur (TRS). 104 The AQI readings are collected by an air moni<strong>to</strong>ring<br />
network composed <strong>of</strong> 40 air moni<strong>to</strong>ring stations located across the province. In Perrotta’s 127 review <strong>of</strong> air<br />
moni<strong>to</strong>ring in Ontario, the <strong><strong>Environment</strong>al</strong> Moni<strong>to</strong>ring and Reporting Branch (EMRB) reported that these<br />
stations continuously measure a combination <strong>of</strong> the six criteria air contaminants.<br />
The Air Quality Health Index (AQHI) is a new single indica<strong>to</strong>r for air quality that has been developed<br />
over many years by a collaborative process led by the federal government. It was developed <strong>to</strong> better<br />
reflect the health impacts associated with the different air pollutants. Within Ontario, this new index is<br />
being piloted in the Greater Toron<strong>to</strong> Area (GTA) and in the National Capital Region (NCR) in a project led<br />
by <strong>Environment</strong> Canada. The EMRB is supporting this project by providing air moni<strong>to</strong>ring data, AQHI<br />
forecasting, and technical advice. 127 To date, moni<strong>to</strong>ring for the AQHI is being done from the same locations<br />
and with the same air moni<strong>to</strong>ring equipment that is used for the AQI in the GTA and the NCR.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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AIR QUALITY<br />
LITERATURE REVIEW<br />
The growth <strong>of</strong> urban communities has important<br />
implications for local air quality issues. Through<br />
increased population sizes and increased demand<br />
for vehicle transportation and other services<br />
which contribute <strong>to</strong> emissions, there is the<br />
potential for higher concentrations <strong>of</strong> air pollutants<br />
as well as greater risk <strong>of</strong> exposure <strong>to</strong> the<br />
public.<br />
While there is a need for data on the built<br />
environment relevant <strong>to</strong> air quality issues,<br />
establishing the link between the two areas is<br />
difficult for multiple reasons:<br />
• The distribution <strong>of</strong> pollutants is impacted<br />
by multiple meteorological variables such<br />
as wind speed and direction, atmospheric<br />
stability and mixing, precipitation,<br />
temperature, etc.<br />
• Physical and chemical properties <strong>of</strong> pollutants<br />
differ, and these individual properties can<br />
impact the life cycle <strong>of</strong> the pollutant, their<br />
distribution in the built environment, and<br />
interaction with the atmosphere and other<br />
pollutants<br />
• The diverse range <strong>of</strong> urban structures<br />
influences air quality through the creation <strong>of</strong><br />
emissions, altered flow patterns <strong>of</strong> pollutants,<br />
and risk <strong>of</strong> exposure <strong>to</strong> the public<br />
• There is a greater need for understanding<br />
emission sources as well as population<br />
exposure in the built environment<br />
The complex relationship between the built environment<br />
and air quality illustrates the need for<br />
more research on the subject before accurate<br />
measures can be developed. Certain measures<br />
may have the potential <strong>to</strong> be generalized <strong>to</strong> a<br />
wide range <strong>of</strong> urban communities. At the same<br />
time, air quality issues can differ significantly between<br />
Public Health Unit (PHU) jurisdictions. This<br />
would require assessments <strong>of</strong> pollution sources<br />
and exposure within local communities.<br />
To manage health risks associated with air pollution,<br />
PHUs and environmental agencies have<br />
adopted various measurement approaches <strong>to</strong><br />
better assess air quality in the built environment.<br />
These methods include surveillance <strong>of</strong> ambient<br />
air pollutant levels through moni<strong>to</strong>ring stations,<br />
maintaining an inven<strong>to</strong>ry <strong>of</strong> emissions levels, and<br />
developing detailed spatial models for air pollution.<br />
Each method has their own strengths and<br />
weaknesses in assessing air quality issues and<br />
requires different data sources and measures.<br />
The purpose <strong>of</strong> the literature review is <strong>to</strong> capture<br />
research on the link between the built environment<br />
and air quality. The literature review does<br />
not focus in detail on any specific aspect <strong>of</strong> air<br />
quality in the built environment, such as traffic<br />
corridors. Rather the approach is <strong>to</strong> provide an<br />
overview <strong>of</strong> a variety <strong>of</strong> ways <strong>to</strong> assess air quality<br />
related <strong>to</strong> the built environment. This includes<br />
reviewing currently available data sources as well<br />
as measurement approaches <strong>to</strong> fill present data<br />
gaps on air quality in the built environment. The<br />
following sections will discuss:<br />
• Common built environment measures relevant<br />
<strong>to</strong> air pollution;<br />
• Air quality indices used <strong>to</strong> moni<strong>to</strong>r pollutant<br />
levels in Ontario;<br />
• Emissions estimates data noted in the<br />
literature review;<br />
• Modelling methods relevant <strong>to</strong> the built<br />
environment and urban areas.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 99<br />
COMMON BUILT<br />
ENVIRONMENT MEASURES<br />
AND AIR POLLUTION<br />
<strong>Built</strong> environment variables commonly investigated<br />
in the literature for their association with<br />
air pollutants are listed below. In general these<br />
variables were mostly extracted from Digital Mapping<br />
Technologies Inc. (DMTI) sources, census<br />
data, other Statistics Canada data sets, as well<br />
as municipal level data.<br />
• Road distance/density<br />
• Transit supply<br />
• Topography (e.g. elevation, terrain)<br />
• Population and or dwelling (i.e. measured as<br />
counts or density)<br />
• Land use type (e.g. industrial, commercial,<br />
residential counts)<br />
• Mixed land use<br />
• Centrality (e.g. residential, job)<br />
• Urban sprawl<br />
• Traffic volume (e.g. vehicle kilometres travelled,<br />
annual average daily traffic)<br />
Through modelling techniques, other built environment<br />
variables have been evaluated. Some<br />
studies have used municipal zoning categories<br />
and investigated the relationship with air pollutant<br />
levels. For example, Parenteau and Sawadra 128<br />
noted how green spaces and their potential linkages<br />
<strong>to</strong> lower level <strong>of</strong> pollutants need <strong>to</strong> be further<br />
assessed, Recent research has provided some<br />
insight on<strong>to</strong> the relationship between the built environment<br />
variables and exposure <strong>to</strong> pollutants.<br />
In a study by Atari and colleagues (2009) dwelling<br />
count and industrialized areas were determinants<br />
<strong>of</strong> intra urban variation in BTEX levels (benzene,<br />
<strong>to</strong>luene, ethylbenzene and xylene). Rosenlund<br />
and colleagues 129 found the most predictive variables<br />
<strong>of</strong> measured NO 2<br />
were distance from busy<br />
roads, the area <strong>of</strong> the city, altitude, inverse population<br />
density, and the size <strong>of</strong> the census block. In<br />
another study, Cesaroni and colleagues 130 evaluated<br />
the linkages <strong>of</strong> three health outcomes (rhinitis,<br />
chronic bronchitis, and asthma) with various<br />
indices measuring and estimating air pollution in<br />
Rome, Italy. The study found that only rhinitis was<br />
associated with traffic exposure, and that selfreported<br />
indices were only not strongly or moderately<br />
correlated <strong>to</strong> objective indices.<br />
Nonetheless, determining how the built environment<br />
influences air pollutants is only one aspect<br />
in understanding the distribution <strong>of</strong> air pollutants<br />
in urban areas. Differences between pollutants<br />
as well as meteorological conditions need <strong>to</strong> be<br />
further considered. Not all pollutants share the<br />
same spatial trends in the built environment, creating<br />
greater challenges <strong>to</strong> assessing exposure.<br />
Some pollutants, such as ozone (O 3<br />
) compared<br />
<strong>to</strong> PM 2.5<br />
, differ more significantly within a city. 110<br />
For O 3<br />
, the most important predic<strong>to</strong>rs were found<br />
<strong>to</strong> be altitude and sprawling. 131 Furthermore, the<br />
impact <strong>of</strong> pollutants is not isolated. They can<br />
undergo chemical reactions and have synergistic<br />
effects that can alter the spatial pattern <strong>of</strong> pollutant<br />
exposure. Ozone concentrations may differ<br />
from other pollutants due <strong>to</strong> O 3<br />
scavenging<br />
by NO close <strong>to</strong> roadways. 110 This spatial heterogeneity<br />
can have important implications for health<br />
outcomes and illustrates the need for assessing<br />
multiple pollutants and their relationships <strong>to</strong> the<br />
built environment.<br />
AIR POLLUTANTS AND INDICES<br />
<strong>An</strong> air quality index (AQI) is a common <strong>to</strong>ol used<br />
for moni<strong>to</strong>ring and communicating pollutant levels<br />
that pose health risks <strong>to</strong> the public. Indices<br />
convert complex data involving time, spatial distribution,<br />
and pollutant concentrations in<strong>to</strong> values<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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or terms that are better unders<strong>to</strong>od by the general<br />
public. Rather than stating pollutant levels in<br />
µg/m 3 , an index communicates air quality using<br />
an aggregated value relative <strong>to</strong> a scale. 132<br />
The majority <strong>of</strong> research in the literature review<br />
focused on moni<strong>to</strong>ring station networks. Table 9<br />
summarizes data sources relevant <strong>to</strong> Ontario for<br />
individual pollutants and indices. These networks<br />
created by government agencies are a favourable<br />
source <strong>of</strong> pollutant data since the information is<br />
available at no cost and can provide high temporal<br />
detail (hourly data on pollutant levels). Forty<br />
moni<strong>to</strong>ring stations are currently located within<br />
Ontario, mostly in urban communities. Presently,<br />
the MOE publishes data on the number <strong>of</strong> smog<br />
advisories at a provincial level, his<strong>to</strong>rical AQI readings,<br />
and air pollutant levels for PM 2.5<br />
, NO 2<br />
, NO,<br />
NO x<br />
, O 3<br />
, SO 2<br />
, and CO. 104 The moni<strong>to</strong>ring stations<br />
managed by MOE feeds in<strong>to</strong> the National Air Pollution<br />
Surveillance (NAPS) network for moni<strong>to</strong>ring<br />
air quality.<br />
AIR QUALITY INDEX<br />
At the provincial level, the Ontario Ministry <strong>of</strong> the<br />
<strong>Environment</strong> (MOE) has developed its own AQI<br />
using a network <strong>of</strong> 40 moni<strong>to</strong>ring stations, providing<br />
air quality readings at a municipal level<br />
across the province. AQI pollutant levels are converted<br />
<strong>to</strong> an index scale, where the pollutant with<br />
the highest value for the hour determines the AQI<br />
reading for the hour. 104 The AQI focuses on six<br />
pollutants <strong>of</strong> concern in Ontario (Table 9). However,<br />
the ability <strong>of</strong> the Ontario AQI <strong>to</strong> prevent adverse<br />
health outcomes had been questioned. A<br />
Toron<strong>to</strong> Public Health study 133 found that 92% <strong>of</strong><br />
premature mortality and hospitalizations relating<br />
<strong>to</strong> respira<strong>to</strong>ry and cardiac admissions occurred<br />
during a good or very good AQI rating.<br />
AIR QUALITY HEALTH INDEX<br />
At the federal level, <strong>Environment</strong> Canada and<br />
Health Canada created the AQHI. The AQHI differs<br />
from the AQI by weighing the impact <strong>of</strong> multiple<br />
pollutants and incorporating findings from<br />
epidemiological studies. 134 Within Ontario, this<br />
AQHI is being piloted in the Greater Toron<strong>to</strong> Area<br />
(GTA) and in the National Capital Region (NCR)<br />
in a project led by <strong>Environment</strong> Canada. According<br />
<strong>to</strong> Perrotta’s review 127 , support for the project<br />
came from the EMRB through the provision <strong>of</strong> air<br />
moni<strong>to</strong>ring data, AQHI forecasting, and technical<br />
advice. Moni<strong>to</strong>ring levels <strong>of</strong> PM 2.5<br />
, NO 2<br />
and O 3<br />
,<br />
the AQHI places a greater weight on NO 2<br />
levels<br />
followed by O 3<br />
and PM 2.5<br />
. At a municipal level, the<br />
AQHI may be less reflective <strong>of</strong> air pollutant health<br />
risks due <strong>to</strong> higher levels <strong>of</strong> PM 2.5<br />
. Furthermore,<br />
the AQHI focuses on regional air quality levels<br />
and does not moni<strong>to</strong>r other pollutants, such as<br />
SO 2<br />
and CO, which may contribute <strong>to</strong> local air<br />
quality health concerns.<br />
To date, AQHI moni<strong>to</strong>ring is done from the same<br />
location and equipment that is used for the AQI.<br />
The pilot project has demonstrated that the AQHI<br />
system tends <strong>to</strong> be more sensitive for urban centres<br />
where health impacts are dominated by air<br />
levels <strong>of</strong> NO 2<br />
. By contrast, the Ontario AQI system<br />
tends <strong>to</strong> be more sensitive for rural areas where<br />
health impacts tend <strong>to</strong> be dominated by air levels<br />
<strong>of</strong> ozone and PM 2.5<br />
and trans-boundary impacts.<br />
Perrotta 127 highlight’s the EMRB’s acknowledgment<br />
that pending the completion <strong>of</strong> additional<br />
analyses, decisions will have <strong>to</strong> be made about<br />
how <strong>to</strong> move forward with the AQHI and the AQ.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 101<br />
DATA ON POINT SOURCE AIR<br />
POLLUTANT LEVELS<br />
With a few exceptions, the MOE Regional Offices<br />
no longer own, operate or maintain the fixed station<br />
air moni<strong>to</strong>ring equipment that is used for ongoing<br />
air moni<strong>to</strong>ring <strong>of</strong> point sources in Ontario.<br />
Responsibility for these moni<strong>to</strong>ring programs<br />
was transferred <strong>to</strong> the organizations responsible<br />
for the point sources under a program called<br />
Source Emissions Moni<strong>to</strong>ring in May 2003. Under<br />
this program, the organizations (i.e. industrial facilities,<br />
generating stations, etc.) are required <strong>to</strong><br />
moni<strong>to</strong>r air quality on an on-going basis, because<br />
<strong>of</strong> the volume <strong>of</strong> their emissions or their potential<br />
<strong>to</strong> negatively impact human health or the environment.<br />
The organizations are responsible for<br />
funding, operating, and maintaining the air moni<strong>to</strong>ring<br />
equipment. Under this program, the MOE<br />
Regional Offices continue <strong>to</strong> have direct access<br />
<strong>to</strong> real-time data, receive regular reports on air<br />
moni<strong>to</strong>ring results, and audit the air moni<strong>to</strong>ring<br />
programs. 127<br />
RESEARCH-BASED AIR<br />
MONITORING AND DATA<br />
When the fixed stations were transferred, MOE<br />
Regional Offices enhanced their capability <strong>to</strong><br />
undertake flexible air moni<strong>to</strong>ring studies by purchasing<br />
more portable equipment. These instruments<br />
can be used <strong>to</strong> identify air emissions and<br />
support compliance and air emission reduction<br />
activities. 127<br />
CURRENT AIR QUALITY DATA IS<br />
LIMITED FOR LOCAL AIRSHEDS<br />
While AQI/AQHI readings are available for most<br />
public health units in Ontario, these readings<br />
provide indica<strong>to</strong>rs <strong>of</strong> ambient or background air<br />
quality only. For the most part, the air moni<strong>to</strong>ring<br />
stations used <strong>to</strong> produce these readings have<br />
been sited intentionally at a distance from local<br />
emissions sources <strong>to</strong> ensure that they reflect ambient<br />
or background air quality for the community.<br />
There are a few exceptions <strong>to</strong> this rule; several <strong>of</strong><br />
these air moni<strong>to</strong>ring stations have been located<br />
adjacent <strong>to</strong> high volume traffic corridors <strong>to</strong> provide<br />
a better understanding <strong>of</strong> air quality as it is<br />
impacted by mobile sources <strong>of</strong> air pollution.<br />
The MOE and <strong>Environment</strong> Canada have indicated<br />
that their organizations use their air moni<strong>to</strong>ring<br />
stations <strong>to</strong> assess ambient air quality or regional<br />
air quality. The MOE reports that it does not, as<br />
a rule, get involved in air moni<strong>to</strong>ring projects that<br />
are directed at understanding how air quality varies<br />
across a community. This means that most<br />
PHUs in Ontario have only one or two air moni<strong>to</strong>ring<br />
stations in their communities providing data<br />
on local air quality.<br />
A few PHUs will have access <strong>to</strong> data collected<br />
by organizations that are required <strong>to</strong> moni<strong>to</strong>r air<br />
quality under the Source Emissions Moni<strong>to</strong>ring<br />
program. This usually applies <strong>to</strong> large point<br />
sources such as steel or nickel-processing facilities,<br />
oil refineries or coal-fired generating stations.<br />
These moni<strong>to</strong>ring stations are usually directed at<br />
air pollutants that are associated with the point<br />
sources. As a rule, these stations are not sited <strong>to</strong><br />
assess air quality across the community from all<br />
sources; nor are they measuring air pollutants beyond<br />
those associated with their own operations.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
102<br />
AIR QUALITY<br />
EMISSIONS ESTIMATES DATA<br />
In addition <strong>to</strong> moni<strong>to</strong>ring stations data, the literature<br />
review noted some important sources <strong>of</strong><br />
information derived from emissions estimates. In<br />
many studies, air pollutant levels in the built environment<br />
were derived by using emissions estimates<br />
from mobile sources (e.g. vehicle and<br />
other transportation sources) and point sources<br />
(e.g. industrial facilities). Table 9 summarizes the<br />
data sources <strong>of</strong> emissions estimates noted in the<br />
literature review.<br />
TRAFFIC EMISSIONS ESTIMATES<br />
In the literature review the most common data<br />
source for emissions obtained at a Municipal level<br />
was traffic volume estimates. <strong>Data</strong> on traffic flow<br />
rates or industrial activities can be converted <strong>to</strong><br />
emissions rates using an emissions fac<strong>to</strong>r, which<br />
converts the known input energy and raw materials<br />
<strong>to</strong> emissions. The variables in calculating<br />
emissions involve data on activity rates, the emissions<br />
fac<strong>to</strong>r, and composition <strong>of</strong> traffic vehicles. 135<br />
Emissions data along with dispersion modelling<br />
has commonly been utilized by the MOE <strong>to</strong> determine<br />
ambient concentration levels. These techniques<br />
have also been used by PHUs in Hal<strong>to</strong>n<br />
and Toron<strong>to</strong>.<br />
As part <strong>of</strong> Ontario Regulation 127/01, all facilities<br />
based in Ontario that emit specific substances<br />
at certain quantities are required <strong>to</strong> provide information<br />
<strong>to</strong> the government, which is also publicly<br />
available. At the federal level, <strong>Environment</strong> Canada<br />
developed the National Pollutant Release<br />
Inven<strong>to</strong>ry (NPRI) which collects information on facilities<br />
emitting specific substances across Canada.<br />
Presently, the requirements for emissions<br />
reporting in Ontario have been harmonized with<br />
NPRI, with the data being made publically available<br />
on the NPRI website. 104 <strong>Environment</strong> Canada<br />
has data available at a facility level and aggregated<br />
data at the provincial level. The provincial<br />
information includes estimates for 17 air pollutants<br />
organized by sec<strong>to</strong>r. In addition <strong>to</strong> industrial<br />
sources, NPRI estimates emissions from various<br />
other sec<strong>to</strong>rs such as transportation, agriculture,<br />
landfills, natural sources, etc. 134<br />
While NPRI is a useful resource for point source<br />
emissions, it does not capture smaller point<br />
sources existing in urban communities such as<br />
printing and au<strong>to</strong>motive service facilities. The<br />
data collection for smaller point source will be<br />
discussed further in the case studies <strong>of</strong> Hal<strong>to</strong>n<br />
and Toron<strong>to</strong>.<br />
INDUSTRIAL EMISSIONS ESTIMATES<br />
DATA<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 103<br />
Table 9: Summary <strong>of</strong> Air Quality and Air Pollutant <strong>Data</strong> Sources at Municipal, Provincial and Federal levels<br />
Measure<br />
Pollutant<br />
Components<br />
<strong>Data</strong> Source <strong>Data</strong> Format Frequency<br />
Municipal level:<br />
Traffic emissions<br />
Traffic related<br />
pollutants<br />
Traffic counts (e.g.<br />
average annual daily<br />
traffic)<br />
Varies between<br />
municipalities<br />
Varies between<br />
municipalities<br />
Provincial level:<br />
Ontario Air Quality<br />
Index<br />
NO 2<br />
, O 3<br />
, SO 2<br />
, CO,<br />
PM 2.5,<br />
and <strong>to</strong>tal<br />
reduced sulphur<br />
compounds<br />
Moni<strong>to</strong>ring stations<br />
from Ministry <strong>of</strong> the<br />
<strong>Environment</strong> Ontario<br />
Air Quality Information<br />
System (AQUIS)<br />
Aggregated<br />
index<br />
Hourly<br />
(AQI reading:<br />
2007 <strong>to</strong><br />
present)<br />
Individual<br />
Pollutants<br />
CO, NO 2<br />
, NO, NO x<br />
,<br />
O 3<br />
, SO 2<br />
, and PM 2.5<br />
,<br />
Moni<strong>to</strong>ring stations<br />
from Ministry <strong>of</strong> the<br />
<strong>Environment</strong> Ontario<br />
Air Quality Information<br />
System (AQUIS)<br />
Ambient<br />
concentrations<br />
Air Quality<br />
Information<br />
System (1953-<br />
2003)<br />
Federal level:<br />
Air Quality Health<br />
Index<br />
O 3<br />
, PM 2.5<br />
, NO 2<br />
Moni<strong>to</strong>ring stations<br />
from National Air<br />
Pollution Surveillance<br />
(NAPS)<br />
Integrates AQUIS<br />
Composite<br />
measure<br />
Hourly<br />
Canada<br />
<strong><strong>Environment</strong>al</strong><br />
Sustainability<br />
Indica<strong>to</strong>rs<br />
O 3<br />
, PM 2.5<br />
, SO 2<br />
, NO 2<br />
,<br />
VOCs<br />
Moni<strong>to</strong>ring stations<br />
from National Air<br />
Pollution Surveillance<br />
(NAPS) and Canadian<br />
Air and Precipitation<br />
Moni<strong>to</strong>ring Network<br />
(CAPMoN)<br />
Ambient<br />
concentrations<br />
Hourly average<br />
2 year lag<br />
period for data<br />
O 3<br />
( (1990 <strong>to</strong><br />
2010)PM 2.5<br />
(2000-2010)<br />
SO 2<br />
, NO 2<br />
, VOC<br />
(1996-2010)<br />
National Pollutant<br />
Release Inven<strong>to</strong>ry<br />
(NPRI)<br />
Over 300 pollutants<br />
Emissions (reporting,<br />
stations, surveys,<br />
and estimates) from<br />
<strong>Environment</strong> Canada<br />
NPRI database<br />
Emissions<br />
estimates<br />
(Does not<br />
provide<br />
atmospheric<br />
concentrations)<br />
Yearly (1 <strong>to</strong> 2<br />
year lag for<br />
most current<br />
data)<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
104<br />
AIR QUALITY<br />
MODELLING AIR QUALITY<br />
Recent research highlights the value <strong>of</strong> more<br />
detailed exposure estimates <strong>to</strong> accurately determine<br />
health risks and for effective public health<br />
programming related <strong>to</strong> the built environment. To<br />
assess air quality issues in the built environment,<br />
it is important <strong>to</strong> consider the spatial scale <strong>of</strong> pollutant<br />
levels. The distribution <strong>of</strong> air pollutants is<br />
complex, ranging from characteristics <strong>of</strong> vehicle<br />
emission flow patterns <strong>to</strong> trans-boundary movements<br />
<strong>of</strong> pollutants. For local air quality issues<br />
relevant <strong>to</strong> public health, the most common approaches<br />
have been <strong>to</strong> evaluate traffic corridors<br />
and measure pollutant levels at a neighbourhood<br />
level.<br />
To accurately map the spatial distribution and<br />
temporal changes <strong>of</strong> air pollutants a large number<br />
<strong>of</strong> air sampling equipment may be required. This<br />
may not be feasible for most public health units.<br />
To overcome this challenge, modelling <strong>to</strong>ols have<br />
been developed and utilized <strong>to</strong> predict pollutant<br />
levels at a high spatial resolution.<br />
The literature review identified a variety <strong>of</strong> models<br />
that were utilized by researchers <strong>to</strong> estimate pollutant<br />
exposure (Table 10). These range from more<br />
basic models such as distance from air moni<strong>to</strong>rs<br />
<strong>to</strong> more complex models such as Kriging, land<br />
use regression, dispersion modelling, and the application<br />
<strong>of</strong> satellite technologies. While various<br />
modelling methods are provided, some <strong>of</strong> these<br />
methods can be combined with other modelling<br />
techniques <strong>to</strong> further assess air quality. For more<br />
detail on options for assessing air quality refer <strong>to</strong><br />
the “Air Quality Assessment Tools: A guide for<br />
Public Health Practitioners” developed by the<br />
National Collaborative Centre for <strong><strong>Environment</strong>al</strong><br />
Health. 135<br />
BASIC MODELS AND INVERSE<br />
DISTANCE WEIGHING<br />
The simplest models determine air pollutant exposure<br />
by the point <strong>of</strong> interest at a certain distance<br />
from an air moni<strong>to</strong>ring station. Some stations<br />
are over 10 km away from the point <strong>of</strong> interest.<br />
136 Using inverse distance weighing (IDW),<br />
studies determined the air pollutant levels at a<br />
specific location by averaging the three nearest<br />
moni<strong>to</strong>ring sites. Pollutant levels at moni<strong>to</strong>ring<br />
stations closer <strong>to</strong> the location <strong>of</strong> interest were<br />
given a greater weighting. 120 The use <strong>of</strong> IDW has<br />
also been applied <strong>to</strong> determine concentrations<br />
close <strong>to</strong> pollutant point sources as well as ambient<br />
concentrations for sensitive recep<strong>to</strong>rs such as<br />
residential areas. These methods take advantage<br />
<strong>of</strong> detailed temporal information and low cost<br />
data from moni<strong>to</strong>ring stations. However, without<br />
considering meteorological or built environment<br />
effects such as wind direction or mixed land use,<br />
the use <strong>of</strong> IDW provides limited accuracy in mapping<br />
the spatial trends <strong>of</strong> pollutant levels.<br />
To gain greater spatial detail on pollutant exposures,<br />
especially for long term exposure and chronic<br />
health outcomes, more sophisticated models<br />
have been developed. The output <strong>of</strong> these models<br />
commonly involves generating spatial maps<br />
which could be used either for pollutant exposure<br />
risk assessments or in air quality moni<strong>to</strong>ring.<br />
Geographic information systems (GIS) are <strong>of</strong>ten<br />
used as a <strong>to</strong>ol in producing pollutant exposure<br />
maps.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 105<br />
Figure 10: Operational land use regression<br />
predicted surface for Toron<strong>to</strong>. 140p.208<br />
KRIGING<br />
Kriging is a method used <strong>to</strong> model air pollutant<br />
exposure in urban environments. The model assumes<br />
that the measure <strong>of</strong> interest can be described<br />
by trends <strong>of</strong> pollutants at various spatial<br />
scales. Based on the association <strong>of</strong> the variable<br />
(e.g. location, land use, etc.) with the measured<br />
pollutant concentration, the model then predicts<br />
what the concentration will be at unmeasured<br />
sites. 131 In the Ottawa air study, Kriging was used<br />
with satellite information <strong>to</strong> assess the spatial distribution<br />
<strong>of</strong> pollutants at a neighborhood level.<br />
©. Taylor & Francis, 2007. Reproduced with permission.<br />
DISPERSION MODELLING<br />
<strong>An</strong>other option for mapping the spatial distribution<br />
<strong>of</strong> air pollutants is dispersion modelling.<br />
These models have been commonly used by<br />
MOE and local municipalities in risk assessments<br />
and evaluating air quality management <strong>of</strong> point<br />
sources emissions. 137 Dispersion modelling is<br />
able <strong>to</strong> consider a wide range <strong>of</strong> issues including<br />
wind patterns, deposition <strong>of</strong> pollutants, filtration<br />
from coniferous trees, and turbulent air flow<br />
caused by urban land cover. In general the model<br />
requires data from traffic volume, composition <strong>of</strong><br />
traffic vehicles, point source emissions (e.g. industrial<br />
sources), and local weather information.<br />
Numerous dispersion models are approved by<br />
the Ontario MOE including ASHRAE, SCREEN3,<br />
CALPUFF/CALMET, ISCPRIME, CALINE4, and<br />
AERMOD. 104 Through the MOE’s Certificate <strong>of</strong><br />
Approval Process, industries could be required <strong>to</strong><br />
perform dispersion modelling for an emitting facility.This<br />
data may be available and useful for public<br />
health decision making. In Ontario CALMET/<br />
CALPUFF has been used by Hal<strong>to</strong>n Region and<br />
City <strong>of</strong> Toron<strong>to</strong> <strong>to</strong> model the spatial distribution<br />
<strong>of</strong> pollutants at a resolution <strong>of</strong> one <strong>to</strong> two kilometres.<br />
127<br />
LAND USE REGRESSION<br />
<strong>An</strong>other commonly cited model in the literature<br />
was land use regression (LUR). The focus<br />
<strong>of</strong> LUR is <strong>to</strong> develop predic<strong>to</strong>rs <strong>of</strong> pollutant<br />
concentration by showing strong linkages <strong>to</strong><br />
certain variables such as road density or land<br />
use type. The level <strong>of</strong> detail can be substantial<br />
with a resolution <strong>of</strong> 10 meters for spatial<br />
maps. 135;137 The approach involves sampling<br />
<strong>of</strong> pollutants at multiple sites. Linear regression<br />
statistics are used <strong>to</strong> correlate measured<br />
pollutant concentrations with the strongest<br />
predictive variable. This correlation can be<br />
linked <strong>to</strong> a variety <strong>of</strong> geographic attributes<br />
relating <strong>to</strong> the built environment. In particular<br />
LUR uses four main variables: population<br />
density, traffic density, road length, and landuse.<br />
The data sources noted for LUR include<br />
DMTI files on roads and land use, local traffic<br />
data, population data, and municipal zoning<br />
data. With various subcategories <strong>of</strong> each<br />
variable, there are a <strong>to</strong>tal <strong>of</strong> 55 possible predic<strong>to</strong>rs<br />
<strong>of</strong> environmental exposure. 137 These<br />
models have previously been developed in<br />
Sarnia, 138 Hamil<strong>to</strong>n, 139 Toron<strong>to</strong> (Figure 10), 140<br />
Windsor, 141 and Ottawa. 128 Refer <strong>to</strong> Brauer and<br />
colleagues 121 for examples <strong>of</strong> LUR models<br />
conducted in Canadian cities.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
106<br />
AIR QUALITY<br />
WHICH MODEL IS MOST EFFECTIVE?<br />
The research has been mixed on which model most<br />
accurately estimates air pollution levels. Ryan and<br />
LeMasters 142 state that LUR models can provide<br />
equal or better spatial information than dispersion<br />
models. The precision <strong>of</strong> LUR modelling was better<br />
for certain pollutants such as <strong>to</strong>tal suspended<br />
particulates (TSP) 143 and NO 2<br />
, 120 but poorer than<br />
nearer moni<strong>to</strong>ring stations for PM 2.5<br />
and black<br />
carbon. 120 Nitrogen dioxide is strongly correlated<br />
with the land use variables, but SO 2<br />
is not<br />
as strongly correlated with all land use variables<br />
used in LUR. 141 Beelen and colleagues 131 noted<br />
that universal Kriging had outperformed LUR and<br />
ordinary Kriging for NO 2<br />
, PM 10<br />
, and O 3<br />
when an<br />
exposure map (1km resolution) for Europe was<br />
developed. Brauer and colleagues 137 notes how<br />
research projects have found dispersion modelling<br />
<strong>to</strong> have similar predictive power <strong>to</strong> LUR, but<br />
with LUR being more valuable when emissions<br />
data is unavailable. Despite noted weaknesses in<br />
LUR predicting pollutant exposure, others have<br />
argued that LUR provides the most reliable results<br />
in comparison <strong>to</strong> distance from road ways<br />
and questionnaires regarding perceived traffic<br />
density. 129<br />
SATELLITE TECHNOLOGIES<br />
The use <strong>of</strong> remote sensing, particularly satellite<br />
imagery was not commonly cited in the literature<br />
review, but has been a <strong>to</strong>ol used by municipalities<br />
such as Ottawa. Satellite moni<strong>to</strong>ring systems<br />
have been developed <strong>to</strong> moni<strong>to</strong>r a variety<br />
<strong>of</strong> pollutants including NO 2<br />
, SO 2<br />
, CO, O 3<br />
, and<br />
PM through indirect measures such as assessing<br />
the number <strong>of</strong> aerosols in a vertical column <strong>of</strong><br />
the earth’s atmosphere and hydrocarbons. 144 The<br />
spatial resolution can vary significantly from 250<br />
m <strong>to</strong> 320 km depending on the instrument used.<br />
While satellite technologies have shown <strong>to</strong> be a<br />
useful <strong>to</strong>ol in moni<strong>to</strong>ring the built environment,<br />
other issues need <strong>to</strong> be considered such as cost,<br />
the quality <strong>of</strong> images, and the accuracy <strong>of</strong> pollutant<br />
concentration through measurements <strong>of</strong><br />
infrared and light scattering. The example <strong>of</strong> Ottawa<br />
and use <strong>of</strong> satellite technologies in air quality<br />
assessments is discussed later.<br />
Generally, these sophisticated models have<br />
shown strong predictive value for pollutants. The<br />
choice <strong>of</strong> model depends on multiple fac<strong>to</strong>rs. Key<br />
questions need <strong>to</strong> be answered before modelling<br />
begins: What is the objective <strong>of</strong> the moni<strong>to</strong>ring<br />
program or study? Is spatial and/or temporal detail<br />
required? Is the required data available? What<br />
financial and logistical issues are involved? What<br />
pollutants and health outcomes are <strong>of</strong> special<br />
concern? Brauer and colleagues 120;137 highlight<br />
that air moni<strong>to</strong>ring stations are a good predic<strong>to</strong>r<br />
<strong>of</strong> urban levels <strong>of</strong> particulate matter but in contrast<br />
ozone, nitric oxide, and black carbon differ<br />
more significantly within urban environments and<br />
can benefit from modelling <strong>to</strong> gain better spatial<br />
exposure maps. 120<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 107<br />
Table 10: Methods <strong>of</strong> modelling air quality and pollution levels noted in the literature review<br />
Proximity Models<br />
(e.g. IDW)<br />
Interpolation<br />
models, Kriging<br />
Land Use<br />
Regression<br />
Dispersion Modelling<br />
Description<br />
Distance from<br />
emission source<br />
or from ambient<br />
measuring station.<br />
Inverse Distance<br />
Weighing formula<br />
calculates average<br />
exposure levels based<br />
on distance from<br />
moni<strong>to</strong>ring stations.<br />
Mathematical<br />
formula predicting<br />
pollutant levels<br />
in unknown area<br />
based on data near<br />
area.<br />
Estimates air<br />
pollution levels<br />
which correlate<br />
well with land cover<br />
type, traffic, road<br />
type, elevation,<br />
and other possible<br />
variables<br />
Commonly used for<br />
point and area sources.<br />
Using emissions data, the<br />
model calculates ambient<br />
air pollutant levels. The<br />
model takes in<strong>to</strong> account<br />
low wind speeds, timevariation<br />
from pollution<br />
sources, and various land<br />
types.<br />
Benefits<br />
• Simple method <strong>of</strong><br />
calculating exposure<br />
• Low financial and<br />
logistics cost<br />
• Can be applied<br />
for point source<br />
emissions<br />
• Models spatial<br />
distribution <strong>of</strong> air<br />
pollutants<br />
• Universal Kriging<br />
considers large<br />
scale fac<strong>to</strong>rs (e.g.<br />
regional or global<br />
levels)<br />
• At a resolution <strong>of</strong><br />
10 meters<br />
• Allows for flexible<br />
sampling in terms<br />
<strong>of</strong> location and<br />
time period<br />
• Better for<br />
between<br />
participant<br />
variability in<br />
epidemiology<br />
studies<br />
• Accounts for<br />
meteorological impacts<br />
(mainly wind) and<br />
temporal changes in<br />
pollutant levels<br />
• Can estimate pollutant<br />
levels from different<br />
scenarios<br />
• Does not require air<br />
moni<strong>to</strong>ring data<br />
• At a resolution <strong>of</strong> 10s <strong>of</strong><br />
meters<br />
• Provides details at<br />
regional and urban level<br />
Challenges<br />
• Does not account<br />
for landscape and<br />
meteorological<br />
features<br />
• Greater error range<br />
in estimates<br />
• Needs large<br />
number <strong>of</strong><br />
observations<br />
• Initially requires<br />
air sampling or<br />
emissions fac<strong>to</strong>r<br />
• Do not take in<strong>to</strong><br />
account other<br />
variables such as<br />
terrain<br />
• May exaggerate<br />
variability<br />
• Limited<br />
predictability and<br />
generalization<br />
from one city <strong>to</strong><br />
another<br />
• Initially requires<br />
air sampling or<br />
emissions fac<strong>to</strong>r<br />
• May produce<br />
higher risk<br />
estimates<br />
• Less developed<br />
for point sources<br />
• Costly, requiring<br />
powerful computers<br />
• Requires emissions,<br />
traffic density data<br />
• Mismatch in temporal<br />
data (e.g. derives hourly<br />
estimates from annual<br />
emissions data<br />
• More sophisticated<br />
models require a lot <strong>of</strong><br />
data <strong>to</strong> be accurate.<br />
• Present applications in<br />
Ontario municipalities<br />
at 1-2 kilometres<br />
resolution<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
108<br />
AIR QUALITY<br />
AIR MONITORING AND MODELLING STUDIES AT THE PUBLIC<br />
HEALTH UNIT LEVEL<br />
This section provides examples <strong>of</strong> air moni<strong>to</strong>ring and modelling projects conducted by three municipalities:<br />
Toron<strong>to</strong>, Hal<strong>to</strong>n Region, and Ottawa. These examples highlight how various measurement approaches<br />
and data sources are combined <strong>to</strong> better assess ambient levels <strong>of</strong> pollution at a neighborhood<br />
level and <strong>to</strong> estimate associated health risks.<br />
ASSESSING AIR QUALITY IN TORONTO<br />
In the early 1990s, the MOE was operating five<br />
AQI air moni<strong>to</strong>ring stations in what is <strong>to</strong>day the<br />
amalgamated City <strong>of</strong> Toron<strong>to</strong>. While the MOE<br />
moni<strong>to</strong>rs provided an indica<strong>to</strong>r <strong>of</strong> ambient air<br />
quality at those sites, they could not provide information<br />
about the quality <strong>of</strong> air between these<br />
stations. Therefore, they were inadequate in addressing<br />
community concerns about air quality in<br />
neighbourhoods that were located in close proximity<br />
<strong>to</strong> local emission sources. Furthermore, air<br />
moni<strong>to</strong>rs could not predict how air quality would<br />
be impacted by changes in municipal policies related<br />
<strong>to</strong> land use or development according <strong>to</strong><br />
the TEO. 127<br />
To address these concerns, the City <strong>of</strong> Toron<strong>to</strong><br />
began modelling air quality. They subsequently<br />
verified the results by comparing them against<br />
the data from the MOE AQI air moni<strong>to</strong>ring stations,<br />
and by undertaking specific air moni<strong>to</strong>ring<br />
projects. Accordingly, the TEO deemed air modelling<br />
<strong>to</strong> be useful for the City <strong>of</strong> Toron<strong>to</strong> as it indicates<br />
what comes from where and when. 127<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 109<br />
AIRSHED MODELLING – COMMON<br />
AIR POLLUTANTS<br />
Toron<strong>to</strong> staff began the process <strong>of</strong> building a Toron<strong>to</strong>-specific<br />
airshed model by using all available<br />
known emissions data; collecting data needed <strong>to</strong><br />
estimate relevant emissions such as traffic count<br />
data and engine emissions standards; modifying<br />
federal estimates <strong>of</strong> fugitive emissions (particulate<br />
matter release from industrial and construction<br />
area sources) and other area emissions; and collecting<br />
federal meteorological data from across<br />
southern Ontario. After evaluating several modelling<br />
options, City <strong>of</strong> Toron<strong>to</strong> staff decided <strong>to</strong> use<br />
the CALMET/CALPUFF model suite that was developed<br />
by a private research corporation under<br />
contract <strong>to</strong> the California Air Resources Board<br />
(CARB) in the late 1980’s; a suite which was later<br />
adopted by the U.S. <strong><strong>Environment</strong>al</strong> Protection<br />
Agency (EPA) and the MOE among others. 127<br />
Originally, the Toron<strong>to</strong>-specific airshed model<br />
work was used <strong>to</strong> estimate annual air levels <strong>of</strong><br />
the common air pollutants, PM 10<br />
/PM 2.5<br />
, NO 2<br />
, SO 2<br />
,<br />
CO and VOCs, at a 2 km resolution for the entire<br />
City. Ozone has also been estimated but using<br />
satellite data. The airshed model was developed<br />
so that the air quality impacts associated with<br />
the four different sec<strong>to</strong>rs - point sources such as<br />
industrial facilities, area sources such as commercial<br />
and residential buildings, mobile sources<br />
such as cars, trucks and trains, and biogenic<br />
sources such as trees and gardens - could be<br />
viewed separately and in combination. 127<br />
AIRSHED MODELLING – AIR TOXICS<br />
The Toron<strong>to</strong> airshed model has been extended <strong>to</strong><br />
examine air <strong>to</strong>xics as well. This effort began with<br />
a project directed at South Riverdale – Leslieville<br />
and the Beach neighbourhoods in Toron<strong>to</strong>. In this<br />
project, air modelling was used <strong>to</strong> estimate the air<br />
levels for the common air pollutants and all <strong>of</strong> the<br />
25 substances that are captured in Toron<strong>to</strong>’s new<br />
Reporting, Disclosure and Innovation By-law. The<br />
TEO indicates that for the entire city, emissions<br />
from adjacent municipalities, southern Ontario<br />
and the United States were modelled at a 1 km<br />
resolution using the CALMET/CALPUFF models<br />
plus the CMAC model. 127<br />
Both the TEO and Toron<strong>to</strong> Public Health indicate<br />
that for the targeted neighbourhoods, 30 air pollutants<br />
were modelled at a 100 metre resolution<br />
using emissions from all potential sources including<br />
mobile sources, residential sources, small<br />
area and point sources such as dry cleaners and<br />
au<strong>to</strong>body shops, as well as the large local and<br />
transboundary point sources that are included in<br />
the National Pollutant Release Inven<strong>to</strong>ry (NPRI)<br />
and the American Toxic Release Inven<strong>to</strong>ry (TRI). 127<br />
While this phase <strong>of</strong> the project focused on local<br />
air quality in these two neighbourhoods, the<br />
background air levels estimated for the entire City<br />
will be used when assessments are conducted in<br />
the future for other neighbourhoods in the City <strong>of</strong><br />
Toron<strong>to</strong>. 127<br />
ASSESSING THE CUMULATIVE<br />
HEALTH IMPACTS ASSOCIATED WITH<br />
MULTIPLE POLLUTANTS & SOURCES<br />
Toron<strong>to</strong> Public Health assessed the cumulative<br />
health impacts associated with multiple air pollutants<br />
for two neighbourhoods in down<strong>to</strong>wn<br />
Toron<strong>to</strong> over several years. The common air pollutants<br />
(ozone, PM 2.5<br />
, NO 2<br />
, CO and SO 2<br />
) and 25<br />
priority substances identified for the City’s new<br />
<strong><strong>Environment</strong>al</strong> Reporting and Disclosure Bylaw<br />
were assessed. The 25 priority substances were<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
110<br />
AIR QUALITY<br />
Figure 11: Distribution <strong>of</strong> risks associated with airborne non-carcinogencic <strong>to</strong>xics for two<br />
neighbourhoods in Toron<strong>to</strong>, 2011 145;146<br />
selected by comparing air quality data for many<br />
different air pollutants, that had been collected<br />
by <strong>Environment</strong> Canada and the Ontario MOE,<br />
against health benchmarks established for these<br />
air pollutants by organizations such as the California<br />
<strong><strong>Environment</strong>al</strong> Protection Agency, the Ontario<br />
MOE, <strong>Environment</strong><br />
Canada, and the<br />
New Jersey Department<br />
<strong>of</strong> <strong><strong>Environment</strong>al</strong><br />
Protection.<br />
As indicated above,<br />
the common air pollutants<br />
and priority<br />
substances were<br />
all modelled using<br />
emissions data directly<br />
available from<br />
sources such as<br />
NPRI and from estimates<br />
<strong>of</strong> emissions<br />
derived for mobile,<br />
residential and commercial<br />
sources in<br />
the two neighbourhoods.<br />
The estimated<br />
levels <strong>of</strong> the air<br />
pollutants were then<br />
compared against<br />
health benchmarks<br />
for each air pollutant<br />
individually and cumulatively with other air<br />
pollutants. 145;146<br />
For non-carcinogenic substances, the average,<br />
maximum and minimum hazard ratio values were<br />
estimated for each non-carcinogenic substance<br />
using the average annual air concentrations from<br />
the 551 recep<strong>to</strong>r sites modelled in the two neighbourhoods.<br />
These hazard ratios were used <strong>to</strong><br />
determine relative risk <strong>of</strong> each compound, and<br />
were then added <strong>to</strong>gether <strong>to</strong> create a cumulative<br />
hazard ratio.<br />
Using this approach,<br />
a cumulative hazard<br />
ratio less than<br />
1 indicates that the<br />
cumulative impact<br />
<strong>of</strong> exposure <strong>to</strong> all<br />
<strong>of</strong> the non-carcinogens<br />
in combination,<br />
does not present a<br />
health concern. A<br />
cumulative hazard<br />
ratio greater than 1<br />
indicates that the<br />
combined exposures<br />
might present a<br />
health concern and<br />
require a more detailed<br />
analysis.<br />
In the study directed<br />
at specific City <strong>of</strong><br />
Toron<strong>to</strong> neighbourhoods,<br />
the cumulative<br />
hazard ratio was<br />
0.31. The conclusion<br />
was that the<br />
Reproduced with permission<br />
non-carcinogenic contaminants are present in<br />
the air at levels below levels <strong>of</strong> concern <strong>to</strong> health,<br />
even when the combined exposure is taken in<strong>to</strong><br />
account 145;146 (Figure 11).<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 111<br />
Figure 12: Distribution <strong>of</strong> risks associated with airborne carcinogens for two neighbourhoods in<br />
Toron<strong>to</strong>, 2011 145;146<br />
For carcinogenic substances, the average,<br />
maximum and minimum cancer risk values were<br />
estimated for each carcinogenic substance<br />
using the average annual air concentrations<br />
from the 551 recep<strong>to</strong>r sites modelled in the two<br />
neighbourhoods.<br />
These cancer risk<br />
values were then<br />
added <strong>to</strong>gether <strong>to</strong><br />
create a cumulative<br />
cancer risk value<br />
which was compared<br />
against a risk level <strong>of</strong><br />
1 in a million excess<br />
cancer cases in a<br />
lifetime. 145;146<br />
In this study, the<br />
cumulative cancer<br />
risk for all <strong>of</strong> the<br />
carcinogenic air<br />
pollutants combined<br />
was 83 in one million.<br />
The study found that<br />
most carcinogens<br />
in these two<br />
n e i g h b o u r h o o d s<br />
are present at<br />
c o n c e n t r a t i o n s<br />
below the one in<br />
a million excess<br />
cancer risk benchmark. However, benzene<br />
and Polycyclic Aromatic Hydrocarbons (PAHs)<br />
exceed their annual ambient air quality criteria in<br />
some areas, on occasion. It also found that the<br />
cumulative risk from airborne carcinogens is very<br />
low when compared <strong>to</strong> the <strong>to</strong>tal incidence rate <strong>of</strong><br />
cancer in Toron<strong>to</strong> 145;146 (Figure 12).<br />
For the common or criteria air pollutants, the<br />
average, maximum and minimum excess per<br />
capita risk for premature death values were estimated<br />
for each substance using the average<br />
annual air concentrations from the 551 recep<strong>to</strong>r<br />
sites modelled in<br />
the two neighbourhoods<br />
(Figure 13).<br />
These per capita risk<br />
values were then<br />
added <strong>to</strong>gether <strong>to</strong><br />
create a cumulative<br />
increased risk<br />
<strong>of</strong> premature death<br />
value. In this study,<br />
the cumulative per<br />
capita risk <strong>of</strong> premature<br />
death was<br />
8.9%. This study<br />
also found that 24-<br />
hour ambient air<br />
quality criteria for<br />
PM 10<br />
and NO x<br />
are<br />
exceeded on occasion<br />
in these neighbourhoods.<br />
The<br />
study found that the<br />
criteria air contaminants<br />
such as ozone,<br />
Reproduced with permission<br />
nitrogen dioxide<br />
and particulate matter,<br />
are responsible for the largest burden <strong>of</strong> illness<br />
associated with air pollution in these two<br />
neighbourhoods 145;146<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
112<br />
AIR QUALITY<br />
Figure 13: Distribution <strong>of</strong> risks associated with criteria air pollutants for two neighbourhoods in Toron<strong>to</strong>,<br />
2011 145;146 Reproduced with permission<br />
CHEMTRAC EMISSIONS DATA FOR<br />
POINT SOURCES ON A FINER SCALE<br />
Toron<strong>to</strong> is establishing the ChemTRAC system <strong>to</strong><br />
implement its <strong><strong>Environment</strong>al</strong> Reporting and Disclosure<br />
Bylaw which requires that certain facilities<br />
report, on an annual<br />
basis, if they use or<br />
release any <strong>of</strong> the<br />
25 priority substances<br />
above certain<br />
thresholds. While all<br />
25 chemicals in Toron<strong>to</strong>’s<br />
ChemTRAC<br />
system are included<br />
in NPRI, Toron<strong>to</strong>’s<br />
ChemTRAC system<br />
has: removed the<br />
employee exemption<br />
which excludes facilities<br />
with 10 or fewer<br />
employees from reporting<br />
under NPRI;<br />
reduced the reporting<br />
mass exemption <strong>to</strong><br />
about one tenth <strong>of</strong><br />
the mass required<br />
for exemption under<br />
NPRI; and applies<br />
<strong>to</strong> the use <strong>of</strong> chemicals<br />
as well as <strong>to</strong><br />
emission releases, while NPRI requires reporting<br />
on releases only. In 2010, with the first phase <strong>of</strong><br />
ChemTRAC rolled out, Toron<strong>to</strong> had 272 facilities<br />
reporting on their use and emissions, 152 <strong>of</strong><br />
which were not reporting under either NPRI or<br />
TRI (Toxic Release Inven<strong>to</strong>ry; a US based data<br />
source). 147<br />
ASSESSING AIR QUALITY IN<br />
HALTON<br />
In June 2007, Hal<strong>to</strong>n Regional Council adopted<br />
a 5-part air quality program that included airshed<br />
modelling, stationary air moni<strong>to</strong>ring, portable<br />
air moni<strong>to</strong>ring,<br />
policy development<br />
directed at the land<br />
use planning process,<br />
and a health<br />
promotion program<br />
directed at air<br />
quality and climate<br />
change. 148<br />
The airshed model<br />
was based on detailed<br />
emissions<br />
data for the industrial,<br />
transportation,<br />
commercial, residential<br />
and agricultural<br />
sec<strong>to</strong>rs as well<br />
as biogenic sources<br />
(i.e., trees and other<br />
natural sources).<br />
This Hal<strong>to</strong>n-specific<br />
model was supposed<br />
<strong>to</strong> be developed<br />
for the five common<br />
air pollutants<br />
(i.e. ozone, PM 2.5<br />
, NO 2<br />
, SO 2<br />
and CO) with a 2 km<br />
resolution using the CALMET/CALPUFF model<br />
adopted by the U.S. <strong><strong>Environment</strong>al</strong> Protection<br />
Agency. The Regional Municipality <strong>of</strong> Hal<strong>to</strong>n, as<br />
cited by Perrotta 127 , suggests the model would<br />
be used <strong>to</strong>:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 113<br />
• Characterize air quality across the Region;<br />
• Demonstrate the contribution <strong>of</strong> different<br />
sec<strong>to</strong>rs <strong>to</strong> air quality across the region;<br />
• Inform the broad land use and transportation<br />
planning processes;<br />
• Identify how a new major point source, such<br />
as a new generating station, might contribute<br />
<strong>to</strong> air quality across the community;<br />
• Inform the review <strong>of</strong> certificates <strong>of</strong> approval<br />
by providing better information about<br />
background air levels across the community;<br />
and<br />
• Inform public education and social marketing.<br />
STATIONARY AIR MONITORING<br />
STATION<br />
The Health Department was authorized <strong>to</strong> establish<br />
a stationary air moni<strong>to</strong>ring station capable <strong>of</strong><br />
moni<strong>to</strong>ring the five common air pollutants - ozone,<br />
PM 2.5<br />
, NO 2<br />
, SO 2<br />
and CO – on a continuous basis<br />
in the Town <strong>of</strong> Mil<strong>to</strong>n in 2007. This station was<br />
sited in Mil<strong>to</strong>n in response <strong>to</strong> concerns about<br />
how air quality was being affected by the rapid<br />
growth being experienced in the <strong>to</strong>wn. 148<br />
PORTABLE AIR MONITORING<br />
In 2007, Hal<strong>to</strong>n purchased two Airpointer® portable<br />
air moni<strong>to</strong>rs capable <strong>of</strong> moni<strong>to</strong>ring the five<br />
common air pollutants (Ozone, PM 2.5<br />
, NO/NO 2<br />
,<br />
SO 2<br />
and CO). These instruments are compact,<br />
relatively light-weight and capable <strong>of</strong> matching<br />
the detection limits and accuracy achieved with<br />
the air moni<strong>to</strong>ring equipment used by the MOE.<br />
They come in their own weather-pro<strong>of</strong> and climate<br />
controlled housing so they do not need <strong>to</strong><br />
be housed in trailers or buildings like traditional<br />
equipment. Once power is available, the machine<br />
can simply be plugged in <strong>to</strong> a 120 V circuit. 148;149<br />
COSTS FOR AIR MONITORING & AIR<br />
MODELLING<br />
