An Environmental Scan of Built Environment Data Related to ...

An Environmental Scan of Built Environment Data Related to Walkability & Environmental Exposures in Urban Ontario

ACKNOWLEDGEMENTS 

We are sincerely grateful and recognize the hard work and contributions of the project collaborators 

over the past year: the Built Environment Locally Driven Collaborative Project (LDCP) team and project 

consultants who helped make this project a reality. 

The Built Environment LDCP project team would like to thank Public Health Ontario (PHO) for its support 

of this project. The team gratefully acknowledges funding received from PHO through the Locally Driven 

Collaborative Projects program. 

We acknowledge and thank Sarah Maaten for her contributions to the project proposal; as well as 

students Caleb Stenzl and Ashley Czerkas for their work on the metadata analysis. 

The project team would also like to thank the study participants for their important contributions. 

This report was prepared through a collaborative effort by the following public health organizations 

and consultants: 

PUBLIC HEALTH UNITS/AGENCIES 

KINGSTON, FRONTENAC AND LENNOX & ADDINGTON PUBLIC HEALTH (PROJECT LEAD) 

NIAGARA REGION PUBLIC HEALTH 

PUBLIC HEALTH AGENCY OF CANADA 

SUDBURY & DISTRICT HEALTH UNIT 

YORK REGION PUBLIC HEALTH 

CONSULTANTS 

Popy Dimoulas-Graham Epidemiologist Consultant; 

Project Coordinator (Primary Editor) 

Kim Perrotta Environmental Health Consultant; 

Creating Healthy and Sustainable Environments (CHASE) 

Kevin Behan Environmental Health Consultant; 

Creating Healthy and Sustainable Environments (CHASE) 

Amanda Northcott Qualitative Data Consultant 

© Kingston, Frontenac and Lennox & Addington (KFL&A) Public Health, 2012

BUILT ENVIRONMENT 

LOCALLY DRIVEN COLLABORATIVE 

PROJECT TEAM MEMBERS 

KINGSTON, FRONTENAC AND LENNOX & ADDINGTON PUBLIC HEALTH (KFL&A) 

Paul Belanger (co-principal investigator), GIS Services Manager 

Daphne Mayer (co-principal investigator), Research Associate 

Novella Martinello, Foundational Standard Specialist 

NIAGARA REGION PUBLIC HEALTH 

Bill Hunter, Manager, Environmental Health 

Deborah Moore, Senior Epidemiologist 

Ryan Waterhouse, GIS Analyst 

PUBLIC HEALTH AGENCY OF CANADA 

Ahalya Mahendra, Epidemiologist 

SUDBURY & DISTRICT HEALTH UNIT 

Marc Lefebvre, Manager Resources, Research, Evaluation and Development Division 

YORK REGION PUBLIC HEALTH 

Asim Qasim, Environmental Research and Policy Analyst 

Caitlyn Paget, Epidemiologist 

Helen Doyle, Manager, Health Protection Division 

Jaime Chow, Environmental Epidemiologist 

Mira Shnabel, Environmental Health Program Coordinator 

Disclaimer 

The views expressed in this publication are the views of the project team and do not necessarily reflect those of Public Health Ontario. 


EXECUTIVE 

SUMMARY 

The Ontario Public Health Standards mandate local Public Health 

Units to address the built environment by increasing public awareness, 

supporting healthy public policy, and creating supportive environments. 

In order to fully understand the relationship between 

the built environment and health, public health practitioners and researchers 

alike need metrics that are current, reliable, and geographically 

commensurate. Objective built environment measures that reflect 

the scale of walkability and environmental exposures (e.g. air 

pollutants and extreme heat) in a community could help public health 

practitioners monitor health outcomes, facilitate research, and guide 

the development of evidenced-informed interventions, programs and 

policies. Yet assessments of the built environment are hampered by 

several inconsistencies, including variations in terminology, computational 

methods and data sources. 

This report presents the results from an environmental scan used 

to support the identification of walkability and select environmental 

exposure (air quality and extreme heat exposure) data for use in the 

assessment of the urban built environment in Ontario. The environmental 

scan consisted of a literature review, key informant interviews, 

and a survey of Ontario Public Health Units (PHUs). The findings were 

synthesized through a gap analysis of measurement approaches 

and data availability, and subsequently applied to the development 

of guiding principles and recommendations to influence future directions 

for research and policy development in Ontario. 

This Locally Driven Collaborative Project (LDCP) was supported by 

Public Health Ontario (PHO). Project co-applicants included representation 

from Kingston, Frontenac and Lennox & Addington 

(KFL&A) Public Health, York Region Public Health, Niagara Region 

Public Health, and the Public Health Agency of Canada (PHAC). 