Hal<strong>to</strong>n Region’s air moni<strong>to</strong>ring and airshed modelling<br />
program cost 148-150 :<br />
• $286,000 <strong>to</strong> purchase the two portable air<br />
moni<strong>to</strong>rs and all <strong>of</strong> the equipment for the<br />
stationary air moni<strong>to</strong>ring station in Mil<strong>to</strong>n.<br />
This includes the equipment needed <strong>to</strong> s<strong>to</strong>re,<br />
transfer, and process air moni<strong>to</strong>ring data.<br />
• $76,000 per year <strong>to</strong> an air moni<strong>to</strong>ring firm <strong>to</strong><br />
install, operate and maintain its stationary air<br />
moni<strong>to</strong>ring station and <strong>to</strong> relocate, operate<br />
and maintain its two portable air moni<strong>to</strong>rs. For<br />
this fee, the firm also manages the Region’s<br />
air moni<strong>to</strong>ring data, generates the AQHI on<br />
an on-going basis, and ensures that the AQHI<br />
readings and continuous air moni<strong>to</strong>ring data<br />
are current for uploading on the Region’s<br />
website on a 24-hour basis.<br />
• Up <strong>to</strong> $50,000 per year for the development<br />
and management <strong>of</strong> the Hal<strong>to</strong>n-specific<br />
airshed model<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
114<br />
AIR QUALITY<br />
ASSESSING AIR QUALITY IN THE NATIONAL CAPITAL REGION (OTTAWA)<br />
In 2005, there was only one MOE AQI air moni<strong>to</strong>ring<br />
station <strong>to</strong> provide indica<strong>to</strong>rs for air quality<br />
for the National Capital Region, an area <strong>of</strong> 2,700<br />
square kilometres. A second MOE AQI air moni<strong>to</strong>ring<br />
station for the NCR was granted in addition<br />
<strong>to</strong> the development <strong>of</strong> a pilot project that aimed<br />
<strong>to</strong> provide a better spatial picture <strong>of</strong> air quality<br />
across the area.<br />
In February 2007, the City <strong>of</strong> Ottawa submitted<br />
a proposal <strong>to</strong> GeoConnections, a Natural Resources<br />
Canada (NRCan) Program, <strong>to</strong> characterize<br />
air quality in the NCR using satellite data as<br />
well as air moni<strong>to</strong>ring data. Air moni<strong>to</strong>ring data<br />
were collected with mobile air moni<strong>to</strong>ring stations<br />
provided by the Transport Canada, <strong>Environment</strong><br />
Canada and the MOE, and passive moni<strong>to</strong>rs on<br />
loan from Health Canada and analyzed by <strong>Environment</strong><br />
Canada. This project allowed for the<br />
testing and calibration <strong>of</strong> a new satellite-based<br />
moni<strong>to</strong>ring technology and provided data <strong>to</strong> support<br />
research in the air quality field. 127<br />
The information used <strong>to</strong> generate maps included:<br />
• Air moni<strong>to</strong>ring data from four NAPS and<br />
Quebec sites and the mobile moni<strong>to</strong>ring units<br />
that were rotating between eight sites;<br />
• Satellite-based air quality data;<br />
• Meteorological data and geographic<br />
characteristics; and<br />
• Road counts and emissions data for<br />
stationary emission sources.<br />
Although the pilot project only ran for one year,<br />
maps were generated for an additional year. City<br />
staff plan on developing a database <strong>of</strong> maps for<br />
the purposes <strong>of</strong>:<br />
• Compilation <strong>of</strong> baseline data for<br />
environmental assessments;<br />
• Educational strategies <strong>to</strong> promote walking,<br />
cycling and public transit;<br />
• To inform land use planning processes (e.g.<br />
community design, land use compatibility);<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 115<br />
Figure 14: Airpointer® belonging <strong>to</strong> the City <strong>of</strong> Ottawa 151<br />
• To inform transportation planning (e.g.<br />
Transportation Master Plan); and<br />
• To support corporate projects such as the<br />
Fleet Emissions Reduction Strategy. 127<br />
Costs for the Project<br />
This pilot project was led by the Air and Energy<br />
team <strong>of</strong> the <strong><strong>Environment</strong>al</strong> Sustainability Division<br />
<strong>of</strong> the <strong><strong>Environment</strong>al</strong> Services Department within<br />
the City <strong>of</strong> Ottawa. This project cost approximately<br />
$340,000, with GeoConnections providing<br />
$149,000 in funding, Ottawa’s Public Health<br />
Department providing $20,000, and the <strong><strong>Environment</strong>al</strong><br />
Sustainability Division providing $10,000<br />
in funding, with in-kind funding provided by<br />
Transport Canada, <strong>Environment</strong> Canada, and the<br />
MOE. The Ottawa International Airport also provided<br />
air traffic data and financially supported the<br />
purchase <strong>of</strong> a roadside moni<strong>to</strong>r (Airpointer®). 127<br />
LIMITATIONS AND<br />
CHALLENGES OF MEASURES,<br />
MODELS & DATA<br />
There is a clear need for more research linking<br />
built environment characteristics <strong>to</strong> air pollutant<br />
exposure. The previous sections discussed various<br />
measures, data sources, and models used<br />
<strong>to</strong> gain a better understanding <strong>of</strong> pollutant exposure<br />
in the built environment. The studies in the<br />
current literature review mainly focused on finding<br />
relationships between various pollutants and<br />
health outcomes, as well as their geographic and<br />
temporal distribution. Research based in Ontario<br />
did not provide specific recommendations about<br />
the air quality indexes presently used by Health<br />
Canada or MOE; it focused on new methods<br />
available for public health units <strong>to</strong> measure air<br />
pollutant exposure.<br />
Portable Air Moni<strong>to</strong>rs<br />
©Urquizo, N. 2009. Reproduced with permission.<br />
In 2009, Ottawa City Council authorized staff <strong>to</strong><br />
move <strong>to</strong> a second phase in its project by approving<br />
the purchase <strong>of</strong> a second Airpointer® (Figure<br />
14) equipped with modules <strong>to</strong> moni<strong>to</strong>r ozone,<br />
PM 2.5<br />
, and NO 2<br />
/NO at a cost <strong>of</strong> $90,000. 127 The<br />
two moni<strong>to</strong>rs have been used <strong>to</strong> acquire street<br />
level air pollution data <strong>to</strong> refine the satellite moni<strong>to</strong>ring<br />
technology, support car free roads and<br />
satisfy public concerns.<br />
A European Space Agency sponsored project used<br />
this data, and incorporated health risk assessment<br />
modelling <strong>to</strong> demonstrate the health effects <strong>of</strong><br />
pollution from traffic corridors. The resulting <strong>to</strong>ol<br />
translates pollution levels in<strong>to</strong> real health impacts<br />
both in monetary terms and in terms <strong>of</strong> morbidity<br />
and mortalit. 127<br />
The next steps will be <strong>to</strong> use more updated transportation<br />
data and more detailed health data in<br />
the mapping process. If funding permits, staff plan<br />
<strong>to</strong> incorporate wood burning emissions data in<strong>to</strong><br />
the mapping process (as one more local emission<br />
source that contributes <strong>to</strong> pollution). 127<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
116<br />
AIR QUALITY<br />
Direct comparisons between studies in the literature<br />
review were challenging since they differed<br />
in the pollutants studied, the health outcomes <strong>of</strong><br />
interest, models used, and populations <strong>of</strong> interest.<br />
While air moni<strong>to</strong>ring stations can be useful<br />
sources <strong>of</strong> data, they are limited in determining air<br />
quality conditions and risk areas between neighborhoods.<br />
Various models have been developed<br />
<strong>to</strong> estimate urban pollutants more accurately.<br />
More sophisticated models evaluate how pollutant<br />
distribution is impacted by the distance from<br />
and density <strong>of</strong> specific built environment traits<br />
(e.g. roads, elevation, land-use type, etc). The<br />
majority <strong>of</strong> studies focused on urban environments,<br />
and noted the benefits and limitations <strong>of</strong><br />
present assessments <strong>of</strong> pollutant exposure. The<br />
use <strong>of</strong> spatial models (e.g. land-use regression),<br />
developed for one city may not translate well <strong>to</strong><br />
other urban centres. 140;141<br />
Epidemiology research challenges<br />
A majority <strong>of</strong> the epidemiology studies assessed<br />
correlation between specific pollutant levels and<br />
health outcomes. Causation from specific pollutants<br />
is difficult <strong>to</strong> ascertain due <strong>to</strong> the high correlation<br />
between pollutants. 152 Nonetheless, the<br />
review <strong>of</strong> literature by Brauer and colleagues 121<br />
identified significant evidence from Canadian<br />
studies relating traffic pollutants <strong>to</strong> health outcomes.<br />
Linking outdoor air quality <strong>to</strong> indoor air<br />
quality<br />
In order <strong>to</strong> better assess the public’s exposure<br />
<strong>to</strong> pollutants, further development is required<br />
<strong>to</strong> understand the relationship between outdoor<br />
air quality and indoor air quality. Behavioural<br />
characteristics such as the time individuals<br />
choose <strong>to</strong> spend outside versus inside,, is<br />
a fac<strong>to</strong>r that present models have generally not<br />
considered. 110;137;153;154 Exposure <strong>to</strong> air pollutants<br />
is commonly defined at residential addresses<br />
and does not consider other key areas where individuals<br />
may be present in a day. 155 Furthermore,<br />
measurements <strong>of</strong> infiltration (outdoor pollutant<br />
exposure entering indoor environments) can enhance<br />
the use <strong>of</strong> modelling and datasets in calculating<br />
health risks. 137;156 Both behaviour and<br />
infiltration measurements have the potential <strong>to</strong><br />
increase the accuracy <strong>of</strong> exposure modelling and<br />
in the assessments <strong>of</strong> risk for acute and chronic<br />
health outcomes.<br />
Temporal limitations<br />
Exposure maps are commonly used for annual<br />
estimates, and may not take in<strong>to</strong> account temporal<br />
changes in pollutant levels. Studies varied<br />
in their use <strong>of</strong> temporal information, ranging from<br />
pollutant readings every minute 157 <strong>to</strong> annual averages.<br />
120 Seasonal trends were noted for some<br />
pollutants as well, 110;141 and need <strong>to</strong> be considered<br />
when developing exposure maps.<br />
The temporal scale can play an important role in<br />
evaluating different health risks from air pollutants.<br />
Land use regression focuses on chronic exposure<br />
using annual averages <strong>to</strong> evaluate the model.<br />
Nonetheless, acute exposure studies can benefit<br />
from a detailed map, that highlights pollutant<br />
levels at different times <strong>of</strong> day (e.g. during rush<br />
hour).<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 117<br />
Development <strong>of</strong> key indica<strong>to</strong>rs for air<br />
pollutants in the built environment<br />
A specific pollutant or land variable may be used<br />
as a marker for predicting exposure and distribution<br />
<strong>of</strong> other pollutants. The most commonly<br />
cited example is NO 2<br />
, which has been used as a<br />
marker for vehicle/traffic emissions 129;158 and potentially<br />
for benzene, <strong>to</strong>luene, and SO 2<br />
. 141 Proximity<br />
<strong>to</strong> traffic corridors is commonly used <strong>to</strong> determine<br />
exposure <strong>to</strong> vehicle emissions and is a<br />
useful measure for land use planning. 120;123 More<br />
research is needed <strong>to</strong> better understand how<br />
various pollutants relate <strong>to</strong> one another as well as<br />
any synergistic effects. 159<br />
Studies have also shown the need for greater<br />
geographic detail on pollutant exposure in urban<br />
areas. This requires an accurate understanding <strong>of</strong><br />
how the built environment contributes <strong>to</strong> sources<br />
<strong>of</strong> pollutants as well as its distribution. Determining<br />
exposure from air pollutants can be challenging<br />
as researchers and public health units try <strong>to</strong><br />
capture all relevant pollutants and optimize the<br />
temporal and spatial detail. While traffic and industrial<br />
emissions are key sources <strong>of</strong> pollution,<br />
there are other sources <strong>of</strong> pollution that require<br />
more research. 160 More research is needed <strong>to</strong><br />
meet these challenges in order <strong>to</strong> optimize both<br />
spatial and temporal detail and <strong>to</strong> address both<br />
acute and chronic health outcomes resulting<br />
from air pollutants in the built environment. Nonetheless,<br />
significant strides have been made in developing<br />
more detailed exposure estimates, with<br />
recent research emphasizing the role <strong>of</strong> the built<br />
environment.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
118<br />
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KEY INFORMANT INTERVIEWS SUMMARY:<br />
AIR QUALITY<br />
In <strong>to</strong>tal, six individuals were interviewed on issues pertaining <strong>to</strong> air quality and the built environment. They<br />
represented federal and provincial stakeholders as well as Public Health Units involved in air quality assessments.<br />
Some <strong>of</strong> the information provided in the interviews overlapped with findings from the literature<br />
review and survey data conducted for this project. As a result the following information focuses on<br />
new information not found in other components <strong>of</strong> this research project.<br />
HIGHLIGHTS<br />
• In Ontario, the network <strong>of</strong> air moni<strong>to</strong>ring stations is an important resource for moni<strong>to</strong>ring air<br />
pollutants, but is not designed for evaluating air quality at the neighborhood level.<br />
• Key informants identified current methods used <strong>to</strong> further assess and collect data on air quality in<br />
the built environment including additional moni<strong>to</strong>ring, modeling, and use <strong>of</strong> satellite technologies.<br />
o There are various air moni<strong>to</strong>ring equipment <strong>to</strong>ols available <strong>to</strong> better assess pollutant levels at a<br />
neighborhood levels and <strong>to</strong> assess the impact <strong>of</strong> the built environment<br />
o Previous projects by public health units highlight the value <strong>of</strong> using modeling and satellite<br />
technologies<br />
• NO and PM were identified as two key pollutants that can be used as indica<strong>to</strong>rs <strong>of</strong> local air<br />
x 2.5<br />
quality<br />
• While local emission sources may be captured from data sources such as the NPRI, smaller<br />
emission sources are not considered.<br />
• There is a vast amount <strong>of</strong> research present on pollutant gradients from traffic corridors, which<br />
could be used <strong>to</strong> develop indica<strong>to</strong>rs and measures for mobile/transportation sources in the built<br />
environment<br />
AIR QUALITY DATA FROM THE MINISTRY OF ENVIRONMENT (MOE) AND<br />
ENVIRONMENT CANADA<br />
As previously mentioned, the MOE has an established network <strong>of</strong> 40 permanent air moni<strong>to</strong>ring stations<br />
across Ontario. The majority <strong>of</strong> the stations are located in urban areas in southern Ontario around the<br />
Greater Toron<strong>to</strong>/Hamil<strong>to</strong>n area, and are focused on ambient air pollutant concentrations not impacted<br />
from local point emissions or traffic corridors. However, the National Air Pollutant Surveillance (NAPS) unit<br />
at <strong>Environment</strong> Canada has provided new criteria <strong>to</strong> include moni<strong>to</strong>ring stations close <strong>to</strong> traffic corridors.<br />
Currently, MOE distinguishes between urban and rural air moni<strong>to</strong>ring stations as well as roadside loca-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 119<br />
tions (within 100 meters from the road) for their AQI sites. Ten stations fall in<strong>to</strong> the roadside moni<strong>to</strong>ring<br />
criteria including Windsor down<strong>to</strong>wn, Hamil<strong>to</strong>n down<strong>to</strong>wn, Toron<strong>to</strong> down<strong>to</strong>wn, Toron<strong>to</strong> East, Toron<strong>to</strong><br />
North, Toron<strong>to</strong> West, Oshawa, Thunder Bay and North Bay.<br />
There are also five research-grade air quality stations that are not public, but are for the MOE, and other<br />
agencies’ academic research. Of the five research stations, three <strong>of</strong> them are permanent, and two are<br />
mobile. The stations are used <strong>to</strong> test equipment and for short-term studies. These stations typically moni<strong>to</strong>r<br />
AQI pollutants, and have additional equipment <strong>to</strong> moni<strong>to</strong>r organic and inorganic compounds, black<br />
carbon, ultrafine particle matter, and BTEX (benzene, <strong>to</strong>luene, ethyl benzene and xylene).<br />
These research stations are managed in collaboration with <strong>Environment</strong> Canada. Presently, Health Canada,<br />
MOE, and <strong>Environment</strong> Canada are working on research projects <strong>to</strong> evaluate traffic corridors and<br />
pollutant levels. The MOE also moni<strong>to</strong>rs VOCs as part <strong>of</strong> the NAPS program. These sites include Toron<strong>to</strong><br />
West and Hamil<strong>to</strong>n down<strong>to</strong>wn. While such data can be useful <strong>to</strong> other communities with traffic corridors,<br />
they need additional data such as traffic volumes <strong>to</strong> determine the relationship <strong>of</strong> the built environment<br />
with pollutant levels.<br />
STRATEGIES TO PRODUCE LOCAL AIR QUALITY DATA<br />
There are a number <strong>of</strong> different approaches that can be used <strong>to</strong> assess air quality across a community.<br />
The following approaches were identified:<br />
• Conduct air dispersion modelling using an inven<strong>to</strong>ry <strong>of</strong> existing emissions and validate and fine<br />
tune the results with air moni<strong>to</strong>ring using the MOE air moni<strong>to</strong>ring stations and portable moni<strong>to</strong>rs;<br />
• Conduct moni<strong>to</strong>ring with an array <strong>of</strong> electro-chemical sensors for NO that are spatially dispersed,<br />
add the results from the MOE/NAPS air moni<strong>to</strong>ring stations, and combine the results with land<br />
use regression modelling <strong>to</strong>ols; and,<br />
• Use satellite based systems, combine them with air moni<strong>to</strong>ring results conducted with mobile and<br />
portable devices, and model the results along with sec<strong>to</strong>r-specific emissions estimates data such<br />
as traffic volume.<br />
MONITORING EQUIPMENT RECOMMENDED FOR ASSESSING LOCAL AIR<br />
QUALITY<br />
Key informants were questioned on potential instruments that can be utilized <strong>to</strong> better understand local<br />
air quality issues. Two different methods <strong>to</strong> approach air moni<strong>to</strong>ring for spatial variation were noted. The<br />
first involves developing a regular grid <strong>of</strong> the community and trying <strong>to</strong> cover the grids as best as one can<br />
with a limited budget. The second option involves focusing moni<strong>to</strong>ring on particular types <strong>of</strong> land uses<br />
and key micro-environments that are <strong>of</strong> concern, such as traffic corridors, or local small point source<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
120<br />
AIR QUALITY<br />
emissions. The following instruments for air moni<strong>to</strong>ring were noted by key informants:<br />
• Airpointers are good portable moni<strong>to</strong>rs that can provide data not only for NO but also particulate<br />
x<br />
matter, sulphur compounds, O 3<br />
, and CO. Airpointers can also be useful in measuring microenvironments<br />
as the instrument can placed in areas with limited space within urban communities.<br />
While the Airpointer equipment was recommended by most interviewees, this equipment can be<br />
quite expensive ($100,000).<br />
• Inexpensive NO / NO electro-chemical sensors (i.e. about $100 /sensor) can be used in multiple<br />
x<br />
locations with relatively little investment. These sensors could be used <strong>to</strong> measure pollutant levels<br />
at multiple distances and heights <strong>to</strong> identify the vertical and horizontal reach <strong>of</strong> the air pollutants<br />
associated with traffic corridors. These sensors can be connected wirelessly, collecting results on<br />
an instantaneous basis, and eventually be incorporated in<strong>to</strong> modelling.<br />
• Passive NO and NO samplers, which are very inexpensive, can be used <strong>to</strong> collect weekly<br />
2 x<br />
air quality results with a greater spatial density, but require a link with an accredited labora<strong>to</strong>ry<br />
<strong>to</strong> analyze the samples. One organization has been doing some studies on variations across<br />
communities using a combination <strong>of</strong> NAPS stations and passive NO 2<br />
moni<strong>to</strong>rs. They are<br />
combining these results with land use regression modelling in different communities across<br />
Canada. This strategy holds some promise as a method that could be used more broadly in the<br />
future.<br />
• There are no inexpensive sensors for PM . There are less expensive portable moni<strong>to</strong>rs that cost<br />
2.5<br />
about $500 that can be used for micro-environments or that could be used with mobile sources<br />
(e.g., on a police car or on city bus) <strong>to</strong> see how air quality varies across an area.<br />
MEASUREMENTS FOR LOCAL AIR QUALITY<br />
POLLUTANTS RELEVANT TO THE BUILT ENVIRONMENT<br />
When we asked four air quality key informants about the two <strong>of</strong> three air pollutants that could be used as<br />
indica<strong>to</strong>rs <strong>of</strong> local air quality from a built environment perspective, all four recommended:<br />
• NO : It is easy <strong>to</strong> measure, it varies substantially across a community, it is strongly linked <strong>to</strong><br />
x<br />
combustion processes in buildings (furnaces) and vehicles, it is a good indica<strong>to</strong>r <strong>of</strong> the fine scale<br />
for air quality, and it is strongly correlated with health impacts.; and<br />
• PM : It is a very good indica<strong>to</strong>r <strong>of</strong> “dirty air”, it builds up when air stagnates, its mass varies<br />
2.5<br />
substantially across a community, it is affected by a variety <strong>of</strong> emission sources in the built<br />
environment including fireplaces, vehicles, industry, and fugitive dust (particulate matter from<br />
industry and the construction sec<strong>to</strong>r), and it is strongly correlated with health impacts.<br />
Other air pollutants identified as potential indica<strong>to</strong>rs <strong>of</strong> local air quality included:<br />
BTEX, UFP, BC, SO 2<br />
, PM 2.5 -10.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 121<br />
MEASURES OF LOCAL POINT SOURCES<br />
When asked about two or three air pollutants that can be used as indica<strong>to</strong>rs <strong>of</strong> local point sources in a<br />
community, the air quality key informants had varied responses. In Toron<strong>to</strong>, where a cumulative risk assessment<br />
was conducted on a variety <strong>of</strong> different air pollutants from all sources, it was suggested that<br />
one should select the point source pollutants <strong>to</strong> be used as indica<strong>to</strong>rs using both, the background levels<br />
for air pollutants in the community derived from nearby NAPS stations, and emissions data for the point<br />
sources in the community.<br />
When key informants were asked about indirect indica<strong>to</strong>rs that might be used for local point sources,<br />
emissions data from NPRI and TRI were identified as imperfect <strong>to</strong>ols that can be used <strong>to</strong> identify large<br />
emitters. It was noted by a few key informants that smaller facilities that are exempted from NPRI and TRI<br />
can be important sources <strong>of</strong> air pollution at a localized level, particularly if the air pollutants are emitted<br />
at ground level. This could be remedied using a a system similar <strong>to</strong> Toron<strong>to</strong>’s ChemTRAC system that<br />
would capture smaller facilities and could have a substantial impact on air quality at the local level. One<br />
key informant noted that dispersion modelling conducted for C<strong>of</strong>As might provide information (e.g., setback<br />
distances) that could be used as indirect indica<strong>to</strong>rs for local point sources.<br />
Estimates <strong>of</strong> greenhouse gas emissions would be one way <strong>of</strong> using inven<strong>to</strong>ries as indirect indica<strong>to</strong>rs <strong>to</strong><br />
moni<strong>to</strong>r potential exposure <strong>to</strong> air pollution from point sources. Greenhouse gas and source emissions<br />
can be used <strong>to</strong> determine residential natural gas consumption due <strong>to</strong> residential heating. This indirect<br />
indica<strong>to</strong>r, again, would only give a limited sense <strong>of</strong> emissions.<br />
MEASURES OF MOBILE SOURCES<br />
When we asked the air quality key informants about the two or three air pollutants that could be used as<br />
indica<strong>to</strong>rs for mobile sources, they all agreed that NO x<br />
(i.e., NO and NO 2<br />
) are the best indica<strong>to</strong>rs for mobile<br />
source because they are so strongly associated with vehicles and are fairly cheap <strong>to</strong> moni<strong>to</strong>r. Other<br />
air pollutants identified included UFP, VOCs (i.e., BTEX), and BC.<br />
When we asked about indirect indica<strong>to</strong>rs that could be used for air quality associated with vehicles, it<br />
was suggested that set-back distances could be used based on modelling, traffic volume and/or fleet<br />
demographics. While the air quality key informants agreed that researchers are still not clear which air<br />
pollutants in the traffic-related air pollutant mix are responsible for negative health impacts, they agreed<br />
that research has demonstrated gradients for levels <strong>of</strong> air pollutants such as NO x<br />
and UFP beside traffic<br />
corridors, which are associated with traffic volume, fleet demographics and meteorology.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
122<br />
AIR QUALITY<br />
HUMAN RESOURCE CAPACITY AT THE PUBLIC HEALTH UNIT LEVEL<br />
In Ontario, a few municipalities have funded additional work for the development <strong>of</strong> an air quality moni<strong>to</strong>ring<br />
program within their jurisdiction. While costs may vary for each program, key informants identified the<br />
human resource requirements for the development and maintenance <strong>of</strong> these programs. For two municipal<br />
level organizations, one full-time employee is responsible <strong>to</strong> manage the air quality program. One key<br />
informant noted additional in-house support may be required for data analysis. Consultants were hired for<br />
more technical aspects <strong>of</strong> the air quality programs, such as modeling, calibration <strong>of</strong> equipment, quality<br />
assurance and quality control issues for moni<strong>to</strong>ring, and maintenance <strong>of</strong> air moni<strong>to</strong>ring equipment<br />
.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 123<br />
SUMMARY OF SURVEY RESULTS:<br />
AIR QUALITY<br />
This section represents a summary <strong>of</strong> the survey results specific <strong>to</strong> air quality measures and data sources.<br />
The survey was administered <strong>to</strong> Ontario Public Health Units (PHU) in July 2012.<br />
Note that the number <strong>of</strong> organizations responding <strong>to</strong> each question varies due <strong>to</strong> the structure (skip pattern)<br />
<strong>of</strong> the survey. For instance, if a PHU indicated that they assess air quality using moni<strong>to</strong>ring stations,<br />
then they were prompted <strong>to</strong> provide further details about specific air pollutants. Whereas a PHU that did<br />
not report using an air moni<strong>to</strong>ring station <strong>to</strong> assess air quality was prompted <strong>to</strong> skip forward <strong>to</strong> other<br />
survey questions. Overall, this impacted the number <strong>of</strong> PHUs (i.e. denomina<strong>to</strong>r) that responded <strong>to</strong> each<br />
survey question.<br />
HIGHLIGHTS<br />
• 28 Ontario PHUs completed the air quality section <strong>of</strong> the survey<br />
• 14 (50%) PHUs assess urban air quality in Ontario<br />
• Most <strong>of</strong> the 14 PHUs have been assessing urban air quality for 1 <strong>to</strong> 5 years (36%) and 6 <strong>to</strong> 10<br />
years (36%)<br />
• PHUs which have been assessing air quality for greater than ten years were in larger cities, or in<br />
areas with industries <strong>of</strong> concern<br />
• Of the 14 PHUs assessing urban air quality using an index, 93% are using the Ontario Air Quality<br />
Index (AQI) and 50% are using the Air Quality Health Index (AQHI)<br />
• Of the 14 PHUs assessing urban air quality, 79% <strong>of</strong> PHUs use meteorological data from<br />
<strong>Environment</strong> Canada weather stations, 50% use air modelling estimates, 43% use data from<br />
portable moni<strong>to</strong>rs, 43% use emission estimates, 29% use data from air moni<strong>to</strong>ring stations not<br />
collected by the MOE, and 7% use satellite data<br />
• <strong>Built</strong> environment data relevant <strong>to</strong> air quality were predominantly acquired from city, regional or<br />
municipal departments. For most <strong>of</strong> the built environment data collected, PHUs indicated that<br />
they had limited information describing the data. Less than half <strong>of</strong> PHUs indicated that the data<br />
was available as a spatial map.<br />
• Of the 28 PHUs responding <strong>to</strong> the survey, 43% said they had access <strong>to</strong> transit s<strong>to</strong>ps per square<br />
kilometre and traffic volumes, and 36% had access <strong>to</strong> modal share (commuting choice) in their<br />
communities. In addition, 29% said they had access <strong>to</strong> data on: vehicle kilometres traveled per<br />
capita in their community; proximity <strong>of</strong> populations <strong>to</strong> industrial emission sources; and residents<br />
and sensitive populations living in close proximity <strong>to</strong> high volume traffic corridors.<br />
• The most common challenges identified by PHUs included (i) human resource capacity (79%),<br />
(ii) data availability (61%) and (iii) financial capacity (61%)<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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The air quality survey response rate was 78% (28/36), with 28 Ontario PHUs completing the survey. Overall,<br />
50% (14/28) <strong>of</strong> PHUs assess air quality in urban environments in Ontario.<br />
Of the 14 PHUs that assess air quality, 36% have been assessing air quality for 1 <strong>to</strong> 5 years, 36% for 6 <strong>to</strong> 10<br />
years, and 29% for over 11 years (Figure 15).<br />
Figure 15: Number <strong>of</strong> years Public Health Units have been assessing urban air quality in Ontario, 2012 (n=14)<br />
36%<br />
36%<br />
1-5 years<br />
6-10 years<br />
11+ years<br />
29%<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 125<br />
All 14 PHUs use an index <strong>to</strong> assess air quality in urban environments. More specifically, <strong>of</strong> the 14 PHUs<br />
that assess urban air quality in Ontario, 93% (13 PHUs) use an Air Quality Index (AQI), 50% (6 PHUs)<br />
use an Air Quality Health Index (AQHI), and 14% (2 PHUs) use other indices (Figure 16). Seven <strong>of</strong> these<br />
PHUs are using AQI alone, 6 PHUs are using both AQI and AQHI, and 2 PHUs are using AQI, AQHI and<br />
another index <strong>to</strong> assess air quality.<br />
Figure 16: Type <strong>of</strong> index used <strong>to</strong> assess urban air quality in Ontario, 2012 (n=14)<br />
No. <strong>of</strong> Public Health Units<br />
14<br />
12<br />
10<br />
8<br />
6<br />
93%<br />
50%<br />
4<br />
2<br />
14%<br />
0<br />
AQI AQHI Other<br />
Type <strong>of</strong> Air Quality Index<br />
Most <strong>of</strong> the 14 (79%) PHUs that assess urban air quality use readings for specific air pollutants from<br />
nearby Ministry <strong>of</strong> <strong>Environment</strong> (MOE) air moni<strong>to</strong>ring stations. Most PHUs track fine particulate matter<br />
(PM 2.5<br />
) (71%), ground level ozone (O 3<br />
) (71%), nitrogen dioxide (NO 2<br />
) (71%), and sulphur dioxide (SO 2<br />
)<br />
(64%) using general MOE moni<strong>to</strong>ring stations (regardless <strong>of</strong> the number <strong>of</strong> years they have been assessing<br />
air quality). A smaller proportion <strong>of</strong> PHUs track carbon monoxide (CO) from general MOE moni<strong>to</strong>ring<br />
stations (Figure 17). All PHUs that assessed urban air quality for 11 or more years moni<strong>to</strong>red CO levels.<br />
Fewer PHUs track air pollutants using MOE stations directed at local industrial sources: 29% are tracking<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
126<br />
AIR QUALITY<br />
PM 2.5,<br />
NO 2,<br />
and SO 2<br />
while 21% are tracking O 3<br />
and CO in this manner. Several PHUs further commented<br />
on the specific air pollutants that are tracked and methods <strong>of</strong> data collection:<br />
• In addition <strong>to</strong> the above noted pollutants, one PHU tracks <strong>to</strong>tal reduced sulphur (TRS), nitric<br />
oxides (NO), oxides <strong>of</strong> nitrogen (NOx), and inhalable particulate matter (PM 10<br />
). As well, they utilize<br />
non-continuous air-moni<strong>to</strong>ring samplers that collect data on pollutants such as <strong>to</strong>tal suspended<br />
particulates (TSP), volatile organic compounds (VOC’s), polycyclic aromatic hydrocarbons (PAH),<br />
and metals.<br />
• One PHU tracks 25 priority chemicals that are in the environment at levels that are <strong>of</strong> concern <strong>to</strong><br />
public health.<br />
• One PHU added that they will soon be tracking Ammonia (NH ) and a suite <strong>of</strong> Volatile Organic<br />
3<br />
Compounds (VOCs).<br />
• One PHU further commented that they moni<strong>to</strong>r the above mentioned pollutants using satellite<br />
data in conjunction with MOE and roadside moni<strong>to</strong>rs that belong <strong>to</strong> the city.<br />
Figure 17: Air pollutants measured by type <strong>of</strong> Ministry <strong>of</strong> <strong>Environment</strong> stations in Ontario, 2012 (n=12)<br />
MOE stations directed at local industrial sources<br />
Both General MOE and MOE stations directed at local sources<br />
No. <strong>of</strong> Public Health Units<br />
12<br />
10<br />
8<br />
6<br />
4<br />
General MOE air moni<strong>to</strong>ring stations<br />
2<br />
0<br />
PM2.5 O3 NO2 SO2 CO<br />
Air Pollutant<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 127<br />
The 14 PHUs use several types <strong>of</strong> data in their assessment <strong>of</strong> air quality including: meteorological<br />
data from <strong>Environment</strong> Canada weather stations (e.g. temperature, humidex, wind speed)<br />
(79%); air modelling estimates (e.g. dispersion modelling, land use regression) (50%); data from<br />
portable or mobile air moni<strong>to</strong>ring equipment (43%); emission estimates (e.g. traffic, industrial,<br />
etc.) (43%); data from stationary air moni<strong>to</strong>ring equipment not collected under the MOE (29%);<br />
and, data from remote sensing technologies (i.e. satellite based) (7%) (Figure 18).<br />
Seven PHUs use data derived from air modelling; one from northern Ontario; one from eastern<br />
Ontario; and five from in and around the Greater Toron<strong>to</strong> Area; and six have access <strong>to</strong> this data<br />
in spatial maps. Six PHUs use data collected with portable air moni<strong>to</strong>ring equipment; two in<br />
eastern Ontario and four in the Greater Toron<strong>to</strong> Area and Hamil<strong>to</strong>n area; and four indicated that<br />
they have spatial maps for this data. Four PHUs use data collected from stationary air moni<strong>to</strong>ring<br />
equipment that does not belong <strong>to</strong> the MOE; three from in and around the Greater Toron<strong>to</strong><br />
Area and one from northern Ontario; and three indicated that the data is available in spatial<br />
maps.<br />
Spatial mapping <strong>of</strong> data<br />
With the exception <strong>of</strong> meteorological data, less than half <strong>of</strong> PHUs indicated that other air quality<br />
data and estimates are available as a spatial map. <strong>Data</strong> were available as spatial maps for: all<br />
PHUs using emission estimates (6 in <strong>to</strong>tal); 86% <strong>of</strong> PHUs using modelling (6/7); 67% using portable<br />
equipment (4/6), and for the one PHU using satellite/remote sensing technologies (Ottawa).<br />
Of the PHUs using various methods <strong>of</strong> data collection, 43% <strong>to</strong> 83% had information describing<br />
the data, depending on the approach.<br />
Only the 4 PHUs that had assessed urban air quality for 11 or more years, had access <strong>to</strong> data<br />
and estimates from stationary air moni<strong>to</strong>ring equipment not collected under the MOE. All 4 <strong>of</strong><br />
these PHUs also used air modelling estimates <strong>to</strong> assess air quality compared <strong>to</strong> 3 PHUs with<br />
less experience using air modelling estimates. Overall, only one PHU had access <strong>to</strong> data from<br />
remote sensing technologies.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
128<br />
AIR QUALITY<br />
Figure 18: <strong>Data</strong> and estimates used <strong>to</strong> assess urban air quality in Ontario, 2012 (n=14)<br />
<strong>Data</strong> and estimates<br />
Meteorological data from<br />
<strong>Environment</strong> Canada<br />
weather stations<br />
Air modelling estimates<br />
Emission estimates<br />
<strong>Data</strong> from portable or mobile air<br />
moni<strong>to</strong>ring equipment<br />
<strong>Data</strong> from stationary air<br />
moni<strong>to</strong>ring equipment<br />
not collected under MOE<br />
<strong>Data</strong> from remote<br />
sensing technologies<br />
7%<br />
29%<br />
43%<br />
43%<br />
54%<br />
79%<br />
0 2 4 6 8 10 12<br />
No. <strong>of</strong> Public Health Units<br />
<strong>Built</strong> environment data access<br />
All PHUs (28), regardless <strong>of</strong> whether or not they assessed urban air quality, were asked if they had access<br />
<strong>to</strong> the following data (Figure 19), including:<br />
• Number <strong>of</strong> transit s<strong>to</strong>ps per square kilometre (43%);<br />
• Volume <strong>of</strong> traffic on regional or municipal roads (43%);<br />
• Modal share (i.e., car, transit, cycling, walking split) in communities (36%);<br />
• Vehicle kilometres travelled (per capita) in communities (29%);<br />
• Residents and sensitive populations living within pre-determined distance from high volume roads<br />
(29%); and,<br />
• Proximity <strong>of</strong> population(s) <strong>to</strong> emission sources that report through the National Pollutant Release<br />
Inven<strong>to</strong>ry (NPRI) (29%).<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 129<br />
The majority <strong>of</strong> PHUs that were assessing urban air quality for 6 <strong>to</strong> 10 years and 11 or more years, had<br />
access <strong>to</strong> the above data. On the other hand, PHUs with 1 <strong>to</strong> 5 years <strong>of</strong> experience had limited access<br />
<strong>to</strong> these data.<br />
For most <strong>of</strong> the built environment data collected, PHUs had limited information describing the data. Less<br />
than half <strong>of</strong> PHUs indicated that the data are available as a spatial map. <strong>Data</strong> were mostly acquired from<br />
city, regional or municipal departments (e.g. Transportation department). Other commonly used data<br />
sources included the census <strong>to</strong> assess data such as residents and sensitive populations living within<br />
pre-determined distance from high volume road.<br />
The Census, Transportation Tomorrow Survey, Smartmoves Transportation Survey, and transportation<br />
departments were used by PHUs <strong>to</strong> assess vehicle kilometres travelled (per capita) in communities and<br />
modal share.<br />
Figure 19: Access <strong>to</strong> data used <strong>to</strong> assess urban air quality in Ontario, 2012 (n=28)<br />
Air Quality <strong>Data</strong><br />
Number <strong>of</strong> transit s<strong>to</strong>ps<br />
per square kilometre<br />
Volume <strong>of</strong> traffic on regional<br />
or municipal roads<br />
Model share in your communities<br />
Vehicle kilometres travelled<br />
(per capita) in your communities<br />
Proximity <strong>of</strong> population(s) <strong>to</strong> emission<br />
sources that report through NPRI<br />
Residents and sensitive populations<br />
living within pre-determined distance<br />
from high volume<br />
43%<br />
43%<br />
36%<br />
29%<br />
29%<br />
29%<br />
0 2 4 6 8 10 12 14<br />
No. <strong>of</strong> Public Health Units<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
130<br />
AIR QUALITY<br />
Challenges<br />
PHUs identified major challenges their organization’s face in assessing air quality in urban environments.<br />
The most common challenges identified by PHUs included human resource capacity (79%), data availability<br />
(61%), and financial capacity (61%) (Figure 20); these challenges were most commonly reported<br />
regardless <strong>of</strong> the length <strong>of</strong> time a PHU had been assessing urban air quality and whether or not a PHU<br />
was assessing air quality. In fact, all 4 PHUs, with 11 or more years <strong>of</strong> experience in assessing urban<br />
air quality, identified human resource capacity as a challenge; 3 <strong>of</strong> these PHUs also identified financial<br />
capacity as a challenge. While all 5 PHUs with 6 <strong>to</strong> 10 years <strong>of</strong> experience, identified data availability as<br />
a challenge; 4 <strong>of</strong> these PHUs also identified data accessibility as a challenge.<br />
Several predominantly rural PHUs commented that air quality moni<strong>to</strong>ring is not a priority:<br />
“The municipality does not assess air quality. Because it is not an issue<br />
here, there is little interest in tracking it.”<br />
“This is not a priority issue for us in a predominantly rural area.”<br />
“Air quality...is rarely an issue except in a couple small municipalities<br />
where operating pulp and paper mills or other large industrial mills are<br />
located very close <strong>to</strong> the residential areas. MOE has some AQ moni<strong>to</strong>rs<br />
in these circumstances. We have access <strong>to</strong> their results if required <strong>to</strong><br />
follow-up on a complaint.”<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 131<br />
Figure 20: Challenges faced by PHUs in assessing air quality in Ontario, 2012 (n=28)<br />
Challenge<br />
Human resource capacity<br />
<strong>Data</strong> availability<br />
Financial capacity<br />
<strong>Data</strong> accessibility<br />
Variations between municipalities<br />
<strong>Data</strong> quality<br />
Lack <strong>of</strong> GIS technical support<br />
Other<br />
No challenges <strong>to</strong> report<br />
4%<br />
11%<br />
46%<br />
39%<br />
36%<br />
29%<br />
61%<br />
61%<br />
79%<br />
0 5 10 15 20 25<br />
No. <strong>of</strong> Public Health Units<br />
Five PHUs commented about the limited coverage <strong>of</strong>fered by MOE air moni<strong>to</strong>ring stations in their<br />
communities:<br />
“Limited number <strong>of</strong> MOE stations/geography covered, stations are purposely<br />
located away from sources <strong>of</strong> emissions (e.g. highway) and needs<br />
<strong>to</strong> be reflective <strong>of</strong> community exposure, outdated air quality standards<br />
(not reflective <strong>of</strong> current health-based evidence), lack data/methodology<br />
<strong>to</strong> assess cumulative air impacts, lack access <strong>to</strong> technical expertise (e.g.<br />
for air modelling), limited resources for assessing micro-environments.”<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
132<br />
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“Regarding data quality: the data from the local MOE moni<strong>to</strong>ring site is<br />
not representative <strong>of</strong> conditions throughout the health unit area.”<br />
One PHU commented that there is no public health mandate from the province. While another PHU indicated<br />
that access <strong>to</strong> data within their own organization is challenging:<br />
“Our counties don’t necessarily share the same viewpoint on sharing their<br />
data with us. Especially spatial data as one county employs a private<br />
sec<strong>to</strong>r GIS provider that is less keen on data sharing.”<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 133<br />
GAP ANALYSIS: AIR QUALITY<br />
Insights gathered from the comprehensive literature review, key informant interviews and survey administered<br />
<strong>to</strong> Ontario’s Public Health Units were compiled <strong>to</strong> identify gaps between the necessary and available<br />
built environment data <strong>to</strong> assess urban air quality in Ontario. A gap analysis for the assessment <strong>of</strong> air<br />
quality in Ontario was conducted for the following measurement approaches and data sources:<br />
• Measurement approaches: air quality indices, moni<strong>to</strong>ring individual pollutants, satellite based<br />
technologies, emissions estimates, modeling (Table 11).<br />
• <strong>Data</strong> sources from Census, Ministry <strong>of</strong> the <strong>Environment</strong>, National Pollutant Release Inven<strong>to</strong>ry, Ontario<br />
Ministry <strong>of</strong> Transportation, and local road data (Table 12).<br />
OVERALL FINDINGS OF GAP ANALYSIS<br />
MEASUREMENT APPROACHES<br />
Challenges in assessing air quality<br />
• The project noted the following challenges in assessing air quality in the built environment:<br />
o The movement <strong>of</strong> pollutants is influenced by various meteorological fac<strong>to</strong>rs such as wind<br />
speed and direction, atmospheric stability and mixing, temperature, etc.<br />
o Pollutants differ in their physical and chemical properties which in turn impacts the spatial<br />
pattern <strong>of</strong> pollutants in the built environment and the pollutant lifecycle. This is further<br />
complicated by meteorological conditions as well as interactions between pollutants.<br />
o Urban structures impact air quality through altering flow patterns <strong>of</strong> pollutants, the creation<br />
<strong>of</strong> emissions, and exposure <strong>to</strong> the public.<br />
Pollutant sources<br />
• In addition <strong>to</strong> transportation and industrial sources, the research noted specific c sources <strong>of</strong> pollut-<br />
ants in the built environment including:<br />
o Wood fireplaces (PM<br />
2.5<br />
, BC, and UFP)<br />
o Traffic emissions (BC, UFP, NO ) x<br />
o Fugitive dust from industry and construction sites (PM ) 2.5<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
134<br />
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Pollution gradients<br />
• There is a significant amount <strong>of</strong> research on pollutant gradients from traffic corridors from academic<br />
and government agencies that can be further developed recommend separation distances<br />
between sources <strong>of</strong> emissions and sensitive populations.<br />
Modelling approaches<br />
• There are various modelling approaches that can be taken <strong>to</strong> better capture pollutant levels in the<br />
built environment. The appropriate approach will depend on the needs for PHU units, financial<br />
costs, and data availability for each modelling approach.<br />
Moni<strong>to</strong>ring approaches<br />
• The literature review and key informant interviews noted that additional sampling <strong>of</strong> pollutants<br />
can be done through a grid system or by focusing on specific areas <strong>of</strong> interest (e.g. small point<br />
sources, traffic corridors, sensitive populations).<br />
o To gain additional data on air quality in the built environment, various instruments were identified<br />
by the key informant interviews including passive NO/ NO x<br />
samplers, electrochemical<br />
NO/ NO x<br />
sensors, and Airpointer. Passive samplers and electrochemical sensors are noted<br />
as more affordable instruments, but sensors for PM 2.5<br />
are more expensive.<br />
Remote sensing<br />
• Remote sensing, especially satellite based technologies, is a noted measurement approach with<br />
potential for better assessing pollutant distribution at a neighborhood level. More work is needed<br />
in assessing its application and feasibility in Ontario for looking at the built environment and air<br />
quality.<br />
Emission estimates<br />
• The use <strong>of</strong> emissions data from NPRI is limited as it does not capture smaller sources <strong>of</strong> emissions<br />
existing in urban areas.<br />
Cumulative impacts<br />
• Currently, MOE permitting does not take in<strong>to</strong> consideration cumulative impacts or traffic<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY 135 155 135<br />
PHU capacity<br />
• There are limited resources available for measuring pollutant levels (e.g. modelling, satellite technologies,<br />
and portable moni<strong>to</strong>ring) by PHUs. PHUs with more resources have generally been in<br />
higher density urban areas around the southern Ontario.<br />
o For a small number <strong>of</strong> rural PHUs, the concern for air quality is not that high.<br />
DATA SETS AND SOURCES<br />
Pollutants <strong>of</strong> concern<br />
• While the five common pollutants (ground level ozone, PM , NO , CO, and SO ) are those which<br />
2.5 2 2<br />
have been associated with the greatest burden <strong>of</strong> illness, other pollutants <strong>of</strong> concern in the built<br />
environment have been noted such as UFP and BC.<br />
Neighbourhood-level air quality data<br />
• While the air quality indexes used in Ontario are a valuable resource for moni<strong>to</strong>ring air pollution for<br />
PHUs, this data source is not designed <strong>to</strong> capture pollutant levels at a neighborhood level.<br />
Traffic data<br />
• PHUs report their traffic data were based on municipal data sets. While provincial data sets on<br />
traffic volumes exist from MTO, the data is limited <strong>to</strong> provincial highways.<br />
Census data<br />
• The use <strong>of</strong> census data was noted in looking at traffic related information as well as population<br />
information for modelling<br />
Local air quality data needs<br />
• PHUs have indicated that they need information on local air quality <strong>to</strong>:<br />
o Assess the impacts <strong>of</strong> air quality on health within their health units;<br />
o Inform land use and transportation planning decisions for the protection <strong>of</strong> human health;<br />
o Assess the impacts <strong>of</strong> public policy on air quality and human health; and<br />
o Moni<strong>to</strong>r local air quality over time <strong>to</strong> assess the impact <strong>of</strong> policies and programs on human<br />
health as it is impacted by local air quality.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
136<br />
AIR QUALITY<br />
Table 11: Measurement approaches and policy relevant information as identified from the literature<br />
review, key informant interviews, survey and GIS metadata).<br />
AIR QUALITY<br />
Table 11: Measurement approaches and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata).<br />
Measurement<br />
Approach<br />
Description Inputs Current Use (SU) Measures <strong>of</strong> interest<br />
Theoretical<br />
Operationalization<br />
Ontario<br />
Demand / Prioritization/ Desirability<br />
Challenges<br />
Link between<br />
Measurement<br />
Approaches<br />
Individual<br />
Pollutants<br />
Direct<br />
Measurement <strong>of</strong> ambient air<br />
concentration <strong>of</strong> common<br />
pollutants (e.g. NO 2 , NO, SO 2 ,<br />
CO, TSR, O 3 , PM 2.5 , PM 10 , etc.)<br />
• Individual pollutant data<br />
• Pro<strong>to</strong>col for determining<br />
averaging time period (LR)<br />
Of 11 PHUs that assess specific<br />
pollutants (SU)<br />
• 83% assess ozone, fine<br />
particulate matter, and nitrogen<br />
dioxide<br />
• 75% assess sulfur dioxide<br />
• 50% assess carbon monoxide<br />
Of the 11 PHUs assessing individual<br />
pollutants, 4 PHUs look at individual<br />
pollutants from air stations directed<br />
at industrial sources<br />
Of 14 PHUs, that assess air quality<br />
6 use portable air moni<strong>to</strong>ring<br />
equipment<br />
Hundreds <strong>of</strong> pollutants have been identified and<br />
are moni<strong>to</strong>red at the federal level. A select few<br />
criteria air pollutants are moni<strong>to</strong>red provincially<br />
due <strong>to</strong> their links <strong>to</strong> health and knowledge <strong>of</strong><br />
sources.<br />
Sources <strong>of</strong> pollutants in the built environment<br />
include: (LR)<br />
• Wood fireplaces (PM , BC, and UFP<br />
2.5<br />
• Traffic emissions (BC, UFP, NO ) x<br />
• Fugitive dust from industry and construction<br />
sites (PM 2.5 )<br />
The development <strong>of</strong> pollutant measures needs<br />
further research <strong>to</strong> better understand the<br />
relationship with the built environment.<br />
Spatial level<br />
• geocoded reference<br />
for moni<strong>to</strong>ring stations<br />
(regional level coverage)<br />
(GM)<br />
Certain moni<strong>to</strong>ring stations<br />
do not measure all criteria air<br />
contaminants (LR)<br />
NO 2 is noted as a good measure <strong>of</strong> traffic pollution that is cost<br />
effective and easy <strong>to</strong> moni<strong>to</strong>r. Nonetheless more research is<br />
needed in developing NO 2 as an indica<strong>to</strong>r (KI, SU)<br />
Black Carbon, UFP, PM 2.5 , PM 10 and NO x have been identified as<br />
pollutant measures <strong>of</strong> health. BTEX is a useful built environment<br />
pollutant <strong>to</strong> measure for its use in combustion fuels and in<br />
solvents, but is costly <strong>to</strong> measure (KII, LR).<br />
Mobile Equipment:<br />
Portable instruments can provide better detail <strong>of</strong> air pollution<br />
through high spatial resolution,<br />
selection <strong>of</strong> specific target areas, and by focusing on vulnerable<br />
populations or household level (LR, KII)<br />
Inexpensive NO/ NO x sensors, passive NO x /NO 2 samplers, or<br />
Airpointer equipment were noted as useful moni<strong>to</strong>ring <strong>to</strong>ols for<br />
traffic related pollutants (KII,LR)<br />
Pollutant sources and their impact on air quality differs for<br />
each region/PHU (SU, LR)<br />
Additional air moni<strong>to</strong>ring stations would be expensive and<br />
resource intensive. Specific pollutants require expensive<br />
moni<strong>to</strong>ring equipment, For instance, PM 2.5 sensors start at<br />
$500 CAD (KII, LR)<br />
Mobile Equipment:<br />
Costs for sampling and equipment may be high (LR)<br />
Limited sampling my not capture seasonal trends (LR)<br />
Required as data inputs<br />
for developing air quality<br />
indexes and models<br />
Air Quality<br />
Indexes<br />
(composite<br />
measures)<br />
Indexes are a direct measure <strong>of</strong><br />
air quality based on individual or<br />
multiple pollutants (LR)<br />
• The Ontario Air Quality Index<br />
(AQI) involves data on the<br />
following pollutants O 3 , NO 2 ,<br />
PM 2.5 , SO 2 , CO, and TRS (LR)<br />
• The Air Quality Health Index<br />
(AQHI) involves data on three<br />
pollutants: PM 2.5 , O 3 , NO 2<br />
(LR)<br />