WALKABILITY 

The prevalence of obesity has more than doubled in Canada over the last twenty years and has become 

a leading public health concern. Thus, creating more walkable and less automobile dependent communities 

can contribute to decreasing the incidence of obese and overweight Canadians and should be 

considered as part of a comprehensive strategy to improve public health. 1 

When assessing the degree of urban walkability, measurements of density, diversity, street connectivity, 

and pedestrian-oriented design are commonly used. These types of built environment measures are 

strongly and consistently associated with walking and health outcomes in the literature. They are often 

correlated with one another, thus making composite indices a compelling measurement approach in 

capturing multiple aspects of the built environment at once. 2;3 

Our findings identified that the most common methods used by Ontario PHUs in assessing urban walkability 

included self-administered surveys and systematic observations; a smaller number of PHUs assessed 

urban walkability using Geographic Information System (GIS) measurements and techniques. GIS 

is required to operationalize several walkability measures, yet many of Ontario’s PHUs identified a lack of 

GIS technical resources as a significant barrier to assessing urban walkability. Overall, human resource 

capacity, measurement variability between municipalities, and data availability were key challenges identified 

by Ontario’s PHUs in the assessment of urban walkability. 

Organizations with the most established walkability assessment programs in the province were using a 

composite index. A walkability index shows the most promise in the application of a standardized assessment 

of urban walkability by PHUs in Ontario. 

AIR QUALITY 

Several studies have shown that air pollution is associated with a broad range of acute and chronic health 

effects. With increasing population sizes, industrial emissions, and demand for vehicle transportation, 

there is potential for increased concentrations of air pollutants as well as a greater risk of exposure for 

the public. 

The environmental scan showed that most Ontario PHUs use indices to assess air quality, primarily the Air 

Quality Index and the Air Quality Health Index. Individual air pollutant data for these indices are collected 

through a network of air monitoring stations in Ontario. However, a number of stations within this network 

do not have adequate spatial resolution to evaluate air quality at the neighborhood level. In addition to 

the air monitoring stations, PHUs use emissions estimates data such as the National Pollutant Release 

Inventory, and other built environment data such as major roadways and traffic volumes. While useful 

approaches, they remain limited in their ability to assess pollutants within a community. To address this 

gap, other approaches have been developed to predict pollutant levels at a high spatial resolution (e.g. 

portable sensors, satellite data, modelling); however, they are not commonly used by Ontario PHUs. 


The relationship between the built environment and air quality is complex and further research is required. 

Overall, human resources, financial capacity, and data availability were key challenges identified by PHUs 

in the assessment of air quality in urban Ontario. 

EXTREME HEAT 

The built environment plays a significant role in exposure and vulnerability of a population to extreme heat. 

A growing population, increasing urbanization and climate change also impact the risk of heat-related 

morbidity and mortality. 

The environmental scan identified a number of measurement approaches and data sources for assessing 

extreme heat. This includes composite indices and models that have been developed to assess extreme 

heat. However, these and other measurement approaches present challenges for operationalization due 

to data and resource limitations in Ontario. For instance, freely available province-wide meteorological 

data do not meet the data requirements for certain indices, nor do these data provide adequate spatial 

resolution to assess extreme heat within communities. 

Alternative measurement approaches using built environment variables have been identified in the literature. 

Some Ontario PHUs have access to built environment data, but most do not use it in the assessment 

of extreme heat. A promising measurement approach is land surface temperature from satellite 

imagery because of its availability and complete coverage across the province. Land surface temperature 

has been used by PHUs in composite measures for the assessment of community heat vulnerability, 

incorporating both individual and community factors. 

The top challenges identified by Ontario PHUs in the assessment of extreme heat included human resources, 

financial capacity, and data availability. 

COMMON THEMES 

Although the topic areas examined in the environmental scan are unique in some respects, many of the 

challenges related to the measures and data were similar. For instance, several organizations were using 

similar types of measures, but they were using different terminology, computational methods and a variety 

of data sources. Data availability, human resource capacity, financial capacity and variations between 

municipalities were reported as prominent challenges in the assessment of the urban built environment. 

The built environment and health are closely tied to equity. Thus, for all topic areas, sociodemographic 

characteristics were identified as important factors to account for in the assessment of the urban built 

environment. 


RECOMMENDATIONS 

The guiding principles and recommendations are closely tied to the results of the literature review, key 

informant interview and survey results, and are applicable to all topic areas. Given the cross-disciplinary 

nature of built environment assessments, the potential audience for the recommendations is broad. The 

overarching guiding principles and related recommendations are presented below. 