• Pollutant data which can be<br />
collected through mobile<br />
or permanent moni<strong>to</strong>ring<br />
equipment (LR)<br />
• Formula calculation (e.g.<br />
weighting for specific<br />
pollutant) (LR)<br />
• Pro<strong>to</strong>col for determining<br />
averaging time period and<br />
threshold levels (LR)<br />
• Use <strong>of</strong> epidemiological data <strong>to</strong><br />
determine health based levels<br />
<strong>of</strong> interest (LR)<br />
Of 14 PHUs assessing air quality<br />
(SU):<br />
• 93% use the AQI<br />
• 53% use the AQHI<br />
While many air quality indexes have been<br />
developed internationally, only 2 indexes are<br />
currently used in Ontario (LR, SU)<br />
Indexes were developed at the provincial and<br />
federal level.<br />
Spatial level<br />
• geocoded reference<br />
for moni<strong>to</strong>ring stations<br />
(regional level coverage)<br />
(GM)<br />
• AQI: available across<br />
Ontario<br />
• AQHI: available in Southern<br />
Ontario<br />
<strong>Built</strong> environment attributes that could be targeted include<br />
fireplaces and old diesel vehicles (KII, LR)<br />
His<strong>to</strong>rical and temporal data available at no cost.<br />
Information provided with high temporal resolution<br />
Availability <strong>of</strong> moni<strong>to</strong>rs throughout province<br />
No cost for data<br />
The Ontario AQI may not capture health risks (LR)<br />
The current composite measures do not capture the built<br />
environment and its relationship <strong>to</strong> the distribution <strong>of</strong><br />
pollutants. (KII, LR)<br />
While O 3 and PM 2.5 are moni<strong>to</strong>red at most stations across<br />
Ontario, not all key air pollutants are moni<strong>to</strong>red by the MOE<br />
(LR)<br />
The concern for air quality issues is lower in less populated<br />
cities situated far from urban areas. However, concern<br />
remains for specific sources from industrial emissions (KII,<br />
SU)<br />
Noted as an important<br />
component for<br />
PHUs evaluating<br />
meteorological data.<br />
A composite measure <strong>of</strong><br />
individual pollutant data<br />
Remote<br />
Sensing<br />
Indirect measure<br />
Satellite images provide detail<br />
on air pollutant levels. Resolution<br />
varies from 250 m <strong>to</strong> 320 km.<br />
Images taken from every 1-7<br />
days.<br />
Satellites with information on<br />
pollutants.<br />
• OMI, TES, CALIOP & GOME<br />
(for NO 2 and hydrocarbons)<br />
• MODIS, OMI, PARASOL, &<br />
MISR (for PM)<br />
• MOPITT, AIRS, PARASOL,<br />
IASI& SCIAMACHY (for CO)<br />
• GOME, SCIAMACHY, OMI,<br />
TES, IASI, & GOME-2 (for O 3 )<br />
Only used in one PHU in<br />
conjunction with air moni<strong>to</strong>ring and<br />
modelling<br />
Spatial level<br />
• need further evaluation <strong>of</strong><br />
what maps or datasets<br />
exist that can be applied <strong>to</strong><br />
Ontario (LR)<br />
• Need further development <strong>of</strong> satellite systems and recognition<br />
as a promising area (KII, LR)<br />
• Potential for greater spatial coverage (LR, KII)<br />
Technical expertise required <strong>to</strong> create air quality maps from<br />
satellite images (LR, KII)<br />
Spatial resolution can vary significantly from 250m <strong>to</strong> 320km<br />
(LR)<br />
Some satellites do not capture air pollutant concentrations at<br />
a resolution relevant <strong>to</strong> the built environment (LR)<br />
Cost for imagery (LR)<br />
Quality <strong>of</strong> images can vary due <strong>to</strong> cloud cover and other<br />
weather conditions (LR)<br />
Limitations in determining ground level estimates from upper<br />
atmosphere estimates (LR)<br />
Relevant <strong>to</strong> pollutant<br />
moni<strong>to</strong>ring<br />
Emissions<br />
Estimates<br />
Emissions data can be used<br />
<strong>to</strong> estimate air pollutant<br />
concentrations and as an indirect<br />
indica<strong>to</strong>r <strong>of</strong> potential exposure<br />
The data required for emission<br />
estimates were cited from<br />
various sources:<br />
• NPRI<br />
• TURI<br />
• MPAC<br />
• Municipal traffic volumes<br />
• Emissions fac<strong>to</strong>rs formula<br />
<strong>to</strong> convert traffic volumes <strong>to</strong><br />
emission rates<br />
• Fleet demographics <strong>to</strong><br />
determine composition <strong>of</strong><br />
traffic sources<br />
• Geocoding <strong>of</strong> point source and<br />
area emissions<br />
Of 14 PHUs assessing air quality, 6<br />
use emissions estimates<br />
Of the 28 PHUs that responded <strong>to</strong><br />
the survey, 43% have access <strong>to</strong><br />
traffic volume data from regional or<br />
municipal roads<br />
Traffic counts can be an indirect measure <strong>of</strong><br />
traffic related air pollution and may translate <strong>to</strong><br />
other traffic corridors with similar counts (KII)<br />
Can be incorporated<br />
in<strong>to</strong> more detailed<br />
assessments <strong>of</strong> air<br />
quality in the built<br />
Greater need for smaller emissions sources for community environment and<br />
based modelling (KII)<br />
with meteorological<br />
Provides useful information on sources <strong>of</strong> air pollutants in the built<br />
environment (LR, KII)<br />
Potential estimation errors in pollutant levels and geographic modelling<br />
distribution (LR)<br />
Important in proximity<br />
May not require air moni<strong>to</strong>ring data depending on purpose <strong>of</strong> air<br />
quality assessment (LR)<br />
Requires dispersion modelling and meteorological<br />
measures<br />
information for ambient air pollutant estimates (LR)<br />
Need more detailed<br />
Can be applied <strong>to</strong> point sources and traffic (LR)<br />
Spatial detail not sufficient for assessment at a municipal evaluation <strong>to</strong> determine<br />
level (LR)<br />
geographic range <strong>of</strong><br />
impact <strong>of</strong> emissions.<br />
This is done with the<br />
support <strong>of</strong> modelling<br />
approaches<br />
Modelling<br />
Indirect measure<br />
• Used <strong>to</strong> provide additional<br />
spatial and temporal detail <strong>of</strong><br />
air pollutant levels<br />
Modelling techniques used <strong>to</strong><br />
capture pollutant information in<br />
urban areas included:<br />
• Land Use Regression<br />
• Dispersion Modelling<br />
• Kriging<br />
• Basic proximity or<br />
interpolation models (LR)<br />
Inputs vary by model, but can<br />
include:<br />
• Emissions data<br />
• Pollutant level measurements<br />
• Meteorological data (e.g. wind<br />
speed, direction, temperature)<br />
• GIS map files related <strong>to</strong> land<br />
use and surface characteristics<br />
54% <strong>of</strong> PHUs assessing air quality<br />
use some form <strong>of</strong> modelling<br />
Identified as being in previous and/<br />
or current use in select few PHUs<br />
(e.g. Toron<strong>to</strong>, Hal<strong>to</strong>n, Ottawa)<br />
Modelling approaches are an important <strong>to</strong>ol for<br />
assessing neighbourhood level differences in<br />
pollutants.<br />
Common variables considered include:<br />
• Road networks (e.g. road length and traffic<br />
density)<br />
• Land use<br />
• Population density<br />
• Topography<br />
• Meteorological conditions<br />
Various spatial models have<br />
been developed at the<br />
municipal level (LR)<br />
• Present work from Health<br />
Canada is using NO 2<br />
measurements and Land<br />
Use Regression modelling<br />
for various communities<br />
across Canada, and may<br />
have potential <strong>to</strong> be applied<br />
more broadly (KII)<br />
Modelling is noted as an important or necessary <strong>to</strong>ol <strong>to</strong> gain<br />
greater spatial detail <strong>of</strong> air pollution distribution in urban<br />
communities (KII, SU)<br />
Can incorporate modelling approaches with built environment<br />
indica<strong>to</strong>rs (LR)<br />
Technical and human resources needed for modelling (LR,<br />
KII)<br />
Higher cost for more detailed maps (LR)<br />
• Important <strong>to</strong>ol for<br />
mapping pollutant<br />
distribution from<br />
emission estimates<br />
and air moni<strong>to</strong>ring<br />
Proximity<br />
Indirect measure<br />
Distance <strong>of</strong> sensitive populations<br />
• Distance <strong>of</strong> major roadways<br />
<strong>to</strong> areas with high pollutant levels<br />
and emitting facilities from<br />
Determination <strong>of</strong> a safe distance sensitive populations<br />
from major roadways or other<br />
sources <strong>of</strong> air pollutants<br />
29% <strong>of</strong> 28 PHUs that answered<br />
the survey have access <strong>to</strong> data<br />
regarding<br />
• Proximity <strong>of</strong> population <strong>to</strong><br />
emission sources reported in<br />
NPRI<br />
• Proximity <strong>of</strong> population <strong>to</strong> high<br />
traffic volume roads<br />
While much research has shown how pollutant<br />
levels decline from a source, the setback<br />
distance for areas <strong>of</strong> high risk is not conclusive<br />
and depends on meteorological conditions,<br />
traffic volumes, and built environment attributes.<br />
Care should be taken in determining safe<br />
heights as well as distance (SU).<br />
• Need <strong>to</strong> better understand which pollutants are appropriate<br />
indica<strong>to</strong>r for proximity <strong>to</strong> traffic sources (KII, LR)<br />
• Need <strong>to</strong> balance need for setback distances with designing<br />
compact communities (LR)<br />
• Still need <strong>to</strong> clarify which pollutants are causing most<br />
harm near high traffic areas (KII, SU)<br />
Pollutant Abbreviations: UFP- Ultra-fine particulate matter BC- Black carbon BTEX- Benzene/Toluene/Ethylbenzene/Xylene (volatile organic compounds) PM2.5- Fine Particulate Matter PM10- Course particulate matter O3- Ozone SO2- Sulfur dioxide NO- Nitric oxide NO2- Nitrogen dioxide NOx- Nitrogen oxides CO– Carbon monoxide TRS- Total reduced sulfur<br />
Click <strong>to</strong> open the full table.<br />
Table 12: <strong>Data</strong> sources for Air Quality and Policy-Relevant Information as Identified in the Literature<br />
Review, Key Informant Interviews, Survey and GIS Metadata Exercise.<br />
AIR QUALITY<br />
Table 12: <strong>Data</strong> sources for Air Quality and Policy-Relevant Information as Identified in the Literature Review, Key Informant Interviews, Survey and GIS Metadata Exercise.<br />
<strong>Data</strong> Source Topic Area Utility in Outcomes Current Use in Ontario PHUs Desirability Cost Challenges<br />
93% <strong>of</strong> PHUs assessing air<br />
quality use the AQI (SU)<br />
Moni<strong>to</strong>ring stations are geocoded, but no standard exists for<br />
geographic area represented by moni<strong>to</strong>ring station<br />
Ministry <strong>of</strong> the <strong>Environment</strong><br />
Air Quality Information System<br />
(AQUIS)<br />
Air quality<br />
Incorporated in<strong>to</strong> National Air<br />
Pollutant Surveillance program<br />
(NAPS) <strong>Environment</strong> Canada <strong>Data</strong><br />
One <strong>of</strong> two air quality indexes applicable <strong>to</strong> Ontario<br />
is produced by the MOE (Air Quality Index) (LR)<br />
<strong>Data</strong> on individual pollutants (PM 2.5 , NO 2 , NO, NO x ,<br />
O 3 , SO 2 , CO) (LR)<br />
Of 11 PHUs that assess specific<br />
pollutants (SU)<br />
• 83% assess ozone, fine<br />
particulate matter, and<br />
nitrogen dioxide<br />
• 75% assess sulfur dioxide<br />
Depends on local air quality<br />
issues<br />
Three PHUs which noted<br />
challenges in assessing air<br />
quality, stated that demand<br />
for moni<strong>to</strong>ring pollutants was<br />
not a high priority (SU)<br />
Free (GM)<br />
Limited relevance <strong>to</strong> small populations. Strong focus on high<br />
population areas in Southern and South-eastern Ontario (KI, LR)<br />
Need for more air moni<strong>to</strong>ring stations and for modelling <strong>to</strong> provide<br />
greater spatial detail <strong>to</strong> current data (SU, LR, KI)<br />
The density <strong>of</strong> moni<strong>to</strong>ring stations can differ, between municipalities<br />
(KI, LR, SU)<br />
• 50% evaluate carbon<br />
monoxide<br />
Of PHUs assessing air quality 5 PHUs commented on the limited<br />
spatial representation <strong>of</strong> the present air moni<strong>to</strong>ring stations<br />
Canada <strong><strong>Environment</strong>al</strong><br />
Sustainability Indica<strong>to</strong>rs<br />
Hourly data on O 3 , PM 2.5 , SO 2 , NO 2 , VOCs Was not evaluated Free Two year lag period for data.<br />
National Pollutant Release<br />
Inven<strong>to</strong>ry<br />
Air Quality<br />
Emissions data for Ontario prior<br />
<strong>to</strong> 2005 available from Ministry <strong>of</strong><br />
<strong>Environment</strong> His<strong>to</strong>rical OnAIR <strong>Data</strong><br />
2001-2004 (MOE)<br />
The NPRI collects data on pollutant emissions from<br />
industrial and non-industrial sources<br />
<strong>Data</strong> available at a facility level, as well as<br />
aggregated data at the provincial level.<br />
The provincial information includes estimates for 17<br />
air pollutants organized by sec<strong>to</strong>r.<br />
In addition <strong>to</strong> industrial sources, NPRI estimates<br />
emissions from various other sec<strong>to</strong>rs such as<br />
transportation, agriculture, landfills, natural sources,<br />
etc.<br />
Of 28 PHUs who responded <strong>to</strong><br />
the survey, 29% had access <strong>to</strong><br />
data on proximity <strong>of</strong> population<br />
<strong>to</strong> emission sources reported<br />
through NPRI (SU)<br />
For large emission sources<br />
which meet the NPRI<br />
requirements for reporting,<br />
the data is <strong>of</strong> good quality<br />
and useful for PHU (KII)<br />
Provides maps on emissions<br />
density at low resolution (LR)<br />
<strong>Data</strong> available for specific<br />
pollutants<br />
Useful for traffic or point<br />
sources pollution (LR)<br />
Free<br />
Estimates only available for annual emission levels (LR)<br />
Limited information on day <strong>to</strong> day variation between communities<br />
(LR, KII)<br />
A lag period <strong>of</strong> 1 or 2 years exists for the data (LR)<br />
Many commercial and industrial sources within a city can be<br />
exempt from reporting <strong>to</strong> NPRI (LR, KII)<br />
Ontario Ministry <strong>of</strong><br />
Transportation<br />
Air Quality (Traffic volume)<br />
Ontario Ministry <strong>of</strong> Transportation annual publication<br />
on traffic volume and accident rates for provincial<br />
highways in Ontario<br />
No PHU reported the use or<br />
access <strong>to</strong> MTO data. However,<br />
such information may be obtained<br />
indirectly through the municipal<br />
transportation and planning<br />
departments.<br />
A useful resource for<br />
high traffic volume<br />
highways situated in urban<br />
communities<br />
Free<br />
To translate in<strong>to</strong> emission estimates, need information on fleet<br />
demographics (LR)<br />
Focus strictly on provincial highways (GM)<br />
Local Road <strong>Data</strong><br />
(collected by<br />
municipalities /regions)<br />
Network <strong>An</strong>alysis (<strong>to</strong> measure<br />
proximity or <strong>to</strong> model population<br />
affected)<br />
Traffic data (e.g. vehicle kilometres<br />
travelled, modal share, traffic<br />
volume)<br />
12 <strong>of</strong> 28 PHUs that answered the<br />
survey stated they have access <strong>to</strong><br />
municipal level traffic data (SU).<br />
Traffic related information is<br />
commonly gathered from<br />
municipal sources (SU).<br />
Free<br />
May require geocoding for GIS use.<br />
Coverage and update frequency will vary – data quality assessment<br />
is required.<br />
Dependent on municipalities <strong>to</strong> compile a local inven<strong>to</strong>ry <strong>of</strong> road<br />
and traffic data.<br />
Click <strong>to</strong> open the full table.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
4EXTREME HEAT<br />
MEASURES & DATA USED IN THE ASSESSMENT OF EXTREME HEAT<br />
BACKGROUND LITERATURE REVIEW KEY INFORMANT INTERVIEWS SUMMARY OF SURVEY RESULTS GAP ANALYSIS<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
139<br />
MEASURES & DATA USED IN THE ASSESSMENT OF EXTREME HEAT<br />
CHAPTER 4:<br />
EXTREME HEAT<br />
BACKGROUND<br />
EXTREME HEAT AND HEALTH<br />
Urban heat islands (UHIs), the phenomenon where cities are warmer than their surrounding non-urban<br />
areas, exacerbate the intensity <strong>of</strong> heat waves and are thus considerably more problematic for urban<br />
dwellers when compared <strong>to</strong> rural residents. Heat related mortality peaks in urban centres, 161 where the<br />
effects <strong>of</strong> extreme heat events can be manifested both directly and indirectly.<br />
Exposure <strong>to</strong> high temperatures over prolonged periods can result in heat-related morbidity and mortality.<br />
Heat exhaustion is the most widespread heat-related illness 7 and occurs as a result <strong>of</strong> extreme salt<br />
and water loss. Associated symp<strong>to</strong>ms include heavy sweating, weakness, dizziness, nausea, headache,<br />
diarrhoea and muscle cramps. 162 Heat exhaustion can exacerbate conditions such as cardiovascular illnesses,<br />
diabetes and respira<strong>to</strong>ry diseases, 163 and if untreated, heat exhaustion can result in heat stroke.<br />
Heat stroke is the most serious heat-related illness, and occurs when core-body temperature exceeds<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
140<br />
EXTREME HEAT<br />
40°C/140°F. This results in loss <strong>of</strong> consciousness and delirium, convulsions, coma or reduced mental<br />
ability. 7;164 Classic heat stroke is most common in vulnerable groups such as children, seniors, and those<br />
with chronic illness. 162 Excessive perspiration does not generally happen with classic heat stroke. Exertional<br />
heat stroke is accompanied by perspiration, and is more common in otherwise healthy individuals<br />
during strenuous exercise or occupational exposure during heat events. 36 More common but less serious,<br />
heat-related illnesses include fainting, heat cramps and heat rash. Fainting occurs due <strong>to</strong> lower blood<br />
pressure as a result <strong>of</strong> excessive water loss due <strong>to</strong> perspiration. Heat cramps are caused by salt imbalance.<br />
Heat rash is a result <strong>of</strong> clogged sweat glands. 162<br />
Deaths during a heat event more commonly occur as a result <strong>of</strong> indirect effects exacerbated by heat<br />
stress and include: cardiovascular disease, cerebrovascular accidents and vascular lesions, respira<strong>to</strong>ry<br />
diseases, and increased susceptibility <strong>to</strong> infectious diseases. 165;166 Secondary pollutants including ground<br />
level ozone (O 3<br />
) and smog are extremely harmful <strong>to</strong> human health, and are also elevated during heat<br />
events. 103 This leads <strong>to</strong> acute and chronic damage <strong>to</strong> the respira<strong>to</strong>ry system, <strong>of</strong> particular concern for<br />
individuals with cardiovascular or pulmonary disease. 23<br />
VULNERABLE POPULATIONS & EXTREME HEAT<br />
Two unique sets <strong>of</strong> fac<strong>to</strong>rs contribute <strong>to</strong> vulnerability <strong>to</strong> extreme heat; individual and community fac<strong>to</strong>rs.<br />
Individual fac<strong>to</strong>rs determine how a person will respond <strong>to</strong> extreme heat and can include health status,<br />
degree <strong>of</strong> social isolation, air conditioner availability, income level, place <strong>of</strong> work characteristics, place<br />
<strong>of</strong> residence characteristics, and personal behavioral characteristics such as levels <strong>of</strong> strenuous activity,<br />
clothing type etc. 36 Previous long-term exposure <strong>to</strong> heat results in individual acclimatization <strong>to</strong> extreme<br />
heat. Short-term exposure also plays a part, and it is for this reason that the most deadly extreme<br />
heat events occur early in the summer before the body’s systems can acclimatize. Older adults and<br />
young children are less capable <strong>of</strong> this acclimatization. 7;29;30 Because individual fac<strong>to</strong>rs are such important<br />
indica<strong>to</strong>rs <strong>of</strong> vulnerability <strong>to</strong> extreme heat, estimating the impact <strong>of</strong> specific temperature increments<br />
on population health is challenging. However, research has shown that cardiovascular and respira<strong>to</strong>ry<br />
deaths increase considerably with each 1°C increase in temperature above a specified critical value<br />
specific <strong>to</strong> a location. 167;168<br />
The second group <strong>of</strong> fac<strong>to</strong>rs that contribute <strong>to</strong> vulnerability are community fac<strong>to</strong>rs. These fac<strong>to</strong>rs include<br />
the local climate, community design and availability <strong>of</strong> services for the community <strong>to</strong> cope with extreme<br />
heat.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT 141<br />
EXTREME HEAT EVENTS IN CANADA<br />
Extreme heat events have increased significantly in Canada<br />
over the last century. 169 While these events are not a natural<br />
hazard <strong>of</strong> major concern <strong>to</strong> most Canadians, 170 the resultant<br />
death <strong>to</strong>lls are substantially higher than those <strong>of</strong> other natural<br />
phenomena. Precise numbers <strong>of</strong> deaths attributed <strong>to</strong> heat<br />
events vary greatly due <strong>to</strong> inconsistencies in reporting. 170<br />
Large numbers <strong>of</strong> deaths in Canada are suspected <strong>to</strong> be<br />
heat-related but are coded as heart attack, stroke etc., 7 likely<br />
resulting in an underestimate <strong>of</strong> heat related morbidity and<br />
mortality.<br />
CLIMATE CHANGE, URBANIZATION AND<br />
DEMOGRAPHIC CHANGE<br />
Heat-related mortality could double in Southern Ontario by<br />
the 2050s, while rates <strong>of</strong> mortality due <strong>to</strong> worsening air pollution<br />
compounded by these temperature increases, could<br />
increase by about 15-25%. 21 Many researchers confirm<br />
this, predicting that heat-related illnesses and deaths will increase<br />
in the future, worsened due <strong>to</strong> the urban heat island<br />
effect related <strong>to</strong> increasing urbanization and Canada’s aging<br />
population. 6;7 Using modest assumptions about greenhouse<br />
gas (GHG) reductions, temperature increases for Ontario are<br />
in the range <strong>of</strong> 2.5°C <strong>to</strong> 3.7°C compared <strong>to</strong> 1961-1990 and<br />
lower emissions reductions raise this projection <strong>to</strong> between<br />
3.0°C and 4.0°C. 21 Researchers project that the number <strong>of</strong><br />
days with average temperatures above 30˚C will increase nationally,<br />
particularly in the Windsor-Quebec corridor. 22 The intensity<br />
and duration <strong>of</strong> extreme heat events in Canada are also<br />
expected <strong>to</strong> rise. 24;25 Over eighty percent <strong>of</strong> Canadians reside<br />
in urban areas, and this number is increasing. 26 Canada’s<br />
population is aging, with the number <strong>of</strong> seniors expected <strong>to</strong><br />
double by 2035, 27;28 dramatically increasing the size <strong>of</strong> Canada’s<br />
population vulnerable <strong>to</strong> extreme heat events.<br />
Prolonged heat events are not<br />
uncommon in Canada. In 1936,<br />
for 12 days in July, temperatures<br />
reached 44˚C contributing <strong>to</strong><br />
the 1,180 deaths, 458 in Ontario<br />
alone. 171;172 In 2005, Toron<strong>to</strong> had<br />
41 days with temperatures above<br />
30˚C and Montreal had 23 days. 171<br />
Due <strong>to</strong> geographic variability in<br />
climatic thresholds, what constitutes<br />
a heat wave is defined neither<br />
regionally nor nationally. <strong>Environment</strong><br />
Canada defines a meteorological<br />
heat wave as three or<br />
more consecutive days where the<br />
maximum temperature is greater<br />
than or equal <strong>to</strong> 32°C. <strong>Environment</strong><br />
Canada may also issue<br />
extreme heat warnings when air<br />
temperature exceeds 30°C and<br />
the humidex exceeds 40. Municipalities<br />
and public health units<br />
also issue extreme heat warnings,<br />
however the criteria for<br />
issuing these warnings varies by<br />
jurisdiction. 173<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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EXTREME HEAT<br />
EXTREME HEAT AND THE BUILT ENVIRONMENT<br />
The urban heat island (UHI) effect is the phenomenon where cities are warmer than their surrounding<br />
non-urban areas. The ability <strong>of</strong> an urban area <strong>to</strong> generate a heat island is the observed temperature<br />
difference between urban and non-urban areas, or ΔT u<br />
- r<br />
. 174 Oke 175 developed the following equation <strong>to</strong><br />
summarize the radiant energy balance in a city; Q* + Q F<br />
= Q H<br />
+ Q E<br />
+ ∆Q S<br />
+ ∆Q A<br />
, where Q* is net allwave<br />
radiation (the difference between incoming and out-going short and long wave radiation captured<br />
by an area); Q F<br />
is the sum <strong>of</strong> all anthropogenic heat generated by the buildings, vehicles and people; Q H<br />
and Q E<br />
represent the heat fluxes in the area; and ∆Q S<br />
and ∆Q A<br />
are the sensible heat s<strong>to</strong>rage and net heat<br />
advection respectively. 176 The temperature difference can be attributed <strong>to</strong> the replacement <strong>of</strong> vegetated<br />
surfaces and surface waters with urban construction materials that retain heat, and anthropogenic heat<br />
generation processes such as industry or vehicle use.<br />
The urban form results in increased surface areas. The three dimensional geometry <strong>of</strong> a city leads <strong>to</strong> the<br />
absorption and s<strong>to</strong>rage <strong>of</strong> solar energy through the vertical faces <strong>of</strong> buildings. These geometric features<br />
restrict horizontal air flow due <strong>to</strong> frictional drag created by the urban surface resulting in reduced windspeeds,<br />
and less convective cooling, trapping radiation in urban ‘’canyons’’. Reduced sky-view fac<strong>to</strong>rs<br />
concurrently reduce opportunities for the re-emission <strong>of</strong> radiant heat in<strong>to</strong> the atmosphere, producing air<br />
temperatures up <strong>to</strong> 5˚C warmer than surrounding non-urban areas. 177<br />
The UHI is influenced by thermal properties including albedo and emissivity. The albedo <strong>of</strong> a surface is<br />
defined by Taha 178 as its hemispherically and wavelength-integrated reflectivity. Measured between 0 and<br />
1.0, a perfectly reflective body would have an albedo <strong>of</strong> 1 while a perfectly black body would have an albedo<br />
<strong>of</strong> 0. Cities have large areas covered by low albedo materials (concrete, asphalt and dark coloured<br />
ro<strong>of</strong>s) and average albedos tend <strong>to</strong> be low, generally no more than 0.2. 178 Vancouver, BC, for example<br />
has an average albedo <strong>of</strong> 0.13-0.15 179 and Hamil<strong>to</strong>n, Ontario an albedo <strong>of</strong> 0.12-0.13. 180 Solar energy<br />
that is not reflected by a surface is absorbed. Emissivity determines the ability <strong>of</strong> a surface <strong>to</strong> release heat<br />
which was not reflected and was instead absorbed. Also measured on a scale <strong>of</strong> 0 (no emittance) <strong>to</strong> 1<br />
(full emittance), a surface material with a high emissivity remains cooler when exposed <strong>to</strong> solar energy,<br />
because it emits more energy per unit area and comes <strong>to</strong> thermal equilibrium at a lower temperature. 181<br />
<strong>An</strong>thropogenic heating adds an additional supply <strong>of</strong> energy, where heating and cooling systems, machinery<br />
and vehicles, electricity use and fossil fuel combustion all contribute <strong>to</strong> the anthropogenic component<br />
<strong>of</strong> a city’s energy balance equation. The resultant air pollution from these activities leads <strong>to</strong> increased<br />
long wave radiation being created, and less re-emission <strong>of</strong> this energy, more commonly known as the<br />
‘’greenhouse effect‘’. Taha 178 notes that anthropogenic heat sources in the urban core can contribute<br />
2-3°C <strong>to</strong> the ambient air temperature in that area.<br />
ASSESSING EXTREME HEAT<br />
Various techniques <strong>to</strong> quantify the relationship between land use and surface temperature have been employed.<br />
Thermal remote sensing is the most common method. 182-185 Landsat Thematic Mapper (TM) and<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT 143<br />
Enhanced Thematic Mapper (ETM) are the most commonly used and widely available remotely sensed<br />
imagery. Operated by the National Aeronautics and Space Administration (NASA), Band 6 <strong>of</strong> this satellite<br />
captures thermal infrared radiation (heat) at resolutions <strong>of</strong> 60m (ETM) and 120m every 16 days. Images<br />
are captured at approximately 10:00 local time. Together with the 120m pixel size and thermal anisotropy<br />
(i.e. what is actually being captured at a single angle by the sensor), this time <strong>of</strong> acquisition has been<br />
noted as a limitation <strong>of</strong> Landsat imagery (given the sun is not at its apex).<br />
Lo and Quattrochi 186 used Landsat imagery <strong>to</strong> examine relationships between land-use / land-cover<br />
change and urban heat islands over the metropolitan area <strong>of</strong> Atlanta, Georgia. Heat islands were concentrated<br />
in the inner city urban area, spreading out along highways where higher density urban uses were<br />
focused. Between 1987 and 1997, several <strong>of</strong> these heat islands were found <strong>to</strong> have merged in<strong>to</strong> one<br />
larger concentration due <strong>to</strong> urbanization. These findings were confirmed for Toron<strong>to</strong> by Maloley. 182 . Lower<br />
density suburban development around the city’s peri-urban fringe was found <strong>to</strong> have lower surface temperatures<br />
than down<strong>to</strong>wn areas. Rinner and Hussain 184 also examined the land-use – surface temperature<br />
relationships, finding commercial and resource/industrial land-uses were hottest, averaging 29.1˚C<br />
as compared <strong>to</strong> parks/recreational uses (25.1˚C), water bodies (23.1˚C) and residential and open areas<br />
(27.5˚C). This study also found larger single use zones positively correlated with surface temperature for<br />
both commercial (r=0.405) and resource/industrial uses (r=0.259) at the 0.01 level <strong>of</strong> significance.<br />
Applying a similar methodology <strong>to</strong> the Pearl River Delta in Guangdong, China, Chen and colleagues 187<br />
used Landsat 5 and Landsat 7 imagery were used <strong>to</strong> examine the relationship between urban heat<br />
islands and land use/cover change. They found that expansions <strong>of</strong> built-up areas were the main contribu<strong>to</strong>r<br />
<strong>to</strong> the regional temperature rise witnessed between 1990 and 2000. 187 Weng and Yang 183 examined<br />
relationships between land-use and heat in the same study area, finding that in 1989, industrial areas<br />
were hottest, followed by commercial, residential and rural. At this time, hotspots were scattered in an<br />
archipelago, but by 1997, many <strong>of</strong> the “islands” in the archipelago had merged in<strong>to</strong> one heat island due<br />
<strong>to</strong> sprawling urban development.<br />
It should be noted that these cited studies sought <strong>to</strong> examine the relationship between surface temperature<br />
and land-use, however the relationship between surface and air temperature is more complex.<br />
Air temperature is measured between 1.5m and 2m above ground. Much <strong>of</strong> the heat transfer at these<br />
heights is not from the immediate surface below, but from surfaces several hundred metres away on a<br />
horizontal plane, with wind as a vec<strong>to</strong>r. Over more uniform surfaces, with lower wind speeds, surface<br />
temperatures inform air temperatures more accurately. 182 Urban areas rarely have such uniform surfaces.<br />
Although air temperature measurements inform human comfort levels and are desirable, it would be an<br />
onerous if not impossible task <strong>to</strong> establish in-situ air temperature moni<strong>to</strong>ring sites at a density that would<br />
provide adequate coverages over urban areas. Modelling has been attempted using several methodologies<br />
including regression functions, objective hysteresis models and through high resolution urban surface<br />
models. However, these processes have limited successes. 185 While weather stations provide this<br />
data <strong>to</strong> a high degree <strong>of</strong> accuracy, the spatial resolutions obtained through such sparse station densities<br />
are not adequate <strong>to</strong> infer measurements over larger geographies such as municipalities.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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EXTREME HEAT<br />
LITERATURE REVIEW<br />
The purpose <strong>of</strong> this literature review is <strong>to</strong> capture research<br />
on the linkages between the built environment and extreme<br />
heat. The literature search identified a number <strong>of</strong> measures<br />
and approaches <strong>to</strong> assess extreme heat. The measurement<br />
approaches identified in the literature review have been categorized<br />
as follows: (i) Measures using meteorological variables;<br />
(ii) Measures <strong>of</strong> the built environment; (iii) Measures <strong>of</strong><br />
community vulnerability.<br />
MEASURES USING<br />
METEOROLOGICAL VARIABLES<br />
MEASURES USING INDIVIDUAL<br />
METEOROLOGICAL VARIABLES<br />
Meteorological variables are commonly used <strong>to</strong> measure<br />
extreme heat. Variables identified included temperature,<br />
relative humidity, dew point temperature, wind speed and<br />
direction and solar radiation. These variables are usually directly<br />
measured and moni<strong>to</strong>red at local airports and weather<br />
stations by government agencies.<br />
<strong>Environment</strong> Canada’s Meteorological Service <strong>of</strong> Canada<br />
(Weather Office) provides public meteorological information<br />
and weather forecasts for Ontario and across the country.<br />
Table 13 lists select meteorological variables available hourly<br />
from <strong>Environment</strong> Canada weather stations. <strong>Environment</strong><br />
Canada weather stations are known <strong>to</strong> have the most complete<br />
data and include a large number <strong>of</strong> meteorological<br />
parameters. 23 However, these weather stations are <strong>of</strong>ten located<br />
at airports and are limited in number for a given jurisdiction.<br />
Meteorological information is therefore not always<br />
representative <strong>of</strong> conditions experienced by a community<br />
since they can be located some distance from the majority<br />
<strong>of</strong> the population. The nature <strong>of</strong> the airport environment<br />
can also influence readings. Airports may not be reflective<br />
<strong>of</strong> the community due <strong>to</strong> reduced tree cover or vegetation,<br />
reduced building density and increased pavement area.<br />
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EXTREME HEAT 145<br />
Table 13: Select hourly meteorological variables available from <strong>Environment</strong> Canada weather stations 188<br />
Measure<br />
<strong>Data</strong> format<br />
Temperature °C<br />
Dew point temperature °C<br />
Relative humidity %<br />
Wind speed<br />
Wind direction<br />
Total cloud cover<br />
(not available from aviation au<strong>to</strong>matic weather stations)<br />
km/h<br />
Ten’s <strong>of</strong> degrees<br />
Clear (0 tenths)<br />
Mainly clear (1 <strong>to</strong> 4 tenths)<br />
Mostly cloudy (5 <strong>to</strong> 9 tenths)<br />
Cloudy (10 tenths)<br />
COMPOSITE MEASURES USING<br />
METEOROLOGICAL VARIABLES<br />
Based on the literature review, there are a number<br />
<strong>of</strong> meteorological variables that are also used<br />
in composite indices <strong>to</strong> determine environmental<br />
exposure <strong>to</strong> heat. Table 14 lists composite measures<br />
identified in the review <strong>of</strong> the literature. Although<br />
the application <strong>of</strong> these indices varies by<br />
user and purpose, common indices included the<br />
heat index (apparent temperature) and humidex.<br />
These indices both combine the effects <strong>of</strong> relative<br />
humidity and temperature <strong>to</strong> describe how hot<br />
weather feels <strong>to</strong> the average person. The humidex<br />
is used in Canada, whereas the heat index (apparent<br />
temperature) is used in the United States.<br />
These indices are pre-calculated and reported <strong>to</strong><br />
the public as current weather conditions and in<br />
forecasts.<br />
on an ongoing basis due <strong>to</strong> their complex nature,<br />
specialized data requirements and availability. The<br />
utility <strong>of</strong> some indices may also be limited due <strong>to</strong><br />
requirements for specialized equipment. For example,<br />
<strong><strong>Environment</strong>al</strong> Heat Moni<strong>to</strong>ring Systems<br />
(EHMS) are used by Health Canada for research<br />
projects across the country. EHMS units capture<br />
all four fac<strong>to</strong>rs that comprise heat: ambient<br />
temperature; radiant solar load; humidity; and air<br />
velocity. EHMS units also calculate the Wet Bulb<br />
Globe Temperature (WBGT) based on the four<br />
fac<strong>to</strong>rs. Without these moni<strong>to</strong>rs, calculating the<br />
WGBT would not be possible through standard<br />
<strong>Environment</strong> Canada data. Ontario PHUs would<br />
either have <strong>to</strong> purchase specialized equipment or<br />
partner with organizations who already own the<br />
equipment <strong>to</strong> be able <strong>to</strong> collect information on<br />
solar load and the WBGT.<br />
Many <strong>of</strong> the other indices identified in the review<br />
<strong>of</strong> the literature were used in academic studies<br />
and may not be realistic <strong>to</strong> calculate or collect<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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EXTREME HEAT<br />
Table 14: Composite measures <strong>of</strong> heat using meteorological variables, identified in the literature review<br />
Composite<br />
Heat Index/<br />
Apparent<br />
Temperature<br />
Humidex<br />
Spatial Synoptic<br />
Classification<br />
Thom Index/<br />
Discomfort Index<br />
Relative Stress<br />
Index<br />
Wet Bulb Globe<br />
Temperature<br />
Index<br />
Heat Exposure<br />
Index<br />
Net Effective<br />
Temperature/<br />
Thermal Index<br />
Heat Load Index<br />
Heat Stress<br />
Index<br />
Perceived<br />
Temperature<br />
Human Thermal<br />
Comfort Index<br />
Description<br />
“The Heat Index or the ’Apparent Temperature’ is an accurate measure <strong>of</strong> how hot it really<br />
189(para. 7)<br />
feels when the relative humidity is added <strong>to</strong> the actual air temperature.”<br />
The humidex “describes how hot, humid weather feels <strong>to</strong> the average person. The<br />
humidex combines the temperature and humidity in<strong>to</strong> one number <strong>to</strong> reflect the perceived<br />
190(para. 24)<br />
temperature.”<br />
“A series <strong>of</strong> meteorological variables (air temperature, dew point temperature, atmospheric<br />
pressure, wind speed and direction) are categorized in<strong>to</strong> air masses using the spatial<br />
synoptic approach. Oppressive air masses are identified as those associated with an<br />
excess in mortality. The latter is calculated as the difference between observed and daily<br />
baseline values.” 191(p.2258)<br />
“Thom Index, also known as Discomfort Index, is useful <strong>to</strong> evaluate how current<br />
temperature and relative humidity can affect the sultriness or the discomfort<br />
sensation”. 192(p.648)<br />
The Relative Stress Index “calculates the ratio <strong>of</strong> sweat evaporation needed for comfort <strong>to</strong><br />
193(para. 18)<br />
the amount <strong>of</strong> evaporation possible given ambient atmospheric conditions.”<br />
“The wet bulb globe temperature (WBGT) is calculated using a formula that takes in<strong>to</strong><br />
account air temperature, speed <strong>of</strong> air movement, radiant heat from hot objects, sunshine<br />
194(para. 4)<br />
and body cooling due <strong>to</strong> sweat evaporation.”<br />
A Poisson model is used <strong>to</strong> relate excess mortality during a heat wave <strong>to</strong> the<br />
meteorological indica<strong>to</strong>rs <strong>of</strong> a given geography. 195<br />
Net Effective Temperature (NET) “reflects the common perception that people tend <strong>to</strong> feel<br />
more stressful on hot and humid days with calm winds in summer. In summer, the higher<br />
the NET, the more stressful is the weather.” 196(p.401)<br />
Heat Load Index is “based on the Man–ENvironment heat EXchange model (MENEX<br />
model), [where] meteorological conditions as well as physiological parameters establish the<br />
heat gain or loss <strong>of</strong> the human body by radiation, convection, conduction, evaporation and<br />
respiration.” 197(p.154)<br />
Heat Stress Index is a relative comfort index. “The HSI has the ability <strong>to</strong> evaluate daily mean<br />
relative stress values for each first-order weather station in the United States. It includes<br />
important variables not used in previous indices, such as consideration <strong>of</strong> the impact <strong>of</strong><br />
consecutive days <strong>of</strong> stressful weather, daily cloud cover (as a surrogate for solar load), and<br />
accumulation <strong>of</strong> heat through the day.” 198(p.504)<br />
Perceived Temperature (PT) is “derived from a heat budget model as a predic<strong>to</strong>r for heatrelated<br />
health impact assessments. PT refers <strong>to</strong> a reference environment in which the<br />
perception <strong>of</strong> cold and/or heat would be the same as under the actual conditions.” 199(p.2)<br />
The Human Thermal Comfort Index (HTCI) “is based on energy balance <strong>of</strong> a hypothetical<br />
person given the weather data from a site and the site’s surrounding solar and thermal<br />
radiative environmental fluxes.” 200(p.2852)<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT 147<br />
MEASURES OF THE BUILT ENVIRONMENT<br />
Evidence suggests that extreme heat is related<br />
<strong>to</strong> various built environment features and characteristics.<br />
In terms <strong>of</strong> assessing extreme heat,<br />
built environment measures differ from those<br />
using meteorological data, since they are indirect<br />
in nature. <strong>Built</strong> environment measures used in<br />
the assessment <strong>of</strong> extreme heat were identified<br />
in the literature review and can be categorized<br />
by community and residential characteristics, as<br />
well as natural and artificial surfaces (Table 15).<br />
Table 15: Examples <strong>of</strong> built environment measures for the assessment <strong>of</strong> extreme heat by category<br />
Community characteristics Natural & artificial surfaces Residential characteristics<br />
• Sprawl index<br />
• Density<br />
• Proximity<br />
• Land use mix<br />
• Green space<br />
• Open space<br />
• Non-green space<br />
• Soil Adjusted Vegetation Index<br />
(SAVI)<br />
• Single family detached homes<br />
• Homes without air conditioning<br />
• Swimming pools<br />
• Dwellings in high rise buildings<br />
REMOTE SENSING TECHNOLOGIES<br />
<strong><strong>Environment</strong>al</strong> exposures <strong>to</strong> extreme heat can<br />
specifically be assessed by indirect moni<strong>to</strong>ring<br />
through remote sensing technologies. Remote<br />
sensing is a way <strong>to</strong> acquire information about the<br />
Earth’s surface without being directly in contact<br />
with it. It involves processing and analyzing information<br />
from reflected or emitted energy. 201 Satellites<br />
are common platforms for remote sensing<br />
technology and this data can be used <strong>to</strong> quantify<br />
a number <strong>of</strong> measures that describe environmental<br />
exposures <strong>to</strong> extreme heat (Table 16).<br />
Thermal satellite imagery depicting land surface<br />
temperatures is commonly used <strong>to</strong> assess the<br />
urban heat island effect. Urban heat islands and<br />
hot spots can also be assessed by analyzing land<br />
cover and more specifically, vegetative cover and<br />
impervious surfaces (surfaces which water cannot<br />
penetrate).<br />
Vegetation is important in determining heat exposure<br />
because it absorbs solar energy and<br />
cools the air during the evaporation process. 202<br />
By contrast, impervious surfaces such as paved<br />
roads, concrete sidewalks and parking lots tend<br />
<strong>to</strong> absorb solar radiation which is released as<br />
heat in<strong>to</strong> the surrounding environment. 202 Therefore<br />
the proportion <strong>of</strong> land use with vegetative<br />
cover compared <strong>to</strong> impervious surfaces can play<br />
an important role in the presence <strong>of</strong> urban heat<br />
islands.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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EXTREME HEAT<br />
Table 16: Examples <strong>of</strong> remotely sensed measures <strong>of</strong> the built environment used <strong>to</strong> assess extreme heat,<br />
as identified in the literature review<br />
Measure<br />
Surface Temperatures<br />
Urban Heat Islands<br />
Land Cover<br />
Impervious surface<br />
Vegetation<br />
Bidirectional Reflectance Distribution Function<br />
Source<br />
Aqua - MODIS<br />
Landsat (5 TM & 7 ETM+)<br />
Terra – Aster<br />
Quickbird<br />
Landsat 5 TM<br />
MODIS<br />
Quickbird<br />
Landsat<br />
Infrared aerial imagery<br />
Landsat 7 ETM+<br />
IKONOS<br />
Quickbird<br />
Aqua - MODIS<br />
Infrared aerial imagery<br />
Landsat (5 TM & 7 ETM+)<br />
Terra – Aster<br />
MODIS<br />
The quality and quantity <strong>of</strong> satellite data also varies<br />
by source. Properties <strong>of</strong> select satellites and<br />
sensors are outlined in Table 17. 203 For example,<br />
different satellites and sensors vary in terms <strong>of</strong><br />
spatial resolution. This can limit the ability <strong>to</strong> precisely<br />
determine local level attributes. <strong>An</strong>other<br />
distinguishing feature <strong>of</strong> satellite data is the temporal<br />
resolution. This refers <strong>to</strong> the amount <strong>of</strong> time<br />
required <strong>to</strong> obtain an image at the exact same<br />
area for a second time. 204 Therefore, a given location<br />
could have access <strong>to</strong> coarse data daily<br />
through the MODIS sensor (TERRA/AQUA satellite)<br />
or have finer resolution data every 16 days<br />
with the Landsat satellite.<br />
It is also important <strong>to</strong> note that parameters such<br />
as land surface temperature do not capture, and<br />
therefore may underestimate indoor temperatures<br />
and only reflect data at one point in time. 205 <strong>An</strong>other<br />
limitation that should be considered is thermal<br />
anisotropy, 206 that is, what is actually being<br />
captured by the satellite-mounted sensor. Three<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT 149<br />
Table 17: Select satellites and their sensors used in heat-related health studies 203(p.722)±<br />
Satellite<br />
Sensor<br />
Spatial Resolution<br />
(in metres)<br />
Temporal Resolution<br />
(in days)<br />
Landsat TM (5)<br />
TM Multispectral 30 16<br />
TM Thermal 120 16<br />
Landsat ETM+ (7)<br />
ETM+ Multispectral 30 16<br />
ETM+ Thermal 60 16<br />
TERRA ASTER 15 – 90 Variable (4 -16)<br />
TERRA/AQUA MODIS 250 - 1000 Twice Daily<br />
±© Wiley Publishing, 2011 Reproduced with permission.<br />
dimensional structures like buildings are viewed<br />
at a single angle by the satellite; therefore thermal<br />
properties <strong>of</strong> lateral walls are not captured. The<br />
satellite-mounted sensor only captures ro<strong>of</strong><strong>to</strong>p<br />
and street surface temperatures.<br />
Despite these limitations, remotely sensed image<br />
acquisition remains the best available <strong>to</strong>ol for<br />
identifying and estimating heat islands and their<br />
intensities because <strong>of</strong> their complete coverage.<br />
Overall, these are common measurement approaches<br />
used in research-focused literature.<br />
Their use may be limited due <strong>to</strong> accessibility<br />
issues, associated costs (acquisition or processing)<br />
and the requirement for expertise <strong>to</strong> collect<br />
and analyze the data. GIS expertise in particular<br />
can be a requirement for the use <strong>of</strong> remotely<br />
sensed data.<br />
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MEASURES OF COMMUNITY<br />
VULNERABILITY<br />
The final category explores community vulnerability<br />
<strong>to</strong> extreme heat. These measures combine<br />
environmental exposure <strong>to</strong> extreme heat,<br />
as well as individual and community vulnerability<br />
<strong>to</strong> determine overall risk. Although measures <strong>of</strong><br />
community vulnerability may integrate measures<br />
previously described (e.g. land surface temperature),<br />
they also use data sets which speak <strong>to</strong><br />
socio-economic and demographic variables <strong>of</strong><br />
the populations <strong>of</strong> interest. Table 18 describes<br />
examples <strong>of</strong> community vulnerability measures<br />
related <strong>to</strong> extreme heat. Examples <strong>of</strong> socio-economic<br />
and demographic variables used <strong>to</strong> assess<br />
community vulnerability <strong>to</strong> heat include:<br />
• Education<br />
• Race<br />
• Age<br />
• Crime<br />
• Income<br />
• Social Isolation<br />
Toron<strong>to</strong>’s Heat Vulnerability Index combines exposure<br />
and sensitivity <strong>to</strong> heat <strong>to</strong> create a vulnerability<br />
index. The index uses GIS <strong>to</strong> identify community<br />
vulnerability <strong>to</strong> heat on a spatial scale.<br />
The exposure component <strong>of</strong> the index uses land<br />
surface temperature data derived from thermal<br />
imagery combined with other exposure variables<br />
from various data sources.<br />
Examples <strong>of</strong> exposure variables and their sources<br />
include 213 :<br />
• Surface temperature - Natural Resources<br />
Canada<br />
• Access <strong>to</strong> green space - City <strong>of</strong> Toron<strong>to</strong>,<br />
Forestry and Recreation<br />
• Percentage <strong>of</strong> tree canopy coverage by<br />
census tract - City <strong>of</strong> Toron<strong>to</strong>, Urban Forestry<br />
division<br />
• Percentage <strong>of</strong> dwelling units that are in high<br />
rises (5+s<strong>to</strong>reys) - Statistics Canada<br />
• Percentage <strong>of</strong> all dwelling units that are renter<br />
high rise dwellings constructed before 1986 -<br />
Statistics Canada Census<br />
• Population density in persons/km2 over net<br />
area - Statistics Canada Census<br />
These measures require a variety <strong>of</strong> data sources,<br />
some <strong>of</strong> which vary in terms <strong>of</strong> their frequency<br />
and availability across Ontario. For example,<br />
many demographic and socio-economic variables<br />
are based on the Canadian census which<br />
occurs every 5 years. In order <strong>to</strong> advance the use<br />
<strong>of</strong> composite indices that incorporate a number<br />
<strong>of</strong> variables and sources, data availability issues<br />
must be reconciled. These measures would also<br />
require the expertise <strong>to</strong> develop the methodology,<br />
use satellite imagery, and create and analyze data<br />
on a spatial scale. It has also been suggested that<br />
the use <strong>of</strong> a composite index may over simplify a<br />
problem and is prone <strong>to</strong> misinterpretation. 208<br />
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EXTREME HEAT 151<br />
Table 18: Measures <strong>of</strong> community vulnerability <strong>to</strong> extreme heat, as identified in the literature review<br />
Table 18: Measures <strong>of</strong> community vulnerability <strong>to</strong> extreme heat, as identified in the literature review<br />
EXTREME HEAT<br />
Measure <strong>Data</strong> Component Location Applied<br />
Hazard layer<br />
• Satellite image <strong>of</strong> near surface air temperature<br />
Composite Heat<br />
Human vulnerability layer<br />
Vulnerability<br />
• Under 5 or over 65 years <strong>of</strong> age<br />
Index 207<br />
• Living on a low income<br />
• Being over 65 and living alone<br />
Exposure index (40%)<br />
• Mean surface temperature<br />
• Green space coverage<br />
• Accessibility <strong>to</strong> green space<br />
• Dwelling units in high rise buildings<br />
• Renter dwellings in older high rises<br />
• Population density<br />
Sensitivity index (60%)<br />
Heat Vulnerability • Children age =50% <strong>of</strong> income on housing<br />
• Recent immigrants (within 5 years or less)<br />
• Racialized groups<br />
• Emergency visits: respira<strong>to</strong>ry or circula<strong>to</strong>ry disease<br />
• Vulnerable seniors<br />
Coping resources<br />
• Social ties index<br />
• % air conditioned<br />
• % swimming pools<br />
• Ro<strong>of</strong> reflectivity (% asphalt, % tile, % wood, % other)<br />
Population<br />
• Median income, % in poverty<br />
• Less than high school, College graduate<br />
Human Thermal<br />
• % minority<br />
Comfort Index 200<br />
• Median age<br />
• % ages 5 and under<br />
• % ages 65 and over<br />
Thermal environment<br />
• Distance from city center (km)<br />
• Population/km2<br />
• % open space<br />
• Vegetation abundance (SAVI)<br />
Land surface temperature<br />
Heat related mortality<br />
Vulnerability variables<br />
• Hispanic population<br />
• Black population<br />
• Asian population<br />
Risk from<br />
• Native American population<br />
extreme heat 209<br />
• Other race population<br />
• Age 65 and over<br />
• Age 65 and over living below poverty<br />
• Age 5 and under<br />
• Persons living below poverty<br />
• Low education (less than high school education)<br />
Social/environmental<br />
• % below the poverty line<br />
• % race other than white<br />
• % with less than a high school diploma<br />
• % <strong>of</strong> non-green space<br />
Social isolation<br />
Heat Vulnerability<br />
• % that live alone<br />
Index (HVI) 210;211<br />
• %> 65 years <strong>of</strong> age that live alone<br />
Air conditioning prevalence<br />
• % homes without central air conditioning<br />
• % homes with no air conditioning <strong>of</strong> any kind<br />
Preexisting health conditions<br />
• % population diagnosed with diabetes<br />
Hazard Layer: (50%)<br />
• Urban Heat Island magnitude (LST)<br />
Heat Health Exposure Layer (25%):<br />
Risk 212 • Household type (Experian MOSAIC data)<br />
Vulnerability Layer (25%):<br />
• Made up <strong>of</strong> vulnerable types extracted from the exposure layer (e.g. Old, Ill, Density, Flats)<br />
Thermal information (Landsat)<br />
Vulnerability indica<strong>to</strong>rs<br />
• Regional deprivation index 2006<br />
• Population density<br />
SUPREME<br />
• Age<br />
system 205<br />
• Housing conditions<br />
• Foreign language population<br />
• Dissemination areas inside heat Islands<br />
• Landed immigrants since 2001<br />
Montreal<br />
Toron<strong>to</strong><br />
Phoenix, AZ<br />
Philadelphia, PA<br />
California, Massachusetts<br />
New Mexico<br />
Oregon<br />
Washing<strong>to</strong>n<br />
Birmingham, UK<br />
Quebec<br />
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KEY INFORMANT INTERVIEWS SUMMARY:<br />
EXTREME HEAT<br />
Five key informant interviews were conducted <strong>to</strong> explore built environment data in Ontario related <strong>to</strong><br />
the assessment <strong>of</strong> extreme heat. Key informants representation included public health, academia and<br />