1. Strengthen Multidisciplinary Cooperation 

1.1 Engage in multidisciplinary collaboration across all sectors, including government, 

academia and private sectors 

2. Provide Methodological Guidance 

2.1 Standardize built environment measures using a multidisciplinary approach across all sectors 

2.2 Put built environment research into practice 

3. Improve Data Availability and Accessibility 

3.1 Increase access to high quality data across the Province 

3.2 Empower local agencies to engage in built environment data initiatives 

3.3 Identify and evaluate the use of current data sources and sets 

3.4 Explore the creation of new data sets 

3.5 Address the need for data auditing and validation 

4. Engage in Systematic Knowledge Transfer and Exchange (KTE) 

4.1 Facilitate engagement of expertise outside of the public health sector 

5. Strengthen Capacity 

5.1 Support Public Health Units in a technical capacity to assess the built environment 

5.2 Enhance education and training for public health professionals 

6. Strengthen Built Environment and Health Research 

6.1 Increase research funding opportunities for exploring the relationship between the built 

environment and health 


TABLE OF CONTENTS 

ACKNOWLEDGEMENTS 

EXECUTIVE SUMMARY 

LIST OF TABLES 

LIST OF FIGURES 

ACRONYMS 

INTRODUCTION 

CHAPTER 1: METHODOLOGY 

CHAPTER 2: WALKABILITY 

BACKGROUND 

LITERATURE REVIEW 

KEY INFORMANT INTERVIEWS 

SUMMARY OF SURVEY RESULTS 

GAP ANALYSIS 

CHAPTER 3: AIR QUALITY 

BACKGROUND 



SUMMARY OF SURVEY 

GAP ANALYSIS 

CHAPTER 4: EXTREME HEAT 

BACKGROUND 



SUMMARY OF SURVEY RESULTS 

GAP ANALYSIS 

CHAPTER 5: DISCUSSION 

GUIDING PRINCIPLES & RECOMMENDATIONS 

REFERENCES 

3 

5 

12 

13 

14 

17 

23 

33 

35 

41 

53 

64 

75 

85 

87 

98 

118 

123 

133 

137 

139 

144 

152 

159 

168 

173 

185 

193 



APPENDICES 

APPENDIX A: Walkability and environmental exposures 

(air quality and extreme heat) literature review summary tables...................................213 

APPENDIX B: Key informant interview letter of invitation (LOI)...........................................................214 

APPENDIX C: Key informant interview guide – walkability.................................................................216 

APPENDIX D: Key informant interview guide – environmental exposures 

(air quality and extreme heat).....................................................................................218 

APPENDIX E: Built environment measures and data sources survey letter of invitation (LOI).............226 

APPENDIX F: Built enviornment measures and data sources survey................................................228 

APPENDIX G: GIS meta data – walkability........................................................................................264 

APPENDIX H: GIS meta data – air quality.........................................................................................283 

APPENDIX I: 

GIS meta data – extreme heat...................................................................................285 


LIST OF TABLES 

Table 1: Sampling strategy for identifying key informants...................................................................................... 28 

Table 2: Built environment measures that comprise Walkability indices in Ontario, 2012....................................... 55 

Table 3: Gap analysis for walkability indices, 2012............................................................................................... 78 

Table 4: Gap analysis for density measures used to assess urban walkability, 2012............................................. 79 

Table 5: Gap analysis for connectivity measures used to assess urban walkability, 2012...................................... 80 

Table 6: Gap analysis for diversity measures used to assess urban walkability, 2012............................................ 81 

Table 7: Gap analysis for pedestrian oriented design measures used to assess urban walkability, 2012............... 82 

Table 8: Gap analysis for data sources and sets used in the assessment of urban walkability, 2012..................... 83 

Table 9: Summary of air quality and air pollutant data sources............................................................................. 103 

Table 10: Methods of modelling air quality and pollution levels noted in the literature review................................... 107 

Table 11: Measurement approaches and policy relevant information as identified from the literature 

review, key informant interviews, survey and GIS metadata).................................................................... 136 

Table 12: Data sources for Air Quality and Policy-Relevant Information as Identified in the Literature 

Review, Key Informant Interviews, Survey and GIS Metadata Exercise.................................................... 136 

Table 13: Select hourly meteorological variables available from Environment Canada weather stations................... 145 

Table 14: Composite measures of heat using meteorological variables, identified in the literature search................ 146 

Table 15: Examples of built environment measures for the assessment of extreme heat by category...................... 147 

Table 16: Examples of remotely sensed measures of the built environment used to assess extreme 

heat, as identified in the literature search................................................................................................. 148 

Table 17: Select satellites and their sensors used in heat-related health studies..................................................... 149 

Table 18: Measures of community vulnerability to extreme heat, as identified in the literature search...................... 151 

Table 19: Accessibility of data used to assess extreme heat in Ontario, 2012......................................................... 161 


review, key informant interviews, survey and GIS metadata..................................................................... 170 

Table 21: Data sources and sets, and policy relevant information as identified from the literature review, 

key informant interviews, survey and GIS metadata................................................................................ 171 


LIST OF FIGURES 

Figure 1: Built environment LDCP key informant interview process, Ontario, 2012............................................ 30 

Figure 2: Number of years PHUs assess urban Walkability in Ontario, 2012...................................................... 65 

Figure 3: Method(s) used by PHUs to assess urban Walkability in Ontario, 2012............................................... 66 

Figure 4: Geographic scale most commonly used by PHUs to assess urban walkability, in Ontario................... 67 

Figure 5: Public Health Unit access to data sources in Ontario, 2012............................................................... 70 

Figure 6: Major challenges faced by PHUs in assessing urban walkability, 2012............................................... 73 

Figure 7: Ontario nitrogen oxides emissions by sector 

(emissions from point/area/transportation sources, 2009 estimates)................................................... 89 

Figure 8: Ontario volatile organic compounds emissions by sector 


Figure 9: Ontario PM2.5 emissions by sector 


Figure 10: Operational land use regression predicted surface for Toronto........................................................... 105 

Figure 11: Distribution of risks associated with airborne non-carcinogenic toxics for two 

neighbourhoods in Toronto................................................................................................................ 110 

Figure 12: Distribution of risks associated with airborne carcinogens for two neighbourhoods in Toronto............ 111 