federal agencies. Key informant interview questions focused on three specific data sources relevant <strong>to</strong><br />
assessing heat in Ontario: <strong>Environment</strong> Canada’s weather data; Landsat imagery; and, syndromic surveillance<br />
systems.<br />
HIGHLIGHTS<br />
• <strong>Environment</strong> Canada weather station data may have limited usefulness in the assessment <strong>of</strong><br />
cross-community heat variation<br />
• Temporal analysis using <strong>Environment</strong> Canada weather station data may also be limited depending<br />
on location and station his<strong>to</strong>ry<br />
• <strong>Environment</strong> Canada weather data is free and undergoes a quality control process<br />
• Urban climate models that simulate weather conditions and estimate spatial variability in heat can<br />
be used <strong>to</strong> identify hot areas within a region<br />
• Surface temperatures can be used by PHUs as a basic indica<strong>to</strong>r <strong>of</strong> relative hot and cool areas<br />
and neighbourhoods; however, a moderate level <strong>of</strong> expertise is required <strong>to</strong> interpret thermal<br />
imagery. For example, when using thermal imagery, there are benefits <strong>to</strong> normalizing several<br />
single shot images in<strong>to</strong> an average single image<br />
• The his<strong>to</strong>rical Landsat data archive is completely open with no privacy restrictions<br />
• Heat data complimented and analyzed in conjunction with health outcome data (e.g. morbidity),<br />
provides a better approach for heat alert and response systems<br />
• A syndromic surveillance system has the capacity <strong>to</strong> moni<strong>to</strong>r heat-related illnesses and Wet Bulb<br />
Globe Temperature (a composite measure <strong>of</strong> heat)<br />
• Locations for environmental heat sensors can reflect a variety <strong>of</strong> land uses in both large and small<br />
urban areas, as well as rural communities<br />
• Currently, data from the syndromic surveillance system and sensors is not available publicly<br />
• The need for more data was identified as a limitation and should be addressed <strong>to</strong> increase<br />
coverage and support evidence-informed decision making<br />
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EXTREME HEAT 153<br />
ENVIRONMENT CANADA WEATHER DATA<br />
Currently, <strong>Environment</strong> Canada maintains 110 weather moni<strong>to</strong>ring stations across Ontario. These stations<br />
collect various weather-related data including ambient temperature, relative humidity, wind speed<br />
and direction (at 10m), pressure, precipitation rate and amount, and snow depth. <strong>Environment</strong> Canada<br />
also provides supplementary measures, including soil temperature and solar radiation at specific sites.<br />
One key informant suggested that:<br />
“[The] Meteorological Service <strong>of</strong> Canada (MSC) is an invaluable resource<br />
for clima<strong>to</strong>logical and weather data in Canada.”<br />
Temperature data is provided on an hourly basis, as well as maximum and minimum temperatures over<br />
each hourly period. <strong>Environment</strong> Canada <strong>of</strong>fers its weather moni<strong>to</strong>ring data feed online and is updated<br />
hourly.<br />
Weather data on the <strong>Environment</strong> Canada website is free but can be limited by incomplete datasets.<br />
Although weather stations are distributed across Ontario, the number <strong>of</strong> stations is limited. Not all communities<br />
are covered by stations and, therefore, may not have weather data specifically for their location.<br />
Moreover, some stations are not currently active or have been inactive for long periods <strong>of</strong> time. Therefore,<br />
temporal analysis using <strong>Environment</strong> Canada weather station data may be limited depending on location<br />
and station his<strong>to</strong>ry.<br />
<strong>Environment</strong> Canada provides a limited amount <strong>of</strong> free metadata. Detailed metadata can be made available<br />
by <strong>Environment</strong> Canada through formal requests; however, data from a specific site, such as observer<br />
name, wind report related <strong>to</strong> a specific event, etc., have privacy restrictions and are not accessible.<br />
Weather data undergoes a quality control process. Hourly and daily data are subject <strong>to</strong> a manual quality<br />
assurance process. Temperature data is collected by a Campbell Scientific 44212EC temperature probe<br />
that is accurate <strong>to</strong> ±0.1°C. It can also measure air, soil, and water temperature in the -50° <strong>to</strong> +50°C temperature<br />
range. Humidity data is collected by a Campbell Scientific Vaisala HMP45C, with an accuracy <strong>of</strong><br />
±2% over 10-90% Relative Humidity (RH) and ±3% over 90-100% RH.<br />
Weather station data may be limited in their usefulness <strong>to</strong> assess variation in heat across a community.<br />
Meteorological variables can vary substantially over short distances and may be influenced by natural<br />
and built environment features. Therefore, data from a weather station may not accurately reflect heat<br />
and humidity at a locale away from the station itself. One key informant provided advice on interpreting<br />
weather station temperature data and extrapolating it across a wider geography:<br />
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EXTREME HEAT<br />
“This would be best answered on a case by case basis, because <strong>of</strong><br />
the location <strong>of</strong> a weather measuring site in relation <strong>to</strong> the locale <strong>of</strong> the<br />
initial request. For example: Temperature taken at Pearson International<br />
Airport may not be representative <strong>of</strong> down<strong>to</strong>wn Toron<strong>to</strong> due <strong>to</strong> lake<br />
effect. Ottawa International Airport may not be representative <strong>of</strong> the City<br />
<strong>of</strong> Ottawa due <strong>to</strong> distance <strong>of</strong> the airport from the down<strong>to</strong>wn core.”<br />
LANDSAT IMAGERY<br />
Natural Resources Canada (NRCan) has collected Landsat satellite imagery since 1975 and began collecting<br />
thermal imagery in the early 1980s. NRCan has images available from 1984 <strong>to</strong> 2011. Landsat<br />
has worldwide coverage and the images are collected in 16-day intervals at a resolution <strong>of</strong> 120 square<br />
meters. The thermal infrared band can be used <strong>to</strong> determine surface temperatures with a thermal accuracy<br />
<strong>of</strong> ±2°C.<br />
It was suggested that surface temperatures can be used by PHUs as a basic indica<strong>to</strong>r <strong>of</strong> relative hot and<br />
cool areas and neighbourhoods. One key informant suggested that Landsat imagery:<br />
“… is probably the best indica<strong>to</strong>r available <strong>of</strong> hot spots and areas <strong>of</strong><br />
extreme heat, and a relative measure <strong>of</strong> where your coolest areas are<br />
also.”<br />
It was also suggested that there are benefits <strong>to</strong> normalizing several single shot images in<strong>to</strong> an average<br />
single image. One key informant explained:<br />
“A single-shot <strong>of</strong> a single event is generally not representative <strong>of</strong> that<br />
week or that summer so it is better <strong>to</strong> have multiple dates and a<br />
normalization <strong>to</strong> capture average meteorological conditions.”<br />
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EXTREME HEAT 155<br />
This information could be used <strong>to</strong> target planning measures or cooling centres. However, the relationship<br />
between air and surface temperatures was identified as a limitation. One key informant explained that<br />
although air and surface temperature are closely related, they do not have a “direct, one-<strong>to</strong>-one-relationship”.<br />
There is also limited research on determining air temperature from surface temperature. One key<br />
informant mentioned that air temperature is difficult <strong>to</strong> characterize and that:<br />
“To measure air temperature at this scale [120 square metres], or even<br />
every kilometre, becomes expensive and complicated.”<br />
In addition, surface temperatures do not infer indoor temperatures. One key informant noted this as a<br />
limitation:<br />
“You can’t measure inside buildings, the surface temperature on the<br />
outside might be high, but inside it might be air-conditioned or<br />
well-insulated.”<br />
It was noted that a moderate level <strong>of</strong> expertise is required <strong>to</strong> interpret such imagery. Expertise may also<br />
be required <strong>to</strong> use calibration data <strong>to</strong> convert pixel values in<strong>to</strong> degrees Celsius.<br />
One key informant explained:<br />
“One must be able <strong>to</strong> interpret what the measurements actually mean,<br />
noting that we are dealing with surface temperatures instead <strong>of</strong><br />
air temperatures.”<br />
The his<strong>to</strong>rical Landsat data archive is completely open with no privacy restrictions. It is freely available<br />
online through a number <strong>of</strong> data centres. <strong>Data</strong> that has not been processed or geometrically corrected<br />
can also be requested.<br />
When asked about other sources <strong>of</strong> imagery, key informants noted MODIS and airborne pho<strong>to</strong>graphy.<br />
MODIS satellite data has a coarser resolution <strong>of</strong> one square kilometre. Airborne pho<strong>to</strong>graphy entails flying<br />
airborne passes over a given location. This can be cus<strong>to</strong>mized by picking certain times and multiple<br />
acquisitions <strong>of</strong> different data sets, however it can be very expensive.<br />
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EXTREME HEAT<br />
SYNDROMIC SURVEILLANCE SYSTEMS<br />
The syndromic surveillance system is a pilot project conducted by Health Canada, Queen’s University<br />
and select public health units in Ontario. This system has been adapted <strong>to</strong> have the capacity <strong>to</strong> moni<strong>to</strong>r<br />
heat related illness, as well as real time weather conditions.<br />
Early detection <strong>of</strong> heat-related illness and death through real time surveillance can be an integral component<br />
<strong>of</strong> a heat alert and response plan. One key informant highlighted the importance <strong>of</strong> identifying weather<br />
conditions that could affect vulnerable populations and increase heat-related deaths and illness:<br />
“His<strong>to</strong>rical climate and health data can be used <strong>to</strong> understand the<br />
relationship between heat-related adverse outcomes but also <strong>to</strong><br />
support the establishment <strong>of</strong> the community-based heat triggers.”<br />
Wet Bulb Globe Temperature (WBGT) is moni<strong>to</strong>red through a network <strong>of</strong> 12 <strong><strong>Environment</strong>al</strong> Heat Moni<strong>to</strong>ring<br />
Systems (EHMS) across southeastern Ontario. In order <strong>to</strong> calculate the WBGT, the sensors measure<br />
temperature, humidity, wind and radiant heat.<br />
Key informants indicated that the accuracy <strong>of</strong> the sensors is provided by the manufacturer. Accuracy<br />
levels are provided below:<br />
• Temperature and globe temperature: ±5°C<br />
• Relative humidity: ±5%<br />
• Air probe: ±0.1 meters per second plus 4% <strong>of</strong> the measured value<br />
• All measures calculated <strong>to</strong>gether, expanded measurement uncertainty: ±1.1°C<br />
Sensor locations were identified <strong>to</strong> include both large and small built up areas and were evaluated based<br />
on space requirements, availability <strong>of</strong> partners and safety. The final locations reflected a variety <strong>of</strong> land<br />
uses in both large and small urban areas, as well as rural communities. Key informants provided insight<br />
on sensor location siting:<br />
“Sensors were distributed in urban settings and mainly away from the<br />
lake <strong>to</strong> adequately reflect any heat umbrella effects within an urban<br />
setting.”<br />
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EXTREME HEAT 157<br />
“In an ideal situation, sites would have been selected using a randomized<br />
point-selection process, but this would have neglected the requirement<br />
for a safe site.”<br />
Currently, data is not publicly available as the syndromic surveillance project is still in a pilot phase operating<br />
under research ethics board approval. Since data is limited <strong>to</strong> internal purposes, it has not yet been<br />
necessary <strong>to</strong> produce a metadata file; however, the sensors do provide a standardized data pocket as a<br />
text file. Key informants also highlighted the importance and need for data interpretation expertise, especially<br />
if data were <strong>to</strong> be made available <strong>to</strong> others.<br />
Key informants explained how this data could be used:<br />
“…<strong>to</strong> verify the Wet Bulb Globe Temperature and associated health<br />
outcomes, comparing it <strong>to</strong> our current method <strong>of</strong> declaring heat alerts<br />
and heat emergencies”<br />
“…[<strong>to</strong>] better inform urban planners and political stakeholders so that we<br />
can modify the built environment and encourage municipal and policy<br />
decision makers <strong>to</strong> start looking at how we make our built environment<br />
cooler.”<br />
Overall, in order <strong>to</strong> address the lack <strong>of</strong> data related <strong>to</strong> spatial variability <strong>of</strong> heat, one key informant suggested<br />
developing and using urban climate models <strong>to</strong> simulate weather conditions. <strong>An</strong>other suggested:<br />
“…. a limited number <strong>of</strong> units [EHMS] could be strategically installed over<br />
a region or a major urban centre <strong>to</strong> assess the spatial variability <strong>of</strong> heat.<br />
This obviously needs <strong>to</strong> be combined with other sources <strong>of</strong> vulnerability<br />
information such as the level <strong>of</strong> deprivation in the community, the size<br />
and design <strong>of</strong> the urban centre, the location <strong>of</strong> cooling centers or the<br />
effect <strong>of</strong> urban heat islands that exacerbate heat exposure.”<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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EXTREME HEAT<br />
Key informants highlighted the need for more data <strong>to</strong> increase coverage and support evidence-informed<br />
decision making:<br />
“More sensors are required <strong>to</strong> give a more complete spatial coverage<br />
and a more accurate picture <strong>of</strong> the relationship between heat and the<br />
built environment.”<br />
“Right now we do not have the data <strong>to</strong> be able <strong>to</strong> scientifically say when<br />
<strong>to</strong> modify your behaviour and when <strong>to</strong> react <strong>to</strong> a heat event.”<br />
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EXTREME HEAT 159<br />
SUMMARY OF SURVEY RESULTS:<br />
EXTREME HEAT<br />
This section represents a summary <strong>of</strong> the built environment survey results specific <strong>to</strong> extreme heat measures<br />
and data sources. The survey was administered <strong>to</strong> Ontario Public Health Units (PHU) in July 2012.<br />
Please note that the number <strong>of</strong> organizations responding <strong>to</strong> each question varies due <strong>to</strong> the structure<br />
(i.e. skip pattern) <strong>of</strong> the survey. For instance, if PHUs indicated that they assess extreme heat using a<br />
model, then they were prompted <strong>to</strong> provide further details about the specific components <strong>of</strong> the model.<br />
Whereas PHUs that reported not using a model <strong>to</strong> assess extreme heat were prompted <strong>to</strong> skip forward<br />
<strong>to</strong> other survey questions. Overall, this impacted the number <strong>of</strong> PHUs (i.e. denomina<strong>to</strong>r) that responded<br />
<strong>to</strong> each survey question.<br />
HIGHLIGHTS<br />
• 29 Ontario PHUs completed the extreme heat section <strong>of</strong> the survey<br />
• 62% <strong>of</strong> PHUs surveyed assess extreme heat in urban environments in Ontario<br />
• Of the PHUs that assess extreme heat:<br />
o Most have been assessing extreme heat for 1 <strong>to</strong> 5 years (44%) and 6 <strong>to</strong> 10 years (33%)<br />
o Most reported using temperature (89%) and the humidex (89%) <strong>to</strong> assess extreme heat<br />
o One PHU uses a Heat Health Alert System <strong>to</strong> determine when a heat alert or extreme heat<br />
alert is declared. This Heat Health Alert System is based on a spatial synoptic classification<br />
system that compares forecasted data <strong>to</strong> his<strong>to</strong>rical meteorological conditions associated<br />
with increased mortality in their jurisdiction.<br />
o Only 2 PHUs identified using models <strong>to</strong> predict extreme heat events and/or health impacts<br />
related <strong>to</strong> extreme heat.<br />
• Most PHUs have access <strong>to</strong> meteorological data (79%)<br />
• Although 52% <strong>of</strong> PHUs identified having access <strong>to</strong> built environment data , very few PHUs<br />
specifically identified having access <strong>to</strong> data on: canopy cover; age stratification <strong>of</strong> the urban forest<br />
s<strong>to</strong>ck; urban sprawl index; average unit size for each land use type; building density data;<br />
data on building age; surface reflectivity /albedo by land use; and surface emissivity by land use<br />
• Only 2 PHUs identified having access <strong>to</strong> thermal imagery<br />
• One PHU also developed heat-related vulnerability indices <strong>to</strong> support heat response initiatives<br />
and climate change adaptation planning.<br />
• Almost half <strong>of</strong> PHUs (48%) reported using demographic data such as age, gender, income, and<br />
language <strong>to</strong> identify populations that are more vulnerable <strong>to</strong> extreme heat<br />
• The most common challenges identified by PHUs included (i) human resource capacity (79%),<br />
(ii) data availability (55%), and (iii) financial capacity (48%)<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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EXTREME HEAT<br />
The extreme heat survey response rate was 81% (29/36), with 29 Ontario PHUs completing the survey.<br />
Overall, 62% (18/29) <strong>of</strong> PHUs assess extreme heat in urban environments in Ontario.<br />
Of the 18 PHUs that assess extreme heat, 6% have been assessing extreme heat for less than one year,<br />
44% for 1 <strong>to</strong> 5 years, 33% for 6 <strong>to</strong> 10 years, and 17% for over 11 years (Figure 21).<br />
Figure 21:<br />
Number <strong>of</strong> years Public Health Units have been assessing extreme heat in Ontario,<br />
2012 (n=18)<br />
17%<br />
6%<br />
EXTREME HEAT 161<br />
All 29 PHUs were asked about data accessibility related <strong>to</strong> extreme heat. They reported greater access<br />
<strong>to</strong> meteorological data (79%) compared <strong>to</strong> built environment data (52%) and thermal imagery (7%) (Table<br />
19). Most <strong>of</strong> the 8 PHUs (75%) who do not assess extreme heat in urban environments also reported<br />
having access <strong>to</strong> meteorological data.<br />
Of the 9 PHUs with 6 <strong>to</strong> 10 years and 11 or more years <strong>of</strong> experience in assessing extreme heat, a higher<br />
proportion (89% versus 22%) had access <strong>to</strong> built environment data compared <strong>to</strong> PHUs with less than<br />
one and 1 <strong>to</strong> 5 years <strong>of</strong> experience.<br />
Table 19: Accessibility <strong>of</strong> data used <strong>to</strong> assess extreme heat in Ontario, 2012 (n=29)<br />
<strong>Data</strong> Type No. <strong>of</strong> PHUs (%)<br />
Meteorological data 23 (79%)<br />
<strong>Built</strong> environment data 15 (52%)<br />
Thermal Imagery 2 (7%)<br />
METEOROLOGICAL DATA<br />
Most <strong>of</strong> the 29 PHUs accessed meteorological data through <strong>Environment</strong> Canada moni<strong>to</strong>ring stations<br />
(91%; 21 PHUs). Three (13%) <strong>of</strong> these PHUs identified other temporary/mobile moni<strong>to</strong>ring stations and<br />
one (4%) PHU identified other permanent moni<strong>to</strong>ring stations as sources <strong>of</strong> data. Even most PHUs that<br />
were not assessing extreme heat identified having access <strong>to</strong> data from <strong>Environment</strong> Canada moni<strong>to</strong>ring<br />
stations (63%; 5 PHUs).<br />
If <strong>Environment</strong> Canada moni<strong>to</strong>ring stations were not being used <strong>to</strong> gain access <strong>to</strong> meteorological data,<br />
then PHUs were asked <strong>to</strong> identify the ownership <strong>of</strong> moni<strong>to</strong>ring stations and data, as well as the specific<br />
variables measured from other data sources. Three PHUs identified Health Canada as owning the moni<strong>to</strong>ring<br />
stations and data and reported the following variables being measured: globe temperature, wet<br />
bulb temperature, dry bulb temperature, air velocity, humidity, and solar radiation. One PHU identified the<br />
Weather Network as a source <strong>of</strong> heat and humidity information <strong>to</strong> measure the humidex.<br />
Of the 18 PHUs that assess extreme heat, most reported using temperature (89%) and the humidex<br />
(89%) <strong>to</strong> assess extreme heat, while four (22%) PHUs reported using wind speed <strong>to</strong> assess extreme<br />
heat.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
162<br />
EXTREME HEAT<br />
Respondents identified other meteorological data that was being used <strong>to</strong> assess extreme heat:<br />
• Air quality was used as one criterion (or trigger) in the issuing <strong>of</strong> a Heat Alert (i.e. smog advisory<br />
combined with humidex <strong>of</strong> 36°C or higher)<br />
• Tracking the UV index and Air Quality Index (AQI)<br />
• Weather Network heat and humidex readings provided hourly<br />
• Modeling the duration <strong>of</strong> heat<br />
One PHU stated that they moni<strong>to</strong>r a Heat Health Alert System <strong>to</strong> determine when the Medical Officer <strong>of</strong><br />
Health should declare a Heat or Extreme Heat Alert. Using spatial synoptic classification, the Heat Health<br />
Alert System compares forecast data <strong>to</strong> his<strong>to</strong>rical meteorological conditions which have in the past, lead<br />
<strong>to</strong> increased mortality in their respective jurisdiction. Forecast data includes weather conditions such as<br />
temperature, dew point, humidity, cloud cover, wind speed and direction. The system also considers the<br />
number <strong>of</strong> consecutive days oppressive conditions occurred.<br />
BUILT ENVIRONMENT DATA<br />
When asked about access <strong>to</strong> specific built environment measures, very few PHUs reported having access<br />
<strong>to</strong> specific built environment measures (as they relate <strong>to</strong> extreme heat) as follows:<br />
• Canopy cover<br />
• Age stratification <strong>of</strong> the urban forest s<strong>to</strong>ck<br />
• Urban sprawl index<br />
• Average unit size for each land use type<br />
• Building density data<br />
• <strong>Data</strong> on building age<br />
• Surface reflectivity/albedo by land use<br />
• Surface emissivity by land use<br />
None <strong>of</strong> the PHUs reported using any <strong>of</strong> the built environment measures <strong>to</strong> assess extreme heat. Two<br />
PHUs indicated that the following built environment measures related <strong>to</strong> extreme heat were in development:<br />
canopy cover; age stratification <strong>of</strong> the urban forest s<strong>to</strong>ck. One PHU indicated that data on building<br />
age was in development.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT 163<br />
THERMAL IMAGERY<br />
Two PHUs reported having access <strong>to</strong> thermal imagery data (Landsat and/or Infrared) <strong>to</strong> assess extreme<br />
heat. They gain access <strong>to</strong> thermal imagery data through federal, regional, local governments, or direct<br />
requests for information.<br />
EXTREME HEAT MODELS<br />
Two PHUs that assess extreme heat identified using models <strong>to</strong> predict extreme heat events and/or health<br />
impacts related <strong>to</strong> extreme heat.<br />
One PHU reported using an <strong>Environment</strong> Canada model for temperature/humidex projections. The inputs<br />
required are confirmed by local sensors in urban/rural settings which moni<strong>to</strong>r Wet Bulb Globe Temperature<br />
(WBGT). While secondary systems moni<strong>to</strong>r visits <strong>to</strong> energy departments, as well as admissions<br />
<strong>to</strong> hospital for heat-related illness syndromes.<br />
The other PHU reported that they developed two heat-related vulnerability indices that can be mapped<br />
spatially for their respective health jurisdiction. One index is for the general population, and the other is<br />
for seniors. Each composite index is composed <strong>of</strong> indica<strong>to</strong>rs for exposure and sensitivity <strong>to</strong> heat and is<br />
mapped at the census tract level. The intent is <strong>to</strong> enable community heat vulnerability <strong>to</strong> be visualized<br />
on a map, so that the most vulnerable areas <strong>of</strong> the city can be identified. The approach was developed<br />
<strong>to</strong> support planning and short-term response <strong>to</strong> hot weather, and long-term climate change adaptation<br />
efforts in the City. The heat vulnerability maps can also be used by a variety <strong>of</strong> stakeholders who are<br />
involved in heat response or long-term climate change adaptation planning..<br />
The data inputs for the above mentioned model are as follows:<br />
Socio-demographic and health/physiological risk fac<strong>to</strong>rs<br />
• Elderly population<br />
• Chronic respira<strong>to</strong>ry and cardiovascular diseases<br />
• Mobility restrictions<br />
• Cognitive impairment<br />
• Infants and young children<br />
• Socially isolated persons (widowed, divorced; homeless)<br />
• Low-income households<br />
• Marginalized groups, new immigrants<br />
• Not English-speaking<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
164<br />
EXTREME HEAT<br />
Behavioural Risk Fac<strong>to</strong>rs<br />
• Workers engaged in heavy labour<br />
• People engaged in sport activities and recreational exertion<br />
• Lack <strong>of</strong> hydration<br />
Risk Fac<strong>to</strong>rs Relating <strong>to</strong> the Physical, <strong>Built</strong>, and Social <strong>Environment</strong><br />
• Urban heat island, local heat islands (e.g. industrialized areas)<br />
• Lack <strong>of</strong> tree canopy, green spaces<br />
• Lack <strong>of</strong> public shelters (cool places)<br />
• High-density dwellings without air conditioning<br />
• Lack <strong>of</strong> social infrastructure, service gaps<br />
• Living in high-crime rate areas<br />
DEMOGRAPHICS<br />
Almost half <strong>of</strong> the 29 PHUs (48%) who responded <strong>to</strong> the survey (whether they assessed extreme heat in<br />
urban environments or not) used demographic data <strong>to</strong> identify populations more vulnerable <strong>to</strong> extreme<br />
heat. For PHUs with more experience in assessing extreme heat (6 <strong>to</strong> 10 years; 11 or more years), a<br />
higher proportion used demographic data <strong>to</strong> identify vulnerable populations.<br />
Fourteen <strong>of</strong> the PHUs that assess extreme heat reported using demographic data such as age, gender,<br />
income, and language <strong>to</strong> identify populations that are more vulnerable <strong>to</strong> extreme heat (Figure 22). Age<br />
was used more than any <strong>of</strong> the other demographic indica<strong>to</strong>rs <strong>to</strong> identify vulnerable populations.<br />
Other demographic data used <strong>to</strong> identify vulnerable populations varied and included occupation, level<br />
<strong>of</strong> education, socio-demographic and health/physiological risk fac<strong>to</strong>rs, immigration status, and type <strong>of</strong><br />
dwelling (e.g. dwellings without air conditioning). One PHU indicated using a ‘Self Registered – Vulnerable<br />
Population Registry’ <strong>to</strong> identify populations that are more vulnerable <strong>to</strong> extreme heat.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT 165<br />
Figure 22: Demographics used <strong>to</strong> identify populations more vulnerable <strong>to</strong> extreme heat in Ontario, 2012 (n=14)<br />
No. <strong>of</strong> Public Health Units<br />
16<br />
14<br />
12<br />
10<br />
8<br />
6<br />
100%<br />
64%<br />
43%<br />
4<br />
2<br />
0<br />
21% 14%<br />
Age Income Other Language Gender<br />
Demographics<br />
Demographic Variables<br />
CHALLENGES<br />
A <strong>to</strong>tal <strong>of</strong> 29 PHUs identified major challenges their organizations face in assessing extreme heat in urban<br />
environments. The most common challenges identified by PHUs included human resource capacity<br />
(79%), data availability (55%), and financial capacity (48%) (Figure 23).<br />
Most PHUs, regardless <strong>of</strong> the number <strong>of</strong> years they had been assessing extreme heat, identified human<br />
resource capacity and data availability as challenges in assessing extreme heat. Those PHUs that were<br />
not assessing extreme heat at the time <strong>of</strong> the survey also identified human resource capacity as their<br />
biggest challenge in assessing extreme heat.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
166<br />
EXTREME HEAT<br />
Figure 23: Challenges faced by PHUs in assessing extreme heat in Ontario, 2012 (n=29)<br />
Challenge<br />
Human resource capacity<br />
<strong>Data</strong> availability<br />
Financial capacity<br />
Variations between municipalities<br />
<strong>Data</strong> accessibility<br />
Lack <strong>of</strong> GIS technical support<br />
<strong>Data</strong> quality<br />
Other<br />
No challenges <strong>to</strong> report<br />
14%<br />
10%<br />
31%<br />
31%<br />
28%<br />
28%<br />
55%<br />
48%<br />
79%<br />
0 5 10 15 20 25<br />
No. <strong>of</strong> Public Health Units<br />
Several PHUs provided additional comments related <strong>to</strong> the challenges they face, as follows:<br />
“The municipality demonstrates little interest in assessing extreme heat,<br />
as it is not a major issue here.”<br />
“Not a priority issue for us in a predominantly rural area.”<br />
“The one issue we have is that the weather data is from the closest<br />
weather station which is about 30 minutes North <strong>of</strong> our location. We<br />
do subscribe <strong>to</strong> <strong>Environment</strong> Canada weather alert emails that provide<br />
relevant forecast conditions for our County.”<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT 167<br />
One particular PHU brought forward several challenges they faced, including the following:<br />
“Lack standards/consistency for assessing heat (e.g. GIS methodologies,<br />
analysis, community thresholds);<br />
Limited access/availability <strong>of</strong> meteorological data at community level;<br />
Inconsistent methods use <strong>to</strong> assess heat in neighbouring PHUs;<br />
Lack access <strong>to</strong> models with high resolution (e.g. ability <strong>to</strong> forecast heat<br />
exposure at street level/micro-environments).”<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
168<br />
EXTREME HEAT<br />
GAP ANALYSIS: EXTREME HEAT<br />
Insights gathered from the literature review, key informant interviews and survey administered <strong>to</strong> Ontario<br />
Public Health Units were compiled <strong>to</strong> identify gaps between the necessary and available data for the assessment<br />
<strong>of</strong> extreme heat in urban Ontario. The gap analysis for the assessment <strong>of</strong> extreme heat was<br />
conducted for the following measures and data sources:<br />
• Measurement categories include meteorological data, composite heat measures (using<br />
meteorological data), urban climate modelling, built environment data, and community<br />
vulnerability.<br />
• <strong>Data</strong> sources and sets include: Health Canada, Meteorological Service <strong>of</strong> Canada (<strong>Environment</strong><br />
Canada), Natural Resources Canada, First Base Solutions, Statistics Canada and municipalities.<br />
OVERALL FINDINGS OF GAP ANALYSIS<br />
The overall findings from the extreme heat gap analysis, including gaps identified for measures, methodologies<br />
and data sources, are presented below (Tables 20 and 21).<br />
Measures<br />
• There are many varying measures <strong>of</strong> the built environment used <strong>to</strong> assess extreme heat<br />
• Many PHUs use meteorological data <strong>to</strong> assess heat and mostly report on temperature and<br />
humidex<br />
• Temporal analysis may be limited by the availability <strong>of</strong> local meteorological data<br />
• Theoretically, composite measures better reflect the relationship between multiple variables<br />
identified as important components <strong>of</strong> heat<br />
• Individual meteorological variables are limited in their interpretation in relation <strong>to</strong> extreme heat and<br />
health (as heat is comprised <strong>of</strong> 4 fac<strong>to</strong>rs: temperature, humidity, wind and solar load)<br />
• Meteorological data is an important data set for composite measures and models <strong>of</strong> extreme<br />
heat, and is also used in assessing air quality<br />
• Some measures have complex data requirements and may not be appropriate for PHUs <strong>to</strong><br />
assess heat exposure<br />
• Composite measures that incorporate the four components <strong>of</strong> heat would theoretically provide a<br />
more accurate depiction <strong>of</strong> exposure and better predict potential health impacts<br />
• A small number <strong>of</strong> PHUs are using models and Wet Bulb Globe Temperature. These<br />
measurement methods may require additional technical, human and financial resources.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT 169<br />
• Remotely sensed built environment data such as satellite-based thermal imagery has the potential<br />
for complete coverage, however it only represents one point in time and requires a level <strong>of</strong><br />
expertise<br />
• While some PHUs have access <strong>to</strong> built environment data, most PHUs are not currently using built<br />
environment data <strong>to</strong> assess exposure <strong>to</strong> extreme heat<br />
• Many built environment measures used <strong>to</strong> assess exposure <strong>to</strong> extreme heat could also be used in<br />
the assessment <strong>of</strong> air quality and walkability<br />
• Many PHUs currently use age <strong>to</strong> identify populations more vulnerable <strong>to</strong> heat<br />
• There are a variety <strong>of</strong> social, built environment and demographic variables that could be combined<br />
with exposure variables <strong>to</strong> produce a measure <strong>of</strong> community vulnerability <strong>to</strong> heat<br />
• Community heat-vulnerability indices are not common in PHUs and it is likely unreasonable that<br />
each PHU undertake this work<br />
• Other private organizations or academia may be collecting relevant local data for their own<br />
purposes, however it may not be consistent or suitable for surveillance activities<br />
<strong>Data</strong> sets and sources<br />
• Limited availability <strong>of</strong> <strong>Environment</strong> Canada stations by region results in limited application for<br />
assessing heat within communities<br />
• <strong><strong>Environment</strong>al</strong> Heat Moni<strong>to</strong>ring System (EHMS) units are currently supplied by Health Canada <strong>to</strong><br />
a number <strong>of</strong> PHUs <strong>to</strong> supplement meteorological data for their regions<br />
• EHMS units provide the benefit <strong>of</strong> assessing 4 parameters that comprise heat (and calculates the<br />
WBGT), as well as capturing heat exposure in areas outside typical weather stations<br />
• EHMS data is limited <strong>to</strong> study duration and information is not publicly available<br />
• <strong>Environment</strong> Canada is a free source <strong>of</strong> standardized meteorological data on a number <strong>of</strong><br />
parameters; many PHUs have access <strong>to</strong> this data and most use information on humidex and<br />
temperature.<br />
• Solar load is not available at all <strong>Environment</strong> Canada stations, which limits the number <strong>of</strong><br />
composite measures that can be calculated from the data set<br />
• There are various satellites and sensors which could provide information relevant <strong>to</strong> extreme heat<br />
exposure<br />
• Landsat imagery is available at no cost and at an appropriate resolution for all <strong>of</strong> Ontario<br />
• <strong>An</strong>y satellite data would still require processing and analysis, as well as a certain level <strong>of</strong> expertise<br />
• Aerial image acquisition has the potential <strong>to</strong> provide information on a variety <strong>of</strong> built environment<br />
data but may be unrealistic due <strong>to</strong> cost and time requirements<br />
• There is the potential for a lot <strong>of</strong> data <strong>to</strong> be available at the local level but this depends on the<br />
municipality and its capacity <strong>to</strong> collect, analyze and disseminate data<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
170<br />
EXTREME HEAT<br />
Table 20: Measurement approaches and policy relevant information as identified from the literature<br />
review, key informant interviews, survey and GIS metadata<br />
EXTREME HEAT<br />
Table 20: Measurement approaches and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata<br />
Description (LR) Inputs Current Use in Ontario PHUs (SU)<br />
Measures<br />
(LR)<br />
Theoretical Op.<br />
Ontario (LR/GM)<br />
Desirability<br />
Challenges<br />
Link b/w Measurement<br />
Approaches<br />
Meteorological<br />
<strong>Data</strong><br />
Direct measurements <strong>of</strong> Individual<br />
meteorological variables<br />
• Temperature<br />
• Dew point temperature<br />
• Relative humidity<br />
• Wind speed<br />
• Wind direction<br />
• Solar load (radiation)<br />
• Atmospheric pressure<br />
Individual met<br />
variables (LR)<br />
*May be real time or<br />
hourly (GM)<br />
Of 18 PHUs that assess heat<br />
• 16 PHUs have access <strong>to</strong> meteorological data (all 16 use temperature <strong>to</strong> asses heat, 4 use<br />
wind speed)<br />
23 PHUs have access <strong>to</strong> meteorological data<br />
• 21 PHUs use <strong>Environment</strong> Canada as source<br />
• 1 PHU uses Weather Network<br />
One PHU uses met data as input <strong>to</strong> a larger heat model <strong>to</strong> predict heat days/alerts<br />
7 variables<br />
6 <strong>of</strong> 7variables are<br />
available across Ontario<br />
Direct measurements<br />
Availability <strong>of</strong> meteorological variables may be<br />
limited by region (LR/KI)<br />
Limited local data (LR)<br />
Temporal analysis is limited (KI)<br />
Does not take in<strong>to</strong> account effect <strong>of</strong> 4 variables<br />
that comprise heat.<br />
Main inputs for heat indices<br />
Used for AQ measures<br />
Used in conjunction with AQ info (e.g.<br />
AQHI) for calling heat alerts advisories<br />
Composite<br />
Heat Measures<br />
(using<br />
meteorological<br />
data)<br />
Composite measures <strong>of</strong> heat that combine<br />
individual meteorological variables based on<br />
given formulae<br />
• Apparent temp/heat Index<br />
• Humidex<br />
• Thom Index/Discomfort index<br />
• Relative stress index<br />
• WGBT index<br />
• Heat exposure index<br />
• Thermal Index (net effective temperature)<br />
• Heat Load Index<br />
• Heat Stress Index<br />
• Perceived temperature<br />
• Spatial synoptic classification<br />
• Human Thermal Comfort Index<br />
Individual met<br />
variables (LR)<br />
Scientific formulae<br />
(LR)<br />
Some require<br />
climate normal/<br />
averages as<br />
reference periods<br />
(LR)<br />
Some require<br />
demographic/<br />
morbidity/mortality<br />
data as reference<br />
for formula<br />
development (LR)<br />
Of 18 PHUs that assess heat<br />
• 16 PHUs have access <strong>to</strong> meteorological data (all 16 use humidex <strong>to</strong> assess heat)<br />
• 2 PHUs use models<br />
23 PHUs have access <strong>to</strong> meteorological data:<br />
• 21 PHUs use <strong>Environment</strong> Canada as source<br />
• 1 PHU uses Weather Network<br />
• 3 PHUs get data from other temp/mobile moni<strong>to</strong>ring stations (all from Health Canada)<br />
o All 3 measure WGBT<br />
11 measures<br />
Standard <strong>Environment</strong><br />
Canada data would be<br />
sufficient <strong>to</strong> populate<br />
some <strong>of</strong> these<br />
measures. However,<br />
special equipment may<br />
be required for other<br />
measures (e.g. WGBT).<br />
(LR,KII).<br />
Some measures<br />
are not realistic for<br />
PHUs <strong>to</strong> calculate at a<br />
population level<br />
According <strong>to</strong> Health Canada, heat is<br />
comprised <strong>of</strong> 4 fac<strong>to</strong>rs (temperature,<br />
humidity, wind and solar load).<br />
Composite heat measures better<br />
address this relationship (LR)<br />
Availability <strong>of</strong> meteorological variables may be<br />
limited by region (LR/KI)<br />
Limited local data (LR)<br />
Temporal analysis is limited (KI)<br />
Most require direct measurements <strong>of</strong><br />
Individual meteorological variables<br />
Used in conjunction with AQ info (e.g.<br />
AQHI) for calling heat alerts advisories<br />
Urban climate<br />
modelling<br />
Models that simulate local climates in urban<br />
environments<br />
Reasonably low spatial resolution (KII)<br />
Could be used <strong>to</strong> extrapolate met data<br />
across a wider geography(KII)<br />
<strong>Built</strong> environment feature and characteristics<br />
related <strong>to</strong> extreme heat.<br />
*Many <strong>of</strong> these measures have been identified<br />
as being collected through remote sensing.<br />
Individual features<br />
and characteristics<br />
Means <strong>of</strong> collecting<br />
data (e.g. satellite/<br />
sensor)<br />
Expertise <strong>to</strong> collect<br />
and analyze data<br />
15 PHUs reported having access <strong>to</strong> built environment data (e.g. land use, forest cover etc.)<br />
Remotely sensed data:<br />
Potential for complete coverage (LR)<br />
Remotely sensed data:<br />
Quality and quantity <strong>of</strong> satellite data varies by<br />
source (e.g. spatial and temporal resolution(LR)<br />
Parameters only reflect data at one point in time<br />
(LR/KII)<br />
Use may be limited due <strong>to</strong> accessibility issues,<br />
cost and expertise <strong>to</strong> collect and analyze data<br />
(LR/KII)<br />
<strong>Built</strong><br />
environment<br />
data<br />
A. Temperature<br />
• Surface temperature<br />
• Urban heat Islands<br />
B. Land cover<br />
• Impervious Surfaces<br />
• Vegetation (NDVI, VCF, EVI, SAVI)<br />
• Albedo (BRDF) Open/green space<br />
C. Community characteristics<br />
• Sprawl index<br />
• Density<br />
• Proximity<br />
• Building height<br />
• Land Use<br />
2 PHUs have access <strong>to</strong> thermal imagery<br />
1 PHU accesses thermal imagery through federal government<br />
1 PHU accesses thermal imagery by 1) directly requesting it from data source 2) through a<br />
regional or local government<br />
Of the 18 PHUs that assess heat:<br />
2 PHUS have identified the following as in development : canopy cover, age stratification <strong>of</strong><br />
urban forest s<strong>to</strong>ck<br />
The PHUs that do not assess heat, identified having access <strong>to</strong> the following: canopy coverage<br />
(3), age stratification (3), surface reflectivity/albedo (1), surface emissivity (1)<br />
Of the 18 PHUs that assess heat:<br />
1 PHU identified the following as in development àbuilding age<br />
PHUs that do not assess heat identified having access <strong>to</strong> the following: urban sprawl (1),<br />
building density (2), building age (2), average size by land use (1)<br />
Unable <strong>to</strong> assess due <strong>to</strong><br />
level <strong>of</strong> detail for each<br />
measure.<br />
Theoretically, should these<br />
measures be accessed<br />
through remote sensing,<br />
there is the potential<br />
for complete coverage<br />
in Ontario. However,<br />
processing would be<br />
required.<br />
Surface temperature can be used as a<br />
basic indica<strong>to</strong>r <strong>of</strong> relative hot and cool<br />
areas and neighbourhoods (KII)<br />
Thermal anisotropy: 3D structures are viewed<br />
at a single angle so thermal properties <strong>of</strong> other<br />
sides are not captured. (LR)<br />
Unable <strong>to</strong> infer the temperature inside buildings<br />
(e.g. may be air conditioned). (KII)<br />
May be used in air quality assessments.<br />
Measures have also been used <strong>to</strong><br />
assess walkability and air quality.<br />
Measures have also been used <strong>to</strong><br />
assess walkability and air quality.<br />
D. Residential characteristics<br />
• Detached homes<br />
• Air conditioning<br />
• Swimming pools<br />
• Dwellings in high rise buildings<br />
Air<br />
Walkability<br />
Community<br />
vulnerability<br />
Measures that combine exposure <strong>to</strong> extreme<br />
heat and individual./community vulnerability <strong>to</strong><br />
determine overall risk<br />
Socio-economic<br />
& demographic<br />
variables<br />
Heat exposure<br />
variables<br />
Platform/<br />
methodology <strong>to</strong><br />
combine the two<br />
<strong>to</strong> create overall<br />
vulnerability<br />
14 PHUs reported using demographic data <strong>to</strong> identify populations more vulnerable <strong>to</strong> heat (SU)<br />
Of these, the following are used: Age (14), gender (2), income (9), language (3)<br />
Other<br />
• Pre-existing medical conditions (4)<br />
• People without A/C (2)<br />
• Lack <strong>of</strong> public shelters (cool places)<br />
• Deprivation index<br />
• Occupation / employment (2)<br />
• Type <strong>of</strong> dwelling unit (2)<br />
• Education<br />
• Mobility restrictions<br />
• Cognitive impairment<br />
• Socially isolated persons<br />
• New immigrants<br />
• Urban heat island<br />
• Lack <strong>of</strong> tree canopy/green spaces<br />
• Lack <strong>of</strong> social infrastructure/ service gaps (2)<br />
• High-crime rate areas<br />
1 PHU has created a heat vulnerability index and maps (Toron<strong>to</strong>) (LR)<br />
7<br />
Measures <strong>of</strong> community<br />
vulnerably can vary in<br />
methodology. In Ontario,<br />
the Deprivation index and<br />
other census data could<br />
be used. Theoretically,<br />
thermal imagery for<br />
Ontario could be overlaid<br />
with a number <strong>of</strong> built<br />
environment, social and<br />
demographic data.<br />
Combines exposure and vulnerability<br />
which <strong>to</strong>gether <strong>to</strong> inform risk<br />
Likely unreasonable <strong>to</strong> expect every local health<br />
department <strong>to</strong> create its own heat vulnerability<br />
map – a national HVI created through freely<br />
available national data sets is useful (LR)<br />
BE data<br />
Composite measures using met data<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario<br />
Click <strong>to</strong> open the full table.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT 171<br />
Table 21: <strong>Data</strong> sources and sets, and policy relevant information as identified from the literature<br />
review, key informant interviews, survey and GIS metadata.<br />
EXTREME HEAT<br />
Table 21: <strong>Data</strong> sources and sets, and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata<br />
Organization<br />
<strong>Data</strong> Source/<br />
Set<br />
Topic Area Utility in Outcomes Current Use in Ontario PHUs Desirability Cost Challenges/Limitations<br />
Health Canada<br />
<strong><strong>Environment</strong>al</strong><br />
Heat Moni<strong>to</strong>ring<br />
Systems<br />
Extreme Heat<br />
Air Quality<br />
Could potentially:<br />
• Contribute all met variables for 8 <strong>of</strong> 11 composite measures<br />
• Contribute <strong>to</strong> some met variables for 10 <strong>of</strong> 11 composite heat<br />
measures<br />
• Collect 4 <strong>of</strong> 7 individual meteorological variables<br />
Can contribute <strong>to</strong> syndromic surveillance<br />
South eastern environmental heat moni<strong>to</strong>ring network: PHUs<br />
covered by network: Hastings & Prince Edward Counties;<br />
Leeds, Grenville & Lanark District; Peterborough County-City;<br />
and Kings<strong>to</strong>n Frontenac and Lennox & Adding<strong>to</strong>n (KFL&A)<br />
(GM)<br />
3 PHUs identified using health Canada EHMS units (SU) (2 <strong>of</strong><br />
these are separate from the south eastern heat network)<br />
Able <strong>to</strong> assess 4 parameters that comprise heat (LR)<br />
Useful for capturing heat exposure in various<br />
environments given the variation <strong>of</strong> meteorological<br />
conditions over distances (e.g. city centres <strong>to</strong> farm<br />
lands). (KII)<br />
Units can be placed in areas outside typical<br />
<strong>Environment</strong> Canada weather stations and<br />
strategically placed <strong>to</strong> assess spatial variability <strong>of</strong> heat<br />
over a region or major urban area (KII)<br />
No cost<br />
Funded<br />
by Health<br />
Canada (KI)<br />
Limited geographic coverage based on pilot projects<br />
Not publicly available (GM/KI)<br />
If data were <strong>to</strong> be released, it would require interpretation (KI)<br />
Meteorological<br />
Service <strong>of</strong><br />
Canada<br />
(<strong>Environment</strong><br />
Canada)<br />
National Climate<br />
Archives<br />
Real time data<br />
from weather<br />
stations<br />
Extreme Heat<br />
Air Quality<br />
EC data<br />
integrated in<strong>to</strong><br />
syndromic<br />
surveillance<br />
system (KII)<br />
Could potentially<br />
• Contribute all met variables for 5 <strong>of</strong> 11 composite measures<br />
• Contribute <strong>to</strong> some met variables for 10 <strong>of</strong> 11 composite heat<br />
measures<br />
• Collect 6 <strong>of</strong> 7 individual meteorological variables<br />
Can contribute <strong>to</strong> syndromic surveillance<br />
• Of 18 PHUs that assess heat (SU)<br />
• 16 PHUs have access <strong>to</strong> meteorological data (all 16 use<br />
temperature and humidex <strong>to</strong> asses heat, 4 use wind<br />
speed)<br />
• 23 PHUs have access <strong>to</strong> meteorological data (SU)<br />
• 21 PHUs use <strong>Environment</strong> Canada as source<br />
Undergoes quality control (KII)<br />
<strong>Data</strong> freely available and accessible across Ontario.<br />
(GM)<br />
Free<br />
GIS formatted data is not provided through the National Climate Archives; however,<br />
the database contains latitude and longitude <strong>of</strong> each observation (Degrees & minutes)<br />
that can be used <strong>to</strong> create a GIS shape file. (GM)<br />
Typically at local airports/weather stations (LR)<br />
Availability may be limited by region (LR/KI)<br />
Limited use <strong>to</strong> assess variation in heat across community due <strong>to</strong> distance from<br />
stations and sparse spatial coverage (KI, GM)Standard <strong>Environment</strong> Canada data<br />
would not be sufficient <strong>to</strong> populate some <strong>of</strong> the measures that address the four<br />
fac<strong>to</strong>rs that comprise heat.<br />
Natural<br />
Resources<br />
Canada<br />
Landsat<br />
Heat<br />
Walkability<br />
Could contribute <strong>to</strong> BE data (LR)<br />
• Surface temperature<br />
• Impervious surfaces<br />
• Vegetation<br />
• Urban heat islands<br />
• Land cover<br />
2 PHUs have access <strong>to</strong> Landsat (SU)<br />
Used Landsat imagery (from NRCan) for the heat exposure<br />
component for Toron<strong>to</strong>’s Heat Vulnerability Index (LR)<br />
His<strong>to</strong>rical archive completely open and accessible<br />
online with no privacy restrictions (MD/KII)<br />
Canada wide coverage (MD)<br />
Includes metadata <strong>to</strong> convert the thermal infrared <strong>to</strong><br />
temperature in Celsius (KII)<br />
Includes calibration data (KII)<br />
Free (MD)<br />
Satellite ending useful lifespan (MD)<br />
Update frequency is every 16 days<br />
Unspecified<br />
Heat<br />
One PHU identified NRCan as the source for data on surface<br />
reflectivity and surface emissivity (1)<br />
USGS Aster Heat<br />
Could contribute <strong>to</strong> BE data (LR)<br />
• Surface temperature<br />
• Vegetation<br />
No PHUs identified having access <strong>to</strong> ASTER (SU)<br />
Associated<br />
fees<br />
Could contribute <strong>to</strong> BE data (LR)<br />
USGS MODIS Heat<br />
• Surface temperature<br />
• Vegetation<br />
Update frequency is 2x a day (GM) Free Scale resolution limited (GM/KII)<br />
• Albedo<br />
Could contribute <strong>to</strong> BE data (LR)<br />
First Base<br />
Solutions<br />
Cus<strong>to</strong>m Aerial<br />
Image Acquisition<br />
Heat<br />
• Surface temperature<br />
• Impervious surfaces<br />
• Vegetation<br />
One PHU identified using aerial pho<strong>to</strong>graphy but did not<br />
specify the source<br />
Cus<strong>to</strong>mizable data set (GM)<br />
High resolution (GM)<br />
Associated<br />
fees (GM)<br />
Potentially expensive<br />
• Urban heat islands<br />
• Land cover<br />
Statistics<br />
Canada<br />
Census<br />
Heat<br />
Air<br />
Walkability<br />
Could contribute <strong>to</strong> socio economic and demographic variables<br />
required for community vulnerability (LR)<br />
One PHU identified Statistics Canada as a source for data on<br />
building age =<br />
Source for dwelling units in high rises, dwelling units in high<br />
rises constructed before 1986 and population density in<br />
Toron<strong>to</strong><br />
Standardized data available across Canada Free Updated every 5 years<br />
Municipalities Local data Heat<br />
Could contribute on built environment data.<br />
May be accessed through municipal departments<br />
• Forestry<br />
• GIS<br />
• Planning<br />
Could contribute <strong>to</strong> socio economic and demographic variables<br />
required for community vulnerability<br />
PHUs identified municipalities as sources for: (SU)<br />
• Canopy cover (5)<br />
• Age stratification <strong>of</strong> forests (4)<br />
• Unit size by land use (2)<br />
• Building density (3)<br />
• Building age (3)<br />
Source for public green space boundaries and land cover (for<br />
canopy coverage) in Toron<strong>to</strong> (LR)<br />
Provides local level data Unknown <strong>Data</strong> may not be consistently gathered or shared.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario<br />
Click <strong>to</strong> open the full table.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
5DISCUSSION
CHAPTER 5: DISCUSSION<br />
The built environment provides the setting and backdrop by which we live our lives, and undoubtedly<br />
impacts health. Creating more walkable and heat-resilient communities as well as reducing emissions<br />
and exposure <strong>to</strong> air pollutants, can contribute <strong>to</strong> decreasing the incidence <strong>of</strong> obesity, acute and chronic<br />
respira<strong>to</strong>ry diseases. 1 Therefore, identifying the specific characteristics <strong>of</strong> the built environment that support<br />
or hinder people from living a healthy lifestyle is important.<br />
Given that an assessment <strong>of</strong> all built environment fac<strong>to</strong>rs associated with health was beyond the scope <strong>of</strong><br />
this study, the focus <strong>of</strong> this study was on the measures and data used for the assessment <strong>of</strong> walkability<br />
and environmental exposures (air quality and extreme heat) in urban environments. A comprehensive literature<br />
review, key informant interviews and a survey <strong>of</strong> Ontario Public Health Units (PHUs) helped inform<br />
these <strong>to</strong>pic-specific assessments.