Figure 13: Distribution of risks associated with criteria air pollutants for two neighbourhoods in Toronto............. 112 

Figure 14: Airpointer belonging to the City of Ottawa.......................................................................................... 115 

Figure 15: Number of years Public Health Units have been assessing urban air quality in Ontario, 2012............. 124 

Figure 16: Type of index used to assess urban air quality in Ontario, 2012.......................................................... 125 

Figure 17: Air pollutants measured by type of Ministry of Environment stations in Ontario, 2012......................... 126 

Figure 18: Data and estimates used to assess urban air quality in Ontario, 2012................................................ 128 

Figure 19: Access to data used to assess urban air quality in Ontario, 2012...................................................... 129 

Figure 20: Challenges faced by PHUs in assessing air quality in Ontario, 2012................................................... 131 

Figure 21: Number of years Public Health Units have been assessing extreme heat in Ontario, 2012.................. 160 

Figure 22: Demographics used to identify populations more vulnerable to extreme heat in Ontario, 2012........... 165 

Figure 23: Challenges faced by PHUs in assessing extreme heat in Ontario, 2012.............................................. 166 


ACRONYMS 

APHEO 

AQI 

AQHI 

BC 

BTEX 

CCHS 

CO 

CofA 

CMA 

DMTI 

EC 

EHMS 

EPA 

ESS 

FTE 

GHG 

GIS 

GTA 

HC 

LIDAR 

LIO 

LDCP 

KFLA 

MPAC 

MODIS 

MOE 

MOHLTC 

MSC 

NAPS 

NASA 

Association of Public Health Epidemiologists in Ontario 

Air Quality Index 

Air Quality Health Index 

Black Carbon 

Benzene, Toluene, Ethylbenzene, Xylene 

Canadian Community Health Survey 

Carbon monoxide 

Certificate of Approval 

Census Metropolitan Area 

Digital Mapping Technologies Inc. 

Environment Canada 

Environmental Heat Monitoring Systems 

U.S.Environmental Protection Agency 

Earth Sciences Sector 

Full-Time Equivalent 

Greenhouse Gas 

Geographic Information System 

Greater Toronto Area 

Health Canada 

Light Detection and Ranging 

Land Information Ontario 

Locally Driven Collaborative Project 

Kingston, Frontenac, Lennox & Addington Health Unit 

Municipal Property Assessment Corporation 

Moderate Resolution Imaging Spectroradiometer 

Ontario Ministry of the Environment 

Ontario Ministry of Health and Long-Term Care 

Meteorological Service of Canada 

National Air Pollutant Surveillance 

National Aeronautics and Space Administration 


ACRONYMS 

NCR 

NO x 

NO 2 

NO 

NPRI 

NRCan 

NRVIS 

OMA 

OPHS 

PAHs 

PHIMS 

PHO 

PHU 

PM 

PM 10 

PM 2.5 

RRFSS 

SES 

SO 2 

TEO 

TPH 

TRI 

TRS 

TSP 

TTS 

UHI 

UFP 

VOCs 

WHO 

National Capital Region 

Nitrogen Oxides 

Nitrogen Dioxide 

Nitric Oxide 

National Pollutant Release Inventory 

Natural Resources Canada 

National Resources and Values Information (Ministry of Natural Resources) 

Ontario Medical Association 

Ontario Public Health Standards 

Polycyclic Aromatic Hydrocarbons 

Public Health Information Management System 

Public Health Ontario 

Public Health Unit 

Particulate Matter 

Coarse particulate matter 

Fine particulate matter 

Rapid Risk Factor Surveillance System 

Socioeconomic status 

Sulphur dioxide 

Toronto Environmental Office 

Toronto Public Health 

Toxic Release Inventory 

Total Reduced Sulphur 

Total Suspended Particulates 

Transportation Tomorrow Survey 

Urban Heat Island 

Ultra-Fine Particles 

Volatile Organic Compounds 

World Health Organization 



INTRODUCTION 

INTRODUCTION 

THE BUILT ENVIRONMENT AND HEALTH 

The ways in which we design and build our neighbourhoods have substantial implications on our health 

and quality of life. 3 Although the intention of many urban planning schemes implemented in the last fifty 

years has been to protect the public’s health, by separating industrial and residential areas for example, 

emerging research reveals that these built environment policies may have had unintended consequences. 

It has been suggested that the land use decisions of the past contribute to current air pollution concerns 

due to increased car reliance, and to sedentary lifestyles due to environments that discourage physical 

activity. 4 Rising obesity rates and physical inactivity threaten to decrease the longevity of future generations 

and places a strain on an already fragile healthcare system. 5 Several studies have shown that air pollution 

increases the risk of death and illness due to heart disease, stroke, and respiratory disease through 

both short-term and long-term exposures. Many researchers also predict that heat-related illnesses and 

deaths are on the rise, worsened by the impacts of increased urbanization (e.g. urban health island effects) 

and Canada’s aging population 6;7 As a result of these concerns, research on the built environment 

has proliferated in recent years and there has been a shift in interest to designing and re-designing built 

environments to promote physical activity and minimize environmental exposures. 8-10 