176<br />
DISCUSSION<br />
WALKABILITY<br />
Walkability describes those qualities <strong>of</strong> the built environment that encourage walking. Early research<br />
relied heavily on self-reported perceptions, rather than objectively measured characterisations <strong>of</strong> the built<br />
environment. While perceptions <strong>of</strong> the built environment are important, new developments in geographical<br />
information system (GIS) s<strong>of</strong>tware and databases have enabled more sophisticated measurements<br />
<strong>of</strong> built environment variables at a variety <strong>of</strong> scales. Several built environment measures have been developed<br />
over the years <strong>to</strong> examine walkability including measures <strong>of</strong> density, diversity, street connectivity,<br />
and pedestrian-oriented design. These types <strong>of</strong> measures represent built environment fac<strong>to</strong>rs that are<br />
strongly and consistently associated with physical activity, walking and health outcomes in the scientific<br />
literature. These built environment metrics are <strong>of</strong>ten correlated with one another as well, which has led <strong>to</strong><br />
the adoption <strong>of</strong> composite indices <strong>to</strong> capture many aspects <strong>of</strong> the built environment at once. 2;73<br />
Evidenced informed decision making is hampered by the absence <strong>of</strong> agreement among public health<br />
researchers and practitioners on how the built environment should be measured and modeled. 2;5 Spatial<br />
extent, source <strong>of</strong> data, and the number and range <strong>of</strong> places compared across studies are so variable<br />
that virtually no two studies have evaluated built environment metrics the same way. 2 The choice <strong>of</strong> methodology<br />
is complicated further by variations in the terminology used <strong>to</strong> describe and measure the built<br />
environment. These challenges were highlighted in the key informant interviews and survey results.<br />
The gap analysis was informed by the literature review, key informant interviews, and survey results. Highlights<br />
from the gap analysis related <strong>to</strong> the assessment <strong>of</strong> urban walkability are provided below:<br />
• It is challenging <strong>to</strong> draw comparisons between the walkability <strong>of</strong> different PHUs. Although several<br />
organizations are using similar types <strong>of</strong> measures, they are using different computational methods,<br />
terminology and a variety <strong>of</strong> data sources. Comparisons become particularly challenging when<br />
organizations use predominantly local data sources.<br />
• The most common methods used by PHUs <strong>to</strong> assess urban walkability included self-administered<br />
survey and systematic observation; some PHUs assessed urban walkability using GIS s<strong>of</strong>tware.<br />
• GIS is required <strong>to</strong> operationalize several walkability measures, yet many <strong>of</strong> Ontario’s PHUs<br />
identified lack <strong>of</strong> GIS technical support and capacity as a challenge.<br />
• Organizations with the most established walkability assessment programs in the province were<br />
using a walkability index. A walkability index shows the most promise in the application <strong>of</strong> a<br />
standardized assessment <strong>of</strong> urban walkability across PHUs in Ontario.<br />
• Human resource capacity, measurement variability between municipalities, and data availability<br />
are key challenges for Ontario’s PHUs in the assessment <strong>of</strong> urban walkability.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
DISCUSSION 177<br />
AIR QUALITY<br />
In order <strong>to</strong> manage health risks associated with air pollution, public health units and environmental agencies<br />
have adopted various measurement approaches <strong>to</strong> better assess air quality in the built environment.<br />
These methods include moni<strong>to</strong>ring ambient air pollutant levels using the Air Quality Index (AQI) and Air<br />
Quality Health Index (AQHI) <strong>to</strong> communicate air quality; maintaining inven<strong>to</strong>ries on emissions levels like<br />
the National Pollutant Release Inven<strong>to</strong>ry (NPRI); and developing detailed spatial models. Each method<br />
has strengths and weaknesses in the assessment <strong>of</strong> air quality in the built environment and requires different<br />
measures and data sources.<br />
Although there have been several improvements in assessing air quality over the years, public health<br />
researchers and practitioners face several challenges in making evidence-informed decisions. Directly<br />
comparing different studies is challenging because <strong>of</strong> the different pollutants studied, health outcomes <strong>of</strong><br />
interest, models used, and populations <strong>of</strong> interest. In addition, while air moni<strong>to</strong>ring stations can be useful<br />
sources <strong>of</strong> data, they are limited in determining air quality between neighborhoods. Thus, more sophisticated<br />
spatial models have been developed <strong>to</strong> evaluate how pollutant distribution is impacted by the<br />
distance from and density <strong>of</strong> specific built environment traits. However, the use <strong>of</strong> these spatial models<br />
which are <strong>of</strong>ten developed for one city may not translate well <strong>to</strong> other urban centres.<br />
Highlights from the gap analysis related <strong>to</strong> the assessment <strong>of</strong> urban air quality exposure are provided<br />
below:<br />
• Most PHUs assessing air quality use an index, specifically the Air Quality Index (AQI) and the Air<br />
Quality Health Index (AQHI).<br />
• There are valuable modelling methods available <strong>to</strong> assess air quality in the built environment.<br />
• While important data sources from the municipal, provincial, and federal level were identified,<br />
there are limitations <strong>to</strong> assess air quality issues at a neighborhood scale.<br />
• NO and PM were noted as two key pollutants which can be used as indica<strong>to</strong>rs <strong>of</strong> local air<br />
x 2.5<br />
quality. While the five common pollutants (O 3<br />
, PM 2.5<br />
, NO 2<br />
, CO, and SO 2<br />
) are those which have<br />
been associated with the greatest burden <strong>of</strong> illness, other pollutants <strong>of</strong> concern in the built<br />
environment have been identified such as UFP and BC.<br />
• Public Health Units need information on local air quality <strong>to</strong>:<br />
o Assess the impacts <strong>of</strong> air quality on health within their jurisdictions;<br />
o Inform land use and transportation planning decisions for the protection <strong>of</strong> human health;<br />
and,<br />
o Assess and moni<strong>to</strong>r the impacts <strong>of</strong> policies and programs on air quality and human health at<br />
the local level.<br />
• Human resources, financial capacity, and data availability are key challenges in the assessment <strong>of</strong><br />
air quality and the built environment in Ontario.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
178<br />
DISCUSSION<br />
EXTREME HEAT<br />
Meteorological variables are commonly used <strong>to</strong> measure extreme heat including temperature, relative<br />
humidity, dew point temperature, wind speed and direction, and solar radiation. These variables are<br />
usually directly measured and moni<strong>to</strong>red at local airports and weather stations by government agencies.<br />
There are a number <strong>of</strong> meteorological variables that are also used in composite indices <strong>to</strong> determine<br />
exposure <strong>to</strong> extreme heat; however, application <strong>of</strong> these indices varies. In addition <strong>to</strong> the use <strong>of</strong> direct<br />
measurements, indirect measures are also used <strong>to</strong> assess exposures <strong>to</strong> extreme heat. Measures <strong>of</strong> the<br />
built environment related <strong>to</strong> community and residential characteristics, as well as natural and artificial<br />
surfaces, provide key insights on exposures <strong>to</strong> extreme heat. Many <strong>of</strong> these measures employ remote<br />
sensing technologies <strong>to</strong> gather data. Lastly, composite measures have been developed which combine<br />
exposure with socio-economic, demographic and built environment data <strong>to</strong> map overall community vulnerability<br />
<strong>to</strong> extreme heat.<br />
Several limitations cited by the research literature were corroborated by the current study’s key informants<br />
and Ontario Public Health Unit survey results. Given the existing data infrastructure, some measures that<br />
are readily available and accessible without cost may not adequately cover the need <strong>to</strong> assess extreme<br />
heat within a community. Primarily, this extends <strong>to</strong> meteorological data provided by <strong>Environment</strong> Canada<br />
weather stations which do not provide adequate spatial resolution. While weather stations provide data<br />
<strong>to</strong> a high degree <strong>of</strong> accuracy, the spatial resolutions obtained through sparse station densities are not<br />
adequate <strong>to</strong> infer measurements within and across communities. This data gap is particularly important<br />
in urban areas where weather stations are not typically located and it would be unreasonable <strong>to</strong> establish<br />
moni<strong>to</strong>ring sites at a higher density, especially in urban areas. In addition, standard weather moni<strong>to</strong>ring<br />
stations do not collect information on solar radiation and therefore many composite measures would<br />
not be feasible without the use <strong>of</strong> additional equipment. Alternative measures have been identified in the<br />
literature, however they have not been widely adopted. This may be due <strong>to</strong> PHU capacity issues and the<br />
lack <strong>of</strong> research or consensus within Ontario on the best measures and approaches.<br />
Highlights from the gap analysis related <strong>to</strong> the assessment <strong>of</strong> extreme heat are provided below:<br />
• Many PHUs use meteorological data <strong>to</strong> assess heat and mostly report on temperature and<br />
humidex.<br />
• Meteorological data is an important data set for composite measures and models <strong>of</strong> extreme heat<br />
and most PHUs have access <strong>to</strong> meteorological data.<br />
• <strong>Environment</strong> Canada is a free source <strong>of</strong> standardized meteorological data on a number <strong>of</strong><br />
extreme heat parameters.<br />
• There are many composite measures that are used <strong>to</strong> assess exposure <strong>to</strong> extreme heat but many<br />
may not be useful for local population health assessments due <strong>to</strong> complex data requirements.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
DISCUSSION 179<br />
• Some PHUs have access <strong>to</strong> built environment data, however most PHUs are not currently using<br />
built environment data <strong>to</strong> assess exposure <strong>to</strong> extreme heat.<br />
• There are a limited number <strong>of</strong> PHUs with access <strong>to</strong> thermal imagery for the assessment <strong>of</strong><br />
extreme heat.<br />
• Demographic data such as age, gender, income, and language are being used <strong>to</strong> identify<br />
populations that are more vulnerable <strong>to</strong> extreme heat.<br />
• Human resources, financial capacity, and data availability are the key challenges for Ontario PHUs<br />
in the assessment <strong>of</strong> extreme heat and the built environment.<br />
CROSS-CUTTING THEMES<br />
Researchers and public health practitioners trying <strong>to</strong> associate built environmental features with walking,<br />
air quality and extreme heat, have been hampered by several data limitations. Most striking were the<br />
commonalities in challenges reported for all three <strong>to</strong>pic-areas.<br />
Lack <strong>of</strong> standardization in measurement approaches<br />
Lack <strong>of</strong> standardization was a common challenge in built environment assessments <strong>of</strong> walkability, air<br />
quality and extreme heat. For instance, several organizations were using similar types <strong>of</strong> measures, but<br />
they were using different terminology, measurement approaches and diverse data sources. Also, while<br />
similar measures are used across multiple jurisdictions, they are <strong>of</strong>ten collected at a different scale and<br />
frequency. As a result, it was challenging <strong>to</strong> draw comparisons between health jurisdictions, especially<br />
for organizations that are predominantly using local data sources. Undoubtedly, different communities will<br />
have different needs, however further research is required <strong>to</strong> identify which measures and approaches<br />
are most appropriate across the province.<br />
Variations between municipalities were reported as a prominent challenge in the assessment <strong>of</strong> urban<br />
walkability. Variations included a lack <strong>of</strong> consensus on standardized methods for measuring or cataloging<br />
GIS measures and no central provincial reposi<strong>to</strong>ry for such data.<br />
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DISCUSSION<br />
Availability and accessibility <strong>of</strong> data<br />
<strong>Data</strong> availability was reported as a major challenge in the assessment <strong>of</strong> the built environment. Access <strong>to</strong><br />
and availability <strong>of</strong> high quality built environment data depends on the resources and policy priorities <strong>of</strong> the<br />
agencies that collect and warehouse the information. Some regions have excellent and accessible data,<br />
while others still have paper-based or nonexistent data. These observations echo other research findings<br />
that data sharing for the most part has been casual and opportunistic, impeded by lack <strong>of</strong> infrastructure,<br />
collaboration, and training. 2<br />
Human resource capacity<br />
The primary challenge in the assessment <strong>of</strong> urban walkability and environmental exposures was human<br />
resource capacity. This challenge can be considered on two levels: (i) the need for staff dedicated <strong>to</strong><br />
working on walkability, air quality and extreme heat assessments and (ii) the need for personnel <strong>to</strong> have<br />
specialized expertise (e.g. spatial analysis, epidemiology, satellite image processing).<br />
Financial capacity<br />
Financial capacity was identified as a major challenge in the assessment <strong>of</strong> air quality and extreme heat.<br />
For example, while some data may be freely available, other data must be purchased from government<br />
and/or private institutions. Some PHUs are collecting their own data (e.g. purchasing air moni<strong>to</strong>ring<br />
equipment), but this expense may not be feasible for all PHUs.<br />
Competing public health priorities<br />
For some PHUs, walkability and environmental exposure assessments were not a key priority due <strong>to</strong> lack<br />
<strong>of</strong> political will and relevance <strong>to</strong> rural environments. While some PHUs identified these issues, there may<br />
be other reasons or competing priorities that were not identified via our environmental scan.<br />
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DISCUSSION 181<br />
HEALTH EQUITY AND THE BUILT ENVIRONMENT<br />
The built environment is closely tied <strong>to</strong> health equity. The built environment and health equity impacts may<br />
be linked <strong>to</strong> socioeconomic status (SES), age, health status, or cultural influences. While this <strong>to</strong>pic was<br />
not identified as an area <strong>of</strong> focus for this project, some interesting findings bear mentioning.<br />
Walkability & Health Equity<br />
It is vital <strong>to</strong> understand the difference between an individual who chooses <strong>to</strong> walk as a result <strong>of</strong> living in a<br />
walkable neighbourhood and someone who, for financial constraints or other reasons, has no choice but<br />
<strong>to</strong> walk in a neighbourhood or circumstance that may or may not be conducive <strong>to</strong> walking. 54 The design<br />
<strong>of</strong> urban environments varies dramatically between high-income and lower income neighborhoods and<br />
the possible implications for physical activity are complex and multifaceted. For instance, lower income<br />
neighborhoods frequently have both features that are hypothesized <strong>to</strong> support walking for transportation<br />
(e.g. higher densities, grid streets that provide direct connections, transit access) and features that are<br />
hypothesized <strong>to</strong> deter walking (e.g. poor maintenance, few nearby destinations, and unpleasant walking<br />
environments.) Also, lower income residents may walk more irrespective <strong>of</strong> neighborhood design due <strong>to</strong><br />
financial barriers <strong>to</strong> owning a car and incidentals like car insurance and a valid drivers’ license. Ultimately,<br />
sociodemographic characteristics warrant serious attention from urban designers and planners and from<br />
public health advocates who are interested in creating walkable urban environments. 214<br />
Air Quality & Health Equity<br />
Canada-wide research found that individuals and families living in low SES neighbourhoods are more<br />
likely <strong>to</strong> live close <strong>to</strong> a highway or industrial area that exposes them <strong>to</strong> higher levels <strong>of</strong> outdoor air pollution.<br />
37 While everyone faces increased health risks due <strong>to</strong> air pollution, the risk is greater for people with<br />
cardiovascular and respira<strong>to</strong>ry conditions, people with diabetes and/or those who are obese, and the<br />
elderly, women (especially those that are pregnant), and young children.<br />
Several PHUs surveyed through this project reported that they had access <strong>to</strong> data on residents and<br />
sensitive populations living within a pre-determined distance from high volume roads; and that commonly<br />
used data sources, such as the census, were used <strong>to</strong> access this data.<br />
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DISCUSSION<br />
Extreme Heat & Health Equity<br />
Research in Montreal and Toron<strong>to</strong> found that neighbourhoods with the lowest SES are more likely <strong>to</strong><br />
reach higher temperatures and less likely <strong>to</strong> have open green space than higher SES neighbourhoods. 37<br />
Some <strong>of</strong> the socio-demographic and health/physiological risk fac<strong>to</strong>rs that have been used <strong>to</strong> assess<br />
heat-health impacts include elderly population, infants and young children, chronic respira<strong>to</strong>ry and<br />
cardiovascular diseases, mobility restrictions, cognitive impairment, socially isolated persons (widowed,<br />
divorced, homeless), low-income households, marginalized groups, new immigrants and non-Englishspeaking<br />
populations.<br />
Almost half <strong>of</strong> PHUs surveyed through this project reported using demographic data such as age, gender,<br />
income, and language <strong>to</strong> identify populations that are more vulnerable <strong>to</strong> extreme heat. Age was used<br />
more than any <strong>of</strong> the other demographic indica<strong>to</strong>rs <strong>to</strong> identify vulnerable populations. Other demographic<br />
data used <strong>to</strong> identify vulnerable populations varied and included occupation, level <strong>of</strong> education,<br />
socio-demographic and health/physiological risk fac<strong>to</strong>rs, immigration status, and type <strong>of</strong> dwelling (e.g.<br />
dwellings without air conditioning).<br />
LIMITATIONS<br />
As with any research endeavor, some methodological challenges were anticipated beforehand, while still,<br />
others arose as the project unfolded. Several caveats and limitations <strong>of</strong> the study are noted below.<br />
Early on, the project team confronted usual definition and terminology challenges. “<strong>An</strong> examination <strong>of</strong><br />
data sources <strong>to</strong> characterize measures <strong>of</strong> the built environment in urban Ontario” may seem intuitive but<br />
what exactly constitutes ‘urban’ Ontario? Or, how is an ‘indica<strong>to</strong>r,’ ‘measure,’ ‘metric,’ or ‘index’ defined?<br />
These questions challenged the project team at the outset and periodically throughout the project. Copious<br />
reading in the field <strong>of</strong> built environment research suggests similarly inexact language as these terms<br />
are used in the scholarly literature, grey literature, and popular press with some imprecision. Thus, the<br />
project team made a deliberate effort <strong>to</strong> explicitly define terms and <strong>to</strong> use them consistently throughout<br />
the project.<br />
Recognizing the scope <strong>of</strong> this study and the recent proliferation <strong>of</strong> built environment research, the keyword<br />
search criteria in the literature review were initially structured in order <strong>to</strong> practically limit the number<br />
<strong>of</strong> articles for review. The keyword lists were subsequently expanded and otherwise relaxed <strong>to</strong> a point<br />
where the team began <strong>to</strong> see saturation in the identified measures and data sources that researchers<br />
have used <strong>to</strong> study the built environment. No doubt, there are other novel and specialized measures that<br />
may have been missed, but the measures and data this review has revealed constitute the current locus<br />
<strong>of</strong> attention in built environment research. Though rigorous, given the scope <strong>of</strong> the project, an exhaustive<br />
search <strong>of</strong> all potentially relevant measures and data sources was not feasible.<br />
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DISCUSSION 183<br />
The project team, among its own collective expertise, identified a number <strong>of</strong> potential key informants. The<br />
key informants were selected from those who have made contributions <strong>to</strong> the study <strong>of</strong> the built environment,<br />
from those who are working at the nexus <strong>of</strong> the built environment and health, and from those who<br />
have considerable knowledge <strong>of</strong> relevant built environment data sources. Although critical insights were<br />
gleaned from the key informants, the project team makes no claims <strong>to</strong> having captured all strands <strong>of</strong><br />
current thinking in the field <strong>of</strong> built environment research. The key informants selected represent more <strong>of</strong><br />
a convenience sample rather than a rigorously vetted expert panel.<br />
Considerable thought and investment went in<strong>to</strong> the development and testing <strong>of</strong> the survey instrument.<br />
However, <strong>to</strong> ensure the project met major miles<strong>to</strong>nes and key deadlines, the survey needed <strong>to</strong> be fielded<br />
during the summer and, thus, may have limited the number and quality <strong>of</strong> responses. Given the response<br />
rate and the lack <strong>of</strong> detail in some <strong>of</strong> the responses, there were insufficient data with which <strong>to</strong> generate<br />
jurisdictional pr<strong>of</strong>iles as planned. Comprehensively enumerating desired built environment indica<strong>to</strong>rs by<br />
public health unit was also not feasible.<br />
While the project has identified and documented key datasets (spatial and non-spatial) that can and are<br />
being leveraged <strong>to</strong> support built environment research programs, there may well be datasets that were<br />
missed. The ‘standard fare’ or ‘core’ datasets in the public domain were first identified, many <strong>of</strong> which<br />
are produced and/or disseminated by public agencies or government; secondly, the project team tried<br />
<strong>to</strong> enumerate key vendor datasets that can inform built environment research. It is recognized, however,<br />
that individual researchers, interest groups, or private sec<strong>to</strong>r firms may hold more proprietary datasets<br />
<strong>of</strong> value <strong>to</strong> built environment research. Moreover, it is important <strong>to</strong> mention that there exists considerable<br />
potential <strong>to</strong> construct derivative datasets (as either a transformation <strong>of</strong> a single core dataset or by<br />
innovatively combining two or more core datasets) that could greatly enhance the understanding <strong>of</strong> the<br />
relationship between the built environment and health outcomes.<br />
CONCLUSION<br />
In order <strong>to</strong> fully understand the impact <strong>of</strong> the built environment on health, the development <strong>of</strong> highquality<br />
measures is essential. Although several limitations have been cited in this report, existing built<br />
environment measures have stimulated rapid advancements in understanding environmental correlates<br />
<strong>of</strong> physical activity and environmental exposures in a variety <strong>of</strong> populations and settings. Many built environment<br />
measures are considered <strong>to</strong> be first-generation measures, so further development is needed.<br />
In particular, further work is required <strong>to</strong> standardize measures and terminology, improve the quality <strong>of</strong><br />
measures, ensure relevance for diverse populations, and integrate measures in<strong>to</strong> public health and planning<br />
systems. 5;61 Moving forward, cross- and inter-disciplinary collaborations will be critical <strong>to</strong> making<br />
these measurement improvements. By working <strong>to</strong>gether, our communities will reap the resulting health,<br />
environmental, and economic benefits. In the end, widespread implementation <strong>of</strong> effective policy interventions<br />
will be necessary <strong>to</strong> achieve these built environment and public health goals. 215<br />
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GUIDING PRINCIPLES &<br />
RECOMMENDATIONS<br />
Several overarching principles were identified <strong>to</strong> help guide the development <strong>of</strong> the study recommendations.<br />
These principles are closely tied <strong>to</strong> the results <strong>of</strong> the literature review, key informant interview and survey<br />
results, and are applicable <strong>to</strong> the assessment <strong>of</strong> walkability, air quality, and extreme heat.<br />
Multiple sec<strong>to</strong>rs and disciplines are interested in the built environment including public health, geography,<br />
transportation, architecture, urban planning and design. Therefore the potential audience for our<br />
recommendations is large. The stakeholders for which our guiding principles and recommendations<br />
could apply <strong>to</strong>, include:<br />
• Ontario’s regional and municipal governments: public health, land use planning, transportation<br />
planning, public works, sustainability, and community services<br />
• Provincial government: Ministry <strong>of</strong> Health and Long Term Care, Public Health Ontario, Ministry<br />
<strong>of</strong> Transportation, Ministry <strong>of</strong> <strong>Environment</strong>, Ministry <strong>of</strong> Municipal Affairs and Housing Federal<br />
government: Statistics Canada, <strong>Environment</strong> Canada, Health Canada, Transport Canada, Public<br />
Health Agency <strong>of</strong> Canada, Natural Resources Canada<br />
• Non-governmental organizations: Ontario Public Health Association (OPHA), Association <strong>of</strong> Public<br />
Health Epidemiologists in Ontario (APHEO), Association <strong>of</strong> Local Public Health Agencies (alPHa),<br />
Association <strong>of</strong> Supervisors <strong>of</strong> Public Health Inspec<strong>to</strong>rs <strong>of</strong> Ontario (ASPHIO), Canadian Institute<br />
<strong>of</strong> Public Health Inspec<strong>to</strong>rs (CIPHI), Council <strong>of</strong> Ontario Medical Officers <strong>of</strong> Health (COMOH),<br />
Canadian Medical Association, Ontario Medical Association, Association <strong>of</strong> Municipalities <strong>of</strong><br />
Ontario, Ontario Association <strong>of</strong> Architects, Ontario Association <strong>of</strong> Pr<strong>of</strong>essional Engineers, Ontario<br />
Pr<strong>of</strong>essional Planners Institute (OPPI)<br />
• Private organizations/agencies including planning, transportation and engineering firms<br />
• Academia<br />
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The guiding principles as well as the specific recommendations that relate <strong>to</strong> each principle are presented<br />
below. It is important <strong>to</strong> note that when ‘local agencies’ are referenced, these stakeholders include Public<br />
Health Units and municipal governments.<br />
1. STRENGTHEN MULTIDISCIPLINARY COOPERATION<br />
A recurring theme in all aspects <strong>of</strong> this project is the need for diversity in both expertise and skills in order<br />
<strong>to</strong> have an in-depth understanding <strong>of</strong> the built environment. More so than several other subject areas,<br />
assessing the impact <strong>of</strong> the built environment on the public’s health requires extensive collaboration.<br />
1.1<br />
Engage in multidisciplinary collaboration across all sec<strong>to</strong>rs, including government,<br />
academia and private sec<strong>to</strong>rs<br />
• Establish and enhance existing multidisciplinary built environment working groups and<br />
associations that are focused on assessing walkability, air quality and extreme heat<br />
through strengthened human and financial resource capacity.<br />
• Membership should include provincial and local built environment stakeholders (e.g.<br />
associations, Public Health Units, municipalities) and academia.<br />
• Preliminary areas <strong>of</strong> focus could include (but are not limited <strong>to</strong>):<br />
o<br />
o<br />
o<br />
Supporting the expansion <strong>of</strong> multidisciplinary groups with mandates <strong>to</strong> identify<br />
province-wide walkability indices and their operationalization.<br />
Creating a multidisciplinary committee <strong>to</strong> further research local air quality<br />
assessments as it relates <strong>to</strong> the built environment and <strong>to</strong> share information on:<br />
different technologies, methodologies and approaches; conducting research<br />
directed at air quality and traffic corridors; and research and assessments<br />
directed at air quality and point sources.<br />
Creating a multidisciplinary task force <strong>to</strong> further research variations in<br />
extreme heat across communities and community vulnerability, and <strong>to</strong> share<br />
information on: heat burden <strong>of</strong> illness and research directed at heat alert<br />
triggers, as well as heat alert and response systems.<br />
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2. PROVIDE METHODOLOGICAL GUIDANCE<br />
Currently, the assessments and research on public health and the built environment are comprised <strong>of</strong><br />
a patchwork <strong>of</strong> different methodologies that are predominantly led by independent efforts. Providing<br />
guidance <strong>to</strong> local agencies on the assessment <strong>of</strong> the built environment, namely walkability, air quality and<br />
extreme heat, would help <strong>to</strong> promote comparability <strong>of</strong> results, reduce duplication <strong>of</strong> efforts, and hasten<br />
the inclusion <strong>of</strong> agencies that are new <strong>to</strong> assessing the built environment.<br />
2.1 Standardize built environment measures using a multidisciplinary approach<br />
across all sec<strong>to</strong>rs<br />
• Establish consistent built environment terminology across the province.<br />
• Develop a standard suite <strong>of</strong> measures (including indices) and methodologies that can be<br />
used <strong>to</strong> characterize the built environment in Ontario.<br />
• Develop guidelines or minimum requirements for built environment metadata.<br />
• Preliminary areas <strong>of</strong> focus could include (but are not limited <strong>to</strong>):<br />
o<br />
o<br />
Developing pro<strong>to</strong>cols for the use and management <strong>of</strong> municipal traffic volume<br />
data for Public Health Units.<br />
Including extreme heat and air quality in the current development<br />
<strong>of</strong> standardized indica<strong>to</strong>rs for the built environment<br />
(e.g. Association <strong>of</strong> Public Health Epidemiologists in Ontario<br />
(APHEO) <strong>Built</strong> <strong>Environment</strong> Core Indica<strong>to</strong>rs Project).<br />
2.2 Put built environment research in<strong>to</strong> practice<br />
• Identify, and use, best practices <strong>to</strong> determine which built environment measures and<br />
data sources are a priority in the assessment <strong>of</strong> air quality, extreme heat and walkability in<br />
Ontario.<br />
• Preliminary areas <strong>of</strong> focus could include (but are not limited <strong>to</strong>):<br />
o Assessing the applicability <strong>of</strong> free and publicly available walkability s<strong>of</strong>tware<br />
(e.g. Walkscore) in the Ontario context.<br />
o Exploring the use <strong>of</strong> thermal imagery <strong>to</strong> assess extreme heat within Ontario<br />
communities.<br />
o Providing complimentary moni<strong>to</strong>ring programs <strong>to</strong> better understand air<br />
pollutant distribution (e.g. AirPointer, NO 2<br />
passive samplers).<br />
o Integrating socio-demographic characteristics in<strong>to</strong> built environment and<br />
health assessments.<br />
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3. IMPROVE DATA AVAILABILITY AND ACCESSIBILITY<br />
Understanding the impact <strong>of</strong> the built environment on walkability, air quality and extreme heat requires<br />
relevant, easy-<strong>to</strong>-understand, and reliable measures and data <strong>to</strong> assess built environment features. Lack<br />
<strong>of</strong> data availability and accessibility was a recurring challenge identified in this study.<br />
3.1 Increase access <strong>to</strong> high quality data across the Province<br />
• Establish and maintain a centralized data reposi<strong>to</strong>ry for standardized built environment<br />
data relevant <strong>to</strong> health in Ontario.<br />
• Preliminary areas <strong>of</strong> focus could include (but are not limited <strong>to</strong>):<br />
o<br />
o<br />
Developing consistent data sharing opportunities <strong>to</strong> use thermal imagery from Natural<br />
Resources Canada.<br />
Developing a mechanism for all Ontario Public Health Units <strong>to</strong> have access <strong>to</strong> the data<br />
reposi<strong>to</strong>ry at minimum cost.<br />
3.2 Empower local agencies <strong>to</strong> engage in built environment data initiatives<br />
• Develop data sharing agreement templates that make data sharing easier between Public<br />
Health Units and other stakeholders (e.g. <strong>to</strong> address privacy barriers).<br />
• Equip Public Health Units with valid <strong>to</strong>ols, instruments and technology <strong>to</strong> collect local data<br />
at low or no cost.<br />
• Preliminary areas <strong>of</strong> focus could include (but are not limited <strong>to</strong>):<br />
o<br />
o<br />
o<br />
Evaluating the ability <strong>to</strong> operationalize a similar walkability index in multiple Public<br />
Health Units across the province.<br />
Developing common methodology for assessing community vulnerability <strong>to</strong><br />
extreme heat across Ontario.<br />
Expanding Health Canada’s use <strong>of</strong> NO sensors for use by Public Health Units in<br />
2<br />
conjunction with air modelling.<br />
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3.3 Identify and evaluate the use <strong>of</strong> current data sources and sets<br />
• Preliminary areas <strong>of</strong> focus could include (but are not limited <strong>to</strong>):<br />
o<br />
o<br />
o<br />
o<br />
Acquiring data from research-based air moni<strong>to</strong>ring stations and determine how<br />
such data could be used in assessing air quality and the built environment (e.g.<br />
emerging pollutants <strong>of</strong> concern; ultrafine particles, black carbon).<br />
Investigating remote sensing as an opportunity <strong>to</strong> complement and support<br />
Public Health Units in the assessment <strong>of</strong> the built environment.<br />
Creating a system or guide <strong>to</strong>ol similar <strong>to</strong> Toron<strong>to</strong>’s ChemTrac, where Public<br />
Health Units can collect data and evaluate smaller emission sources not<br />
considered in the NPRI.<br />
Using data sources and data collection methods for air pollutant distribution<br />
modelling used by Ottawa, Hal<strong>to</strong>n, and Toron<strong>to</strong> municipalities in Public Health<br />
Units across the province.<br />
3.4 Explore the creation <strong>of</strong> new data sets<br />
• Preliminary areas <strong>of</strong> focus could include (but are not limited <strong>to</strong>):<br />
o Investigating publicly available GIS maps for National Pollutant Release Inven<strong>to</strong>ry<br />
(NPRI) data sets.<br />
o Improving province-wide meteorological data by including the collection <strong>of</strong> solar load<br />
(radiation) at <strong>Environment</strong> Canada weather stations.<br />
3.5 Address the need for data auditing and validation<br />
• Develop strategies <strong>to</strong> ensure data auditing and validation becomes standard practice,<br />
with a priority on province-wide data sets.<br />
• Develop guidance documentation or pro<strong>to</strong>cols on built environment data auditing<br />
procedures.<br />
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4. ENGAGE IN SYSTEMATIC KNOWLEDGE TRANSFER AND EXCHANGE (KTE)<br />
Knowledge transfer and exchange activities related <strong>to</strong> the development, use and adoption <strong>of</strong> built<br />
environment measures and data sources are necessary <strong>to</strong> support evidence informed decision making.<br />
Networks are characterized by social interaction and knowledge-sharing related <strong>to</strong> a common goal within<br />
a specific domain <strong>of</strong> knowledge and practice. They are valuable in enhancing the management, sharing<br />
and co-creation <strong>of</strong> knowledge in public health, and augment pr<strong>of</strong>essional and organizational capacity<br />
development, and system change. 216<br />
4.1 Facilitate engagement <strong>of</strong> expertise outside <strong>of</strong> the public health sec<strong>to</strong>r<br />
• Invest in the engagement <strong>of</strong> non-public health built environment stakeholders through<br />
KTE activities that demonstrate the value that public health can bring <strong>to</strong> built environment<br />
issues and vice versa. Audiences could include land use planners, police departments,<br />
engineers, conservations authorities, community groups, etc.<br />
• Preliminary areas <strong>of</strong> focus could include (but are not limited <strong>to</strong>):<br />
o<br />
o<br />
o<br />
Providing logistical aid and capacity <strong>to</strong> foster networking and communication with<br />
other subject area experts (e.g. suggesting appropriate contacts, covering incidental<br />
costs, liaising between organizations, etc.).<br />
Creating an online portal for sharing data, methods for collection, and best practices<br />
on the built environment and health.<br />
Creating opportunities for public health <strong>to</strong> demonstrate the impact the built<br />
environment has on health <strong>to</strong> other organizations that have traditionally worked in the<br />
built environment (e.g. land use planning).<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
191<br />
5. STRENGTHEN CAPACITY<br />
Human resource and financial capacity are challenges that Ontario’s Public Health Units face when<br />
assessing the urban built environment. Having a highly skilled and competent public health workforce<br />
will ultimately help strengthen capacity <strong>to</strong> protect and improve the public’s health and reduce pressures<br />
on the health-care system.<br />
5.1 Support Public Health Units in a technical capacity <strong>to</strong> assess the built environment<br />
• Develop the appropriate technical infrastructure at provincial and local levels <strong>to</strong> improve<br />
capacity <strong>to</strong> generate, manage, and communicate spatial and non-spatial information<br />
related <strong>to</strong> the built environment across all sec<strong>to</strong>rs.<br />
• Preliminary areas <strong>of</strong> focus could include (but are not limited <strong>to</strong>):<br />
o<br />
o<br />
o<br />
Providing Public Health Units with technical support through pooling <strong>of</strong> resources<br />
(e.g. GIS analysts).<br />
Providing spatial modelling <strong>to</strong> complement current air moni<strong>to</strong>ring networks and<br />
providing better spatial detail <strong>of</strong> pollutant distribution at the community level.<br />
Providing technical support and resources <strong>to</strong> those Public Health Units and/or<br />
municipalities that are doing air moni<strong>to</strong>ring and/or air modelling studies <strong>of</strong> local<br />
airsheds and/or micro-environments.<br />
5.2 Enhance education and training for public health pr<strong>of</strong>essionals<br />
• Strengthen post-secondary training <strong>of</strong> public health pr<strong>of</strong>essionals <strong>to</strong> include the<br />
built environment-health relationships as part <strong>of</strong> the core curriculum and increase<br />
competencies around the use <strong>of</strong> spatial and non-spatial <strong>to</strong>ols <strong>to</strong> evaluate these<br />
relationships.<br />
• Implement policies and programs <strong>to</strong> train local public health pr<strong>of</strong>essionals on the roles<br />
and responsibilities, legislation, policy, standards, terminology/concepts, and utilization <strong>of</strong><br />
spatial and non-spatial data related <strong>to</strong> land use planning and GIS (and vice versa for land<br />
use planners).<br />
• Strengthening Public Health Units and/or municipalities’ understanding <strong>of</strong> the air<br />
moni<strong>to</strong>ring and modelling <strong>to</strong>ols, technologies, and strategies that can be used <strong>to</strong><br />
assess local airsheds and micro-environments, along with their strengths, limitations,<br />
and applications.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
192<br />
6. STRENGTHEN BUILT ENVIRONMENT AND HEALTH RESEARCH<br />
Although several advancements have been made in the measures and measurement approaches<br />
used in the assessment <strong>of</strong> urban walkability and environmental exposures, several gaps in the<br />
research have emerged.<br />
6.1 Increase research funding opportunities for exploring the relationship between the built<br />
environment and health<br />
Preliminary areas <strong>of</strong> focus could include (but are not limited <strong>to</strong>):<br />
o<br />
o<br />
o<br />
o<br />
o<br />
o<br />
o<br />
Providing additional data on pollutant distribution from key traffic sources <strong>to</strong> better<br />
understand variability in setback distances and heights.<br />
Evaluating elements <strong>of</strong> the built environment relevant <strong>to</strong> land use planning and air<br />
quality (e.g. impact <strong>of</strong> vegetation, noise walls, etc.).<br />
Exploring urban climate modelling for the assessment <strong>of</strong> extreme heat.<br />
Examining how meteorological conditions contribute <strong>to</strong> the distribution <strong>of</strong> air<br />
pollutants in communities in urban areas.<br />
Investigating how multiple pollutants contribute <strong>to</strong> health outcomes through<br />
additive or synergistic effects.<br />
Evaluating the South Eastern Ontario environmental extreme heat moni<strong>to</strong>ring<br />
system <strong>to</strong> better understand the potential <strong>of</strong> syndromic surveillance.<br />
Determining how built environmental fac<strong>to</strong>rs influence physical activity levels in<br />
youth, particularly <strong>to</strong> assist in the development <strong>of</strong> youth-oriented interventions that<br />
promote life-long healthy behaviors.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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(195) Rey G, Fouillet A, Bessemoulin P, Frayssinet P, Dufour A, Jougla E, et al. Heat exposure<br />
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Epidemiol. 2009;24(9):495-502.<br />
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Heat waves observed in 2007 in Athens, Greece: synoptic conditions, bioclima<strong>to</strong>logical<br />
assessment, air quality levels and health effects. Environ Res. 2010;110(2):152-61.<br />
(198) Watts J, Kalkstein L. The development <strong>of</strong> a warm-weather relative stress index for<br />
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10.5620/eht.2011.26.e2011009.<br />
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pubs/climat/adapt_bulletin-adapt1/index-eng.php<br />
(203) Johnson D, Lulla V, Stanforth A, Webber J. Remote sensing <strong>of</strong> heat-related health risks: the<br />
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[cited 2012 Nov 15]. Available from: http://www.nrcan.gc.ca/earth-sciences/geographyboundary/remote-sensing/fundamentals/1924<br />
(205) Uejio C, Wilhelmi O, Golden J, Mills D, Gulino S, Samenow J. Intra-urban societal<br />
vulnerability <strong>to</strong> extreme heat: the role <strong>of</strong> heat exposure and the built environment,<br />
socioeconomics, and neighborhood stability. Health Place. 2011;17(2):498-507.<br />
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(210) Reid C, Mann J, Alfasso R, English P, King G, Lincoln R, et al. Evaluation <strong>of</strong> a heat<br />
vulnerability index on abnormally hot days: an environmental public health tracking study.<br />
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(211) Reid C, O’Neill M, Gronlund C, Brines S, Brown D, Diez-Roux ASJ. Mapping community<br />
determinants <strong>of</strong> heat vulnerability. Environ Health Perspect. 2009;117(11):1730-6.<br />
(212) Tomlinson C, Chapman L, Thornes J, Baker C. Including the urban heat island in spatial<br />
heat health risk assessment strategies: a case study for Birmingham, UK. Int J Health Geogr.<br />
2011;10(42). doi: 10.1186/1476-072X-10-42.<br />
(213) Toron<strong>to</strong> Public Health. Questions & answers - heat vulnerability maps for Toron<strong>to</strong> [Internet]<br />
[Internet]. 2010 [cited 2012 Nov 21]. Available from: http://<strong>to</strong>ron<strong>to</strong>healthpr<strong>of</strong>iles.ca/a_<br />
documents/aboutThe<strong>Data</strong>/9_1_QandA_HeatVulner_HV_2010.pd<br />
(214) Boarnet M, Forsyth A, Day K, Oakes J. The street level built environment and physical activity<br />
and walking. Environ Behav. 2011;43(6):735-75.<br />
(215) McKinnon R, Bowles H, Towbridge M. Engaging physical activity policymakers. J Phys Act<br />
Health. 2011;8(Suppl 1):S145-S147.<br />
(216) Robeson P. Networking in public health: exploring the value <strong>of</strong> networks <strong>to</strong> the National<br />
Collaborating Centres for Public Health. Hamil<strong>to</strong>n: National Collaborating Centre for Methods<br />
and Tools; 2009.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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APPENDICES<br />
211<br />
APPENDICES<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX A<br />
213<br />
APPENDIX A:<br />
WALKABILITY AND ENVIRONMENTAL EXPOSURES<br />
(AIR QUALITY AND EXTREME HEAT) LITERATURE REVIEW SUMMARY TABLES<br />
The literature review summary tables can be found at the following link:<br />
http://www.kflapublichealth.ca/files/research/AppendixA_LiteratureReviewSummaries.xls<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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APPENDIX B<br />
APPENDIX B:<br />
KEY INFORMANT INTERVIEW LETTER OF INVITATION (LOI)<br />
<br />
<br />
Re: Invitation <strong>to</strong> Participate in a Research Project<br />
Title <strong>of</strong> Project: <strong>An</strong> environmental scan <strong>of</strong> built environment data related <strong>to</strong> walkability and<br />
environmental exposure in urban Ontario.<br />
Dear <br />
On behalf <strong>of</strong> our investigation team, we would like <strong>to</strong> invite you <strong>to</strong> participate in a Public Health Ontario (PHO)<br />
funded study. Through PHO’s Locally Driven Collaborate Project (LDCP) initiative, several Ontario local public<br />
health agencies are working <strong>to</strong>gether on a project that will support the identification <strong>of</strong> standardized walkability<br />
and environmental exposure data and measures that can be used in the assessment <strong>of</strong> the urban built<br />
environment in Ontario, and <strong>to</strong> develop policy recommendations that would promote the development and use<br />
<strong>of</strong> such data and measures.<br />
We would like <strong>to</strong> gather the insights <strong>of</strong> those with relevant experience in the area <strong>of</strong> the urban built environment,<br />
specifically in relation <strong>to</strong> walkability and/or environmental exposures. At his time, would like <strong>to</strong> invite you or<br />
members <strong>of</strong> your team <strong>to</strong> participate in a key informant interview. We are interested in interview a person (or<br />
group) who possess knowledge related <strong>to</strong> areas <strong>of</strong> strength and weakness in the collection, availability, and<br />
comparability <strong>of</strong> built environment data within Ontario. We hope <strong>to</strong> briefly explore <strong>to</strong>pics such as data availability,<br />
data quality, data gaps, internal capacity, suggestions for acquiring data, and ongoing challenges as it relates <strong>to</strong><br />
data for built environment walkability and/or environmental exposures measures.<br />
The results <strong>of</strong> the interview will help inform the development <strong>of</strong> an electronic survey <strong>of</strong> built environment<br />
data sources that will be administered <strong>to</strong> public health departments, municipalities, the private sec<strong>to</strong>r and<br />
other agencies (e.g. provincial) across Ontario in May or June 2012. This information will later be applied in<br />
the development <strong>of</strong> policy recommendations related <strong>to</strong> the development and application <strong>of</strong> walkability and<br />
environmental exposure measures in Ontario. We expect <strong>to</strong> present our final results in report format on our<br />
project website (www.builtenvironment.ca).<br />
In the near future, you will be contacted <strong>to</strong> set up an appointment for a brief interview (about 30 minutes <strong>to</strong><br />
one hour). We encourage you <strong>to</strong> participate or <strong>to</strong> indicate another time that is more convenient for you. Once<br />
an interview date is confirmed, we will share a copy <strong>of</strong> the interview questions and consent form prior <strong>to</strong> the<br />
interview.<br />
If you have any questions about this project, please contact our Project Coordina<strong>to</strong>r, Popy Dimoulas-Graham,<br />
at 226-338-8004. If you have any concerns about your rights as a research participant please contact<br />
- Dr. Albert Clark, Chair <strong>of</strong> the Queen’s University and Affiliated Teaching Hospitals Research Ethics Board at<br />
(613)- 533-6081.<br />
We look forward <strong>to</strong> hearing from you and having you participate in this collaborative initiative.<br />
Sincerely,<br />
Paul Belanger, PhD<br />
GIS Services Manager<br />
KFL&A Public Health<br />
221 Portsmouth Ave<br />
Kings<strong>to</strong>n, ON K7M 1V5<br />
613 549- 1232, ext. 1602<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX B<br />
215<br />
APPENDIX B:<br />
KEY INFORMANT INTERVIEW LETTER OF INVITATION (LOI)<br />
INVESTIGATION TEAM<br />
Paul Belanger, PhD<br />
GIS Services<br />
Kings<strong>to</strong>n, Frontenac and Lennox &<br />
Adding<strong>to</strong>n Public Health<br />
Helen Doyle<br />
Manager, Public Health<br />
York Region Public Health<br />
Deborah Moore<br />
Senior Epidemiologist<br />
Niagara Region Public Health<br />
Daphne Mayer, MPH<br />
Research Associate<br />
Kings<strong>to</strong>n, Frontenac and Lennox &<br />
Adding<strong>to</strong>n Public Health<br />
Mira Shnabel<br />
<strong><strong>Environment</strong>al</strong> Health<br />
Program Coordina<strong>to</strong>r<br />
York Region Public Health<br />
Ryan Waterhouse<br />
GIS <strong>An</strong>alyst<br />
Niagara Region Public Health<br />
Novella Martinello, MSc<br />
Foundational Standard Specialist<br />
Kings<strong>to</strong>n, Frontenac and Lennox &<br />
Adding<strong>to</strong>n Public Health<br />
Asim Qasim<br />
<strong><strong>Environment</strong>al</strong> Research<br />
and Policy <strong>An</strong>alyst<br />
York Region Public Health<br />
Ahalya Mahendra<br />
Epidemiologist<br />
Public Health Agency <strong>of</strong> Canada<br />
Caitlyn Paget<br />
Epidemiologist<br />
York Region Public Health<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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APPENDIX C<br />
APPENDIX C:<br />
KEY INFORMANT INTERVIEW GUIDE – WALKABILITY<br />
Date <strong>of</strong> Interview:<br />
Name <strong>of</strong> Interviewer:<br />
DEMOGRAPHICS<br />
Name <strong>of</strong> key informant:<br />
Position:<br />
Organization:<br />
No. <strong>of</strong> years working on the built environment:<br />
Experience working on the built environment:<br />
QUESTIONS<br />
1. Can you tell me about the types <strong>of</strong> walkability measures your organization uses <strong>to</strong> measure, describe<br />
or evaluate walkability?<br />
The next set <strong>of</strong> questions will focus on data sources required <strong>to</strong> objectively measure walkability in<br />
Ontario:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX C<br />
217<br />
APPENDIX C:<br />
KEY INFORMANT INTERVIEW GUIDE – WALKABILITY<br />
DATA AVAILABILITY/QUALITY<br />
2. In your experience with these measures, can you comment on:<br />
(i)<br />
<strong>Data</strong> coverage (i.e. equally available across your jurisdiction);<br />
(ii) Frequency <strong>of</strong> data submission (i.e. how <strong>of</strong>ten is data collected; how current is the data);<br />
(iii) How data is collected (e.g. electronically, aerial images, specific databases)<br />
and at what level (geographic, population, political (e.g. municipal, provincial));<br />
(iv) <strong>Data</strong> quality (e.g. reputable source, consistency, current);<br />
(v) Accessibility (e.g. readily available; cost).<br />
DATA GAPS<br />
3. What are the major challenges with the current walkability data infrastructure<br />
(i.e. collection, processing and dissemination)?<br />
CAPACITY<br />
4. What capacity challenges do you think public health practitioners (at the provincial or local levels)<br />
experience when using objective walkability data? (e.g. internal resources <strong>to</strong> analyze spatial data)<br />
NEXT STEPS<br />
5. Do you have any suggestions related <strong>to</strong> where <strong>to</strong> get the most appropriate sources <strong>of</strong> built<br />
environment data <strong>to</strong> measure walkability across Ontario?<br />
6. We would appreciate feedback on how best <strong>to</strong> communicate the results <strong>of</strong> this project so that it<br />
will useful for <strong>Built</strong> environment stakeholders in Ontario. Do you have any suggestions?<br />
Thank you for your time. If you have any questions or comments, please feel free <strong>to</strong> contact the project<br />
coordina<strong>to</strong>r (contact information) at any time.<br />
-END-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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APPENDIX D<br />
APPENDIX D:<br />
KEY INFORMANT INTERVIEW GUIDE – ENVIRONMENTAL EXPOSURES<br />
(AIR QUALITY AND EXTREME HEAT)<br />
Date <strong>of</strong> Interview:<br />
Name <strong>of</strong> Interviewer:<br />
DEMOGRAPHICS<br />
Name <strong>of</strong> key informant:<br />
Position:<br />
Organization:<br />
No. <strong>of</strong> years working on the built environment:<br />
Experience working on the built environment:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX D<br />
219<br />
APPENDIX D:<br />
KEY INFORMANT INTERVIEW GUIDE – ENVIRONMENTAL EXPOSURES<br />
(AIR QUALITY AND EXTREME HEAT)<br />
AIR QUALITY KEY INFORMANTS<br />
Questions for <strong>Environment</strong> Canada and the Ministry <strong>of</strong> the <strong>Environment</strong> key informants<br />
1.<br />
2.<br />
3.<br />
4.<br />
5.<br />
6.<br />
7.<br />
How many air moni<strong>to</strong>ring stations do you operate in Ontario?<br />
What air pollutants are being moni<strong>to</strong>red by these stations?<br />
Where are these air moni<strong>to</strong>ring stations located?<br />
Are these stations moni<strong>to</strong>ring regional or local air quality?<br />
Are these stations targeted at specific emission sources, and if so, what types <strong>of</strong> emission<br />
sources?<br />
Do you have any air modelling or air moni<strong>to</strong>ring stations that are directed at local air quality related<br />
<strong>to</strong> non-industrial sources? If so, at which types <strong>of</strong> sources and where are they located?<br />
For public health units that are concerned about variations in air quality across their communities<br />
related <strong>to</strong> the built environment, what advice would you <strong>of</strong>fer about:<br />
a.<br />
b.<br />
c.<br />
d.<br />
e.<br />
f.<br />
Which 2 or 3 air pollutants <strong>to</strong> target as indica<strong>to</strong>rs <strong>of</strong> local air quality as it is impacted by the<br />
built environment, and why?<br />
How <strong>to</strong> collect data that can be used as an indica<strong>to</strong>r <strong>of</strong> exposure across the community (i.e.,<br />
with air moni<strong>to</strong>ring stations, local air moni<strong>to</strong>ring devices, airshed modelling, other?)<br />
Are there indirect indica<strong>to</strong>rs that can be used <strong>to</strong> moni<strong>to</strong>r potential exposure <strong>to</strong> air pollution from<br />
point sources such as set-back distances and/or NPRI or TURI reporting data?<br />
Which air pollutant(s) <strong>to</strong> target as indica<strong>to</strong>rs <strong>of</strong> traffic-related air pollution and why?<br />
How <strong>to</strong> collect data that can be used as indica<strong>to</strong>rs <strong>of</strong> exposure <strong>to</strong> traffic-related air pollution?<br />
(i.e., with portable air moni<strong>to</strong>ring devices, air modelling, other)?<br />
Are there indirect indica<strong>to</strong>rs that can be used <strong>to</strong> moni<strong>to</strong>r potential exposure <strong>to</strong> air pollution<br />
associated with traffic corridors such as set-back distances and/or traffic volumes ?<br />
8. We would appreciate feedback on how best <strong>to</strong> communicate the results <strong>of</strong> this project so that it<br />
will useful for <strong>Built</strong> environment stakeholders in Ontario. Do you have any suggestions?<br />
Thank you for your time. If you have any questions or comments, please feel free <strong>to</strong> contact the project<br />
coordina<strong>to</strong>r (contact information) at any time.<br />
-END-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
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APPENDIX D<br />
APPENDIX D:<br />
KEY INFORMANT INTERVIEW GUIDE – ENVIRONMENTAL EXPOSURES<br />
(AIR QUALITY AND EXTREME HEAT)<br />
Questions for air quality academics or research key informants<br />
1.<br />
For public health units that are concerned about variations in air quality across their communities, what<br />
advice would you <strong>of</strong>fer about:<br />
a.<br />
b.<br />
c.<br />
d.<br />
e.<br />
f.<br />
Which 2 or 3 air pollutants <strong>to</strong> target as indica<strong>to</strong>rs <strong>of</strong> local point sources and why?<br />
How <strong>to</strong> collect data that can be used as indica<strong>to</strong>rs <strong>of</strong> local air quality and why (i.e., with air<br />
moni<strong>to</strong>ring stations, local air moni<strong>to</strong>ring devices, airshed modelling, other?)<br />
Are there indirect indica<strong>to</strong>rs that can be used for air pollutants associated with point sources such as<br />
set-back distances or NPRI or TURI data?<br />
Which air pollutants <strong>to</strong> target as indica<strong>to</strong>rs <strong>of</strong> traffic-related air pollution and why?<br />
How <strong>to</strong> collect data that can be used as indica<strong>to</strong>rs <strong>of</strong> traffic-related air pollution and why (i.e., with<br />
portable air moni<strong>to</strong>rs, air modelling, other).<br />
Are there indirect indica<strong>to</strong>rs that can be used for air pollutants associated with traffic corridors such<br />
as set-back distances and/or traffic volume?<br />
2. We would appreciate feedback on how best <strong>to</strong> communicate the results <strong>of</strong> this project so that it will<br />
useful for <strong>Built</strong> environment stakeholders in Ontario. Do you have any suggestions?<br />
Thank you for your time. If you have any questions or comments, please feel free <strong>to</strong> contact the project<br />
coordina<strong>to</strong>r (contact information) at any time.<br />
-END-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX D<br />
221<br />
APPENDIX D:<br />
KEY INFORMANT INTERVIEW GUIDE – ENVIRONMENTAL EXPOSURES<br />
(AIR QUALITY AND EXTREME HEAT)<br />
Questions for local public health agencies or municipalities key informants<br />
who collect their own air quality data<br />
1.<br />
Does the air modelling and/or moni<strong>to</strong>ring program being run by your municipalities provide data that<br />
could be used as indica<strong>to</strong>rs for air quality as it is affected by the built environment?<br />
a. no (if no, skip <strong>to</strong> question 7)<br />
b. yes (if yes, continue <strong>to</strong> next question)<br />
2.<br />
3.<br />
4.<br />
5.<br />
6.<br />
7.<br />
8.<br />
9.<br />
What air pollutants do you model and/or moni<strong>to</strong>r?<br />
What modelling <strong>to</strong>ols and/or moni<strong>to</strong>ring equipment do you use and for what purpose?<br />