BUILT ENVIRONMENT MEASURES AND DATA 

Inquiries into the connections between the built environment and health outcomes have revived the need 

for comprehensive and detailed measures to identify elements of the physical and natural environment 

that impact environmental exposures and support or detract from walking. 11 However, research into the 

relationship between the built environment, walking and environmental exposures is complex and the 

diverse nature of built environment data presents multiple challenges for public health stakeholders. In 

the application of these data to examine the built environment, it is important to recognize that there are 

issues around data consistency, the operationalization of measures, data resolution, data accuracy and 

completeness, and temporality of data. 12;13 These built environment data challenges affect most jurisdictions, 

including Ontario. Moreover public health researchers and practitioners are increasingly using 

objective built environment indicators, often measured using geographic information systems (GIS) and 

spatial analytic techniques, to explore built environment-health relationships. An examination of these 

relationships requires current, relevant, and consistent measures and data. 


INTRODUCTION 

THE WALKABILITY PICTURE 

Walking is an important piece of the built environment puzzle from a public health perspective because it 

is the most common physical activity among Canadians. The Canadian Physical Activity Guidelines recommend 

that adults accumulate at least 150 minutes of moderate to vigorous physical activity (e.g. walking) 

per week and that children and youth be physically active at least sixty minutes per day. 14 However, 

a national survey demonstrated that only 49% of Canadians meet these guidelines. 15;16 Many Canadians 

would like to become more physically active but are deterred largely by concerns about the environment, 

distance, and convenience. 17;18 Therefore, identifying the specific characteristics of the urban environment 

that support or hinder people from living an active lifestyle is important given the inadequate and 

declining levels of physical activity in both adults and children, increasing sedentary time related to electronic 

media use and car travel times, and rising levels of chronic diseases and obesity. 10 

THE AIR QUALITY PICTURE 

Air pollution in Ontario is responsible for a significant burden of illness and death. In 2004, Canadian researchers 

estimated that five common air pollutants (i.e. fine particulate matter, nitrogen dioxide, ground 

level ozone, sulphur dioxide and carbon monoxide) contributed to approximately 2,900 non-traumatic 

deaths each year in four Ontario cities: Windsor, Hamilton, Toronto, and Ottawa. They attributed one third 

of those deaths to acute health impacts associated with a mix of air pollutants, and two thirds to chronic 

health impacts associated with fine particulate matter (PM2.5) alone. They concluded that air pollution in 

Ontario cities is responsible for 7 percent to 10 percent of all non-traumatic deaths. 19 

Motor vehicles are a major source of air pollutants, and people who spend significant amounts of time near 

high-traffic roads are often exposed to elevated levels of traffic-related pollutants. Studies have shown 

that childhood exposures to traffic-related pollution are associated with chronic respiratory symptoms, 

reduced lung function, impaired lung development, development of asthma, increased asthma incidence, 

respiratory infections, and middle ear infections. Recent research also shows that traffic-related pollution 

exposure is associated with decreased cognitive performance and language abilities in children. 20 

THE EXTREME HEAT PICTURE 

Episodes of extreme heat are increasingly more common in Canada, including Ontario. The Expert Panel 

on Climate Change Adaptation 21 advised that heat-related mortality could double in Southern Ontario by 

the 2050s, while rates of mortality due to worsening air pollution compounded by rising temperatures, 

could increase by about 15 to 25 percent during the same period. It has been predicted that the number 

of days with average temperatures above 30˚C will increase in cities across Canada, particularly those 

located in the Windsor-Quebec corridor. 22 The intensity and duration of extreme heat events in Canada 

are also expected to rise, and have been identified as key climate change risks for human health. 23-25 


INTRODUCTION 

In Canada, over eighty percent of the population live in urban areas, and this number continues to 

increase. 26 Canada’s population is aging, with the number of people over age sixty-five expected to 

double within the next twenty-five years. 27;28 Senior citizens are at an elevated risk from extreme heat 

events. 7;29;30 

THE BUILT ENVIRONMENT LOCALLY DRIVEN COLLABORATIVE PROJECT 

This project was guided by the following central research question: 

What objective 

measures of urban 

walkability and 

select environmental 

exposures (air 

pollutants and 

extreme heat) are 

in current use, 

and what data 

are available, and 

necessary, to assess 

the urban built 

environment 

in Ontario? 

The Ontario Public Health Standards mandate public health units in the province 

to address the built environment by increasing public awareness, supporting 

healthy public policy, and creating supportive environments. 31 To this 

end, Ontario’s public health practitioners must be guided by the best available 

evidence from population heath assessment, surveillance, research and knowledge 

exchange, and program evaluation to inform public program, services 

and policies related to the built environment. 