What emissions sources have you targeted with modelling? What was the source <strong>of</strong> data for point<br />
sources? What resolution do you get with the modelling being done?<br />
What types <strong>of</strong> emission sources have had a substantial impact on local air quality in your community?<br />
How frequently will you be repeating the modelling and/or moni<strong>to</strong>ring under the same<br />
parameters if at all?<br />
What budget is directed <strong>to</strong>wards air modelling/air moni<strong>to</strong>ring? Capital and operating?<br />
How is the program managed (i.e., consultants? Internal staff?)<br />
How many FTEs per year are directed at this activity?<br />
10. How is this data currently being used?<br />
11. Are there plans <strong>to</strong> repeat the modelling or moni<strong>to</strong>ring with the same parameters <strong>to</strong><br />
track changes over time?<br />
12. We would appreciate feedback on how best <strong>to</strong> communicate the results <strong>of</strong> this project so that it will<br />
useful for <strong>Built</strong> environment stakeholders in Ontario. Do you have any suggestions?<br />
Thank you for your time. If you have any questions or comments, please feel free <strong>to</strong> contact the project<br />
coordina<strong>to</strong>r (contact information) at any time.<br />
-END-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
222<br />
APPENDIX D<br />
APPENDIX D:<br />
KEY INFORMANT INTERVIEW GUIDE – ENVIRONMENTAL EXPOSURES<br />
(AIR QUALITY AND EXTREME HEAT)<br />
EXTREME HEAT KEY INFORMANTS<br />
Questions for <strong>Environment</strong> Canada key informants<br />
1.<br />
2.<br />
3.<br />
4.<br />
5.<br />
6.<br />
7.<br />
8.<br />
9.<br />
How many weather stations do you operate in Ontario?<br />
What temperature measurements are obtained by these stations? (frequency, height <strong>of</strong> observation etc).<br />
Where are these stations located?<br />
What quality control procedures are in place for station temperature data?<br />
What is the accuracy <strong>of</strong> the temperature data?<br />
For public health units that are concerned about variation in heat across their communities, what advice<br />
would you <strong>of</strong>fer regarding the interpretation <strong>of</strong> station temperature values and their extrapolation across<br />
a wider geography?<br />
What are the limitations <strong>to</strong> using this data?<br />
Does this data come with standardized metadata?<br />
Are there privacy restrictions around this data?<br />
10. How frequently is this data updated?<br />
11. We would appreciate feedback on how best <strong>to</strong> communicate the results <strong>of</strong> this project so that it will<br />
useful for <strong>Built</strong> environment stakeholders in Ontario. Do you have any suggestions?<br />
Thank you for your time. If you have any questions or comments, please feel free <strong>to</strong> contact the project<br />
coordina<strong>to</strong>r (contact information) at any time.<br />
-END-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX D<br />
223<br />
APPENDIX D:<br />
KEY INFORMANT INTERVIEW GUIDE – ENVIRONMENTAL EXPOSURES<br />
(AIR QUALITY AND EXTREME HEAT)<br />
Questions for Natural Resources Canada key informants<br />
1.<br />
2.<br />
3.<br />
4.<br />
5.<br />
6.<br />
7.<br />
8.<br />
9.<br />
When did NRCan start <strong>to</strong> collect LANDSAT thermal imagery?<br />
What years are currently available?<br />
Is there a fee for accessing the imagery?<br />
What level <strong>of</strong> end-user expertise is required <strong>to</strong> use/interpret this imagery?<br />
What geographic areas have been covered by these images?<br />
What is the resolution <strong>of</strong> this imagery?<br />
What is the accuracy?<br />
What benefits are there <strong>to</strong> normalizing several single shot images in<strong>to</strong> an average seasonal image?<br />
How can public health workers use these images <strong>to</strong> assess exposure <strong>to</strong> heat?<br />
10. What are the limitations <strong>of</strong> using this data <strong>to</strong> assess exposure <strong>to</strong> heat?<br />
11. What are the benefits <strong>of</strong> using this data <strong>to</strong> assess exposure <strong>to</strong> heat?<br />
12. How <strong>of</strong>ten do you anticipate these images will be available in the future?<br />
13. Are there other sources from which this imagery can be obtained?<br />
14. Does this data come with standardized metadata?<br />
15. Are there privacy restrictions around this data?<br />
16. How frequently is this data updated?<br />
17. We would appreciate feedback on how best <strong>to</strong> communicate the results <strong>of</strong> this project so that it will<br />
useful for <strong>Built</strong> environment stakeholders in Ontario. Do you have any suggestions?<br />
Thank you for your time. If you have any questions or comments, please feel free <strong>to</strong> contact the project<br />
coordina<strong>to</strong>r (contact information) at any time.<br />
-END-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
224<br />
APPENDIX D<br />
APPENDIX D:<br />
KEY INFORMANT INTERVIEW GUIDE – ENVIRONMENTAL EXPOSURES<br />
(AIR QUALITY AND EXTREME HEAT)<br />
Questions for public health research key informants<br />
1.<br />
2.<br />
3.<br />
4.<br />
5.<br />
6.<br />
7.<br />
8.<br />
9.<br />
How many wet-bulb globe temperature sensors do you use in the syndromic surveillance system for<br />
extreme heat events?<br />
How were their locations decided?<br />
Do their locations reflect a variety <strong>of</strong> land uses?<br />
What is the accuracy <strong>of</strong> these sensors?<br />
What parameters are measured by these sensors?<br />
Does this data come with standardized metadata?<br />
Are there privacy restrictions around this data?<br />
How frequently is this data updated?<br />
Do you also incorporate environment Canada weather data in the system?<br />
10. We would appreciate feedback on how best <strong>to</strong> communicate the results <strong>of</strong> this project so that it will<br />
useful for <strong>Built</strong> environment stakeholders in Ontario. Do you have any suggestions?<br />
Thank you for your time. If you have any questions or comments, please feel free <strong>to</strong> contact the project<br />
coordina<strong>to</strong>r (contact information) at any time.<br />
-END-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX D<br />
225<br />
APPENDIX D:<br />
KEY INFORMANT INTERVIEW GUIDE – ENVIRONMENTAL EXPOSURES<br />
(AIR QUALITY AND EXTREME HEAT)<br />
Questions for Health Canada key informant<br />
1.<br />
Could you describe the <strong><strong>Environment</strong>al</strong> Heat Moni<strong>to</strong>ring Systems?<br />
a. What parameters are collected?<br />
b. How do you determine the locations <strong>of</strong> the moni<strong>to</strong>rs?<br />
c. What is the accuracy <strong>of</strong> these moni<strong>to</strong>rs?<br />
d. What are the limitations <strong>of</strong> using /accessing data?<br />
e. How does this compare <strong>to</strong> data collected by <strong>Environment</strong> Canada weather stations?<br />
f. Are there plans <strong>to</strong> expand these moni<strong>to</strong>rs <strong>to</strong> make this data across the province?<br />
2.<br />
3.<br />
4.<br />
Based on your pilot projects for the heat & health resiliency initiative, what advice would you give <strong>to</strong><br />
public health units in terms <strong>of</strong> using available heat data <strong>to</strong> build heat alert and response programs<br />
(e.g. triggers)?<br />
For public health units that are concerned about variations in heat across their communities,<br />
what advice would you <strong>of</strong>fer regarding the interpretation <strong>of</strong> EHMS data and their extrapolation<br />
across a wider geography?<br />
We would appreciate feedback on how best <strong>to</strong> communicate the results <strong>of</strong> this project so<br />
that it will useful for <strong>Built</strong> environment stakeholders in Ontario. Do you have any suggestions?<br />
-END-<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
226<br />
APPENDIX E<br />
APPENDIX E:<br />
BUILT ENVIRONMENT MEASURES<br />
AND DATA SOURCES SURVEY LETTER OF INVITATION (LOI)<br />
[Date]<br />
Re: Invitation <strong>to</strong> participate in a Public Health Ontario research project<br />
<strong>An</strong> environmental scan <strong>of</strong> built environment data sources related <strong>to</strong> walkability and environmental<br />
exposures in urban Ontario.<br />
Dear [Medical Officer <strong>of</strong> Health; Direc<strong>to</strong>r or <strong><strong>Environment</strong>al</strong> Health, and Direc<strong>to</strong>r <strong>of</strong> Chronic Diseases]<br />
On behalf <strong>of</strong> our investigation team, we would like <strong>to</strong> invite your public health unit <strong>to</strong> participate in a Public Health<br />
Ontario (PHO) funded study. Through PHO’s Locally Driven Collaborative Project (LDCP) initiative, several Ontario<br />
public health agencies are working <strong>to</strong>gether on a project that will support the identification <strong>of</strong> walkability and<br />
environmental exposure indica<strong>to</strong>rs and related data sources. This information will be used in the assessment<br />
<strong>of</strong> the urban built environment in Ontario, subsequently leading <strong>to</strong> the development <strong>of</strong> policy recommendations<br />
based on our findings.<br />
We are gathering insights from public health units with relevant experience in the area <strong>of</strong> the urban built<br />
environment, specifically in relation <strong>to</strong> walkability and environmental exposure indica<strong>to</strong>rs and data sources/<br />
infrastructure. We are inviting representatives from your public health unit <strong>to</strong> participate in this voluntary survey.<br />
Given the in-depth nature <strong>of</strong> this survey, your public health unit may be required <strong>to</strong> contact other departments<br />
and/or municipalities <strong>to</strong> accurately complete this survey. We kindly request that your public health unit complete<br />
the following survey by [date] [link <strong>to</strong> electronic survey]. Note, the questions for walkability and environmental<br />
exposures have been separated <strong>to</strong> facilitate survey completion; please complete both parts.<br />
The results <strong>of</strong> the survey will assist in the development <strong>of</strong> jurisdictional pr<strong>of</strong>iles <strong>of</strong> built environment data<br />
infrastructure across Ontario. In the reporting <strong>of</strong> our results, your public health unit may be identified in the<br />
jurisdictional pr<strong>of</strong>iles outlining data sources and infrastructure. This information will later be applied in the<br />
development <strong>of</strong> policy recommendations. All final results will be published in report format on our project website<br />
(www.builtenvironment.ca).<br />
If you have any questions about this project, then please do not hesitate <strong>to</strong> contact our Project Coordina<strong>to</strong>r,<br />
Popy Dimoulas-Graham, at 226-338-8004. If you have any concerns about your rights as a research participant<br />
please contact - Dr. Albert Clark, Chair <strong>of</strong> the Queen’s University and Affiliated Teaching Hospitals Research<br />
Ethics Board at (613) - 533-6081.<br />
We look forward <strong>to</strong> your participation in this collaborative initiative.<br />
Sincerely,<br />
Paul Belanger and Daphne Mayer<br />
KFL&A Public Health<br />
221 Portsmouth Ave<br />
Kings<strong>to</strong>n, ON K7M 1V5<br />
613 549- 1232<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX E<br />
227<br />
APPENDIX E:<br />
BUILT ENVIRONMENT MEASURES<br />
AND DATA SOURCES SURVEY LETTER OF INVITATION (LOI)<br />
INVESTIGATION TEAM<br />
Paul Belanger (co-lead)<br />
GIS Services Manager<br />
Kings<strong>to</strong>n, Frontenac and Lennox &<br />
Adding<strong>to</strong>n Public Health<br />
Helen Doyle<br />
Manager,<br />
Health Protection Division<br />
York Region Public Health<br />
Deborah Moore<br />
Senior Epidemiologist<br />
Niagara Region Public Health<br />
Daphne Mayer (co-lead)<br />
Research Associate<br />
Kings<strong>to</strong>n, Frontenac and Lennox &<br />
Adding<strong>to</strong>n Public Health<br />
Mira Shnabel<br />
<strong><strong>Environment</strong>al</strong> Health<br />
Program Coordina<strong>to</strong>r<br />
York Region Public Health<br />
Ryan Waterhouse<br />
GIS <strong>An</strong>alyst<br />
Niagara Region Public Health<br />
Asim Qasim<br />
<strong><strong>Environment</strong>al</strong> Research<br />
and Policy <strong>An</strong>alyst<br />
York Region Public Health<br />
Bill Hunter<br />
Manager, <strong><strong>Environment</strong>al</strong> Health<br />
Niagara Region Public Health<br />
Caitlyn Paget<br />
Epidemiologist<br />
York Region Public Health<br />
Ahalya Mahendra<br />
Epidemiologist<br />
Public Health Agency <strong>of</strong> Canada<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
228<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
<strong>Built</strong> <strong>Environment</strong> Measures and <strong>Data</strong><br />
Sources <strong>Related</strong> <strong>to</strong> Walkability and<br />
<strong><strong>Environment</strong>al</strong> Exposures in Ontario<br />
Background<br />
On behalf <strong>of</strong> our investigation team, we would like <strong>to</strong> invite your public health unit <strong>to</strong> participate<br />
in a Public Health Ontario (PHO) funded study. Through PHO’s Locally Driven Collaborative<br />
Project (LDCP) initiative, several public health organizations, including representatives<br />
from the Association <strong>of</strong> Public Health Epidemiologists in Ontario (APHEO) <strong>Built</strong> <strong>Environment</strong><br />
Indica<strong>to</strong>r Subgroup, are working <strong>to</strong>gether on a project that will support the identification <strong>of</strong><br />
walkability and environmental exposure measures (or indica<strong>to</strong>rs) as well as related data sources.<br />
This information will be used in the assessment <strong>of</strong> the urban built environment in Ontario,<br />
and <strong>to</strong> subsequently develop policy recommendations based on our findings.<br />
We are collecting information from Ontario public health units in the area <strong>of</strong> the urban built<br />
environment, specifically in relation <strong>to</strong> walkability and environmental exposure (air quality and<br />
extreme heat) measures and data sources.<br />
Given the in-depth nature <strong>of</strong> this survey, your public health unit may need <strong>to</strong> include other<br />
departments (e.g. Geographic Information System (GIS); Transportation Planning; Land Use<br />
Planning) and/or municipalities, as well as employee(s) with knowledge <strong>of</strong> GIS <strong>to</strong> accurately<br />
complete this survey.<br />
We kindly request that your public health unit complete the following survey by July 31, 2012.<br />
Note, each survey page contains a "save and continue later" but<strong>to</strong>n. Click this but<strong>to</strong>n <strong>to</strong> save<br />
and continue the survey at a later time.<br />
Thank-you,<br />
Project investigation team:<br />
Kings<strong>to</strong>n, Frontenac and Lennox Adding<strong>to</strong>n Public Health (Study Lead)<br />
Niagara Region Public Health<br />
Public Health Agency <strong>of</strong> Canada<br />
Sudbury District Health Unit<br />
York Region Community and Health Services<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
229<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Survey Consent<br />
By submitting this survey electronically, your public health unit is agreeing <strong>to</strong> participate<br />
in this study and is aware <strong>of</strong> the following: This study aims <strong>to</strong> support the identification <strong>of</strong><br />
walkability and environmental exposure measures and data sources that can be used in the<br />
assessment <strong>of</strong> the urban built environment in Ontario. The results <strong>of</strong> the survey will assist<br />
in the development <strong>of</strong> jurisdictional pr<strong>of</strong>iles <strong>of</strong> built environment data infrastructure across<br />
Ontario. In the reporting <strong>of</strong> study results, your public health unit may be identified in the<br />
jurisdictional pr<strong>of</strong>iles outlining data sources and data infrastructure. This information will later<br />
be applied in the development <strong>of</strong> policy recommendations. Participation in this survey is<br />
voluntary. At any time, you can refuse <strong>to</strong> answer certain questions or put an end <strong>to</strong> this survey<br />
without prejudice <strong>to</strong> your public health unit. All final results will be published in report format<br />
on our project website: www.builtenvironment.ca<br />
For any information about the study, you may contact the co-principal investiga<strong>to</strong>rs, Paul<br />
Belanger (613 549-1232 ext. 1602) and Daphne Mayer (613 549-1232 ext. 1125); or the<br />
Project Coordina<strong>to</strong>r, Popy Dimoulas-Graham (226-338-8004).<br />
If you have questions about your rights as a participant in this study, you can contact Dr.<br />
Albert Clark, Chair, Queen’s University Health Sciences<br />
and Affiliated Teaching Hospitals Research Ethics Board at 613 533-6061.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
230<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Please identify which Ontario<br />
Public Health Unit you represent:<br />
Please select from the list below.<br />
Algoma Public Health Unit<br />
Brant County Health Unit<br />
Chatham-Kent Health Unit<br />
City <strong>of</strong> Hamil<strong>to</strong>n - Public Health Services<br />
Durham Region Health Department<br />
Eastern Ontario Health Unit<br />
Elgin-St. Thomas Health Unit<br />
Grey Bruce Health Unit<br />
Haldimand-Norfolk Health Unit<br />
Halibur<strong>to</strong>n, Kawartha, Pine Ridge District Health Unit<br />
... 15 additional choices hidden ...<br />
Region <strong>of</strong> Waterloo, Public Health<br />
Renfrew County and District Health Unit<br />
Simcoe Muskoka District Health Unit<br />
Sudbury and District Health Unit<br />
Thunder Bay District Health Unit<br />
Timiskaming Health Unit<br />
Toron<strong>to</strong> Public Health<br />
Welling<strong>to</strong>n-Dufferin-Guelph Health Unit<br />
Windsor-Essex County Health Unit<br />
York Region Community and Health Services<br />
Please indicate which section(s) <strong>of</strong> the survey you are completing at this time:<br />
Walkability Measures and <strong>Data</strong> Sources<br />
Air Quality Measures and <strong>Data</strong> Sources<br />
Extreme Heat Measures and <strong>Data</strong> Sources<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
231<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Walkability: Measures and <strong>Data</strong> Sources<br />
Does your organization assess walkability in urban environments?<br />
Yes<br />
No<br />
Don't know<br />
How many years has your organization been assessing walkability in urban<br />
environments?<br />
1-5 years<br />
6-10 years<br />
11+ years<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
232<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Please identify which method(s) your<br />
organization uses <strong>to</strong> assess urban<br />
walkability:<br />
Please select all that apply.<br />
Systematic observation (i.e. audit <strong>to</strong>ol)<br />
Self-administered survey (assessing perceptions)<br />
Interview (e.g. RRFSS)<br />
Geographic Information System (GIS) (spatial analytics)<br />
Accelerometers<br />
Other, please specify: __________________________<br />
Does your organization use individual<br />
measures <strong>to</strong> assess urban walkability?<br />
Yes<br />
No<br />
Don't know<br />
Does your organization use an index (or<br />
composite measure) <strong>to</strong> assess urban<br />
walkability?<br />
Yes<br />
No<br />
Don't know<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
233<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Please identify which measures are<br />
being used in the walkability index<br />
(or composite measure) and specify<br />
respective data source(s) for each:<br />
Density, please specify:<br />
Land Use Mix, please specify:<br />
Retail Floor Area, please specify:<br />
Connectivity, please specify:<br />
Other (please specify indica<strong>to</strong>r(s) and related data source(s) for each):<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
234<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Which individual Connectivity measures<br />
does your organization use?<br />
Please select from the list provided below and specify the data source(s) for each.<br />
Does your organization use this measure? If yes, please specify data source:<br />
(e.g. DMTI, CCHS, Census, etc.)<br />
Block size and length<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Intersection density<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Street density<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Connected node ratio<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Alpha index<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Gamma index<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Other, please specify measure(s) and related data source(s) for each:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
235<br />
Which individual Density measures does<br />
your organization use?<br />
Please select from the list provided below and specify the data source(s) for each.<br />
Does your organization use this measure? If yes, please specify data source: (e.g.<br />
DMTI, CCHS, Census, etc.)<br />
Population density<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Residential density<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Employment density<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Other, please specify measure(s) and related data source(s) for each:<br />
Which individual Diversity measures does<br />
your organization use?<br />
Please select from the list provided below and specify the data source(s) for each<br />
Does your organization use this measure? If yes, please specify data source: (e.g.<br />
DMTI, CCHS, Census, etc.)<br />
Land Use Mix (LUM) (or mixed land use)<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Proximity (e.g. <strong>to</strong> school, park, trail, etc.)<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Retail Floor Area (FAR)<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Other, please specify measure(s) and related data source(s) for each:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
236<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Which individual Pedestrian<br />
Oriented Design measures does<br />
your organization use?<br />
Please select from the list provided below and specify the data source(s) for each.<br />
Does your organization use this measure? If yes, please specify data source: (e.g.<br />
DMTI, CCHS, Census, etc.)<br />
Posted speed limits<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Sidewalk width<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Presence <strong>of</strong> traffic circles<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
No. <strong>of</strong> traffic lanes<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Presence <strong>of</strong> street furniture<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
Cleanliness<br />
Yes<br />
Yes, in development<br />
No<br />
Don't know<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
237<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Crime rates<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Presence <strong>of</strong> graffiti<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Street lighting<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Presence <strong>of</strong> abandoned or vacant buildings<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Proximity <strong>to</strong> sources <strong>of</strong> air pollution<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Canopy coverage (trees)<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Other, please specify measure(s) and related data source(s) for each:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
238<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
At what geographic scale does your<br />
organization most commonly assess<br />
urban walkability?<br />
Please select all that apply.<br />
Street Network<br />
Dissemination Block (i.e. area equivalent <strong>to</strong> city block, bounded by intersecting streets)<br />
Dissemination Area (i.e. area composed <strong>of</strong> dissemination blocks with ~400 <strong>to</strong> 700 persons)<br />
Census Tract (i.e. area composed <strong>of</strong> dissemination area’s with ~2500 <strong>to</strong> 8000 persons)<br />
Census Division (Region)<br />
Census Subdivision (i.e. Municipality)<br />
Census Division (i.e. Region)<br />
Don't know<br />
Other, please specify (i.e. cus<strong>to</strong>m geography or street segment or road network)<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
239<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Of the measures currently in use by your<br />
organization, which are <strong>of</strong> most value in<br />
assessing urban walkability?<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
240<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
In the future, which measures would<br />
be <strong>of</strong> most value <strong>to</strong> your organization in<br />
assessing urban walkability?<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
241<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Please identify the data (sources) your<br />
organization has access <strong>to</strong>:<br />
Please select all that apply. If necessary, please inquire about data (sources) from<br />
other departments, municipalities, and/or with employee(s) who have knowledge <strong>of</strong><br />
GIS, in order <strong>to</strong> accurately complete this question.<br />
Business registrations<br />
Census boundary files<br />
Crime statistics<br />
Digital elevation model (<strong>to</strong>pography)<br />
DMTI Spatial<br />
Location <strong>of</strong> trails<br />
Location <strong>of</strong> parks<br />
Location <strong>of</strong> schools<br />
Locations <strong>of</strong> recreation centres<br />
Location <strong>of</strong> traffic controls (e.g. speed bumps)<br />
Location <strong>of</strong> controlled cross walks<br />
Municipal Property Assessment Corporation (MPAC)<br />
Presence <strong>of</strong> sidewalks<br />
Public transit data<br />
Rapid Risk Fac<strong>to</strong>r Surveillance System (RRFSS)<br />
Speed limits per street<br />
Street network files<br />
Street lighting standards<br />
Traffic counts<br />
Other, please specify data source(s):<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
242<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
What major challenges does your<br />
organization face in assessing urban<br />
walkability?<br />
Please select all that apply.<br />
<strong>Data</strong> availability<br />
<strong>Data</strong> quality<br />
<strong>Data</strong> accessibility<br />
Financial capacity<br />
Human resource capacity<br />
Lack <strong>of</strong> Geographic Information Systems (GIS) technical support<br />
Variations between municipalities<br />
No challenges <strong>to</strong> report<br />
Other, please specify:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
243<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Air Quality: Measures and <strong>Data</strong> Sources<br />
Does your organization assess air quality in urban environments?<br />
Yes<br />
No<br />
Don't know<br />
How many years has your organization been assessing air quality in urban environments?<br />
1-5 years<br />
6-10 years<br />
11+ years<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
244<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Does your organization use an index<br />
(or composite measure) <strong>to</strong> assess air<br />
quality?<br />
Yes, the Air Quality Index (AQI)<br />
Yes, the Air Quality Health Index (AQHI)<br />
Yes, a different index:<br />
No<br />
Don't know<br />
Does your organization use readings for<br />
specific air pollutants from nearby Ministry<br />
<strong>of</strong> <strong>Environment</strong> (MOE) air moni<strong>to</strong>ring<br />
stations?<br />
Yes<br />
No<br />
Don't know<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
245<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Which air pollutants does your organization<br />
track?<br />
Please specify the specific air pollutants tracked at each type <strong>of</strong> air moni<strong>to</strong>ring station:<br />
Fine particulate matter (PM2.5)<br />
Ground level ozone (O3)<br />
Nitrogen dioxide (NO2)<br />
Sulphur dioxide (SO2)<br />
Carbon Monoxide (CO)<br />
General MOE air<br />
moni<strong>to</strong>ring stations<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
MOE stations directed<br />
at local industrial sources<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Other, please specify air pollutant(s) and type <strong>of</strong> air moni<strong>to</strong>ring station (as above):<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
246<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Does your organization use any <strong>of</strong> the<br />
following <strong>to</strong> assess air quality?<br />
Please select from the list provided below and specify the data source(s) for each. If<br />
necessary, please inquire about data and relevant data sources from other departments<br />
and/or municipalities in order <strong>to</strong> accurately complete this question. Examples <strong>of</strong><br />
"information describing the data": inven<strong>to</strong>ry describing the available data such as<br />
available time periods, frequency <strong>of</strong> updating, spatial scale, etc.<br />
<strong>Data</strong> from portable or mobile air<br />
moni<strong>to</strong>ring<br />
<strong>Data</strong> from remote sensing<br />
technologies (i.e. satellite based)<br />
Emission estimates (e.g. traffic,<br />
industrial, etc.)<br />
Air modelling estimates (e.g.<br />
dispersion modelling, land use<br />
regression)<br />
Meteorological data from<br />
<strong>Environment</strong> Canada<br />
Do you use this data/<br />
estimate?<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
If yes, is it available as a<br />
spatial map?<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
If yes, do you have information describing the data?<br />
<strong>Data</strong> from stationary air moni<strong>to</strong>ring equipment not collected under the MOE<br />
<strong>Data</strong> from portable or mobile air moni<strong>to</strong>ring equipment<br />
<strong>Data</strong> from remote sensing technologies (i.e. satellite based)<br />
Emission estimates (e.g. traffic, industrial, etc.)<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
247<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Air modelling estimates (e.g. dispersion<br />
modelling, land use regression)<br />
Meteorological data from <strong>Environment</strong> Canada<br />
weather stations (e.g. temperature, humidex,<br />
wind speed)<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Other, please specify data, access, availability <strong>of</strong> spatial map and information (as above):<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
248<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Does your organization have access <strong>to</strong><br />
the following data?<br />
Examples <strong>of</strong> data sources: DMTI, CCHS, Census, organization's own research, etc<br />
Examples <strong>of</strong> "information describing the data": inven<strong>to</strong>ry describing the available<br />
data such as available time periods, frequency <strong>of</strong> updating, spatial scale, etc.<br />
Do you have access?<br />
If yes, specify data source:<br />
If yes, is it available<br />
as a spatial map?<br />
Volume <strong>of</strong> traffic on regional or<br />
municipal roads<br />
Residents and sensitive populations<br />
living within pre-determined distance<br />
from high volume roads<br />
Proximity <strong>of</strong> population(s) <strong>to</strong> emission<br />
sources that report through the National<br />
Pollutant Release Inven<strong>to</strong>ry (NPRI)<br />
Vehicle kilometres travelled (per capita)<br />
in your communities<br />
Modal share (i.e., car, transit, cycling,<br />
walking split) in your communities<br />
Number <strong>of</strong> transit s<strong>to</strong>ps per square<br />
kilometer<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Volume <strong>of</strong> traffic on regional or<br />
municipal roads<br />
Residents and sensitive populations<br />
living within pre-determined distance<br />
from high volume roads<br />
Proximity <strong>of</strong> population(s) <strong>to</strong> emission<br />
sources that report through the National<br />
Pollutant Release Inven<strong>to</strong>ry (NPRI)<br />
If yes, do you have information describing the data?<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
249<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Vehicle kilometres travelled (per capita)<br />
in your communities<br />
Modal share (i.e., car, transit, cycling,<br />
walking split) in your communities<br />
Number <strong>of</strong> transit s<strong>to</strong>ps per square<br />
kilometer<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Other, please specify data, data source, availability <strong>of</strong> spatial map and information (as above):<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
250<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
What major challenges does your<br />
organization face in assessing air quality?<br />
Please check all that apply.<br />
<strong>Data</strong> availability<br />
<strong>Data</strong> quality<br />
<strong>Data</strong> accessibility<br />
Financial capacity<br />
Human resource capacity<br />
Lack <strong>of</strong> Geographic Information Systems (GIS) technical support<br />
Variations between municipalities<br />
No challenges <strong>to</strong> report<br />
Other, please specify:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
251<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Extreme Heat: Measures and <strong>Data</strong><br />
Sources<br />
Does your organization assess extreme heat in urban environments?<br />
Yes<br />
No<br />
Don't know<br />
How many years has your organization been assessing extreme heat in urban<br />
environments?<br />
< 1 year<br />
1-5 years<br />
6-10 years<br />
11+ years<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
252<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Does your organization have access <strong>to</strong><br />
any <strong>of</strong> the following data types related <strong>to</strong><br />
extreme heat?<br />
For each data source (row), please specify:<br />
Meteorological data<br />
<strong>Built</strong> environment data (e.g. land use, forest cover, etc.)<br />
Thermal Imagery<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Does your organization use models<br />
<strong>to</strong> predict extreme heat events and/or<br />
health impacts related <strong>to</strong> extreme heat?<br />
Yes<br />
No<br />
Don't know<br />
Does your organization use demographic<br />
data <strong>to</strong> identify populations more<br />
vulnerable <strong>to</strong> extreme heat?<br />
Yes<br />
No<br />
Don't know<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
253<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
How does your organization gain access<br />
<strong>to</strong> meteorogical data?<br />
<strong>Environment</strong> Canada Moni<strong>to</strong>ring Stations<br />
Other Permanent Moni<strong>to</strong>ring Stations<br />
Other Temporary/Mobile Moni<strong>to</strong>ring Stations<br />
Don't know<br />
For the data sources outside <strong>of</strong> <strong>Environment</strong> Canada, please specify:<br />
Who owns the moni<strong>to</strong>ring stations and data?<br />
What variables are measured?<br />
What meteorological data does your<br />
organization use <strong>to</strong> assess extreme heat?<br />
Temperature<br />
Yes<br />
No<br />
Don’t Know<br />
Not available<br />
Humidex Yes<br />
Yes<br />
No<br />
Don’t Know<br />
Not available<br />
Wind Speed<br />
Yes<br />
No<br />
Don’t Know<br />
Not available<br />
Other, please specify data:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
254<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Which built environment measures<br />
related <strong>to</strong> extreme heat does your<br />
organization have access <strong>to</strong>?<br />
Please select from the list provided below and specify the data source(s) for each. If<br />
necessary, please inquire about data and relevant data sources from other departments<br />
and/or municipalities in order <strong>to</strong> accurately complete this question. Examples<br />
<strong>of</strong> data sources: DMTI, CCHS, Census, organization’s own research, etc. Examples <strong>of</strong><br />
“information describing the data”: inven<strong>to</strong>ry describing the available data such as available<br />
time periods, frequency <strong>of</strong> updating, spatial scale, etc.<br />
If you have access <strong>to</strong> this<br />
data, please specify source:<br />
If you have access, is it<br />
available as a spatial map?<br />
Canopy cover<br />
Age stratification <strong>of</strong> the urban<br />
forest s<strong>to</strong>ck<br />
Urban sprawl index<br />
Average unit size for each land<br />
use type<br />
Building density data<br />
<strong>Data</strong> on building age<br />
<strong>Data</strong>base <strong>of</strong> multiunit residential<br />
buildings without access <strong>to</strong> air<br />
conditioning or cool rooms<br />
Surface reflectivity /albedo by<br />
land use<br />
Surface emissivity by land use<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
255<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
If you have access, do you have information<br />
describing the data?<br />
Canopy cover<br />
Age stratification <strong>of</strong> the urban<br />
forest s<strong>to</strong>ck<br />
Urban sprawl index<br />
Average unit size for each land<br />
use type<br />
Building density data<br />
<strong>Data</strong> on building age<br />
<strong>Data</strong>base <strong>of</strong> multiunit residential<br />
buildings without access <strong>to</strong> air<br />
conditioning or cool rooms<br />
Surface reflectivity /albedo by<br />
land use<br />
Surface emissivity by land use<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Other, please specify data, data source, availability <strong>of</strong> spatial map and information (as above):<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
256<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Which built environment measures<br />
related <strong>to</strong> extreme heat does your<br />
organization use?<br />
Please select from the list provided below and specify the data source(s) for each. If<br />
necessary, please inquire about data and relevant data sources from other departments<br />
and/or municipalities in order <strong>to</strong> accurately complete this question. Examples<br />
<strong>of</strong> data sources: DMTI, CCHS, Census, organization's own research, etc. Examples <strong>of</strong><br />
"information describing the data": inven<strong>to</strong>ry describing the available data such as available<br />
time periods, frequency <strong>of</strong> updating, spatial scale, etc.<br />
Do you use this measure?<br />
If you have access <strong>to</strong> this<br />
data, please specify source:<br />
Canopy cover<br />
Age stratification <strong>of</strong> the urban<br />
forest s<strong>to</strong>ck<br />
Urban sprawl index<br />
Average unit size for each land<br />
use type<br />
Building density data<br />
<strong>Data</strong> on building age<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
257<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
<strong>Data</strong>base <strong>of</strong> multiunit residential<br />
buildings without access <strong>to</strong> air<br />
conditioning or cool rooms<br />
Surface reflectivity<br />
/albedo by land use<br />
Surface emissivity by land use<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
Yes<br />
Yes, in development<br />
No<br />
Don’t know<br />
If you have access, is it available as a spatial map?<br />
Canopy cover<br />
Age stratification <strong>of</strong> the urban<br />
forest s<strong>to</strong>ck<br />
Urban sprawl index<br />
Average unit size for each land<br />
use type<br />
Building density data<br />
<strong>Data</strong> on building age<br />
<strong>Data</strong>base <strong>of</strong> multiunit residential<br />
buildings without access <strong>to</strong> air<br />
conditioning or cool rooms<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
258<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Surface reflectivity /albedo by land use<br />
Surface emissivity by land use<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
If you have access, do you have information describing the data?<br />
Canopy cover<br />
Age stratification <strong>of</strong> the urban<br />
forest s<strong>to</strong>ck<br />
Urban sprawl index<br />
Average unit size for each land<br />
use type<br />
Building density data<br />
<strong>Data</strong> on building age<br />
<strong>Data</strong>base <strong>of</strong> multiunit residential<br />
buildings without access <strong>to</strong> air<br />
conditioning or cool rooms<br />
Surface reflectivity /albedo by<br />
land use<br />
Surface emissivity by land use<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
Yes<br />
No<br />
Don’t Know<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
259<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Other, please specify data, data source, availability <strong>of</strong> spatial map and information (as above):<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
260<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
How does your organization gain access<br />
<strong>to</strong> thermal imagery?<br />
Please check all that apply.<br />
Information is accessed through a regional or local government<br />
Public health unit directly requests information from data source<br />
Information is accessed through the federal government<br />
Other, please specify: __________________________<br />
What type(s) <strong>of</strong> thermal imagery is available <strong>to</strong> your organization?<br />
Landsat Y<br />
Yes<br />
No<br />
Don’t Know<br />
Modis<br />
Yes<br />
No<br />
Don’t Know<br />
Infrared (IR)<br />
Yes<br />
No<br />
Don’t Know<br />
Other, please specify type(s) <strong>of</strong> thermal imagery available:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
261<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
Please provide the following information<br />
as it relates <strong>to</strong> the extreme heat model(s)<br />
used by your organization:<br />
What is the name <strong>of</strong> the model?<br />
What outcome does the model predict?<br />
What inputs does the model require?<br />
What are the data sources for the inputs?<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
262<br />
APPENDIX F<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
What demographic considerations<br />
does your organization use <strong>to</strong> identify<br />
populations more vulnerable <strong>to</strong> extreme<br />
heat?<br />
Please select all that apply:<br />
Age<br />
Gender<br />
Income<br />
Language<br />
Other, please specify:<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX F<br />
263<br />
APPENDIX F:<br />
BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY<br />
What major challenges does your<br />
organization face in assessing extreme<br />
heat?<br />
Please check all that apply.<br />
<strong>Data</strong> availability<br />
<strong>Data</strong> quality<br />
<strong>Data</strong> accessibility<br />
Financial capacity<br />
Human resource capacity<br />
Lack <strong>of</strong> Geographic Information Systems (GIS) technical support<br />
Variations between municipalities<br />
No challenges <strong>to</strong> report<br />
Other, please specify:<br />
Thank you for your time. Click the SUBMIT but<strong>to</strong>n below <strong>to</strong> complete this survey.<br />
Our final study results will be published in report format on our project website:<br />
http://builtenvironment.ca/<br />
If you have any questions, please contact the co-principal investiga<strong>to</strong>rs,<br />
Paul Belanger (613 549-1232 ext. 1602) and Daphne Mayer (613 549-1232 ext. 1125);<br />
or the Project Coordina<strong>to</strong>r, Popy Dimoulas-Graham (226-338-8004).<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
264<br />
APPENDIX G<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
1. Street Network Metadata (2012-07-03)<br />
a) National Road Network (NRN)<br />
Metadata Element<br />
Title / Short Name<br />
Description<br />
Rationale<br />
National Road Network (NRN)<br />
The National Road Network (NRN) product contains quality geospatial and<br />
aspatial data <strong>of</strong> Canadian road phenomena.<br />
Vintage Varies depending on Province/ Terri<strong>to</strong>ry. Ontario release (2012-06)<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing<br />
(projection and datum)<br />
Format<br />
Primary Contact and<br />
Contact Details<br />
Thematic Keywords<br />
Entity and Attribute<br />
Enumeration<br />
Online Resource:<br />
Suitability and<br />
comparability<br />
Yearly<br />
None. Registration required <strong>to</strong> download.<br />
National Coverage (i.e. Provinces and Terri<strong>to</strong>ries)<br />
The planimetric accuracy aimed for the product is 10 meters or better. Under<br />
the data maintenance phase, no systematic validation <strong>of</strong> geometric and<br />
attributive accuracies is performed on all NRN datasets.<br />
Horizontal Datum Name: NAD83CSRS (North American Datum 1983 in<br />
Canadian Spatial Reference System);<br />
Ellipsoid Name: GRS80;<br />
ESRI, KML, GML and WMS<br />
Geobase Technical Support:<br />
Government <strong>of</strong> Canada, Natural Resources Canada, Earth Sciences Sec<strong>to</strong>r<br />
Email: supportGeobase@nrcan.gc.ca<br />
NRCan, Transportation Network, Infrastructure<br />
Major Entities include: Address Range, Alternate Name Link, Blocked<br />
Passage, Ferry Connection, Junction, Road Segment, Street <strong>An</strong>d Place<br />
Names, Toll Points<br />
http://www.geobase.ca/geobase/en/data/nrn/index.html<br />
Standardized national dataset with detailed documentation and regular<br />
update frequency. Maintained from its national road network partners the NRN<br />
incorporates data from the ORN <strong>to</strong> build its Ontario level file.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX G<br />
265<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
b) Statistics Canada Road Network File (RNF)<br />
Metadata Element<br />
Title / Short Name<br />
Description<br />
Rationale<br />
Road Network File (RNF)<br />
The Road Network File is a digital representation <strong>of</strong> Canada’s national road<br />
network, containing information such as street names, types, directions and<br />
address ranges.<br />
Vintage 2005-2011<br />
Cost<br />
Update Frequency<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing<br />
(projection and datum)<br />
Format<br />
Primary Contact and<br />
Contact Details<br />
Thematic Keywords<br />
Entity and Attribute<br />
Enumeration<br />
Online Resource:<br />
Suitability and<br />
comparability<br />
None. Free <strong>to</strong> download.<br />
Yearly<br />
National Coverage (i.e. Provinces and Terri<strong>to</strong>ries)<br />
No formal assessment <strong>of</strong> relative positional accuracy has been undertaken.<br />
In 2011, the ORN provided updates from six Census Divisions (i.e. Hal<strong>to</strong>n,<br />
Hamil<strong>to</strong>n, Ottawa, Peel, Toron<strong>to</strong> and Waterloo) in Ontario.<br />
Horizontal Datum Name:<br />
NAD83 (North American Datum); Ellipsoid Name: GRS80;<br />
ESRI, GML,MapInfo<br />
Statistics Canada National Contact Centre. Phone: 1-800-263-1136<br />
Email: infostats@statcan.gc.ca<br />
Highways, Road transport, Street networks, Streets, Census.<br />
Major Attribute fields names include: Name, Type, Dir, Address Ranges,<br />
Csdname, Csdtype, Cmaname, Prname, Street Rank,Class<br />
http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=92-<br />
500-X<br />
Standardized national dataset with detailed documentation and irregular update<br />
frequency. Designed principally for the purposes <strong>of</strong> conducting Statistics<br />
Canada activities (i.e. census) it maintains that it is not suitable for route<br />
optimization and other critical or engineering applications. With the exception<br />
<strong>of</strong> the census areas that incorporated the ORN, visual comparison with the<br />
other reviewed datasets <strong>of</strong>ten produced inconsistent positional discrepancies.<br />
Additionally, it was found that segmentation also differed from that <strong>of</strong> the NRN,<br />
ORN and CanMap Street file.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
266<br />
APPENDIX G<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
c) Ontario Road Network File (ORN)<br />
Metadata Element<br />
Title / Short Name<br />
Description<br />
Rationale<br />
Ontario Road Network (ORN)<br />
A geospatial database <strong>of</strong> Ontario’s Road Network (ORN) and its<br />
associated attributes, e.g. its street name or road number, its address<br />
information, road classification, etc.<br />
Vintage 2010-02-01<br />
Cost<br />
Update Frequency<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and<br />
datum)<br />
Format<br />
Primary Contact and Contact<br />
Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Online Resource:<br />
None. Registration or membership <strong>to</strong> OGDE required <strong>to</strong> download.<br />
On-going/Continuous<br />
Province <strong>of</strong> Ontario<br />
Within 10 meters (Average)<br />
Horizontal Datum Name:<br />
NAD83 (North American Datum);Ellipsoid Name: GRS80<br />
ESRI, WMS<br />
William Millar,<br />
Ministry <strong>of</strong> Natural Resources,<br />
Land Information Ontario – Support, Email: william.millar@ontario.ca<br />
Transportation, Transport, Highways, Streets, Roads, Mapping,<br />
Emergency Services, Land Use Planning.<br />
Major Attribute fields names include: Street Name, Street Type, Street<br />
Dir, Address Ranges, Road Class, Surface Types, Direction Of Traffic<br />
Flow, Speed Limits .<br />
https://www.appliometadata.lrc.gov.on.ca:/<br />
geonetwork?uuid=c7c7202d-942d-47dc-bb15-259eb71f2551<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.html<br />
Suitability and comparability<br />
Standardized provincial dataset with detailed documentation and<br />
regular update frequency. ORN is the authoritative source <strong>of</strong> roads<br />
data for the Ontario Government.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX G<br />
267<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
d) Table 4.0 CanMap ® Street files (DMTI)<br />
Metadata Element<br />
Title / Short Name<br />
Description<br />
Rationale<br />
CanMap ® Streetfiles<br />
Detailed geospatial database representing nationwide coverage <strong>of</strong><br />
roads and highways.<br />
Vintage 2012<br />
Cost<br />
Update Frequency<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and<br />
datum)<br />
Format<br />
Primary Contact and Contact<br />
Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Online Resource:<br />
Suitability and comparability<br />
Available at no cost through most University and College library’s via<br />
DLI. Interested users outside these institutions should contact DMTI<br />
Spatial as public sec<strong>to</strong>r pricing models vary with requirements etc.<br />
Quarterly, semi-annual, or annual maintenance.<br />
National Coverage (i.e. Provinces and Terri<strong>to</strong>ries)<br />
Ranging from the National Topographic <strong>Data</strong> Base (NTDB) standard<br />
<strong>to</strong><br />
sub-meter accuracy.<br />
Horizontal Datum Name:<br />
NAD83 (North American Datum); Ellipsoid Name: GRS80<br />
ESRI, MapInfo or Cus<strong>to</strong>m Format<br />
DMTI Spatial Inc. 15 Allstate Parkway, Suite 400 Markham, Ontario<br />
L3R 5B4 Canada<br />
Road, Line, Highway, Expressway, Major Road, Local Road, Trail,<br />
Transportation, Center Line, Centre-Line<br />
Street, Address Ranges, PreDir, PreType, Street Name, Street Suffix,<br />
CSDName, FSAName, ProvName & Additional Lookup Tables.<br />
http://www.dmtispatial.com/en/Products/<strong>Data</strong>Management/<br />
CanMapProductSuite/CanMapStreetfiles.aspx<br />
Standardized national dataset with detailed documentation and<br />
regular update frequency. Previous usage in walkability type projects.<br />
Fairly commonplace in research institutions but not all public sec<strong>to</strong>r<br />
health units have access.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
268<br />
APPENDIX G<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
2. Provincial-Level GIS <strong>Data</strong>sets: Topography, Imagery, and Land Use Mix<br />
Topography<br />
a) Digital Elevation Model – Version 2.00 – Provincial Tiled <strong>Data</strong>set<br />
Metadata Source: http://www.mnr.gov.on.ca/en/Business/LIO/index.html<br />
Metadata Element<br />
Title/Short Name<br />
Description<br />
Rationale<br />
Ontario Digital Elevation Model<br />
DEM, Provincial DEM v2.00<br />
<strong>Data</strong>set <strong>of</strong> 3-dimensional raster data which captures terrain elevations.<br />
Available in vec<strong>to</strong>r.<br />
Vintage January 2001 <strong>to</strong> December 2002<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial Referencing (projection<br />
and datum)<br />
Primary Contact and Contact<br />
Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Suitability and comparability<br />
Updated as deemed necessary.<br />
None.<br />
Available from the Ontario Geospatial <strong>Data</strong> Exchange (OGDE),<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.html<br />
Covers the province <strong>of</strong> Ontario <strong>to</strong> the 51 st parallel.<br />
Scale: 10 m in southern Ontario and 20m in northern Ontario.<br />
Horizontal: +/- 10m<br />
Vertical: +/- 5m<br />
Projection: Transverse Merca<strong>to</strong>r (UTM)<br />
Datum: NAD83<br />
Ken Todd, Ministry <strong>of</strong> Natural Resources, 705.755.5023<br />
Forest management, water, water management, environment,<br />
watersheds, digital <strong>to</strong>pographic data base, elevation models, digital<br />
terrain model, water analysis, <strong>to</strong>pography.<br />
Latitude, longitude, elevation.<br />
Additional attribute information is available with data acquisition.<br />
Coverage is available for a large part <strong>of</strong> Ontario. For a walkability index,<br />
the scale may be <strong>to</strong>o coarse.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX G<br />
269<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
b) Shuttle Radar Topography Mission (SRTM)<br />
Metadata Sources: 3-arc-second, http://dds.cr.usgs.gov/srtm/version2_1/Documentation/SRTM_Topo.pdf<br />
30 arc second metadata, http://library.mcmaster.ca/maps/SRTM30_Documentation.pdf<br />
Metadata Element<br />
Title/Short Name<br />
Description<br />
Rationale<br />
Shuttle Radar Topography Mission, SRTM<br />
Near-global elevation data. Digital elevation data consists <strong>of</strong> an ordered<br />
array <strong>of</strong> ground elevations sampled at regular intervals over each grid<br />
area. Aside from estimating point elevations, digital elevation has a variety<br />
<strong>of</strong> applications, including terrain modeling, line <strong>of</strong> sight analysis, water<br />
flow and flooding analysis, and many others.<br />
Vintage 2000<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection<br />
and datum)<br />
Primary Contact and Contact<br />
Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Suitability and comparability<br />
Completed.<br />
None.<br />
<strong>Data</strong>sets available for free <strong>to</strong> the public.<br />
Full coverage <strong>of</strong> Canada.<br />
Resolution: 3 arc-second (90m) and 30 arc-second (1km).<br />
Projection: Universal Transverse Merca<strong>to</strong>r (UTM) and geographic (lat/long)<br />
Datum: WGS84<br />
Available from the USGS,<br />
http://srtm.usgs.gov/index.php and http://earthexplorer.usgs.gov/.<br />
Water bodies, digital terrain elevation, digital elevation model, <strong>to</strong>pography.<br />
Additional theme information available from data acquisition.<br />
Latitude, longitude, elevation. Additional theme information available from<br />
data acquisition.<br />
Available SRTM resolution is between 90m and 1km. SRTM resolution is<br />
coarser than the DEM scale. The SRTM resolution is less suitable for a<br />
walkability index than the DEM.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
270<br />
APPENDIX G<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
Imagery<br />
a) Ontario SPOT Pansharpened Orthoimagery 2005-2010 (SPOT)<br />
Metadata Sources:<br />
http://www.geobase.ca/geobase/en/metadata.do?id=17FB0A31-D9FE-A35B-3BDD-B8AFB93BDB66<br />
http://www.geobase.ca/doc/specs/pdf/GeoBase_Orthoimage_2005-2010_specs_EN.pdf<br />
Metadata Element<br />
Title/Short Name<br />
Description<br />
Rationale<br />
Pansharpened SPOT Imagery, SPOT<br />
Spot 4/5 was acquired and orthorectified for the entire<br />
country by the Natural Resources Canada.<br />
Raster and vec<strong>to</strong>r data available.<br />
Vintage 2005-2010<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and datum)<br />
None planned.<br />
None. Available from GeoBase, www.geobase.ca and<br />
LIO, http://www.mnr.gov.on.ca/en/Business/LIO/<br />
index.html.<br />
Available for entire country and entire coverage <strong>of</strong> the<br />
province <strong>of</strong> Ontario and orthorectified.<br />
Resolution: 10m for panchromatic band and 20 m for<br />
three multispectral bands and 10 m multispectral images<br />
covering Ontario.<br />
Horizontal: +/- 30m<br />
Projection: Transverse Merca<strong>to</strong>r (UTM)<br />
Datum: NAD83<br />
Primary Contact and Contact Details Carey Gibson, Ministry Natural Resources, 705.755.2150<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Suitability and comparability<br />
Farming, Biota, Economy, <strong>Environment</strong>,<br />
ImageryBaseMapsEarthCover, IntelligenceMillitary,<br />
InlandWaters, Structure, Transportation,<br />
UtilitiesCommunication<br />
Attribute information available with dataset.<br />
Coverage is available for the entire province <strong>of</strong> Ontario.<br />
The resolution is finer than DEM and SRTM, but may still<br />
be <strong>to</strong>o coarse for a walkability index.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX G<br />
271<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
b) Digital Raster Acquisition Project Eastern Ontario (DRAPE)<br />
Metadata Source:<br />
http://www.library.carle<strong>to</strong>n.ca/find/gis/geospatial-data/eastern-ontario-air-pho<strong>to</strong>s-drape-2008<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.html<br />
Metadata Element<br />
Title/Short Name<br />
Description<br />
Rationale<br />
Digital Raster Acquisition Project Eastern Ontario (DRAPE)<br />
Digital imagery collected using DMC digital aerial sensors <strong>to</strong> 20<br />
cm resolution for eastern Ontario. Covering 54,000 km sq. Of<br />
Ontario.<br />
Off-leaf conditions.<br />
Raster data.<br />
Imagery available in 1km tiles or mosaiced 20km tiles.<br />
Vintage 2008-2009<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and<br />
datum)<br />
Primary Contact and Contact Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Suitability and comparability<br />
Updated as necessary.<br />
None.<br />
Available from the Ontario Geospatial <strong>Data</strong> Exchange (OGDE),<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.html.<br />
Approximately 54,000 sq km covering an area stretching from<br />
the City <strong>of</strong> Kawartha Lakes/Northumberland County, eastward<br />
along the St. Lawrence River <strong>to</strong> the Quebec border and north <strong>to</strong><br />
the County <strong>of</strong> Renfrew.<br />
20cm and 50cm resolution available.<br />
Projection: Transverse Merca<strong>to</strong>r<br />
Datum: NAD83<br />
Mike Robertson, Ontario Ministry <strong>of</strong> Natural Resources,<br />
705.755.1280<br />
Remote sensing, geographic imagery collections, orthoimage,<br />
elevation, con<strong>to</strong>ur, air pho<strong>to</strong>, aerial pho<strong>to</strong>graphy, aerial pho<strong>to</strong>,<br />
aerial colour pho<strong>to</strong>graphy, pho<strong>to</strong>graphy, raster, remote sensing,<br />
orthopho<strong>to</strong>.<br />
Attribute information available with dataset.<br />
DRAPE provides fine resolution imagery for a walkability index.<br />
Coverage is only available for eastern Ontario.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
272<br />
APPENDIX G<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
c) Landsat 7<br />
Metadata Source: http://library.mcmaster.ca/maps/ogde_ls7.htm<br />
Metadata Element<br />
Rationale<br />
Title/Short Name Landsat 7<br />
Description<br />
Vintage<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and<br />
datum)<br />
Primary Contact and Contact Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Suitability and comparability<br />
Satellite land coverage available for the province <strong>of</strong> Ontario.<br />
Orthorectified imagery over Canada.<br />
Images available are False Colour Infrared and Fused True Colour.<br />
Coverage is generated as mosaics.<br />
Raster format.<br />
1999 <strong>to</strong> 2001, +/- 3 years<br />
None planned.<br />
None. Available at no cost from the Ontario Geospatial <strong>Data</strong><br />
Exchange (OGDE),<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.htm.<br />
A large amount <strong>of</strong> Ontario is covered, but some areas are missing.<br />
<strong>An</strong> index showing the available frames is available from McMaster<br />
University,<br />
http://library.mcmaster.ca/maps/images/ogde_ls7indx.pdf.<br />
Scale: 1:50,000<br />
Resolution: Panchromatic resolution is 15m and other bands are<br />
30m.<br />
Projection: UTM<br />
Datum: NAD83<br />
Cus<strong>to</strong>mer Support Group, Natural Resources <strong>of</strong> Canada,<br />
800.661.2638<br />
Available from Ontario Geospatial <strong>Data</strong> Exchange (OGDE),<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.html<br />
Theme information is available with dataset.<br />
Attribute information is available with dataset.<br />
The geographic coverage <strong>of</strong> Ontario good, but not complete. The<br />
resolution is comparable <strong>to</strong> the Ontario DEM and SRTM and is<br />
coarse for a walkability index.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX G<br />
273<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
d) Southwestern Ontario Orthopho<strong>to</strong>graphy Project (SWOOP)<br />
Metadata Sources:<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.html<br />
http://www.lib.uoguelph.ca/resources/data_resource_centre/geospatial_data_resources/southwestern_<br />