Using an intensive collaborative priority setting process, Public Health Ontario 

(PHO) identified the built environment as one of the priority funding areas in 

their 2011 Locally Driven Collaborative Project (LDCP) initiative. Given that a 

comprehensive examination of all the built environment factors associated with 

health is beyond the scope of the LDCP initiative, the focus of this collaborative 

project is on walkability and environmental exposures, namely air quality 

and extreme heat. Since eighty-five percent of Ontario residents lived in urban 

areas in 2011 and that urban migration is projected to continue to grow, this 

project focuses on urban areas. 26;32 

More specifically, through an environmental scan, this collaborative project aimed to: 

(i) 

(ii) 

Identify existing objective measures and indices of urban walkability and environmental exposures 

(air quality and extreme heat); 

Identify built environment data necessary for the construction of those measures and indices; 

(iii) Identify data currently collected by municipalities, the private sector, partners, and other sources 

that could contribute to and enhance the assessment of urban walkability and environmental 

exposure in Ontario; 

(iv) Identify gaps between necessary and available data collected in Ontario related to walkability 

and select environmental exposures; and, 

(v) 

Develop policy recommendations that would promote the development and use of such data 

and measures. 


INTRODUCTION 

These project objectives were achieved using a collaborative approach, including core representation 

from the following project co-applicants: Kingston, Frontenac and Lennox & Addington (KFL&A) Public 

Health (Study Lead), York Region Public Health, Niagara Region Public Health, and the Public Health 

Agency of Canada (PHAC). 

This report provides an overview of the evidence and foundational information for built environment 

measures and relevant data sources, as well as future directions for research and policy development in 

Ontario. 


INTRODUCTION 

KEY DEFINITIONS 

The key definitions that were applied in this study for the built environment, walkability and environmental 

exposures (air quality and extreme heat) are presented below. 

THE BUILT ENVIRONMENT 

The built environment has been defined in different 

ways by researchers in diverse fields of study 

including public health, urban design and transportation. 

For the purposes of this study, the built 

environment encompasses all buildings, spaces 

and products that are created, or modified, by 

people. Features of the built environment are 

numerous and include homes, schools, workplaces, 

parks/recreation areas, greenways, business 

areas and transportation systems. It extends 

overhead in the form of electric transmission lines, 

underground in the form of waste disposal sites 

and subway trains, and across the country in 

the form of highways. It includes land-use planning 

and policies that impact our communities in 

urban, rural and suburban areas. 33 

WALKABILITY 

Environments that make walking feasible and 

appealing have been labelled as “pedestrianoriented” 

or “walkable”. 34 Walkability is a concept 

to describe the extent to which characteristics of 

the built environment and land use mix encourage 

leisure activities, exercise, recreation, and 

ease of travel for residents in a neighbourhood. 35 

Researchers have focused on different types of 

walking, whether walking for recreation or exercise, 

or walking to reach a destination. This latter 

category of walking has a variety of labels, including 

active travel, non-motorized travel, transportrelated 

physical activity, destination-oriented 

walking, and utilitarian walking. 8 

ENVIRONMENTAL EXPOSURES: 

AIR QUALITY & EXTREME HEAT 

Exposure to contaminants and environmental 

conditions is both directly and indirectly impacted 

by the built environment, including, but not limited 

to air quality, water quality, vector-borne disease, 

climate change and extreme heat. For the 

purpose of this study, environmental exposure is 

defined as air quality and extreme heat in the built 

environment that can impact human health. 

Air quality is a term used to describe ambient 

atmospheric conditions based on the presence 

or absence of chemical, biological or physical 

compounds that impact human and/or environmental 

health. Air quality is affected by hundreds 

of pollutants associated with a wide range of activities 

and processes. 

Extreme heat is a term used to describe conditions 

of high temperature and/or humidity that 

can put people at risk for heat-related illnesses. 36 

URBAN ENVIRONMENT 

Urban environment in the context of this study refers 

to an area with a population of at least 1,000 

and no fewer than 400 people per square kilometre. 

26 


1METHODOLOGY


CHAPTER 1: 

METHODOLOGY 

An environmental scan was conducted using a mixed method approach to achieve the specific project 

objectives. A literature review was performed to understand the relationship between the built environment 

and the three topic areas (walkability, air quality, extreme heat) within the scope of the project. Key 

informant interviews provided the Canadian and Ontario context of built environment research and 

initiatives. A survey of public health units in Ontario gathered information on measures and data sources 

currently in use to assess the built environment, as well as the challenges faced by these organizations. 

The results from all three research activities were synthesized together in the gap analysis to form the 

basis for discussion and the development of guiding principles and recommendations. 

As the scope of this project addressed three distinct subject areas, the methodology was developed to be 

consistent across the whole project. In all cases, the primary focus remained the built environment. The 

study received ethics approval from Queen’s University Health Science and Affiliated Teaching Hospitals 

Research Ethics Board. 


26 

METHODOLOGY 


A comprehensive literature review was performed to find and report on studies that identified measures 

and explored the relationship between the urban built environment and walkability, air quality and extreme 

heat. 

Search Strategy 

The literature search focused on peer-reviewed academic research articles but also included grey literature 

sources. Online resources were searched for relevant content, specifically PubMed, Medline, Google 

Scholar, Google, and Academic Search Premier. In addition, to capture articles not indexed in these 

databases, journals recognized for examining associations between the built environment or community 

planning and health were also searched. These included the Journal of Physical Activity & Health, International 

Journal of Health Geographics and American Journal of Preventive Medicine. 