ontario_orthopho<strong>to</strong>graphy_project.cfm<br />
Metadata Element<br />
Title/Short Name<br />
Description<br />
Rationale<br />
Southwestern Ontario Orthopho<strong>to</strong>graphy Project, 2010; SWOOP,<br />
2010<br />
The orthopho<strong>to</strong>graphy provides high-resolution, true colour aerial<br />
coverage for Southwestern Ontario. This data serves as a valuable<br />
information resource, providing a spatial context <strong>to</strong> features<br />
existing on the landscape in 2010.<br />
Vintage 2010<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and<br />
datum)<br />
Not completed. Updates as necessary.<br />
None.<br />
Available at no cost through the Ontario Geospatial <strong>Data</strong><br />
Exchange (OGDE),<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.html.<br />
45,000 sq m covering. Coverage includes: Brant, Bruce, Dufferin,<br />
Elgin, Essex and Chatham, Gray, Haldimand, Huron, Lamb<strong>to</strong>n,<br />
Middlesex, Norfolk, Oxford, Perth, Waterloo, Welling<strong>to</strong>n.<br />
Resolution: 20 cm resolution panchromatic imagery and 40 cm<br />
multi spectral.<br />
Projection: Universal Transverse Merca<strong>to</strong>r (UTM)<br />
Datum: NAD83<br />
Primary Contact and Contact Details Mike Robertson, Ministry <strong>of</strong> Natural Resources, 705.755.1280<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Suitability and comparability<br />
Geographic imagery collections, remote sensing. More information<br />
available with dataset.<br />
Attribute information available with dataset.<br />
Geographic coverage is only available for southwestern Ontario.<br />
The resolution is fine and is appropriate for a walkability study.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
274<br />
APPENDIX G<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
e) Light Detection <strong>An</strong>d Ranging (LiDAR)<br />
Metadata Source: http://agrg.cogs.nscc.ca/projects/LiDAR_Metadata. See Appendix Note 3.<br />
http://www.lidarbasemaps.org/. See Appendix Note 3.<br />
Metadata Element<br />
Title/Short Name<br />
Description<br />
Vintage<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and datum)<br />
Primary Contact and Contact Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Suitability and comparability<br />
Rationale<br />
Light Detection <strong>An</strong>d Ranging, LiDAR<br />
Type <strong>of</strong> remote sensing similar <strong>to</strong> radar. Uses light or laser<br />
beams <strong>to</strong> detect distance <strong>to</strong> surface and calculate elevation<br />
data (ie. DEMs).<br />
2006 <strong>to</strong> Present<br />
Updated as necessary or as new information becomes<br />
available.<br />
None through Applied Geomatics Research Group (AGRG),<br />
http://agrg.cogs.nscc.ca/projects/LiDAR_Metadata<br />
Coverage available for North America. Fragments <strong>of</strong> Ontario<br />
covered.<br />
Resolution: Differs depending on area. Resolution may be as<br />
fine as 0.80m.<br />
Projection: NAD83<br />
Ministry <strong>of</strong> Natural Resources and Ministry <strong>of</strong> <strong>Environment</strong><br />
Thematic keywords are available with datasets.<br />
Elevation (x,y,z), Transportation, Boundaries, Hydrography,<br />
Orthoimagery, Land Cover. More information about attributes<br />
is available with individual datasets.<br />
LiDAR is subject <strong>to</strong> conditions and requires the area <strong>of</strong><br />
interest <strong>to</strong> be well researched before using data. Coverage <strong>of</strong><br />
Ontario is not comprehensive and is fragmented. Resolution is<br />
medium, possibly <strong>to</strong>o coarse for a walkability study.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX G<br />
275<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
Land Use Mix<br />
a) CanVec<br />
Metadata Source: http://geogratis.cgdi.gc.ca/geogratis/en/collection/5460AA9D-54CD-8349-C95E-<br />
1A4D03172FDF.html<br />
Metadata Element<br />
Title/Short Name<br />
Description<br />
Rationale<br />
CanVec<br />
CanVec is a digital car<strong>to</strong>graphic reference product produced by Natural<br />
Resources Canada. CanVec originates from the best available data sources<br />
(Landsat 7, NTDB, and Spot Imagery) covering Canadian terri<strong>to</strong>ry and<br />
<strong>of</strong>fers quality <strong>to</strong>pographic information in vec<strong>to</strong>r format that complies with<br />
international geomatics standards.<br />
Vec<strong>to</strong>r data.<br />
Vintage 1945 <strong>to</strong> 2012<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Bi-annually<br />
None <strong>to</strong> the public. Available for free through Geogratis.<br />
Coverage available for all <strong>of</strong> Canada by consolidating data from best available<br />
sources.<br />
Scale/Resolution Scale: 1:10,000 and 1:50,000<br />
Spatial referencing<br />
(projection and datum)<br />
Primary Contact and<br />
Contact Details<br />
Thematic Keywords<br />
Entity and Attribute<br />
Enumeration<br />
Suitability and<br />
comparability<br />
Projection: Geographic (lat/long)<br />
Datum: NAD83CSRS<br />
Cus<strong>to</strong>mer Support Group, Natural Resources Canada, Earth Sciences Sec<strong>to</strong>r,<br />
Centre for Topographic Information, 800.661.2638<br />
Available from Geogratis, http://geogratis.cgdi.gc.ca<br />
Administrative Boundaries, Buildings and Structures, Energy, Hydrography,<br />
Industrial and Commercial Areas, Places <strong>of</strong> Interest, Relief and Landforms,<br />
Toponymy, Transportation, Vegetation and Water Saturated Soils.<br />
89 entities organised in<strong>to</strong> 11 distribution themes.<br />
See Appendix 4 for description <strong>of</strong> Feature Catalogue and link <strong>to</strong> document.<br />
Considered <strong>to</strong> be the most up-<strong>to</strong>-date source for land use mix data in<br />
Canada. CanVec is recommended <strong>to</strong> replace NTDB. Geographic coverage is<br />
extensive, but the scale is <strong>to</strong>o coarse for a walkability index.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
276<br />
APPENDIX G<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
b) National Topographic <strong>Data</strong> Base (NTDB)<br />
Metadata Source: http://geogratis.cgdi.gc.ca/geogratis/en/collection/F3D83500-2564-D61E-4F37-<br />
FEF860E6DDC0.html;jsessionid=4BB38783CA33505707E655049A394921<br />
Metadata Element<br />
Title/Short Name<br />
Description<br />
Rationale<br />
National Topographic <strong>Data</strong> Base, NTDB<br />
Digital vec<strong>to</strong>r data sets that cover the entire Canadian landmass.<br />
Geomatics Canada has digitised and structured thousands <strong>of</strong><br />
<strong>to</strong>pographic maps, creating a complete and uniform product that<br />
can be highly useful in a broad range <strong>of</strong> industries. The NTDB<br />
includes features such as watercourses, urban areas, railways,<br />
roads, vegetation, and relief.<br />
Vintage 1944-2002<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
As <strong>of</strong> 2008 completed and no updates planned.<br />
None.<br />
Available for no cost <strong>to</strong> the public through Geogratis,<br />
http://geogratis.cgdi.gc.ca.<br />
Coverage available for all <strong>of</strong> Canada.<br />
Scale/Resolution 1:50,000 and 1:250,000<br />
Spatial referencing (projection and<br />
datum)<br />
Primary Contact and Contact Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Suitability and comparability<br />
Datum: NAD83<br />
Projection: Geographic (lat/long)<br />
Cus<strong>to</strong>mer Support Group, Ministry <strong>of</strong> Natural Resources, Earth<br />
Sciences Sec<strong>to</strong>r, Centre for Topographic Information<br />
800.661.2638<br />
Available from Geogratis, http://geogratis.cgdi.gc.ca.<br />
Designated Area, Roads, Manmade Features, Relief and<br />
Landform, General, Hydrology, Hypsography, Administrative<br />
Boundaries, Toponymy, Power Network, Rail network, Road<br />
network, Water saturated soils, and Vegetation.<br />
122 entities divided in<strong>to</strong> 13 themes<br />
See user guide link in Appendix 5.<br />
http://library.mcmaster.ca/maps/ntdb250.htm<br />
NTDB is now static and users should transition <strong>to</strong> CanVec.<br />
Appendix 5 for link <strong>to</strong> Transition Guide.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX G<br />
277<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
c) DMTI – CanMap Route Logistics<br />
Metadata Source:<br />
2010 User Manual, http://gsg.uottawa.ca/geo/metadata/dmti_doc/CanMap_RouteLogistics_v2010_3.pdf<br />
Metadata Element<br />
Title/Short Name<br />
Description<br />
Rationale<br />
CanMap Route Logistics, DMTI<br />
CanMap RouteLogisitics produced by DMTI, can be used for location<br />
based service applications, routing and fleet management, market<br />
analysis, target marketing, site location analysis, cus<strong>to</strong>mer service<br />
and asset management. Calculated gradient, distance and travel<br />
times based on the slope <strong>of</strong> each segment is also available.<br />
Vec<strong>to</strong>r data.<br />
Vintage Current <strong>to</strong> 2011<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and<br />
datum)<br />
Primary Contact and Contact<br />
Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Suitability and comparability<br />
Continuous<br />
Available at no cost <strong>to</strong> university students and faculty for educational<br />
purposes if educational institution is a subscriber.<br />
For all other purposes, must be purchased from the provider, DMTI<br />
Spatial Inc., http://www.dmtispatial.com/.<br />
Coverage available for all <strong>of</strong> Canada.<br />
Resolution: High<br />
Scales: Various<br />
Projection Unprojected latitude and longitude.<br />
Datum: NAD83<br />
DMTI Spatial Inc.<br />
Ivan Barron<br />
877.477.3684 x 2181<br />
Hydrology, Roads, Drainage, Political, Parks, Addresses, Postal<br />
Geography, Vegetation, Recreation, Streets – One way, Streets –<br />
Speed limits, Highways – Speed limits, Highways – Exits, Utilities,<br />
Place Names, Land Use, Boundaries – Political, Industrial, Food, Travel<br />
Time, Census, Services, Cultural, Emergency, Transportation, Train,<br />
Bus, Building – Points, Building – Footprints, Trails, Streets, Highways,<br />
Railroads, Hydrography. See User Guide link above for more details.<br />
Topographic, Point <strong>of</strong> Interest (POI), Streets. See 2010 User Guide link<br />
above for more details.<br />
Great detail and realism for a walkability index. Ability <strong>to</strong> optimise travel<br />
distances and time. Recommended <strong>to</strong> use the dataset with Canada<br />
Base Map (DMTI).<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
278<br />
APPENDIX G<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
d) Ontario Land Parcel - Teranet/Teraview and Municipal Property Assessment Corporation (MPAC)<br />
Metadata Source: http://publicdocs.mnr.gov.on.ca/View.asp?Document_ID=13873&Attachment_ID=43863<br />
Metadata Element<br />
Title/Short Name<br />
Description<br />
Vintage<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Rationale<br />
Ontario Land Parcel, MPAC, POLARIS, Teranet<br />
The Ontario Parcel database was brought <strong>to</strong>gether by the Ministry <strong>of</strong> Natural<br />
Resources, Teranet, and MPAC and consists <strong>of</strong> Ontario’s estimated 4 million<br />
parcels <strong>of</strong> land. It is an integrated set <strong>of</strong> assessment, Crown, and parcel data<br />
layers. The layers consist <strong>of</strong> polygons with unique identifiers and civic addressing<br />
linked <strong>to</strong> the assessment parcel. Can be used for land planning. Three structured<br />
layers are available: Digital Assessment Parcel Fabric (DAPF), Digital Ownership<br />
Parcel Fabric (DOPF), and Digital Crown Parcel Fabric (DCPF).<br />
DAPF is a digital index map describing the limits <strong>of</strong> parcels for property assessment<br />
purposes in Ontario. Contains 4,260,975 parcels with over 80,000 parcels inserted<br />
each year.<br />
DOPF data are compiled using legal information, including cadastral surveys, which<br />
are available in the land registry <strong>of</strong>fices and contain graphical information describing<br />
the limits <strong>of</strong> the properties represented in the POLARIS title database.<br />
DCPF This is a digital index map describing the limits <strong>of</strong> Crown parcels within<br />
Ontario. Some examples include patented parcels, federal parks, First Nations<br />
lands and Crown land-use permits<br />
Vec<strong>to</strong>r.<br />
2002 <strong>to</strong> Present.<br />
Quarterly.<br />
Available at no cost through the Ontario Geospatial <strong>Data</strong> Exchange (OGDE),<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.htm.<br />
Coverage available for all <strong>of</strong> Ontario.<br />
Scale/Resolution Scale: 1:5,000<br />
Spatial referencing<br />
(projection and<br />
datum)<br />
Primary Contact and<br />
Contact Details<br />
Thematic Keywords<br />
Entity and Attribute<br />
Enumeration<br />
Suitability and<br />
comparability<br />
Datum: NAD83<br />
Projection: geographic (lat./long.) and UTM<br />
Ontario Geospatial <strong>Data</strong> Exchange (OGDE)<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.htm<br />
DAPF themes: Assessment Parcel, Parcel, assessment (estimation in general), land<br />
management, MPAC.<br />
DCPF themes: Crown parcel<br />
DOPF themes: Ownership parcel<br />
Additional theme information is available with data acquisition. See Appendix 6.<br />
See Metadata source link above for attributes contained in layers. Additional theme<br />
information is available with data acquisition. See Appendix 6.<br />
Geographic coverage is complete and frequently up-dated for the province <strong>of</strong><br />
Ontario. Easily integrated with land data from other resources.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX G<br />
279<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
3. Street Network Metadata (2012-07-03)<br />
a) Table 1.0 National Road Network (NRN)<br />
Metadata Element<br />
Title / Short Name<br />
Description<br />
Vintage<br />
Update Frequency<br />
Cost<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and datum)<br />
Format<br />
Primary Contact and Contact Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Online Resource:<br />
Suitability and comparability<br />
Rationale<br />
National Road Network (NRN)<br />
The National Road Network (NRN) product contains quality<br />
geospatial and aspatial data <strong>of</strong> Canadian road phenomena.<br />
Varies depending on Province/ Terri<strong>to</strong>ry. Ontario release<br />
(2012-06)<br />
Yearly<br />
None. Registration required <strong>to</strong> download.<br />
National Coverage(i.e. Provinces and Terri<strong>to</strong>ries)<br />
The planimetric accuracy aimed for the product is 10<br />
meters or better. Under the data maintenance phase, no<br />
systematic validation <strong>of</strong> geometric and attributive accuracies<br />
is performed on all NRN datasets.<br />
Horizontal Datum Name: NAD83CSRS (North American<br />
Datum 1983 in Canadian Spatial Reference System);<br />
Ellipsoid Name: GRS80;<br />
ESRI, KML, GML and WMS<br />
Geobase Technical Support:<br />
Government <strong>of</strong> Canada, Natural Resources Canada, Earth<br />
Sciences Sec<strong>to</strong>r<br />
Email: supportGeobase@nrcan.gc.ca<br />
NRCan, Transportation Network, Infrastructure<br />
Major Entities include: Address Range, Alternate Name<br />
Link, Blocked Passage, Ferry Connection, Junction, Road<br />
Segment, Street <strong>An</strong>d Place Names, Toll Points<br />
http://www.geobase.ca/geobase/en/data/nrn/index.html<br />
Standardized national dataset with detailed documentation<br />
and regular update frequency. Maintained from its national<br />
road network partners the NRN incorporates data from the<br />
ORN <strong>to</strong> build its Ontario level file.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
280<br />
APPENDIX G<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
b) Statistics Canada Road Network File (RNF)<br />
Metadata Element<br />
Title / Short Name<br />
Description<br />
Rationale<br />
Road Network File (RNF)<br />
The Road Network File is a digital representation <strong>of</strong> Canada’s national road<br />
network, containing information such as street names, types, directions and<br />
address ranges.<br />
Vintage 2005-2011<br />
Cost<br />
Update Frequency<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing<br />
(projection and datum)<br />
Format<br />
Primary Contact and<br />
Contact Details<br />
Thematic Keywords<br />
Entity and Attribute<br />
Enumeration<br />
Online Resource:<br />
None. Free <strong>to</strong> download.<br />
Yearly<br />
National Coverage (i.e. Provinces and Terri<strong>to</strong>ries)<br />
No formal assessment <strong>of</strong> relative positional accuracy has been undertaken.<br />
In 2011, the ORN provided updates from six Census Divisions (i.e. Hal<strong>to</strong>n,<br />
Hamil<strong>to</strong>n, Ottawa, Peel, Toron<strong>to</strong> and Waterloo) in Ontario.<br />
Horizontal Datum Name:<br />
NAD83 (North American Datum); Ellipsoid Name: GRS80;<br />
ESRI, GML,MapInfo<br />
Statistics Canada National Contact Centre. Phone: 1-800-263-1136<br />
Email: infostats@statcan.gc.ca<br />
Highways, Road transport, Street networks, Streets, Census.<br />
Major Attribute fields names include: Name, Type, Dir, Address Ranges,<br />
Csdname, Csdtype, Cmaname, Prname, Street Rank,Class<br />
http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=92-<br />
500-X<br />
Suitability and comparability<br />
Standardized national dataset with detailed documentation and irregular<br />
update frequency. Designed principally for the purposes <strong>of</strong> conducting<br />
Statistics Canada activities (i.e. census) it maintains that it is not suitable for<br />
route optimization and other critical or engineering applications. With the<br />
exception <strong>of</strong> the census areas that incorporated the ORN, visual comparison<br />
with the other reviewed datasets <strong>of</strong>ten produced inconsistent positional<br />
discrepancies. Additionally, it was found that segmentation also differed from<br />
that <strong>of</strong> the NRN, ORN and CanMap Street file.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX G<br />
281<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
c) 3.0 Ontario Road Network File (ORN)<br />
Metadata Element<br />
Title / Short Name<br />
Description<br />
Rationale<br />
Ontario Road Network (ORN)<br />
A geospatial database <strong>of</strong> Ontario’s Road Network (ORN) and its<br />
associated attributes, e.g. its street name or road number, its<br />
address information, road classification, etc.<br />
Vintage 2010-02-01<br />
Cost<br />
Update Frequency<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and<br />
datum)<br />
Format<br />
Primary Contact and Contact Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Online Resource:<br />
None. Registration or membership <strong>to</strong> OGDE required <strong>to</strong> download.<br />
On-going/Continuous<br />
Province <strong>of</strong> Ontario<br />
Within 10 meters (Average)<br />
Horizontal Datum Name:<br />
NAD83 (North American Datum);Ellipsoid Name: GRS80<br />
ESRI, WMS<br />
William Millar,<br />
Ministry <strong>of</strong> Natural Resources,<br />
Land Information Ontario – Support,<br />
Email: william.millar@ontario.ca<br />
Transportation, Transport, Highways, Streets, Roads, Mapping,<br />
Emergency Services, Land Use Planning.<br />
Major Attribute fields names include: Street Name, Street Type,<br />
Street Dir, Address Ranges, Road Class, Surface Types, Direction<br />
Of Traffic Flow, Speed Limits .<br />
https://www.appliometadata.lrc.gov.on.ca:/<br />
geonetwork?uuid=c7c7202d-942d-47dc-bb15-259eb71f2551<br />
http://www.mnr.gov.on.ca/en/Business/LIO/index.html<br />
Suitability and comparability<br />
Standardized provincial dataset with detailed documentation and<br />
regular update frequency. ORN is the authoritative source <strong>of</strong> roads<br />
data for the Ontario Government.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
282<br />
APPENDIX G<br />
APPENDIX G:<br />
GIS META DATA – WALKABILITY<br />
d) Table 4.0 CanMap® Street files (DMTI)<br />
Metadata Element<br />
Title / Short Name<br />
Description<br />
Rationale<br />
CanMap ® Streetfiles<br />
Detailed geospatial database representing nationwide coverage <strong>of</strong><br />
roads and highways.<br />
Vintage 2012<br />
Cost<br />
Update Frequency<br />
Geographic Coverage<br />
Scale/Resolution<br />
Spatial referencing (projection and<br />
datum)<br />
Format<br />
Primary Contact and Contact Details<br />
Thematic Keywords<br />
Entity and Attribute Enumeration<br />
Online Resource:<br />
Suitability and comparability<br />
Available at no cost through most University and College library’s<br />
via DLI. Interested users outside these institutions should<br />
contact DMTI Spatial as public sec<strong>to</strong>r pricing models vary with<br />
requirements etc.<br />
Quarterly, semi-annual, or annual maintenance.<br />
National Coverage (i.e. Provinces and Terri<strong>to</strong>ries)<br />
Ranging from the National Topographic <strong>Data</strong> Base (NTDB)<br />
standard <strong>to</strong> sub-meter accuracy.<br />
Horizontal Datum Name:<br />
NAD83 (North American Datum); Ellipsoid Name: GRS80<br />
ESRI, MapInfo or Cus<strong>to</strong>m Format<br />
DMTI Spatial Inc. 15 Allstate Parkway, Suite 400 Markham,<br />
Ontario L3R 5B4 Canada<br />
Road, Line, Highway, Expressway, Major Road, Local Road, Trail,<br />
Transportation, Center Line, Centre-Line<br />
Street, Address Ranges, PreDir, PreType, Street Name, Street<br />
Suffix, CSDName, FSAName, ProvName & Additional Lookup<br />
Tables.<br />
http://www.dmtispatial.com/en/Products/<strong>Data</strong>Management/<br />
CanMapProductSuite/CanMapStreetfiles.aspx<br />
Standardized national dataset with detailed documentation and<br />
regular update frequency. Previous usage in walkability type<br />
projects. Fairly commonplace in research institutions but not all<br />
public sec<strong>to</strong>r health units have access.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX H<br />
283<br />
APPENDIX H:<br />
GIS META DATA – AIR QUALITY<br />
Metadata<br />
Element<br />
Air Quality<br />
Title /<br />
Short Name<br />
Description<br />
Ambient Air Quality<br />
Network<br />
The Ambient Air Quality<br />
Network provides AQI<br />
readings and hourly air<br />
pollutant concentrations<br />
measured from ambient air<br />
moni<strong>to</strong>ring stations.<br />
Air Quality Health<br />
Index<br />
The AQHI is an index<br />
incorporating health<br />
outcomes with<br />
ambient pollutant<br />
levels<br />
National Pollutant<br />
Release Inven<strong>to</strong>ry<br />
The NPRI collects data<br />
on pollutant emissions<br />
from industrial and nonindustrial<br />
sources<br />
Vintage 2000-Present 1993-2010 1998-2008<br />
Update<br />
Frequency<br />
Geographic<br />
Coverage<br />
Hourly Hourly <strong>An</strong>nual emissions <strong>An</strong>nual<br />
Cost None None None None<br />
Ontario Canada (Ontario) Canada (Ontario) Ontario<br />
Scale/Resolution n/a n/a<br />
Spatial<br />
referencing<br />
(projection and<br />
datum)<br />
Primary Contact<br />
and Contact<br />
Details<br />
WG84 decimal degrees<br />
Publically available<br />
information<br />
Publically available<br />
information (not on<br />
website)<br />
Emission density (10km<br />
x 10km)<br />
WG84 decimal degrees<br />
(facility location),<br />
jpeg (aggregate <strong>of</strong><br />
emission sources), Kmz<br />
spatial maps<br />
Publically available<br />
information<br />
<strong>Environment</strong> Canada<br />
1-877-877-8375<br />
inrp-npri@ec.gc.ca<br />
Provincial Highway Traffic<br />
volume<br />
Ontario Ministry <strong>of</strong> Transportation<br />
annual publication on traffic volume<br />
and accident rates <strong>of</strong> provincial<br />
highways in Ontario<br />
n/a<br />
Highway segment defined by<br />
intersecting or proximate roads<br />
(e.g. W END GARDEN CITY<br />
SKYWAY BRIDGE)<br />
Publically available information<br />
Traffic Office (905)-704-2960<br />
Thematic<br />
Keywords<br />
Air quality index, AQI,<br />
pollutants, ozone, O3,<br />
nitrogen dioxide, NO2, nitric<br />
oxide, NO, nitrogen oxides,<br />
NOx, sulphur dioxide, SO2,<br />
particulate matter, PM2.5,<br />
carbon monoxide, CO,<br />
AQHI, ozone, O3,<br />
Nitrogen dioxide,<br />
NO2, particulate<br />
matter, PM2.5,<br />
PM10<br />
Industry, emissions,<br />
pollutants, emission<br />
density<br />
Traffic, road, highway, traffic<br />
volume, <strong>An</strong>nual Average Daily<br />
Traffic volume (AADT), Summer<br />
Average Daily Traffic volume<br />
(SADT), Summer Average Weekday<br />
Traffic volume<br />
(SAWDT), Winter Average Daily<br />
Traffic volume (WADT), Accident<br />
Rate (AR)<br />
Entity and<br />
Attribute<br />
Enumeration<br />
-Address location<br />
-AQI<br />
-Pollutant<br />
-Station type (urban/rural,<br />
etc)<br />
-Elevation<br />
-Air intake height<br />
-AQHI reading<br />
-Time and Date<br />
-<strong>An</strong>nual <strong>to</strong>tal emissions<br />
-Various facility<br />
descrip<strong>to</strong>rs<br />
-census location<br />
-<strong>An</strong>nual Average Daily Traffic<br />
-Summer Average Daily Traffic;<br />
-Summer Average Weekday Traffic;<br />
-Winter Average Daily Traffic;<br />
-Length <strong>of</strong> section (km)<br />
-Road pattern type<br />
-Accident rate<br />
-Location description<br />
Suitability and<br />
comparability<br />
The data source is suitable<br />
as a general indica<strong>to</strong>r <strong>of</strong> air<br />
quality. It provides limited<br />
spatial detail for the built<br />
environment. Some sites<br />
do not capture all pollutants<br />
<strong>of</strong> interest, and may have<br />
missing his<strong>to</strong>rical data.<br />
<strong>Data</strong> gaps exist for some<br />
years. Spatial detail on<br />
distribution <strong>of</strong> pollutant<br />
is limited.<br />
The information provided is suitable<br />
indica<strong>to</strong>r <strong>of</strong> traffic volumes for<br />
significant sources <strong>of</strong> pollution.<br />
However, the information only<br />
covers provincial highways and<br />
does not provide additional<br />
information on city roads.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
284<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX I<br />
285<br />
APPENDIX I:<br />
GIS META DATA – EXTREME HEAT<br />
APPENDIX I<br />
APPENDIX I:GIS META DATA – EXTREME HEAT<br />
Metadata Element<br />
Title / Short Name<br />
Common name <strong>of</strong> the<br />
dataset<br />
Description<br />
More complete description<br />
Vintage<br />
Timeframe that the data<br />
refers <strong>to</strong><br />
Update Frequency To<br />
assess how current the<br />
dataset may be at any<br />
given time<br />
Cost<br />
Cost <strong>to</strong> a public-benefit<br />
corporation and limits on<br />
redistribution <strong>of</strong> derived<br />
layers/ information<br />
products<br />
Geographic Coverage<br />
Relevant areal coverage<br />
Scale/Resolution<br />
<strong>Data</strong> Sources & Sets<br />
Southeastern Ontario<br />
Landsat Aster Modis Aerial Imagery Weather Stations<br />
environmental heat<br />
Census<br />
moni<strong>to</strong>ring network<br />
Wet-bulb globe temperature<br />
sensors were installed over SE<br />
Advanced Spaceborne Thermal<br />
Information <strong>of</strong><br />
Landsat 7 Orthorectified Imagery over<br />
Moderate Resolution Imaging<br />
Cus<strong>to</strong>m Aerial Image Acquisition <strong>Environment</strong> Canada Weather Stations / National Ontario <strong>to</strong> capture real-time heat<br />
Emission and Reflection<br />
socio- economic and<br />
Canada<br />
Spectrometer<br />
(e.g. First Base Solutions)<br />
Climate <strong>Data</strong><br />
data (air temperature, humidity,<br />
Radiometer<br />
demographic attributes<br />
wind, speed, solar load, and<br />
waterless wet bulb temperature).<br />
2000 - present (with First Base Hourly; Daily; Monthly; Almanac (Period <strong>of</strong> record varies<br />
1984 - present 1999 - present 1999 - present<br />
2009-Present Up <strong>to</strong> 2011<br />
Solutions)<br />
by station)<br />
Currently operating under<br />
a Queens REB so there are<br />
Every 16 days Varies by request, 4-16 day range 2x per day Varies by request Varies depending on station <strong>of</strong> interest<br />
Every 5 years<br />
restrictions and it has not been<br />
released <strong>to</strong> others.<br />
Free: Hourly (temp, dew point temp, relative humidity,<br />
None. His<strong>to</strong>rical LANDSAT data archive<br />
wind speed, wind direction, station pressure, humidex,<br />
is completely open, accessed over the Varies, go <strong>to</strong> http://eros.usgs.<br />
Varies, go <strong>to</strong> http://www.<br />
weather); daily (max/ min/mean temp, cooling degree<br />
No cost, though data cannot yet No cost for standard data<br />
web through established data centres or gov/#/Find_<strong>Data</strong>/Products_and_ None<br />
firstbasesolutions.com/contact. days); monthly (max/ min/mean temp, extreme max<br />
be released.<br />
products<br />
USGS centres such as Earth Resources <strong>Data</strong>_Available/Aster<br />
php<br />
temp. Other observations not available online can only<br />
Observation System (EROS).<br />
be obtained by order <strong>of</strong> a cost-recovered cus<strong>to</strong>mized<br />
dataset.<br />
SE Ontario (PHUs involved:<br />
Hastings & Prince Edward<br />
Canada Canada Canada Canada Canada (coverage is limited <strong>to</strong> station availability)<br />
Canada<br />
; Leeds, Grenville & Lanark;<br />
KLF&A, Peterborough)<br />
15m (VNIR) - 90m (TIR), 60km<br />
30m-60m, 185km swath<br />
250m - 1km, 10km swath 3cm - 65cm Individual stations Individual sensors Census block<br />
swath<br />
Spatial referencing<br />
(projection and datum)<br />
For GIS ingest<br />
NAD1983 NAD1983 NAD1983 Cus<strong>to</strong>mizable<br />
Contains latitude and longitude (Degrees & minutes)<br />
that can be used <strong>to</strong> create a GIS shape file. This can<br />
be provided by ordering a cus<strong>to</strong>mized dataset.<br />
WGS84 Geographic Coordinates<br />
<strong>of</strong> each station are available<br />
Census tract (larger) and<br />
dissemination area (smaller).<br />
Primary Contact and<br />
Contact Details Who <strong>to</strong><br />
contact <strong>to</strong> discuss access<br />
NRCan, Earth Sciences Sec<strong>to</strong>r, Centre<br />
for Topographic Information 010- 2144<br />
King Street West, Sherbrooke, Quebec<br />
J1J 2E8<br />
1-819-564-5600,<br />
geoginfo@NRCan.gc.ca<br />
USGS National Center<br />
12201 Sunrise Valley Dr<br />
Res<strong>to</strong>n, VA 20192, USA<br />
703-648-5953<br />
custserv@usgs.gov<br />
USGS National Center<br />
12201 Sunrise Valley Dr<br />
Res<strong>to</strong>n, VA 20192, USA<br />
703-648-5953<br />
custserv@usgs.gov<br />
First Base Solutions<br />
100-140 Renfrew Drive<br />
Markham, ON L3R 6B3<br />
Phone: 905.477.3600<br />
sales@firstbasesolutions.com<br />
EC -Ontario<br />
Tel: 1 900 565-1111 ($2.99/min)<br />
ontario.climate@ec.gc.ca<br />
Dr. Ge<strong>of</strong>f Hall<br />
Department <strong>of</strong> Civil Engineering<br />
Department <strong>of</strong> Family Medicine<br />
Queen’s University<br />
Kings<strong>to</strong>n, Ontario<br />
K7L 3N6<br />
Statistics Canada<br />
150 Tunney’s Pasture<br />
Driveway<br />
Ottawa, ON K1A 0T6<br />
1-800-263-1136<br />
infostats@statcan.gc.ca<br />
Thematic Keywords<br />
For searching<br />
Entity & Attribute<br />
Enumeration Besides<br />
georeferencing, what<br />
attribute data are<br />
captured?<br />
Suitability and<br />
comparability<br />
How suitable is it for<br />
generating the metrics <strong>of</strong><br />
interest and/or, how does it<br />
compare?<br />
Source<br />
Landsat, NRCan, thermal, IR, infrared,<br />
USGS, raster<br />
Pixel digital number (converted <strong>to</strong><br />
degrees Celsius)<br />
Useful for heat primarily. Compares well<br />
<strong>to</strong> other data sources that estimate<br />
surface temperature. Satellite may be<br />
ending useful lifespan.<br />
Natural Resources Canada. Satellite<br />
Acquisition Services [Internet]. Ottawa<br />
(ON): Public Works and Government<br />
Services Canada; [updated 2009 Jul;<br />
cited 2012 Oct ]. Available from: http://<br />
www.nrcan.gc.ca/earth-sciences/<br />
products-services/satellitepho<strong>to</strong>graphy-imagery/satelliteacquisition-services/2350.<br />
Aerial, orthopho<strong>to</strong>, image, IR, infra Heat, temperature, environment, Canada, station, Heat sensors, wet bulb globe,<br />
Aster, USGS<br />
MODIS, USGS<br />
Census, demographic<br />
red<br />
his<strong>to</strong>rical, weather, data<br />
heat stress<br />
Was not assessed Was not assessed Was not assessed Was not assessed Was not assessed Was not assessed<br />
Useful for obtaining thermal imagery<br />
at a high resolution, but this will only<br />
Useful for creation <strong>of</strong><br />
Generates air temperatures, but sparse spatial<br />
Was not assessed<br />
Was not assessed<br />
be surface temperature, so again,<br />
Was not assessed<br />
community vulnerability<br />
coverage<br />
limited in its application given the<br />
indices<br />
cost <strong>of</strong> acquisition<br />
Earth Resources Observation and<br />
Belanger, P. (Department<br />
Science (EROS) Center. Advanced Earth Resources Observation and First Base Solutions.<br />
<strong>of</strong> Geography, Queen’s<br />
Statistics Canada. Census.<br />
<strong>Environment</strong> Canada.<br />
Spaceborne Thermal Emission and Science (EROS) Center. Moderate Aerial Imagery and<br />
University, Kings<strong>to</strong>n, Frontenac, [Internet]. Ottawa (ON):<br />
National Climate <strong>Data</strong> and Information Archive<br />
Reflection Radiometer (ASTER) Resolution Imaging Spectroradiometer Orthopho<strong>to</strong>graphy [Internet].<br />
Lennox and Adding<strong>to</strong>n Public Statistics Canada. [ cited<br />
[Internet]. [Frederic<strong>to</strong>n, (NB): Public Works and<br />
[Internet]. [Sioux Falls, (SD) ]: U.S. (MODIS) [Internet]. [Sioux Falls, (SD) ]: Markham (ON): First Base Solutions;<br />
Health Unit, Kings<strong>to</strong>n, ON), 2012 Nov]. Available<br />
Government Services Canada; [updated 2012 Nov;<br />
Geological Survey; [ updated 2012 U.S. Geological Survey; [ updated 2012 c2001-2012 [cited 2012 Nov].<br />
Conversation with: Ge<strong>of</strong>f Hall ( from: http://www12.<br />
cited 2012 Nov]. Available from:<br />
Nov; cited 2012 Oct . Available Jul; cited 2012 Oct. Available from: Available from:<br />
Department <strong>of</strong> Civil Engineering, statcan.gc.ca/censusrecensement/index-eng.<br />
http://www.climate.weather<strong>of</strong>fice.gc.ca/<br />
from: http://eros.usgs.gov/#/ http://eros.usgs.gov/#/Find_<strong>Data</strong>/ http://www.firstbasesolutions.<br />
Department <strong>of</strong> Family Medicine,<br />
Welcome_e.html<br />
Find_<strong>Data</strong>/Products_and_<strong>Data</strong>_ Products_and_<strong>Data</strong>_Available/MODIS com/imagery.php<br />
Queen’s University, Kings<strong>to</strong>n, cfm<br />
Available/Aster<br />
ON). 2012 Oct.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario<br />
Click <strong>to</strong> open the full table.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
APPENDIX I<br />
APPENDIX I:GIS META DATA – EXTREME HEAT<br />
Metadata Element<br />
Title / Short Name<br />
Common name <strong>of</strong> the<br />
dataset<br />
Description<br />
More complete description<br />
Vintage<br />
Timeframe that the data<br />
refers <strong>to</strong><br />
Update Frequency To<br />
assess how current the<br />
dataset may be at any<br />
given time<br />
Cost<br />
Cost <strong>to</strong> a public-benefit<br />
corporation and limits on<br />
redistribution <strong>of</strong> derived<br />
layers/ information<br />
products<br />
Geographic Coverage<br />
Relevant areal coverage<br />
Scale/Resolution<br />
<strong>Data</strong> Sources & Sets<br />
Landsat Aster Modis Aerial Imagery Weather Stations<br />
Landsat 7 Orthorectified Imagery over<br />
Canada<br />
Advanced Spaceborne Thermal<br />
Emission and Reflection<br />
Radiometer<br />
Moderate Resolution Imaging<br />
Spectrometer<br />
1984 - present 1999 - present 1999 - present<br />
Cus<strong>to</strong>m Aerial Image Acquisition<br />
(e.g. First Base Solutions)<br />
2000 - present (with First Base<br />
Solutions)<br />
<strong>Environment</strong> Canada Weather Stations / National<br />
Climate <strong>Data</strong><br />
Hourly; Daily; Monthly; Almanac (Period <strong>of</strong> record varies<br />
by station)<br />
Every 16 days Varies by request, 4-16 day range 2x per day Varies by request Varies depending on station <strong>of</strong> interest<br />
None. His<strong>to</strong>rical LANDSAT data archive<br />
is completely open, accessed over the<br />
web through established data centres or<br />
USGS centres such as Earth Resources<br />
Observation System (EROS).<br />
Varies, go <strong>to</strong> http://eros.usgs.<br />
gov/#/Find_<strong>Data</strong>/Products_and_<br />
<strong>Data</strong>_Available/Aster<br />
None<br />
Varies, go <strong>to</strong> http://www.<br />
firstbasesolutions.com/contact.<br />
php<br />
Free: Hourly (temp, dew point temp, relative humidity,<br />
wind speed, wind direction, station pressure, humidex,<br />
weather); daily (max/ min/mean temp, cooling degree<br />
days); monthly (max/ min/mean temp, extreme max<br />
temp. Other observations not available online can only<br />
be obtained by order <strong>of</strong> a cost-recovered cus<strong>to</strong>mized<br />
dataset.<br />
Canada Canada Canada Canada Canada (coverage is limited <strong>to</strong> station availability)<br />
30m-60m, 185km swath<br />
15m (VNIR) - 90m (TIR), 60km<br />
swath<br />
Southeastern Ontario<br />
environmental heat<br />
moni<strong>to</strong>ring network<br />
Wet-bulb globe temperature<br />
sensors were installed over SE<br />
Ontario <strong>to</strong> capture real-time heat<br />
data (air temperature, humidity,<br />
wind, speed, solar load, and<br />
waterless wet bulb temperature).<br />
Census<br />
2009-Present Up <strong>to</strong> 2011<br />
Currently operating under<br />
a Queens REB so there are<br />
restrictions and it has not been<br />
released <strong>to</strong> others.<br />
No cost, though data cannot yet<br />
be released.<br />
SE Ontario (PHUs involved:<br />
Hastings & Prince Edward<br />
; Leeds, Grenville & Lanark;<br />
KLF&A, Peterborough)<br />
Information <strong>of</strong><br />
socio- economic and<br />
demographic attributes<br />
Every 5 years<br />
No cost for standard data<br />
products<br />
250m - 1km, 10km swath 3cm - 65cm Individual stations Individual sensors Census block<br />
Canada<br />
Spatial referencing<br />
(projection and datum)<br />
For GIS ingest<br />
NAD1983 NAD1983 NAD1983 Cus<strong>to</strong>mizable<br />
Contains latitude and longitude (Degrees & minutes)<br />
that can be used <strong>to</strong> create a GIS shape file. This can<br />
be provided by ordering a cus<strong>to</strong>mized dataset.<br />
WGS84 Geographic Coordinates<br />
<strong>of</strong> each station are available<br />
Census tract (larger) and<br />
dissemination area (smaller).<br />
Primary Contact and<br />
Contact Details Who <strong>to</strong><br />
contact <strong>to</strong> discuss access<br />
NRCan, Earth Sciences Sec<strong>to</strong>r, Centre<br />
for Topographic Information 010- 2144<br />
King Street West, Sherbrooke, Quebec<br />
J1J 2E8<br />
1-819-564-5600,<br />
geoginfo@NRCan.gc.ca<br />
USGS National Center<br />
12201 Sunrise Valley Dr<br />
Res<strong>to</strong>n, VA 20192, USA<br />
703-648-5953<br />
custserv@usgs.gov<br />
USGS National Center<br />
12201 Sunrise Valley Dr<br />
Res<strong>to</strong>n, VA 20192, USA<br />
703-648-5953<br />
custserv@usgs.gov<br />
First Base Solutions<br />
100-140 Renfrew Drive<br />
Markham, ON L3R 6B3<br />
Phone: 905.477.3600<br />
sales@firstbasesolutions.com<br />
EC -Ontario<br />
Tel: 1 900 565-1111 ($2.99/min)<br />
ontario.climate@ec.gc.ca<br />
Dr. Ge<strong>of</strong>f Hall<br />
Department <strong>of</strong> Civil Engineering<br />
Department <strong>of</strong> Family Medicine<br />
Queen’s University<br />
Kings<strong>to</strong>n, Ontario<br />
K7L 3N6<br />
Statistics Canada<br />
150 Tunney’s Pasture<br />
Driveway<br />
Ottawa, ON K1A 0T6<br />
1-800-263-1136<br />
infostats@statcan.gc.ca<br />
Thematic Keywords<br />
For searching<br />
Entity & Attribute<br />
Enumeration Besides<br />
georeferencing, what<br />
attribute data are<br />
captured?<br />
Suitability and<br />
comparability<br />
How suitable is it for<br />
generating the metrics <strong>of</strong><br />
interest and/or, how does it<br />
compare?<br />
Source<br />
Landsat, NRCan, thermal, IR, infrared,<br />
USGS, raster<br />
Pixel digital number (converted <strong>to</strong><br />
degrees Celsius)<br />
Useful for heat primarily. Compares well<br />
<strong>to</strong> other data sources that estimate<br />
surface temperature. Satellite may be<br />
ending useful lifespan.<br />
Natural Resources Canada. Satellite<br />
Acquisition Services [Internet]. Ottawa<br />
(ON): Public Works and Government<br />
Services Canada; [updated 2009 Jul;<br />
cited 2012 Oct ]. Available from: http://<br />
www.nrcan.gc.ca/earth-sciences/<br />
products-services/satellitepho<strong>to</strong>graphy-imagery/satelliteacquisition-services/2350.<br />
Aster, USGS<br />
MODIS, USGS<br />
Aerial, orthopho<strong>to</strong>, image, IR, infra<br />
red<br />
Heat, temperature, environment, Canada, station,<br />
his<strong>to</strong>rical, weather, data<br />
Heat sensors, wet bulb globe,<br />
heat stress<br />
Census, demographic<br />
Was not assessed Was not assessed Was not assessed Was not assessed Was not assessed Was not assessed<br />
Was not assessed<br />
Earth Resources Observation and<br />
Science (EROS) Center. Advanced<br />
Spaceborne Thermal Emission and<br />
Reflection Radiometer (ASTER)<br />
[Internet]. [Sioux Falls, (SD) ]: U.S.<br />
Geological Survey; [ updated 2012<br />
Nov; cited 2012 Oct . Available<br />
from: http://eros.usgs.gov/#/<br />
Find_<strong>Data</strong>/Products_and_<strong>Data</strong>_<br />
Available/Aster<br />
Was not assessed<br />
Earth Resources Observation and<br />
Science (EROS) Center. Moderate<br />
Resolution Imaging Spectroradiometer<br />
(MODIS) [Internet]. [Sioux Falls, (SD) ]:<br />
U.S. Geological Survey; [ updated 2012<br />
Jul; cited 2012 Oct. Available from:<br />
http://eros.usgs.gov/#/Find_<strong>Data</strong>/<br />
Products_and_<strong>Data</strong>_Available/MODIS<br />
Useful for obtaining thermal imagery<br />
at a high resolution, but this will only<br />
be surface temperature, so again,<br />
limited in its application given the<br />
cost <strong>of</strong> acquisition<br />
First Base Solutions.<br />
Aerial Imagery and<br />
Orthopho<strong>to</strong>graphy [Internet].<br />
Markham (ON): First Base Solutions;<br />
c2001-2012 [cited 2012 Nov].<br />
Available from:<br />
http://www.firstbasesolutions.<br />
com/imagery.php<br />
Generates air temperatures, but sparse spatial<br />
coverage<br />
<strong>Environment</strong> Canada.<br />
National Climate <strong>Data</strong> and Information Archive<br />
[Internet]. [Frederic<strong>to</strong>n, (NB): Public Works and<br />
Government Services Canada; [updated 2012 Nov;<br />
cited 2012 Nov]. Available from:<br />
http://www.climate.weather<strong>of</strong>fice.gc.ca/<br />
Welcome_e.html<br />
Was not assessed<br />
Belanger, P. (Department<br />
<strong>of</strong> Geography, Queen’s<br />
University, Kings<strong>to</strong>n, Frontenac,<br />
Lennox and Adding<strong>to</strong>n Public<br />
Health Unit, Kings<strong>to</strong>n, ON),<br />
Conversation with: Ge<strong>of</strong>f Hall (<br />
Department <strong>of</strong> Civil Engineering,<br />
Department <strong>of</strong> Family Medicine,<br />
Queen’s University, Kings<strong>to</strong>n,<br />
ON). 2012 Oct.<br />
Useful for creation <strong>of</strong><br />
community vulnerability<br />
indices<br />
Statistics Canada. Census.<br />
[Internet]. Ottawa (ON):<br />
Statistics Canada. [ cited<br />
2012 Nov]. Available<br />
from: http://www12.<br />
statcan.gc.ca/censusrecensement/index-eng.<br />
cfm<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY<br />
Table 3: Gap analysis for walkability indices, 2012<br />
Measure Description Inputs †‡ Current Use in<br />
Ontario<br />
Measures<br />
Count ¥<br />
Theoretical Op.<br />
Ontario<br />
Desirability<br />
Challenges<br />
Link bw<br />
Measurement<br />
Approaches ¥<br />
Walkability<br />
Indices<br />
(Also known<br />
as Composite<br />
Measures)<br />
Walkability indices<br />
are derived from<br />
a combination<br />
<strong>of</strong> individual built<br />
environment<br />
measures<br />
(mostly using GIS<br />
methods). †‡¥<br />
Indirect measure <strong>of</strong><br />
walkability ¥<br />
Urban sprawl<br />
indices: features<br />
<strong>of</strong> sprawl include<br />
‘‘leapfrog’’<br />
development<br />
pattern, low density,<br />
homogeneous<br />
and segregated<br />
land uses, and<br />
an extensive<br />
disconnected,<br />
hierarchical road<br />
network, making<br />
mo<strong>to</strong>rized travel a<br />
necessity and active<br />
transport unsafe<br />
and impractical.<br />
Indices vary by the<br />
components they<br />
include, the scale<br />
at which they are<br />
measured, and the<br />
methods used in<br />
computation.<br />
Indices <strong>of</strong>ten<br />
include measures <strong>of</strong><br />
density, diversity &<br />
connectivity.<br />
The relative<br />
importance <strong>of</strong> each<br />
measure depends<br />
on the specific<br />
formula employed.<br />
All indices require<br />
a reference<br />
geographic area.<br />
<strong>Data</strong> sources<br />
include: Census,<br />
MPAC, DMTI,<br />
Environic <strong>An</strong>alytics,<br />
Ontario Road<br />
Network or National<br />
Road Network .†‡¥<br />
10 PHUs reported<br />
using an index,<br />
out <strong>of</strong> the 19 that<br />
currently assess<br />
walkability. †‡<br />
4 PHUs are more<br />
advanced in<br />
implementation;<br />
modifications have<br />
been made <strong>to</strong><br />
make the index<br />
specific <strong>to</strong> their<br />
jurisdiction .†‡<br />
1 research<br />
institution has<br />
implemented an<br />
index in multiple<br />
jurisdictions across<br />
Ontario (generalized<br />
index) ‡<br />
Some PHUs work<br />
in conjunction with<br />
external consultants<br />
<strong>to</strong> implement<br />
and calculate the<br />
index. †‡<br />
2 PHUs are using<br />
a GIS based <strong>to</strong>ol<br />
developed by Larry<br />
Frank. †‡<br />
All PHUs use spatial<br />
measures and<br />
methods (i.e. GIS). †‡<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary by health<br />
jurisdiction. †‡¥<br />
13 different<br />
walkability indices<br />
were identified<br />
in the literature<br />
review (50 articles<br />
reviewed), created<br />
by various<br />
academics and<br />
using a variety<br />
<strong>of</strong> data sources;<br />
5 applied in the<br />
Canadian context<br />
(ON, BC, QC), 1<br />
in Australia and all<br />
others applied in<br />
the USA.<br />
> 15 different<br />
walkability indices<br />
identified in the<br />
literature review.<br />
Several PHUs have<br />
implemented a<br />
walkability index<br />
but they differ in the<br />
components used<br />
and application,<br />
with some<br />
overlap. †‡<br />
1 Ontario research<br />
institute has<br />
implemented a<br />
walkability index in<br />
multiple jurisdictions<br />
across Ontario<br />
(generalized index )‡<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary, making<br />
comparisons<br />
between health<br />
jurisdictions<br />
challenging .†‡¥<br />
Walkability indices<br />
have been successful<br />
in describing the<br />
walking environment<br />
in several<br />
jurisdictions. †‡¥<br />
Strongly and<br />
consistently<br />
associated with<br />
physical activity,<br />
walking and health<br />
outcomes in the<br />
literature. ¥<br />
<strong>Built</strong> environment<br />
metrics are <strong>of</strong>ten<br />
correlated with<br />
one another & thus<br />
has motivated the<br />
use <strong>of</strong> composite<br />
indices <strong>to</strong> capture<br />
many aspects <strong>of</strong> the<br />
built environment at<br />
once.¥<br />
Research has<br />
suggested that<br />
composite measures<br />
<strong>of</strong> walkability are<br />
more consistent<br />
predic<strong>to</strong>rs <strong>of</strong> walking<br />
behavior than<br />
single component<br />
measures. ¥<br />
Urban sprawl is<br />
associated with<br />
higher levels <strong>of</strong><br />
car dependence<br />
& overweight<br />
populations than<br />
cities with more<br />
compact buildings. ¥<br />
Indices vary in<br />
both structure<br />
& availability <strong>of</strong><br />
data. Thus, lack<br />
<strong>of</strong> consistency in<br />
measurements<br />
(including bw<br />
municipalities within<br />
same jurisdiction). †‡¥<br />
Methodological<br />
concerns regarding<br />
validity, reliability,<br />
and generalizability. ¥<br />
Less useful for<br />
intervention, as one<br />
cannot identify the<br />
specific component<br />
that should be the<br />
highest priority for<br />
change. ¥<br />
Human resource<br />
capacity, data<br />
availability, financial<br />
capacity & lack <strong>of</strong><br />
GIS expertise .†<br />
4/5 <strong>of</strong> most<br />
advanced orgs in<br />
Ontario are working<br />
independently from<br />
one another; limited<br />
collaboration. ‡<br />
Captures interrelatedness<br />
<strong>of</strong> many built<br />
environment<br />
characteristics.<br />
Minimize the<br />
effect <strong>of</strong> spatial<br />
collinearity.<br />
Eases the<br />
communication <strong>of</strong><br />
results.<br />
†Survey ‡Key informant interview ¥Literature review<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY<br />
Table 4: Gap analysis for density measures used <strong>to</strong> assess urban walkability, 2012<br />
Measure Description ¥ Inputs †‡¥ Current Use in<br />
Ontario<br />
Measures<br />
Count ¥<br />
Theoretical Op.<br />
Ontario<br />
Desirability ¥<br />
Challenges<br />
Link bw<br />
Measurement<br />
Approaches ¥<br />
Density<br />
(Population and<br />
Land-Use)<br />
Density is a measure<br />
<strong>of</strong> the amount <strong>of</strong><br />
activity found in an<br />
area and can be<br />
defined in terms <strong>of</strong><br />
population, housing<br />
unit, or employment<br />
density.<br />
Important correlate<br />
<strong>of</strong> walking.<br />
High density<br />
represents compact<br />
land development<br />
and reduces travel<br />
distances between<br />
trip origin and<br />
destination; reduces<br />
dependence<br />
on mo<strong>to</strong>rized<br />
transportation;<br />
supports higher<br />
levels <strong>of</strong> public<br />
transit service and<br />
ridership, including<br />
walking <strong>to</strong> and from<br />
transit.<br />
Density is a ratio in<br />
which a measure <strong>of</strong><br />
population or built<br />
form serves as the<br />
numera<strong>to</strong>r and a<br />
measure <strong>of</strong> land area<br />
(e.g. per unit area) as<br />
the denomina<strong>to</strong>r .†‡¥<br />
The denomina<strong>to</strong>r can<br />
be either <strong>to</strong>tal land<br />
area (as in “gross<br />
density”), or a pared<br />
down measure <strong>of</strong><br />
usable land area (as in<br />
“net density”).<br />
Density measures<br />
include: ¥<br />
• Population density<br />
• Residential<br />
(household)<br />
density (e.g. ratio<br />
<strong>of</strong> residential<br />
units <strong>to</strong> the land<br />
area devoted <strong>to</strong><br />
residential use per<br />
block group)<br />
• Employment<br />
density<br />
Recommend<br />
measures <strong>of</strong> net<br />
density (as opposed<br />
<strong>to</strong> gross density)<br />
because it excludes<br />
other land uses. ¥<br />
<strong>Data</strong> sources include:<br />
census, MPAC, DMTI,<br />
Environic <strong>An</strong>alytics,<br />
Ontario Road<br />
Network or National<br />
Road Network †‡¥<br />
PHUs use<br />
these individual<br />
measures: †<br />
• Population<br />
density (6 PHUs)<br />
• Residential<br />
density (3)<br />
5 (<strong>of</strong> 10) PHUs<br />
identified using<br />
density as part <strong>of</strong><br />
their index. †‡<br />
1 Ontario research<br />
institute uses<br />
population density<br />
(per square<br />
kilometre <strong>of</strong><br />
residential area)<br />
as part <strong>of</strong> their<br />
walkability index. ‡¥<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary by health<br />
jurisdiction. †‡¥<br />
40 (80%) studies<br />
identified using<br />
density measures<br />
in the literature<br />
review (50 articles<br />
reviewed); 24<br />
applied in the<br />
Canadian context<br />
(ON, BC, QC), 1<br />
in Australia and all<br />
others applied in the<br />
USA.<br />
~ 25 different<br />
density measures<br />
identified in the<br />
literature review.<br />
Several PHUs have<br />
Simplemented<br />
density measures,<br />
namely population<br />
and residential<br />
density. †‡<br />
1 Ontario<br />
research institute<br />
implemented<br />
density measures<br />
in several health<br />
jurisdictions<br />
across Ontario,<br />
thus applicability<br />
in more than one<br />
health jurisdicion<br />
is possible as part<br />
<strong>of</strong> a generalizable<br />
index. ‡<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary, making<br />
comparisons<br />
challenging. †‡¥<br />
Simplicity <strong>of</strong><br />
computation,<br />
readily available<br />
data, and ease <strong>of</strong><br />
interpretation make<br />
density a commonly<br />
used metric. ¥<br />
Strongly and<br />
consistently<br />
associated with<br />
physical activity,<br />
walking and health<br />
outcomes in the<br />
literature. ¥<br />
Consistency in<br />
data sources<br />
for calculating<br />
population<br />
density in multiple<br />
jurisdictions in<br />
Ontario census<br />
and parcel-level<br />
data available<br />
from government<br />
sources) .†‡¥<br />
Census data<br />
(Statistics Canada)<br />
is comprehensive<br />
for the entire<br />
population and is<br />
produced every<br />
five years, allowing<br />
changes in density<br />
<strong>to</strong> be moni<strong>to</strong>red<br />
over time. ¥<br />
Many ways <strong>to</strong><br />
calculate densities<br />
using different units<br />
<strong>of</strong> measurement.<br />
Scale <strong>of</strong> density<br />
measurement is<br />
another challenge <strong>to</strong><br />
measuring density<br />
(e.g. calculations<br />
<strong>of</strong> parcel density,<br />
block density,<br />
neighbourhood<br />
density, and gross<br />
density for the<br />
same area will each<br />
produce distinct<br />
results).<br />
Census data<br />
has limitations<br />
including it’s focus<br />
on residential<br />
population counts<br />
thereby being less<br />
useful for examining<br />
employment<br />
density. It also<br />
uses predefined<br />
geographic units for<br />
measurement that<br />
may not capture the<br />
types <strong>of</strong> changes<br />
that are <strong>of</strong> most<br />
interest.<br />
To measure change<br />
before 2001, only<br />
Census Tracts (CTs)<br />
are available.<br />
Integrated in<strong>to</strong><br />
most walkability<br />
and sprawl indices<br />
(composite<br />
measures); namely<br />
residential density<br />
measures. †‡¥<br />
Proximity is<br />
a function <strong>of</strong><br />
both density<br />
(compactness) <strong>of</strong><br />
development and<br />
the level <strong>of</strong> land<br />
use mix. ¥<br />
Density and land<br />
use mix work<br />
in tandem <strong>to</strong><br />
determine how<br />
many activities are<br />
within a convenient<br />
distance. ¥<br />
Residential density<br />
is important<br />
because it serves<br />
as a proxy for other<br />
urban form fac<strong>to</strong>rs,<br />
and is <strong>of</strong> particular<br />
importance at<br />
larger geographic<br />
scales <strong>of</strong><br />
measurement or in<br />
cases where data<br />
is missing. ¥<br />
†Survey ‡Key informant interview ¥Literature review<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY<br />
Table 5: Gap analysis for connectivity measures used <strong>to</strong> assess urban walkability, 2012<br />
Measure Description ¥ Inputs †‡¥ Current Use in<br />
Ontario<br />
Measures<br />
Count ¥<br />
Theoretical Op.<br />
Ontario<br />
Desirability ¥<br />
Challenges<br />
Link bw<br />
Measurement<br />
Approaches ¥<br />
Connectivity<br />
(related <strong>to</strong> street<br />
pattern)<br />
Connectivity affects<br />
the ease <strong>of</strong> travel<br />
between places<br />
& represents the<br />
degree <strong>to</strong> which<br />
roads, pedestrian<br />
walkways, trails,<br />
etc. are connected<br />
so that moving from<br />
point A <strong>to</strong> point B is<br />
relatively easy.<br />
Measures quantify<br />
the network<br />
connections<br />
between trips <strong>to</strong><br />
describe directness<br />
<strong>of</strong> possible paths<br />
& no. <strong>of</strong> mobility<br />
options available.<br />
The denser the<br />
street network<br />
is in terms <strong>of</strong><br />
intersections and<br />
blocks, the higher its<br />
connectivity will be.<br />
Indirect measure <strong>of</strong><br />
walkability.<br />
Measures are related<br />
<strong>to</strong> the physical<br />
design & layout<br />
<strong>of</strong> transportation<br />
infrastructure.<br />
Measures <strong>of</strong><br />
connectivity include:<br />
block size and length;<br />
intersection density;<br />
street density;<br />
connected node ratio;<br />
segment/intersections<br />
ratio; number <strong>of</strong><br />
intersections per<br />
length <strong>of</strong> street<br />
network; alpha index;<br />
gamma index.<br />
Easiest way <strong>to</strong><br />
operationalize street<br />
network connectivity<br />
in a GIS environment<br />
is by measuring<br />
the number <strong>of</strong><br />
intersections.<br />
<strong>Data</strong> sources include:<br />
Local database,<br />
DMTI, Ontario Road<br />
Network or National<br />
Road Network.<br />
PHUs use these<br />
individual measures † :<br />
• Block size and<br />
length (4 PHUs)<br />
• Intersection<br />
density (3)<br />
• Street density (2)<br />
• Connected node<br />
ratio (1)<br />
9 (<strong>of</strong> 10) PHUs<br />
use connectivity<br />
measures as part<br />
<strong>of</strong> their walkability<br />
index; 1 research<br />
institution uses<br />
connectivity as<br />
well .†‡<br />
1 PHU uses<br />
the ‘I can walk’<br />
<strong>to</strong>ol <strong>to</strong> assess<br />
connectivity at the<br />
neighbourhood level<br />
(icanwalk.ca). †<br />
Most PHUs use<br />
spatial methods for<br />
measurement (i.e.<br />
GIS). †‡<br />
When asked which<br />
measures (in current<br />
use) are <strong>of</strong> most<br />
value in assessing<br />
urban walkability,<br />
3 PHUs reported<br />
connectivity (bike<br />
paths, multi-use<br />
paths, trails,<br />
sidewalks, and<br />
streets). †<br />
36 (72%) studies<br />
identified using<br />
connectivity<br />
measures in the<br />
literature review (50<br />
articles reviewed)<br />
using a variety<br />
<strong>of</strong> data sources;<br />
17 applied in the<br />
Canadian context<br />
(ON, BC, QC), 1<br />
in Australia and all<br />
others applied in the<br />
USA.<br />
> 30 connectivity<br />
measures identified<br />
in the literature<br />
review.<br />
Several PHUs<br />
have implemented<br />
connectivity<br />
measures, namely<br />
the intersection<br />
density measure. †‡<br />
1 Ontario<br />
research institute<br />
implemented<br />
connectivity<br />
measures in several<br />
health jurisdictions<br />
across Ontario<br />
(as part <strong>of</strong> a<br />
generalized index )‡<br />
<strong>Data</strong> sources vary<br />
considerable by<br />
PHU: community<br />
walkabout; surveys;<br />
focus groups and<br />
GIS inven<strong>to</strong>ry for<br />
one PHU; includes<br />
streets, sidewalks,<br />
multiuse paths and<br />
sometimes parks;<br />
Road Network GIS<br />
Layer .†<br />
Street networks<br />
that are more<br />
connected<br />
are thought <strong>to</strong><br />
increase walkability<br />
by <strong>of</strong>fering<br />
shorter and many<br />
alternate routes. ¥<br />
Several<br />
studies have<br />
found positive<br />
associations<br />
between measures<br />
<strong>of</strong> connectivity and<br />
walkability. ¥<br />
Greater street<br />
connectivity<br />
supports higher<br />
levels <strong>of</strong> public<br />
transit service and<br />
ridership, including<br />
walking <strong>to</strong> and<br />
from transit. ¥<br />
<strong>Data</strong> sources vary<br />
considerably by<br />
health jurisdiction. †¥<br />
No standard<br />
methods for<br />
operationalizing<br />
measures. †‡¥<br />
Different <strong>to</strong>ols are<br />
used <strong>to</strong> characterize<br />
road network<br />
configuration in<br />
relation <strong>to</strong> physical<br />
activity. †‡¥<br />
Not all studies<br />
have found positive<br />
associations<br />
between measures<br />
<strong>of</strong> connectivity and<br />
walkability. ¥<br />
Most measures<br />
use data from the<br />
street network,<br />
but omitting<br />
pedestrian networks<br />
(e.g., sidewalks,<br />
park paths) may<br />
appreciably<br />
underestimate<br />
connectivity.¥<br />
Determining how <strong>to</strong><br />
handle freeways or<br />
other limited-access<br />
roads is another<br />
methodological<br />
issue. ¥<br />
Integrated in<strong>to</strong><br />
most walkability<br />
indices (composite<br />
measures); namely<br />
the intersection<br />
density measure.<br />
†Survey ‡Key informant interview ¥Literature review<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY<br />
Table 6: Gap analysis for diversity measures used <strong>to</strong> assess urban walkability, 2012<br />
Measure Description ¥ Inputs †‡¥ Current Use in<br />
Ontario<br />
Measures<br />
Count ¥<br />
Theoretical Op.<br />
Ontario<br />
Desirability ¥<br />
Challenges<br />
Link bw<br />
Measurement<br />
Approaches ¥<br />
Diversity<br />
(Land Use Mix<br />
and Proximity)<br />
Diversity refers<br />
<strong>to</strong> the spatial<br />
arrangement <strong>of</strong><br />
land use that<br />
influences the<br />
distance and mode<br />
<strong>of</strong> travel.<br />
Mixed land use<br />
brings different and<br />
necessary uses in<strong>to</strong><br />
relative proximity,<br />
thereby shortening<br />
trip distances and<br />
encouraging active<br />
modes <strong>of</strong> transport.<br />
Proximity describes<br />
the no. & variety<br />
<strong>of</strong> destinations<br />
within a specified<br />
distance <strong>of</strong> any<br />
location; function<br />
<strong>of</strong> both density <strong>of</strong><br />
development &<br />
level <strong>of</strong> land use<br />
mix.<br />
As proximity and<br />
directness between<br />
destinations<br />
increases,<br />
distance between<br />
destinations<br />
decreases.<br />
The entropy index<br />
(entropy-based<br />
measure) is frequently<br />
used; land use types<br />
include: residential,<br />
retail, entertainment,<br />
<strong>of</strong>fice and institutional. ¥<br />
Dissimilarity index<br />
measures dissimilarity<br />
based on predominant<br />
use <strong>of</strong> neighbouring<br />
squares. ¥<br />
With appropriate parcel<br />
data, the calculation<br />
<strong>of</strong> land use mix is<br />
possible through a GIS<br />
or database interface. ¥<br />
LUM data are typically<br />
obtained from land<br />
ownership records. ¥<br />
Proximity is <strong>of</strong>ten<br />
calculated using<br />
circular or road<br />
network buffers. ¥<br />
Distances (e.g. 400m,<br />
800m) commonly used<br />
<strong>to</strong> analyze walking<br />
distance vary. ¥<br />
<strong>Data</strong> sources include:<br />
Census, MPAC, DMTI,<br />
Environic <strong>An</strong>alytics,<br />
Ontario Road Network<br />
or National Road<br />
Network .†‡¥<br />
PHUs use these<br />
individual measures † :<br />
• Land Use Mix (3<br />
PHUs)<br />
• Proximity<br />
(schools, food<br />
outlets, parks,<br />
trails) (6)<br />
studies identified<br />
using diversity<br />
measures in the<br />
literature review<br />
(50 articles<br />
reviewed);<br />
21 applied in<br />
the Canadian<br />
context (ON,<br />
BC, QC), 1 in<br />
Australia and all<br />
others applied in<br />
the USA.<br />
43 (86%)<br />
7 (<strong>of</strong> 10) PHUs<br />
identified using<br />
LUM as part <strong>of</strong> their<br />
index. †‡<br />
1 Ontario research<br />
institute uses LUM<br />
as part <strong>of</strong> their<br />
walkability index. ‡¥<br />
<strong>Data</strong> sources and<br />
methodologies <strong>to</strong><br />
calculate LUM vary<br />
considerably. †‡¥<br />
PHUs did not identify<br />
using dissimilarity<br />
indices. †‡<br />
When asked which<br />
measures (in current<br />
use) are <strong>of</strong> most<br />
value in assessing<br />
urban walkability,<br />
3 PHUs reported<br />
proximity (2 PHUs)<br />
and LUM (1). †<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary by health<br />
jurisdiction. †‡¥<br />
> 80 diversity<br />
measures<br />
identified in the<br />
literature review.<br />
Most PHUs are<br />
using diversity<br />
measures (either<br />
individually or as<br />
part <strong>of</strong> an index)<br />
<strong>to</strong> assess urban<br />
walkability. †‡<br />
1 Ontario<br />
research institute<br />
implemented<br />
diveristy measures<br />
in several health<br />
jurisdictions<br />
across Ontario,<br />
thus applicability<br />
in more than one<br />
health jurisdiction<br />
is possible as part<br />
<strong>of</strong> a generalizable<br />
index. ‡<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary, making<br />
comparisons<br />
between health<br />
jurisdictions<br />
challenging. †‡¥<br />
Mixed land use<br />
brings different and<br />
necessary uses in<strong>to</strong><br />
relative proximity,<br />
thereby shortening<br />
trip distances and<br />
encouraging active<br />
modes <strong>of</strong> transport.<br />
Strongly and<br />
consistently<br />
associated with<br />
physical activity,<br />
walking and health<br />
outcomes in the<br />
literature.<br />
Parks, trails,<br />
recreational<br />
facilities, pathways,<br />
and schools<br />
within walking<br />
distance have<br />
been consistently<br />
correlated with<br />
physical activity,<br />
particularly in<br />
children.<br />
Distance <strong>to</strong> retail<br />
activity is important<br />
in creating inviting<br />
pedestrian<br />
environments and in<br />
predicting levels <strong>of</strong><br />
walking in cities.<br />
Distances used <strong>to</strong><br />
analyze walking<br />
distance vary,<br />
making comparisons<br />
between jurisdictions<br />
challenging.<br />
The entropy index<br />
does not consider<br />
the type or intensity<br />
<strong>of</strong> mixing. ¥<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary, making<br />
comparisons<br />
challenging. †‡¥<br />
<strong>Data</strong> availability<br />
is <strong>of</strong>ten a limiting<br />
fac<strong>to</strong>r since parcellevel<br />
data are<br />
required <strong>to</strong> compute<br />
many land-use mix<br />
measures. Parcellevel<br />
data may be<br />
unavailable in some<br />
locations and in<br />
others may lack<br />
detail about land<br />
use. ¥<br />
Many studies<br />
have opted <strong>to</strong> use<br />
survey items <strong>to</strong><br />
approximate land<br />
use mix (e.g. ‘‘Are<br />
there shops where<br />
you live?’’) but such<br />
approaches do not<br />
allow betweenstudy<br />
comparisons<br />
because <strong>of</strong><br />
unspecified<br />
definitions <strong>of</strong> place. ¥<br />
The retail floor area<br />
ratio (FAR) is <strong>of</strong>ten<br />
used in conjunction<br />
with LUM.<br />
Proximity is<br />
a function <strong>of</strong><br />
both density <strong>of</strong><br />
development and<br />
the level <strong>of</strong> land<br />
use mix.<br />
†Survey ‡Key informant interview ¥Literature review<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY<br />
Table 7: Gap analysis for pedestrian oriented design measures used <strong>to</strong> assess urban walkability, 2012<br />
Measure Description ¥ Inputs †‡¥ Current Use<br />
in Ontario<br />
Measures<br />
Count ¥<br />
Theoretical Op.<br />
Ontario<br />
Desirability ¥<br />
Challenges<br />
Link bw<br />
Measurement<br />
Approaches ¥<br />
Street design refers<br />
<strong>to</strong> the scale &<br />
design <strong>of</strong> sidewalks<br />
and roads, and how<br />
they are managed<br />
for various uses.<br />
POD includes<br />
measures <strong>of</strong><br />
neighborhood<br />
comfort (including<br />
aesthetics),<br />
cleanliness and<br />
safety.<br />
Retail floor area<br />
ratio (FAR) is used<br />
as an indica<strong>to</strong>r <strong>of</strong><br />
POD measuring<br />
retail density and<br />
site design. Low<br />
ratio indicates a<br />
low density retail<br />
development likely<br />
surrounded by<br />
substantial parking;<br />
high ratio indicates<br />
smaller setbacks<br />
& less surface<br />
parking; two fac<strong>to</strong>rs<br />
thought <strong>to</strong> facilitate<br />
pedestrian access.<br />
The normalized<br />
difference<br />
vegetation index<br />
(NDVI) is used<br />
<strong>to</strong> estimate<br />
vegetation biomass,<br />
greenness, and<br />
dominant species.<br />
Various methods<br />
can be used <strong>to</strong><br />
assess street design<br />
(audit, survey <strong>of</strong><br />
perceptions, GIS);<br />
method depends on<br />
specific measures<br />
being assessed.<br />
Aesthetics and<br />
cleanliness are<br />
<strong>of</strong>ten assessed by<br />
observation (e.g.<br />
audit) or survey (<strong>of</strong><br />
perceptions).<br />
FAR = retail building<br />
floor area footprint<br />
divided by retail land<br />
floor area footprint;<br />
included in walkability<br />
indices. <strong>Data</strong><br />
source: DMTI (Route<br />
Logistics);<br />
Environic <strong>An</strong>alytics<br />
(Direc<strong>to</strong>ry <strong>of</strong><br />
Shopping Centres).<br />
NDVI: ratio between<br />
measured reflectivity<br />
in the red and near<br />
infrared band, in<br />
satellite images<br />
(DEM/SRTM).<br />
Safety and<br />
crime statistics<br />
obtained from<br />
police department;<br />
otherwise, through<br />
observation (audit) or<br />
survey.<br />
PHUs use individual<br />
measures <strong>of</strong><br />
comfort and safety,<br />
including: †<br />
• Crime rates (4<br />
PHUs)<br />
• Street lighting (4)<br />
• Canopy coverage<br />
(trees) (4)<br />
• Posted speed<br />
limits (3)<br />
• Presence <strong>of</strong> street<br />
furniture (3)<br />
5 (<strong>of</strong> 10) PHUs<br />
identified using<br />
FAR as part <strong>of</strong> their<br />
index. †‡<br />
1 Ontario research<br />
institute uses LUM<br />
as part <strong>of</strong> their<br />
walkability index. ‡¥<br />
PHUs did not identify<br />
using NDVI indices. †‡<br />
When asked which<br />
measures (in current<br />
use) are <strong>of</strong> most<br />
value in assessing<br />
urban walkability,<br />
2 PHUs reported<br />
sidewalk information<br />
(including condition<br />
affected by seasonal<br />
variances). †<br />
34 (68%) studies<br />
identified using<br />
POD measures<br />
in the literature<br />
review (50<br />
articles reviewed);<br />
14 applied in the<br />
Canadian context<br />
(ON, BC, QC)<br />
and all others<br />
applied in the<br />
USA.<br />
> 100 diversity<br />
measures<br />
identified in the<br />
literature review.<br />
Several PHUs<br />
are using POD<br />
measures (either<br />
individually or as<br />
part <strong>of</strong> an index)<br />
<strong>to</strong> assess urban<br />
walkability. †‡<br />
1 Ontario<br />
research institute<br />
implemented safety<br />
measures in several<br />
health jurisdictions<br />
across Ontario,<br />
thus applicability<br />
in more than one<br />
health jurisdicion<br />
is possible as part<br />
<strong>of</strong> a generalizable<br />
index. ‡<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary considerably,<br />
making<br />
comparisons<br />
between health<br />
jurisdictions<br />
extremely<br />
challenging. †‡¥<br />
Higher levels<br />
<strong>of</strong> objectively<br />
measured safety<br />
(e.g. traffic safetly)<br />
and comfort<br />
(e.g. aesthetically<br />
appealing<br />
communities) are<br />
positively associated<br />
with physical activity<br />
engagement.<br />
Since building<br />
setbacks are<br />
important predic<strong>to</strong>rs<br />
<strong>of</strong> walking and POD,<br />
FAR is introduced<br />
<strong>to</strong> increase the<br />
sensitivity <strong>to</strong> retail<br />
use believed <strong>to</strong><br />
stimulate pedestrian<br />
activity.<br />
Higher levels <strong>of</strong><br />
neighborhood<br />
vegetation have<br />
been associated<br />
with higher levels <strong>of</strong><br />
physical activity.<br />
FAR is a standard<br />
planning<br />
measure and is<br />
frequently used<br />
in development<br />
regulations - and<br />
therefore is useful<br />
<strong>to</strong> apply <strong>to</strong> policy or<br />
existing regulations.<br />
Higher levels<br />
<strong>of</strong> objectively<br />
measured safety<br />
(e.g. traffic safetly)<br />
and comfort<br />
(e.g. aesthetically<br />
appealing<br />
communities)<br />
are positively<br />
associated with<br />
physical activity<br />
engagement.<br />
Since building<br />
setbacks are<br />
important<br />
predic<strong>to</strong>rs <strong>of</strong><br />
walking and POD,<br />
FAR is introduced<br />
<strong>to</strong> increase the<br />
sensitivity <strong>to</strong> retail<br />
use believed <strong>to</strong><br />
stimulate pedestrian<br />
activity.<br />
Higher levels <strong>of</strong><br />
neighborhood<br />
vegetation have<br />
been associated<br />
with higher levels <strong>of</strong><br />
physical activity.<br />
FAR is a standard<br />
planning<br />
measure and is<br />
frequently used<br />
in development<br />
regulations - and<br />
therefore is useful<br />
<strong>to</strong> apply <strong>to</strong> policy or<br />
existing regulations.<br />
<strong>Data</strong> sources and<br />
methodologies<br />
vary (especially<br />
for comfort,<br />
aesthetics and<br />
safety measures),<br />
making comparisons<br />
challenging. †‡¥<br />
Self-reported<br />
measures implicated<br />
in same-source<br />
bias and issues with<br />
reliability, validity, low<br />
response rates and<br />
a biased sample <strong>of</strong><br />
respondents. ¥<br />
Systematic field<br />
observations can<br />
be very laborious<br />
(i.e. time-intensive<br />
and have multiple<br />
logistical constraints),<br />
<strong>of</strong>ten require<br />
significant specialized<br />
training and are<br />
no<strong>to</strong>rious for being<br />
very costly. ¥<br />
Retail floor area<br />
ratio (FAR),<br />
also known as<br />
commercial<br />
density, is a<br />
diverse measure<br />
that can be<br />
applied not only<br />
as a density<br />
indica<strong>to</strong>r but also<br />
as an indica<strong>to</strong>r<br />
<strong>of</strong> pedestrianoriented<br />
design<br />
and used in<br />
conjunction with<br />
land use mix<br />
(LUM).<br />
†Survey ‡Key informant interview ¥Literature review<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
WALKABILITY<br />
Table 8: Gap analysis for data sources and sets used in the assessment <strong>of</strong> urban walkability, 2012<br />
<strong>Data</strong> Source <strong>Data</strong>set Topic Area Current Use in Ontario PHUs Desirability Access/Availability Barriers/Challenges/Limitations<br />
MPAC /<br />
Teranet /<br />
OMNR<br />
Ontario Parcel<br />
database<br />
3 components:<br />
• Digital Assessment<br />
Parcel Fabric<br />
(MPAC)<br />
• Digital Ownership<br />
Parcel Fabric<br />
(Teranet)<br />
• Digital Crown Parcel<br />
Fabric (OMNR)<br />
LUM<br />
Retail Density<br />
56% <strong>of</strong> 25 PHUs that responded <strong>to</strong> the survey<br />
report having access <strong>to</strong> MPAC data †<br />
7 (<strong>of</strong> 10) PHUs identified using LUM as part <strong>of</strong> their<br />
walkability index. †‡<br />
5 (<strong>of</strong> 10) PHUs identified using retail floor area as<br />
part <strong>of</strong> their walkability index †‡<br />
PHUs identified using the following as individual<br />
measures † :<br />
• Land Use Mix (3 PHUs)<br />
• Retai floor area (1)<br />
Long list <strong>of</strong> Property Codes including detailed Residential, Commercial and<br />
Industrial codes<br />
Updated quarterly<br />
3 mapping specifications dictate supporting spatial data— POLARIS (Province<br />
<strong>of</strong> Ontario Land Registration Information System) mapping, Basic Index<br />
Mapping (BIM) and Pre-Basic Index Mapping (Pre-BIM):<br />
• Where POLARIS, features related <strong>to</strong> survey plans (including reference &<br />
subdivision), roads, major easements, <strong>to</strong>wnship fabric, railways and major<br />
water bodies are captured.<br />
• Where BIM, features relate <strong>to</strong> survey plan text, roads, major easements, and<br />
geographic <strong>to</strong>wnship fabric, railways and major water bodies.<br />
• Where Pre-BIM much less extensive. Features relate primarily <strong>to</strong> road text,<br />
geographic <strong>to</strong>wnship fabric and major water bodies.<br />
Available at no cost through MNR’s LIO Warehouse <strong>to</strong> Ontario<br />
municipalities, conservation authorities, and provincial ministries. Eligible<br />
organizations must enter in<strong>to</strong> the relevant license agreement(s) and<br />
become members <strong>of</strong> the Ontario Geospatial <strong>Data</strong> Exchange (ODGE) <strong>to</strong><br />
access.<br />
Municipalities wishing <strong>to</strong> access the ownership data must be licensed<br />
through Teranet.<br />
Through Teranet the only costs incurred by municipalities for standard<br />
deliveries is a modest delivery and support charge.<br />
Variable licensing fees depending on derived products.<br />
Internal use for walkability does not appear <strong>to</strong> be subject <strong>to</strong> fee/royalty<br />
Some municipalities report quality issues with the spatial component<br />
<strong>of</strong> Ontario Parcel database. They use their own digital files and merge<br />
with MPAC data.<br />
Accuracy variable from location <strong>to</strong> location and depends on the source<br />
data available and the build procedure employed.<br />
• Where POLARIS standards were used <strong>to</strong> assemble the ownership<br />
mapping and where good control and legal or cadastral surveys<br />
were available, the data has better accuracy than other types <strong>of</strong><br />
Ontario Parcel mapping.<br />
• Where the product is assembled upon 1:10,000 (or smaller) scale<br />
<strong>to</strong>pographic mapping, and where control and surveys are not<br />
available, or not used, the data is much less accurate. http://www.<br />
ontarioparcel.ca/<br />
http://www.ontarioparcel.ca/<br />
http://www.ontarioparcel.ca/<br />
Good coverage <strong>of</strong> the entire province. ‡<br />
DMTI<br />
CanMap Route<br />
Logistics<br />
Network <strong>An</strong>alysis (<strong>to</strong><br />
measure proximity or <strong>to</strong><br />
model population affected)<br />
Connectivity<br />
Posted Speed Limits<br />
DMTI data used <strong>to</strong> assess connectivity measures. †‡<br />
Excellent level <strong>of</strong> detail, appropriate for assessing walkability. £<br />
The dataset is GIS-ready and requires minimal, if any, massaging prior <strong>to</strong><br />
analysis.<br />
Vendor <strong>of</strong>fers dataset in multiple formats.<br />
The dataset is designed for location-based service (LBS) applications and so is<br />
already <strong>to</strong>pologically clean.<br />
Fee ‡£<br />
Preferential pricing may be available <strong>to</strong> public sec<strong>to</strong>r clients.<br />
Presents a continuing expense <strong>to</strong> acquire annual updates (subscription<br />
model).<br />
Depending on new residential development activity, point-in-time<br />
extracts may weaken the representativeness <strong>of</strong> local calculations. Note<br />
also that not all streets are bordered by sidewalks.<br />
Enhanced Points <strong>of</strong><br />
Interest (EPOI)<br />
Business Facilities &<br />
Amenities<br />
(for measures that capture<br />
proximity and accessibility <strong>to</strong><br />
destinations)<br />
DMTI data used <strong>to</strong> assess connectivity measures. †‡<br />
Offers provincial coverage and enhanced locational precision. Includes SIC and<br />
NAICS codes <strong>to</strong> identify business types (e.g., restaurants, retail, etc.).<br />
Vendor <strong>of</strong>fers dataset in multiple formats.<br />
Fee £<br />
Preferential pricing may be available <strong>to</strong> public sec<strong>to</strong>r clients.<br />
Presents a continuing expense <strong>to</strong> acquire annual updates (subscription<br />
model).<br />
Depending on business churn, point-in-time extracts may weaken the<br />
representativeness <strong>of</strong> local calculations.<br />
Environics<br />
<strong>An</strong>alytics<br />
BusinessWhere<br />
Business Facilities &<br />
Amenities<br />
(for measures that capture<br />
proximity and accessibility <strong>to</strong><br />
destinations)<br />
DMTI data used <strong>to</strong> assess connectivity measures. †‡<br />
Offers provincial coverage and enhanced locational precision. Includes SIC and<br />
NAICS codes <strong>to</strong> identify business types (e.g. restaurants, retail, etc.).<br />
Businesses are verified annually.<br />
Vendor <strong>of</strong>fers dataset in multiple formats.<br />
Fee £<br />
Preferential pricing may be available <strong>to</strong> public sec<strong>to</strong>r clients.<br />
Presents a continuing expense <strong>to</strong> acquire annual updates (subscription<br />
model).<br />
Depending on business churn, point-in-time extracts may weaken the<br />
representativeness <strong>of</strong> local calculations.<br />
GeoBase<br />
National Road Network<br />
(NRN)<br />
Network <strong>An</strong>alysis (<strong>to</strong><br />
measure proximity or <strong>to</strong><br />
model population affected)<br />
Connectivity<br />
Walkability Indices<br />
Density<br />
Diversity<br />
Unknown use across Public Health Units.<br />
No specific example <strong>of</strong> NRN usage.<br />
68% <strong>of</strong> PHUs indicated having access <strong>to</strong> street<br />
network files. †<br />
44% <strong>of</strong> PHUs indicated that Street networks (44%)<br />
are the most common geographic scale used <strong>to</strong><br />
assess urban walkability. †<br />
Good coverage at both national and provincial scales. The Ontario Road<br />
Network (ORN) is used <strong>to</strong> populate the NRN’s Ontario level file. £<br />
Excellent level <strong>of</strong> detail, appropriate for assessing walkability. £<br />
The dataset is GIS-ready and requires minimal, if any, manipulation prior <strong>to</strong><br />
analysis.<br />
Vendor <strong>of</strong>fers dataset in multiple spatial formats. £<br />
Detailed Metadata records with online access and retrieval.<br />
Free with no cost user registration. £<br />
Excludes select street attributes that could be required <strong>to</strong> compute<br />
specific calculations. £<br />
Depending on new residential development activity, point-in-time<br />
extracts may weaken the representativeness <strong>of</strong> local calculations.<br />
Public Health unit road authority or “Local” Road Network file may<br />
override NRN usage due <strong>to</strong> technical specifications (i.e. Accuracy,<br />
Update Frequency, Maintenance etc.).<br />
Ministry<br />
<strong>of</strong> Natural<br />
Resources<br />
Ontario Road Network<br />
(ORN)<br />
Network <strong>An</strong>alysis (<strong>to</strong><br />
measure proximity or <strong>to</strong><br />
model population affected)<br />
Connectivity<br />
Walkability Indices<br />
Density<br />
Diversity<br />
Unknown use across Public Health Units.<br />
No specific example <strong>of</strong> ORN usage.<br />
68% <strong>of</strong> PHUs indicate having access <strong>to</strong> street<br />
network files. †<br />
44% <strong>of</strong> PHUs indicated that street networks (44%)<br />
are the most common geographic scale used <strong>to</strong><br />
assess urban walkability. †<br />
Standardized provincial dataset with detailed documentation and regular<br />
update frequency. ORN is the authoritative source <strong>of</strong> roads data for the Ontario<br />
Government. £<br />
Excellent level <strong>of</strong> detail, appropriate for assessing walkability. £<br />
The dataset is GIS-ready and requires minimal, if any, manipulation prior <strong>to</strong><br />
analysis.<br />
Detailed Metadata records with online access and retrieval.<br />
Free with no cost user registration. £<br />
Depending on new residential development activity, point-in-time<br />
extracts may weaken the representativeness <strong>of</strong> local calculations.<br />
Public Health unit road authority or “Local” Road Network file may<br />
override NRN usage due <strong>to</strong> technical specifications (i.e. Accuracy,<br />
Update Frequency, Maintenance etc.).<br />
† Survey ‡ Key Informant Interview ¥ Literature Review £ GIS Metadata<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
AIR QUALITY<br />
Table 11: Measurement approaches and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata).<br />
Measurement<br />
Approach<br />
Description Inputs Current Use (SU) Measures <strong>of</strong> interest<br />
Theoretical<br />
Operationalization<br />
Ontario<br />
Demand / Prioritization/ Desirability<br />
Challenges<br />
Link between<br />
Measurement<br />
Approaches<br />
Individual<br />
Pollutants<br />
Direct<br />
Measurement <strong>of</strong> ambient air<br />
concentration <strong>of</strong> common<br />
pollutants (e.g. NO 2<br />
, NO, SO 2<br />
,<br />
CO, TSR, O 3<br />
, PM 2.5<br />
, PM 10<br />
, etc.)<br />
• Individual pollutant data<br />
• Pro<strong>to</strong>col for determining<br />
averaging time period (LR)<br />
Of 11 PHUs that assess specific<br />
pollutants (SU)<br />
• 83% assess ozone, fine<br />
particulate matter, and nitrogen<br />
dioxide<br />
• 75% assess sulfur dioxide<br />
• 50% assess carbon monoxide<br />
Of the 11 PHUs assessing individual<br />
pollutants, 4 PHUs look at individual<br />
pollutants from air stations directed<br />
at industrial sources<br />
Of 14 PHUs, that assess air quality<br />
6 use portable air moni<strong>to</strong>ring<br />
equipment<br />
Hundreds <strong>of</strong> pollutants have been identified and<br />
are moni<strong>to</strong>red at the federal level. A select few<br />
criteria air pollutants are moni<strong>to</strong>red provincially<br />
due <strong>to</strong> their links <strong>to</strong> health and knowledge <strong>of</strong><br />
sources.<br />
Sources <strong>of</strong> pollutants in the built environment<br />
include: (LR)<br />
• Wood fireplaces (PM , BC, and UFP<br />
2.5<br />
• Traffic emissions (BC, UFP, NO ) x<br />
• Fugitive dust from industry and construction<br />
sites (PM 2.5<br />
)<br />
The development <strong>of</strong> pollutant measures needs<br />
further research <strong>to</strong> better understand the<br />
relationship with the built environment.<br />
Spatial level<br />
• geocoded reference<br />
for moni<strong>to</strong>ring stations<br />
(regional level coverage)<br />
(GM)<br />
Certain moni<strong>to</strong>ring stations<br />
do not measure all criteria air<br />
contaminants (LR)<br />
NO 2<br />
is noted as a good measure <strong>of</strong> traffic pollution that is cost<br />
effective and easy <strong>to</strong> moni<strong>to</strong>r. Nonetheless more research is<br />
needed in developing NO 2<br />
as an indica<strong>to</strong>r (KI, SU)<br />
Black Carbon, UFP, PM 2.5<br />
, PM 10<br />
and NO x<br />
have been identified as<br />
pollutant measures <strong>of</strong> health. BTEX is a useful built environment<br />
pollutant <strong>to</strong> measure for its use in combustion fuels and in<br />
solvents, but is costly <strong>to</strong> measure (KII, LR).<br />
Mobile Equipment:<br />
Portable instruments can provide better detail <strong>of</strong> air pollution<br />
through high spatial resolution,<br />
selection <strong>of</strong> specific target areas, and by focusing on vulnerable<br />
populations or household level (LR, KII)<br />
Inexpensive NO/ NO x<br />
sensors, passive NO x<br />
/NO 2<br />
samplers, or<br />
Airpointer equipment were noted as useful moni<strong>to</strong>ring <strong>to</strong>ols for<br />
traffic related pollutants (KII,LR)<br />
Pollutant sources and their impact on air quality differs for<br />
each region/PHU (SU, LR)<br />
Additional air moni<strong>to</strong>ring stations would be expensive and<br />
resource intensive. Specific pollutants require expensive<br />
moni<strong>to</strong>ring equipment, For instance, PM 2.5<br />
sensors start at<br />
$500 CAD (KII, LR)<br />
Mobile Equipment:<br />
Costs for sampling and equipment may be high (LR)<br />
Limited sampling my not capture seasonal trends (LR)<br />
Required as data inputs<br />
for developing air quality<br />
indexes and models<br />
Air Quality<br />
Indexes<br />
(composite<br />
measures)<br />
Indexes are a direct measure <strong>of</strong><br />
air quality based on individual or<br />
multiple pollutants (LR)<br />
• The Ontario Air Quality Index<br />
(AQI) involves data on the<br />
following pollutants O 3<br />
, NO 2<br />
,<br />
PM 2.5<br />
, SO 2<br />
, CO, and TRS (LR)<br />
• The Air Quality Health Index<br />
(AQHI) involves data on three<br />
pollutants: PM 2.5<br />
, O 3<br />
, NO 2<br />
(LR)<br />
• Pollutant data which can be<br />
collected through mobile<br />
or permanent moni<strong>to</strong>ring<br />
equipment (LR)<br />
• Formula calculation (e.g.<br />
weighting for specific<br />
pollutant) (LR)<br />
• Pro<strong>to</strong>col for determining<br />
averaging time period and<br />
threshold levels (LR)<br />
• Use <strong>of</strong> epidemiological data <strong>to</strong><br />
determine health based levels<br />
<strong>of</strong> interest (LR)<br />
Of 14 PHUs assessing air quality<br />
(SU):<br />
• 93% use the AQI<br />
• 53% use the AQHI<br />
While many air quality indexes have been<br />
developed internationally, only 2 indexes are<br />
currently used in Ontario (LR, SU)<br />
Indexes were developed at the provincial and<br />
federal level.<br />
Spatial level<br />
• geocoded reference<br />
for moni<strong>to</strong>ring stations<br />
(regional level coverage)<br />
(GM)<br />
• AQI: available across<br />
Ontario<br />
• AQHI: available in Southern<br />
Ontario<br />
<strong>Built</strong> environment attributes that could be targeted include<br />
fireplaces and old diesel vehicles (KII, LR)<br />
His<strong>to</strong>rical and temporal data available at no cost.<br />
Information provided with high temporal resolution<br />
Availability <strong>of</strong> moni<strong>to</strong>rs throughout province<br />
No cost for data<br />
The Ontario AQI may not capture health risks (LR)<br />
The current composite measures do not capture the built<br />
environment and its relationship <strong>to</strong> the distribution <strong>of</strong><br />
pollutants. (KII, LR)<br />
While O 3<br />
and PM 2.5<br />
are moni<strong>to</strong>red at most stations across<br />
Ontario, not all key air pollutants are moni<strong>to</strong>red by the MOE<br />
(LR)<br />
The concern for air quality issues is lower in less populated<br />
cities situated far from urban areas. However, concern<br />
remains for specific sources from industrial emissions (KII,<br />
SU)<br />
Noted as an important<br />
component for<br />
PHUs evaluating<br />
meteorological data.<br />
A composite measure <strong>of</strong><br />
individual pollutant data<br />
Remote<br />
Sensing<br />
Indirect measure<br />
Satellite images provide detail<br />
on air pollutant levels. Resolution<br />
varies from 250 m <strong>to</strong> 320 km.<br />
Images taken from every 1-7<br />
days.<br />
Satellites with information on<br />
pollutants.<br />
• OMI, TES, CALIOP & GOME<br />
(for NO 2<br />
and hydrocarbons)<br />
• MODIS, OMI, PARASOL, &<br />
MISR (for PM)<br />
• MOPITT, AIRS, PARASOL,<br />
IASI& SCIAMACHY (for CO)<br />
• GOME, SCIAMACHY, OMI,<br />
TES, IASI, & GOME-2 (for O 3<br />
)<br />
Only used in one PHU in<br />
conjunction with air moni<strong>to</strong>ring and<br />
modelling<br />
Spatial level<br />
• need further evaluation <strong>of</strong><br />
what maps or datasets<br />
exist that can be applied <strong>to</strong><br />
Ontario (LR)<br />
• Need further development <strong>of</strong> satellite systems and recognition<br />
as a promising area (KII, LR)<br />
• Potential for greater spatial coverage (LR, KII)<br />
Technical expertise required <strong>to</strong> create air quality maps from<br />
satellite images (LR, KII)<br />
Spatial resolution can vary significantly from 250m <strong>to</strong> 320km<br />
(LR)<br />
Some satellites do not capture air pollutant concentrations at<br />
a resolution relevant <strong>to</strong> the built environment (LR)<br />
Cost for imagery (LR)<br />
Quality <strong>of</strong> images can vary due <strong>to</strong> cloud cover and other<br />
weather conditions (LR)<br />
Limitations in determining ground level estimates from upper<br />
atmosphere estimates (LR)<br />
Relevant <strong>to</strong> pollutant<br />
moni<strong>to</strong>ring<br />
Emissions<br />
Estimates<br />
Emissions data can be used<br />
<strong>to</strong> estimate air pollutant<br />
concentrations and as an indirect<br />
indica<strong>to</strong>r <strong>of</strong> potential exposure<br />
The data required for emission<br />
estimates were cited from<br />
various sources:<br />
• NPRI<br />
• TURI<br />
• MPAC<br />
• Municipal traffic volumes<br />
• Emissions fac<strong>to</strong>rs formula<br />
<strong>to</strong> convert traffic volumes <strong>to</strong><br />
emission rates<br />
• Fleet demographics <strong>to</strong><br />
determine composition <strong>of</strong><br />
traffic sources<br />
• Geocoding <strong>of</strong> point source and<br />
area emissions<br />
Of 14 PHUs assessing air quality, 6<br />
use emissions estimates<br />
Of the 28 PHUs that responded <strong>to</strong><br />
the survey, 43% have access <strong>to</strong><br />
traffic volume data from regional or<br />
municipal roads<br />
Traffic counts can be an indirect measure <strong>of</strong><br />
traffic related air pollution and may translate <strong>to</strong><br />
other traffic corridors with similar counts (KII)<br />
Provides useful information on sources <strong>of</strong> air pollutants in the built<br />
environment (LR, KII)<br />
May not require air moni<strong>to</strong>ring data depending on purpose <strong>of</strong> air<br />
quality assessment (LR)<br />
Can be applied <strong>to</strong> point sources and traffic (LR)<br />
Greater need for smaller emissions sources for community<br />
based modelling (KII)<br />
Potential estimation errors in pollutant levels and geographic<br />
distribution (LR)<br />
Requires dispersion modelling and meteorological<br />
information for ambient air pollutant estimates (LR)<br />
Spatial detail not sufficient for assessment at a municipal<br />
level (LR)<br />
Can be incorporated<br />
in<strong>to</strong> more detailed<br />
assessments <strong>of</strong> air<br />
quality in the built<br />
environment and<br />
with meteorological<br />
modelling<br />
Important in proximity<br />
measures<br />
Need more detailed<br />
evaluation <strong>to</strong> determine<br />
geographic range <strong>of</strong><br />
impact <strong>of</strong> emissions.<br />
This is done with the<br />
support <strong>of</strong> modelling<br />
approaches<br />
Modelling<br />
Indirect measure<br />
• Used <strong>to</strong> provide additional<br />
spatial and temporal detail <strong>of</strong><br />
air pollutant levels<br />
Modelling techniques used <strong>to</strong><br />
capture pollutant information in<br />
urban areas included:<br />
• Land Use Regression<br />
• Dispersion Modelling<br />
• Kriging<br />
• Basic proximity or<br />
interpolation models (LR)<br />
Inputs vary by model, but can<br />
include:<br />
• Emissions data<br />
• Pollutant level measurements<br />
• Meteorological data (e.g. wind<br />
speed, direction, temperature)<br />
• GIS map files related <strong>to</strong> land<br />
use and surface characteristics<br />
54% <strong>of</strong> PHUs assessing air quality<br />
use some form <strong>of</strong> modelling<br />
Identified as being in previous and/<br />
or current use in select few PHUs<br />
(e.g. Toron<strong>to</strong>, Hal<strong>to</strong>n, Ottawa)<br />
Modelling approaches are an important <strong>to</strong>ol for<br />
assessing neighbourhood level differences in<br />
pollutants.<br />
Common variables considered include:<br />
• Road networks (e.g. road length and traffic<br />
density)<br />
• Land use<br />
• Population density<br />
• Topography<br />
• Meteorological conditions<br />
Various spatial models have<br />
been developed at the<br />
municipal level (LR)<br />
• Present work from Health<br />
Canada is using NO 2<br />
measurements and Land<br />
Use Regression modelling<br />
for various communities<br />
across Canada, and may<br />
have potential <strong>to</strong> be applied<br />
more broadly (KII)<br />
Modelling is noted as an important or necessary <strong>to</strong>ol <strong>to</strong> gain<br />
greater spatial detail <strong>of</strong> air pollution distribution in urban<br />
communities (KII, SU)<br />
Can incorporate modelling approaches with built environment<br />
indica<strong>to</strong>rs (LR)<br />
Technical and human resources needed for modelling (LR,<br />
KII)<br />
Higher cost for more detailed maps (LR)<br />
• Important <strong>to</strong>ol for<br />
mapping pollutant<br />
distribution from<br />
emission estimates<br />
and air moni<strong>to</strong>ring<br />
Proximity<br />
Indirect measure<br />
Distance <strong>of</strong> sensitive populations<br />
<strong>to</strong> areas with high pollutant levels<br />
Determination <strong>of</strong> a safe distance<br />
from major roadways or other<br />
sources <strong>of</strong> air pollutants<br />
• Distance <strong>of</strong> major roadways<br />
and emitting facilities from<br />
sensitive populations<br />
29% <strong>of</strong> 28 PHUs that answered<br />
the survey have access <strong>to</strong> data<br />
regarding<br />
• Proximity <strong>of</strong> population <strong>to</strong><br />
emission sources reported in<br />
NPRI<br />
• Proximity <strong>of</strong> population <strong>to</strong> high<br />
traffic volume roads<br />
While much research has shown how pollutant<br />
levels decline from a source, the setback<br />
distance for areas <strong>of</strong> high risk is not conclusive<br />
and depends on meteorological conditions,<br />
traffic volumes, and built environment attributes.<br />
Care should be taken in determining safe<br />
heights as well as distance (SU).<br />
• Need <strong>to</strong> better understand which pollutants are appropriate<br />
indica<strong>to</strong>r for proximity <strong>to</strong> traffic sources (KII, LR)<br />
• Need <strong>to</strong> balance need for setback distances with designing<br />
compact communities (LR)<br />
• Still need <strong>to</strong> clarify which pollutants are causing most<br />
harm near high traffic areas (KII, SU)<br />
Pollutant Abbreviations: UFP- Ultra-fine particulate matter BC- Black carbon BTEX- Benzene/Toluene/Ethylbenzene/Xylene (volatile organic compounds) PM2.5- Fine Particulate Matter PM10- Course particulate matter O3- Ozone SO2- Sulfur dioxide NO- Nitric oxide NO2- Nitrogen dioxide NOx- Nitrogen oxides CO– Carbon monoxide TRS- Total reduced sulfur
AIR QUALITY<br />
Table 12: <strong>Data</strong> sources for Air Quality and Policy-Relevant Information as Identified in the Literature Review, Key Informant Interviews, Survey and GIS Metadata Exercise.<br />
<strong>Data</strong> Source Topic Area Utility in Outcomes Current Use in Ontario PHUs Desirability Cost Challenges<br />
Ministry <strong>of</strong> the <strong>Environment</strong><br />
Air Quality Information System<br />
(AQUIS)<br />
Air quality<br />
Incorporated in<strong>to</strong> National Air<br />
Pollutant Surveillance program<br />
(NAPS) <strong>Environment</strong> Canada <strong>Data</strong><br />
One <strong>of</strong> two air quality indexes applicable <strong>to</strong> Ontario<br />
is produced by the MOE (Air Quality Index) (LR)<br />
<strong>Data</strong> on individual pollutants (PM 2.5<br />
, NO 2<br />
, NO, NO x<br />
,<br />
O 3<br />
, SO 2<br />
, CO) (LR)<br />
93% <strong>of</strong> PHUs assessing air<br />
quality use the AQI (SU)<br />
Of 11 PHUs that assess specific<br />
pollutants (SU)<br />
• 83% assess ozone, fine<br />
particulate matter, and<br />
nitrogen dioxide<br />
• 75% assess sulfur dioxide<br />
• 50% evaluate carbon<br />
monoxide<br />
Depends on local air quality<br />
issues<br />
Three PHUs which noted<br />
challenges in assessing air<br />
quality, stated that demand<br />
for moni<strong>to</strong>ring pollutants was<br />
not a high priority (SU)<br />
Free (GM)<br />
Moni<strong>to</strong>ring stations are geocoded, but no standard exists for<br />
geographic area represented by moni<strong>to</strong>ring station<br />
Limited relevance <strong>to</strong> small populations. Strong focus on high<br />
population areas in Southern and South-eastern Ontario (KI, LR)<br />
Need for more air moni<strong>to</strong>ring stations and for modelling <strong>to</strong> provide<br />
greater spatial detail <strong>to</strong> current data (SU, LR, KI)<br />
The density <strong>of</strong> moni<strong>to</strong>ring stations can differ, between municipalities<br />
(KI, LR, SU)<br />
Of PHUs assessing air quality 5 PHUs commented on the limited<br />
spatial representation <strong>of</strong> the present air moni<strong>to</strong>ring stations<br />
Canada <strong><strong>Environment</strong>al</strong><br />
Sustainability Indica<strong>to</strong>rs<br />
Hourly data on O 3<br />
, PM 2.5<br />
, SO 2<br />
, NO 2<br />
, VOCs Was not evaluated Free Two year lag period for data.<br />
National Pollutant Release<br />
Inven<strong>to</strong>ry<br />
Air Quality<br />
Emissions data for Ontario prior<br />
<strong>to</strong> 2005 available from Ministry <strong>of</strong><br />
<strong>Environment</strong> His<strong>to</strong>rical OnAIR <strong>Data</strong><br />
2001-2004 (MOE)<br />
The NPRI collects data on pollutant emissions from<br />
industrial and non-industrial sources<br />
<strong>Data</strong> available at a facility level, as well as<br />
aggregated data at the provincial level.<br />
The provincial information includes estimates for 17<br />
air pollutants organized by sec<strong>to</strong>r.<br />
In addition <strong>to</strong> industrial sources, NPRI estimates<br />
emissions from various other sec<strong>to</strong>rs such as<br />
transportation, agriculture, landfills, natural sources,<br />
etc.<br />
Of 28 PHUs who responded <strong>to</strong><br />
the survey, 29% had access <strong>to</strong><br />
data on proximity <strong>of</strong> population<br />
<strong>to</strong> emission sources reported<br />
through NPRI (SU)<br />
For large emission sources<br />
which meet the NPRI<br />
requirements for reporting,<br />
the data is <strong>of</strong> good quality<br />
and useful for PHU (KII)<br />
Provides maps on emissions<br />
density at low resolution (LR)<br />
<strong>Data</strong> available for specific<br />
pollutants<br />
Useful for traffic or point<br />
sources pollution (LR)<br />
Free<br />
Estimates only available for annual emission levels (LR)<br />
Limited information on day <strong>to</strong> day variation between communities<br />
(LR, KII)<br />
A lag period <strong>of</strong> 1 or 2 years exists for the data (LR)<br />
Many commercial and industrial sources within a city can be<br />
exempt from reporting <strong>to</strong> NPRI (LR, KII)<br />
Ontario Ministry <strong>of</strong><br />
Transportation<br />
Air Quality (Traffic volume)<br />
Ontario Ministry <strong>of</strong> Transportation annual publication<br />
on traffic volume and accident rates for provincial<br />
highways in Ontario<br />
No PHU reported the use or<br />
access <strong>to</strong> MTO data. However,<br />
such information may be obtained<br />
indirectly through the municipal<br />
transportation and planning<br />
departments.<br />
A useful resource for<br />
high traffic volume<br />
highways situated in urban<br />
communities<br />
Free<br />
To translate in<strong>to</strong> emission estimates, need information on fleet<br />
demographics (LR)<br />
Focus strictly on provincial highways (GM)<br />
Local Road <strong>Data</strong><br />
(collected by<br />
municipalities /regions)<br />
Network <strong>An</strong>alysis (<strong>to</strong> measure<br />
proximity or <strong>to</strong> model population<br />
affected)<br />
Traffic data (e.g. vehicle kilometres<br />
travelled, modal share, traffic<br />
volume)<br />
12 <strong>of</strong> 28 PHUs that answered the<br />
survey stated they have access <strong>to</strong><br />
municipal level traffic data (SU).<br />
Traffic related information is<br />
commonly gathered from<br />
municipal sources (SU).<br />
Free<br />
May require geocoding for GIS use.<br />
Coverage and update frequency will vary – data quality assessment<br />
is required.<br />
Dependent on municipalities <strong>to</strong> compile a local inven<strong>to</strong>ry <strong>of</strong> road<br />
and traffic data.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT<br />
Table 18: Measures <strong>of</strong> community vulnerability <strong>to</strong> extreme heat, as identified in the literature review<br />
Measure <strong>Data</strong> Component Location Applied<br />
Composite Heat<br />
Vulnerability<br />
Index 207<br />
Hazard layer<br />
• Satellite image <strong>of</strong> near surface air temperature<br />
Human vulnerability layer<br />
• Under 5 or over 65 years <strong>of</strong> age<br />
• Living on a low income<br />
• Being over 65 and living alone<br />
Index 208 • Low income persons (after tax)<br />
Exposure index (40%)<br />
• Mean surface temperature<br />
• Green space coverage<br />
• Accessibility <strong>to</strong> green space<br />
• Dwelling units in high rise buildings<br />
• Renter dwellings in older high rises<br />
• Population density<br />
Sensitivity index (60%)<br />
Heat Vulnerability • Children age =50% <strong>of</strong> income on housing<br />
• Recent immigrants (within 5 years or less)<br />
• Racialized groups<br />
• Emergency visits: respira<strong>to</strong>ry or circula<strong>to</strong>ry disease<br />
• Vulnerable seniors<br />
Human Thermal<br />
Comfort Index 200<br />
Risk from<br />
extreme heat 209<br />
Heat Vulnerability<br />
Index (HVI) 210;211<br />
Coping resources<br />
• Social ties index<br />
• % air conditioned<br />
• % swimming pools<br />
• Ro<strong>of</strong> reflectivity (% asphalt, % tile, % wood, % other)<br />
Population<br />
• Median income, % in poverty<br />
• Less than high school, College graduate<br />
• % minority<br />
• Median age<br />
• % ages 5 and under<br />
• % ages 65 and over<br />
Thermal environment<br />
• Distance from city center (km)<br />
• Population/km2<br />
• % open space<br />
• Vegetation abundance (SAVI)<br />
Land surface temperature<br />
Heat related mortality<br />
Vulnerability variables<br />
• Hispanic population<br />
• Black population<br />
• Asian population<br />
• Native American population<br />
• Other race population<br />
• Age 65 and over<br />
• Age 65 and over living below poverty<br />
• Age 5 and under<br />
• Persons living below poverty<br />
• Low education (less than high school education)<br />
Social/environmental<br />
• % below the poverty line<br />
• % race other than white<br />
• % with less than a high school diploma<br />
• % <strong>of</strong> non-green space<br />
Social isolation<br />
• % that live alone<br />
• %> 65 years <strong>of</strong> age that live alone<br />
Air conditioning prevalence<br />
• % homes without central air conditioning<br />
• % homes with no air conditioning <strong>of</strong> any kind<br />
Preexisting health conditions<br />
• % population diagnosed with diabetes<br />
Hazard Layer: (50%)<br />
• Urban Heat Island magnitude (LST)<br />
Heat Health Exposure Layer (25%):<br />
Risk 212 • Household type (Experian MOSAIC data)<br />
Vulnerability Layer (25%):<br />
• Made up <strong>of</strong> vulnerable types extracted from the exposure layer (e.g. Old, Ill, Density, Flats)<br />
SUPREME<br />
system 205<br />
Thermal information (Landsat)<br />
Vulnerability indica<strong>to</strong>rs<br />
• Regional deprivation index 2006<br />
• Population density<br />
• Age<br />
• Housing conditions<br />
• Foreign language population<br />
• Dissemination areas inside heat Islands<br />
• Landed immigrants since 2001<br />
Montreal<br />
Toron<strong>to</strong><br />
Phoenix, AZ<br />
Philadelphia, PA<br />
California, Massachusetts<br />
New Mexico<br />
Oregon<br />
Washing<strong>to</strong>n<br />
Birmingham, UK<br />
Quebec<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT<br />
Table 20: Measurement approaches and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata<br />
Description (LR) Inputs Current Use in Ontario PHUs (SU)<br />
Measures<br />
(LR)<br />
Theoretical Op.<br />
Ontario (LR/GM)<br />
Desirability<br />
Challenges<br />
Link b/w Measurement<br />
Approaches<br />
Meteorological<br />
<strong>Data</strong><br />
Direct measurements <strong>of</strong> Individual<br />
meteorological variables<br />
• Temperature<br />
• Dew point temperature<br />
• Relative humidity<br />
• Wind speed<br />
• Wind direction<br />
• Solar load (radiation)<br />
• Atmospheric pressure<br />
Individual met<br />
variables (LR)<br />
*May be real time or<br />
hourly (GM)<br />
Of 18 PHUs that assess heat<br />
• 16 PHUs have access <strong>to</strong> meteorological data (all 16 use temperature <strong>to</strong> asses heat, 4 use<br />
wind speed)<br />
23 PHUs have access <strong>to</strong> meteorological data<br />
• 21 PHUs use <strong>Environment</strong> Canada as source<br />
• 1 PHU uses Weather Network<br />
One PHU uses met data as input <strong>to</strong> a larger heat model <strong>to</strong> predict heat days/alerts<br />
7 variables<br />
6 <strong>of</strong> 7variables are<br />
available across Ontario<br />
Direct measurements<br />
Availability <strong>of</strong> meteorological variables may be<br />
limited by region (LR/KI)<br />
Limited local data (LR)<br />
Temporal analysis is limited (KI)<br />
Does not take in<strong>to</strong> account effect <strong>of</strong> 4 variables<br />
that comprise heat.<br />
Main inputs for heat indices<br />
Used for AQ measures<br />
Used in conjunction with AQ info (e.g.<br />
AQHI) for calling heat alerts advisories<br />
Composite<br />
Heat Measures<br />
(using<br />
meteorological<br />
data)<br />
Composite measures <strong>of</strong> heat that combine<br />
individual meteorological variables based on<br />
given formulae<br />
• Apparent temp/heat Index<br />
• Humidex<br />
• Thom Index/Discomfort index<br />
• Relative stress index<br />
• WGBT index<br />
• Heat exposure index<br />
• Thermal Index (net effective temperature)<br />
• Heat Load Index<br />
• Heat Stress Index<br />
• Perceived temperature<br />
• Spatial synoptic classification<br />
• Human Thermal Comfort Index<br />
Individual met<br />
variables (LR)<br />
Scientific formulae<br />
(LR)<br />
Some require<br />
climate normal/<br />
averages as<br />
reference periods<br />
(LR)<br />
Some require<br />
demographic/<br />
morbidity/mortality<br />
data as reference<br />
for formula<br />
development (LR)<br />
Of 18 PHUs that assess heat<br />
• 16 PHUs have access <strong>to</strong> meteorological data (all 16 use humidex <strong>to</strong> assess heat)<br />
• 2 PHUs use models<br />
23 PHUs have access <strong>to</strong> meteorological data:<br />
• 21 PHUs use <strong>Environment</strong> Canada as source<br />
• 1 PHU uses Weather Network<br />
• 3 PHUs get data from other temp/mobile moni<strong>to</strong>ring stations (all from Health Canada)<br />
o All 3 measure WGBT<br />
11 measures<br />
Standard <strong>Environment</strong><br />
Canada data would be<br />
sufficient <strong>to</strong> populate<br />
some <strong>of</strong> these<br />
measures. However,<br />
special equipment may<br />
be required for other<br />
measures (e.g. WGBT).<br />
(LR,KII).<br />
Some measures<br />
are not realistic for<br />
PHUs <strong>to</strong> calculate at a<br />
population level<br />
According <strong>to</strong> Health Canada, heat is<br />
comprised <strong>of</strong> 4 fac<strong>to</strong>rs (temperature,<br />
humidity, wind and solar load).<br />
Composite heat measures better<br />
address this relationship (LR)<br />
Availability <strong>of</strong> meteorological variables may be<br />
limited by region (LR/KI)<br />
Limited local data (LR)<br />
Temporal analysis is limited (KI)<br />
Most require direct measurements <strong>of</strong><br />
Individual meteorological variables<br />
Used in conjunction with AQ info (e.g.<br />
AQHI) for calling heat alerts advisories<br />
Urban climate<br />
modelling<br />
Models that simulate local climates in urban<br />
environments<br />
Reasonably low spatial resolution (KII)<br />
Could be used <strong>to</strong> extrapolate met data<br />
across a wider geography(KII)<br />
<strong>Built</strong> environment feature and characteristics<br />
related <strong>to</strong> extreme heat.<br />
*Many <strong>of</strong> these measures have been identified<br />
as being collected through remote sensing.<br />
Individual features<br />
and characteristics<br />
Means <strong>of</strong> collecting<br />
data (e.g. satellite/<br />
sensor)<br />
Expertise <strong>to</strong> collect<br />
and analyze data<br />
15 PHUs reported having access <strong>to</strong> built environment data (e.g. land use, forest cover etc.)<br />
Remotely sensed data:<br />
Potential for complete coverage (LR)<br />
Remotely sensed data:<br />
Quality and quantity <strong>of</strong> satellite data varies by<br />
source (e.g. spatial and temporal resolution(LR)<br />
Parameters only reflect data at one point in time<br />
(LR/KII)<br />
Use may be limited due <strong>to</strong> accessibility issues,<br />
cost and expertise <strong>to</strong> collect and analyze data<br />
(LR/KII)<br />
<strong>Built</strong><br />
environment<br />
data<br />
A. Temperature<br />
• Surface temperature<br />
• Urban heat Islands<br />
B. Land cover<br />
• Impervious Surfaces<br />
• Vegetation (NDVI, VCF, EVI, SAVI)<br />
• Albedo (BRDF) Open/green space<br />
C. Community characteristics<br />
• Sprawl index<br />
• Density<br />
• Proximity<br />
• Building height<br />
• Land Use<br />
2 PHUs have access <strong>to</strong> thermal imagery<br />
1 PHU accesses thermal imagery through federal government<br />
1 PHU accesses thermal imagery by 1) directly requesting it from data source 2) through a<br />
regional or local government<br />
Of the 18 PHUs that assess heat:<br />
2 PHUS have identified the following as in development : canopy cover, age stratification <strong>of</strong><br />
urban forest s<strong>to</strong>ck<br />
The PHUs that do not assess heat, identified having access <strong>to</strong> the following: canopy coverage<br />
(3), age stratification (3), surface reflectivity/albedo (1), surface emissivity (1)<br />
Of the 18 PHUs that assess heat:<br />
1 PHU identified the following as in development àbuilding age<br />
PHUs that do not assess heat identified having access <strong>to</strong> the following: urban sprawl (1),<br />
building density (2), building age (2), average size by land use (1)<br />
Unable <strong>to</strong> assess due <strong>to</strong><br />
level <strong>of</strong> detail for each<br />
measure.<br />
Theoretically, should these<br />
measures be accessed<br />
through remote sensing,<br />
there is the potential<br />
for complete coverage<br />
in Ontario. However,<br />
processing would be<br />
required.<br />
Surface temperature can be used as a<br />
basic indica<strong>to</strong>r <strong>of</strong> relative hot and cool<br />
areas and neighbourhoods (KII)<br />
Thermal anisotropy: 3D structures are viewed<br />
at a single angle so thermal properties <strong>of</strong> other<br />
sides are not captured. (LR)<br />
Unable <strong>to</strong> infer the temperature inside buildings<br />
(e.g. may be air conditioned). (KII)<br />
May be used in air quality assessments.<br />
Measures have also been used <strong>to</strong><br />
assess walkability and air quality.<br />
Measures have also been used <strong>to</strong><br />
assess walkability and air quality.<br />
D. Residential characteristics<br />
• Detached homes<br />
• Air conditioning<br />
• Swimming pools<br />
• Dwellings in high rise buildings<br />
Air<br />
Walkability<br />
Community<br />
vulnerability<br />
Measures that combine exposure <strong>to</strong> extreme<br />
heat and individual./community vulnerability <strong>to</strong><br />
determine overall risk<br />
Socio-economic<br />
& demographic<br />
variables<br />
Heat exposure<br />
variables<br />
Platform/<br />
methodology <strong>to</strong><br />
combine the two<br />
<strong>to</strong> create overall<br />
vulnerability<br />
14 PHUs reported using demographic data <strong>to</strong> identify populations more vulnerable <strong>to</strong> heat (SU)<br />
Of these, the following are used: Age (14), gender (2), income (9), language (3)<br />
Other<br />
• Pre-existing medical conditions (4)<br />
• People without A/C (2)<br />
• Lack <strong>of</strong> public shelters (cool places)<br />
• Deprivation index<br />
• Occupation / employment (2)<br />
• Type <strong>of</strong> dwelling unit (2)<br />
• Education<br />
• Mobility restrictions<br />
• Cognitive impairment<br />
• Socially isolated persons<br />
• New immigrants<br />
• Urban heat island<br />
• Lack <strong>of</strong> tree canopy/green spaces<br />
• Lack <strong>of</strong> social infrastructure/ service gaps (2)<br />
• High-crime rate areas<br />
1 PHU has created a heat vulnerability index and maps (Toron<strong>to</strong>) (LR)<br />
7<br />
Measures <strong>of</strong> community<br />
vulnerably can vary in<br />
methodology. In Ontario,<br />
the Deprivation index and<br />
other census data could<br />
be used. Theoretically,<br />
thermal imagery for<br />
Ontario could be overlaid<br />
with a number <strong>of</strong> built<br />
environment, social and<br />
demographic data.<br />
Combines exposure and vulnerability<br />
which <strong>to</strong>gether <strong>to</strong> inform risk<br />
Likely unreasonable <strong>to</strong> expect every local health<br />
department <strong>to</strong> create its own heat vulnerability<br />
map – a national HVI created through freely<br />
available national data sets is useful (LR)<br />
BE data<br />
Composite measures using met data<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario
EXTREME HEAT<br />
Table 21: <strong>Data</strong> sources and sets, and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata<br />
Organization<br />
<strong>Data</strong> Source/<br />
Set<br />
Topic Area Utility in Outcomes Current Use in Ontario PHUs Desirability Cost Challenges/Limitations<br />
Health Canada<br />
<strong><strong>Environment</strong>al</strong><br />
Heat Moni<strong>to</strong>ring<br />
Systems<br />
Extreme Heat<br />
Air Quality<br />
Could potentially:<br />
• Contribute all met variables for 8 <strong>of</strong> 11 composite measures<br />
• Contribute <strong>to</strong> some met variables for 10 <strong>of</strong> 11 composite heat<br />
measures<br />
• Collect 4 <strong>of</strong> 7 individual meteorological variables<br />
Can contribute <strong>to</strong> syndromic surveillance<br />
South eastern environmental heat moni<strong>to</strong>ring network: PHUs<br />
covered by network: Hastings & Prince Edward Counties;<br />
Leeds, Grenville & Lanark District; Peterborough County-City;<br />
and Kings<strong>to</strong>n Frontenac and Lennox & Adding<strong>to</strong>n (KFL&A)<br />
(GM)<br />
3 PHUs identified using health Canada EHMS units (SU) (2 <strong>of</strong><br />
these are separate from the south eastern heat network)<br />
Able <strong>to</strong> assess 4 parameters that comprise heat (LR)<br />
Useful for capturing heat exposure in various<br />
environments given the variation <strong>of</strong> meteorological<br />
conditions over distances (e.g. city centres <strong>to</strong> farm<br />
lands). (KII)<br />
Units can be placed in areas outside typical<br />
<strong>Environment</strong> Canada weather stations and<br />
strategically placed <strong>to</strong> assess spatial variability <strong>of</strong> heat<br />
over a region or major urban area (KII)<br />
No cost<br />
Funded<br />
by Health<br />
Canada (KI)<br />
Limited geographic coverage based on pilot projects<br />
Not publicly available (GM/KI)<br />
If data were <strong>to</strong> be released, it would require interpretation (KI)<br />
Meteorological<br />
Service <strong>of</strong><br />
Canada<br />
(<strong>Environment</strong><br />
Canada)<br />
National Climate<br />
Archives<br />
Real time data<br />
from weather<br />
stations<br />
Extreme Heat<br />
Air Quality<br />
EC data<br />
integrated in<strong>to</strong><br />
syndromic<br />
surveillance<br />
system (KII)<br />
Could potentially<br />
• Contribute all met variables for 5 <strong>of</strong> 11 composite measures<br />
• Contribute <strong>to</strong> some met variables for 10 <strong>of</strong> 11 composite heat<br />
measures<br />
• Collect 6 <strong>of</strong> 7 individual meteorological variables<br />
Can contribute <strong>to</strong> syndromic surveillance<br />
• Of 18 PHUs that assess heat (SU)<br />
• 16 PHUs have access <strong>to</strong> meteorological data (all 16 use<br />
temperature and humidex <strong>to</strong> asses heat, 4 use wind<br />
speed)<br />
• 23 PHUs have access <strong>to</strong> meteorological data (SU)<br />
• 21 PHUs use <strong>Environment</strong> Canada as source<br />
Undergoes quality control (KII)<br />
<strong>Data</strong> freely available and accessible across Ontario.<br />
(GM)<br />
Free<br />
GIS formatted data is not provided through the National Climate Archives; however,<br />
the database contains latitude and longitude <strong>of</strong> each observation (Degrees & minutes)<br />
that can be used <strong>to</strong> create a GIS shape file. (GM)<br />
Typically at local airports/weather stations (LR)<br />
Availability may be limited by region (LR/KI)<br />
Limited use <strong>to</strong> assess variation in heat across community due <strong>to</strong> distance from<br />
stations and sparse spatial coverage (KI, GM)Standard <strong>Environment</strong> Canada data<br />
would not be sufficient <strong>to</strong> populate some <strong>of</strong> the measures that address the four<br />
fac<strong>to</strong>rs that comprise heat.<br />
Natural<br />
Resources<br />
Canada<br />
Landsat<br />
Heat<br />
Walkability<br />
Could contribute <strong>to</strong> BE data (LR)<br />
• Surface temperature<br />
• Impervious surfaces<br />
• Vegetation<br />
• Urban heat islands<br />
• Land cover<br />
2 PHUs have access <strong>to</strong> Landsat (SU)<br />
Used Landsat imagery (from NRCan) for the heat exposure<br />
component for Toron<strong>to</strong>’s Heat Vulnerability Index (LR)<br />
His<strong>to</strong>rical archive completely open and accessible<br />
online with no privacy restrictions (MD/KII)<br />
Canada wide coverage (MD)<br />
Includes metadata <strong>to</strong> convert the thermal infrared <strong>to</strong><br />
temperature in Celsius (KII)<br />
Includes calibration data (KII)<br />
Free (MD)<br />
Satellite ending useful lifespan (MD)<br />
Update frequency is every 16 days<br />
Unspecified<br />
Heat<br />
One PHU identified NRCan as the source for data on surface<br />
reflectivity and surface emissivity (1)<br />
USGS Aster Heat<br />
Could contribute <strong>to</strong> BE data (LR)<br />
• Surface temperature<br />
• Vegetation<br />
No PHUs identified having access <strong>to</strong> ASTER (SU)<br />
Associated<br />
fees<br />
Could contribute <strong>to</strong> BE data (LR)<br />
USGS MODIS Heat<br />
• Surface temperature<br />
• Vegetation<br />
Update frequency is 2x a day (GM) Free Scale resolution limited (GM/KII)<br />
• Albedo<br />
Could contribute <strong>to</strong> BE data (LR)<br />
First Base<br />
Solutions<br />
Cus<strong>to</strong>m Aerial<br />
Image Acquisition<br />
Heat<br />
• Surface temperature<br />
• Impervious surfaces<br />
• Vegetation<br />
One PHU identified using aerial pho<strong>to</strong>graphy but did not<br />
specify the source<br />
Cus<strong>to</strong>mizable data set (GM)<br />
High resolution (GM)<br />
Associated<br />
fees (GM)<br />
Potentially expensive<br />
• Urban heat islands<br />
• Land cover<br />
Statistics<br />
Canada<br />
Census<br />
Heat<br />
Air<br />
Walkability<br />
Could contribute <strong>to</strong> socio economic and demographic variables<br />
required for community vulnerability (LR)<br />
One PHU identified Statistics Canada as a source for data on<br />
building age =<br />
Source for dwelling units in high rises, dwelling units in high<br />
rises constructed before 1986 and population density in<br />
Toron<strong>to</strong><br />
Standardized data available across Canada Free Updated every 5 years<br />
Municipalities Local data Heat<br />
Could contribute on built environment data.<br />
May be accessed through municipal departments<br />
• Forestry<br />
• GIS<br />
• Planning<br />
Could contribute <strong>to</strong> socio economic and demographic variables<br />
required for community vulnerability<br />
PHUs identified municipalities as sources for: (SU)<br />
• Canopy cover (5)<br />
• Age stratification <strong>of</strong> forests (4)<br />
• Unit size by land use (2)<br />
• Building density (3)<br />
• Building age (3)<br />
Source for public green space boundaries and land cover (for<br />
canopy coverage) in Toron<strong>to</strong> (LR)<br />
Provides local level data Unknown <strong>Data</strong> may not be consistently gathered or shared.<br />
<strong>An</strong> <strong><strong>Environment</strong>al</strong> <strong>Scan</strong> <strong>of</strong> <strong>Built</strong> <strong>Environment</strong> <strong>Data</strong> <strong>Related</strong> <strong>to</strong> Walkability & <strong><strong>Environment</strong>al</strong> Exposures in Urban Ontario