A Boolean search strategy was employed with keywords developed for each topic area. The walkability 

search terms were: (“Indicator” OR “Measure” OR “GIS” OR “Geospatial” OR “Health Outcome”) AND 

(“Built Environment”) AND (“Physical Activity” OR “Walking” OR “Walkability). For the air quality literature 

review, the initial search terms were: (“Air Quality” OR “Air Pollution” OR “Adverse Effect”); however, 

due to the limited studies found pertaining to air quality and the built environment, a second phase of 

the literature review was conducted with additional search terms, as follows: “Modelling” OR “Land 

Use Regression” OR “Dispersion Modelling” OR “Kriging” OR “Spatial Distribution” OR “Micro-environments” 

OR “Neighbourhood Level”) AND (“Built Environment” OR “Land Use”) AND (“Pollution” OR “Air 

Quality” OR “Air Emissions” OR “Traffic Related Air Pollutants” OR “Point Sources”). The extreme heat 

literature review used the search terms: (Extreme) Heat” OR “Heat Index” OR “Heat Wave” OR “Humidex” 

or “Urban Heat Island” OR “Adverse Effect”. 

Article Selection and Data Extraction 

Fifty articles for walkability and seventy articles for environmental exposures were selected for formal 

review mainly from the disciplines of public health, environmental science, urban design, and planning. 

For inclusion in the literature review, a study had to come from a reputable source and include some discussion 

of indicators, measures, tools, data sources, or databases in keeping with the project objectives. 

In order to focus on outdoor air quality, studies related to the following topics were excluded: indoor/ 

household air pollution, tobacco smoke/exposure, occupational studies, and soil quality. 

Evidence from Ontario was prioritized, but where it was absent or incomplete, Canadian studies were 

also drawn upon, followed by international studies. Recently published studies were given preference for 

inclusion in the literature review (last 5-7 years). 

Information was extracted from each article using a standardized format to describe the country, objective, 

population studied, public health measure, indicator(s) measured, methods/data components, 

source of data, results, and conclusions (See Appendix A) 


METHODOLOGY 27 


The purpose of the key informant interviews was to help identify both areas of strength and weakness in 

the collection, availability, and comparability of built environment data in Ontario. Key informant interviews 

were conducted from April to June 2012 with a total of 12 key informants interviewed for walkability, 6 

for air quality and 5 for extreme heat. Key informants represented a diverse cross-section of disciplines 

(e.g. public health, land use planning, academia) as well as local, provincial, and federal agencies. For the 

most part, key informants were experts in their respective fields and/or represented the most established 

organizations (from the project team’s perspective) as it related to the assessment of urban walkability, air 

quality and extreme heat exposure in Ontario. 

Interview Questionnaire 

The questionnaire for the key informant interviews was developed by the project team and consultants 

(see Appendices A-C). The completed literature review helped inform questionnaire development. Questions 

focused on issues of data availability, data quality, data gaps, internal capacity, data acquisition, and 

other challenges. Consensus on the number of questions and types of questions was reached among 

the project team before implementation of the survey. The questionnaire was piloted with the first key 

informants; no significant changes to the questions were required. 

Sampling 

The key informants for walkability, air quality, and extreme heat differed; therefore the criteria for 

inclusion also differed. In general, key informants were interviewed if they: 

(i) 

Had expertise in the urban built environment, specific to walkability and/or environmental 

exposures (air quality and extreme heat); and, 

(ii) Were familiar with measures and respective data sources used to assess the built environment 

in Ontario. 

Key informants were identified by the project team using the sampling criteria outlined below (Table 1). 


28 

METHODOLOGY 

Table 1: Sampling strategy for identifying key informants 

Walkability Key Informants 

Environmental Exposure Key Informants 

(air quality and extreme heat) 

Criteria 

Those with expertise related to the 

urban built environment, specifically 

walkability (may include physical 

activity) measures and data sources. 

Those who have operationalized 

walkability measures and/or indices 

in Ontario. 

Those who have expertise or responsibility for 

modelling and/or monitoring air quality and/or 

extreme heat at the provincial and federal levels of 

government; 

Those who have conducted research on air quality 

as it is impacted by traffic corridors and local point 

sources; 

Those who have conducted air modelling and/or 

air monitoring at a municipal level; and 

Those with expertise in extreme heat. 

Interviews 

Figure 1 outlines the key informant interview process. Potential key informants were invited to participate 

in the interviews by email and/or telephone. A meeting was then scheduled based on availability of both 

the interviewer and key informant. 

The questionnaire and consent form were shared with the key informant prior to the meeting for review in 

order to provide adequate notice and time to prepare for the interview. Prior to the interview commencing, 

verbal consent was provided by the key informant. Semi-structured interviews that were comprised of 

open-ended questions, were conducted either face-to-face or by telephone (preference was given to 

face-to-face interviews where possible). Interviews were conducted for approximately one hour and were 

audio recorded for transcription purposes. All transcriptions were sent to interviewees for approval and 

confirmation of content after the interview. 

One of the initial air quality key informant interviews took place using a questionnaire intended for a different 

interviewee. Due to the small number of interviews, this change in interview content would have 

had a large impact on the evidence collected. To correct this error, the interview was repeated with the 

appropriate questionnaire. However, as the repeated interview took place after the rest of the interviews, 

it was not included in the NVivo transcription and qualitative analysis process. Instead, the air quality 

consultant integrated the results into the air quality key informant analysis. 



Qualitative Analysis 

Directed qualitative content analysis of the transcribed interviews was performed using NVivo software. 

Thematic analysis guided the built environment data needs identified by the project team (data availability, 

data gaps, internal capacity, data acquisition, and other challenges). A start list of parent nodes (codes) 

was first created in NVivo 10 to mirror the initial start list of themes. Child nodes (sub-codes) were created 

as warranted to reflect emerging concepts. Ill-fitting codes were discarded, and similar codes were combined. 

Thus, the directed content analysis approach allowed for the identification of emerging themes. 

For consistency, one qualitative coder completed the thematic analysis. 

Trustworthiness of the qualitative interview data and analysis for the current study was established by 

addressing the dependability, credibility, and transferability of findings. 

The dependability of study results was ensured by: 

• The development of an interview guide; 

• The audio recording and the verbatim transcription of the interviews; and, 

• The detailing of data collection and analysis, including an audit trail of the analyst’s 

interaction with the data. 

The credibility of the findings (internal validity) was enhanced by the following actions: 

• The interviewers summarized respondent’s answers during the interview; 

• Participants were given opportunities to provide additional thoughts as well as to clarify any 

misunderstandings during the interview; 

• All interviews were audio recorded and transcribed verbatim, enabling sufficient time to review 

and check data; 

• Transcribed interviews were returned to interview participants for review; 

• The qualitative analyst sought alternative explanations and opinions of interviewees during 

analysis; and, 

• Triangulation of interview data with other project component findings enhanced the credibility of 

the qualitative findings. 

The transferability of findings helps to determine a study’s external validity. The purpose of this project 

was to support the identification of walkability and environmental exposure measures and data specific 

to urban Ontario. Sufficient detail of the study protocol has been provided in this report to enable outside 

researchers and practitioners to judge if the findings can be applied to other settings through transparency 

and reflexivity. 


30 

METHODOLOGY 

Figure 1: Built environment LDCP key informant interview process, Ontario, 2012 

Submit for and receive REB approval 

Create list of nominated walkability & environmental exposure stakeholders 

Send the invitation letter to 15-20 of nominated stakeholders 

Recruit key informants that represent the criteria identified in Table 1 

Schedule interview time 

Email questions and consent form to interviewee 

Conduct and record interview via telephone or in-person 

Transcribe key informant interview data 

Analyze key informant notes using NVivo software 

Summarize results 

SURVEY 

The project team developed and administered an electronic survey to all Ontario Public Health Units (36 

PHUs) in July 2012 (see Appendices D and E for survey questions and documentation). The purpose of 

the survey was to inquire about built environment measures and data used by Ontario PHUs to assess 

urban walkability, air quality and extreme heat. 

The survey included three distinct sections, one for each subject area: walkability, air quality and extreme 

heat. Only one completed survey was accepted from each Public Health Unit jurisdiction; however, each 

section could be completed separately. 



Survey Development 

The survey was developed in consultation with the project team and consultants. The literature review 

and key informant interviews were used to inform survey question development. Consensus on number 

of questions and types of questions asked was reached among the project team before surveying PHUs. 

The survey was pilot tested with project team organizations and was modified based on the feedback 

received. This survey included complex skip patterns to reduce the number of questions asked of health 

units that were not currently assessing the built environment. 

Administration of Survey 

The survey invitation was addressed to PHUs across Ontario; more specifically, to the Medical Officer of 

Health (MOH), Director of Chronic Diseases, and Director of Environmental Health. In some cases, an 

Epidemiologist and/or Planner also received the initial invitation to participate. An electronic copy of the 

survey and a copy of the consent form (Microsoft Word attachment) were also shared with each participating 

Public Health Unit. Given the cross-disciplinary nature of the topics and detailed information 

being sought from respondents, Public Health Units were encouraged to include other departments (e.g. 

Geographic Information System (GIS); Transportation Planning; Land Use Planning) and/or municipalities, 

as well as employee(s) with knowledge of GIS in order to accurately complete the survey. 

On July 11, 2012, the survey was administered to Public Health Units via email and electronically using 

Fluid Surveys (2012), an online survey software program. Reminders for survey completion were sent at 

the end of weeks one and two, and two days prior to the survey deadline. If the survey was not completed 

by the end of the second week, the project coordinator and designated project team members 

followed up with PHUs by email or telephone. 

Survey Analysis 

Survey data were exported from Fluid Surveys (2012) to perform qualitative and quantitative analyses for 

all three topic-areas using Microsoft Excel (2007) and IBM SPSS (2012). Descriptive reports exported 

from Fluid Surveys were also reviewed. Questions in a fixed-response format were analyzed using descriptive 

analyses and cross-tabulations. Qualitative analysis was used to analyze open-ended questions 

to determine common themes. All data were aggregated for the purposes of confidentiality. 


32 

METHODOLOGY 

GAP ANALYSIS AND RECOMMENDATIONS 

The results from the literature review, key informant interviews, and survey were synthesised for the 

assessment of gaps between data requirements and availability. This was achieved by organizing and 

describing measures using the following categories: 

• A general description of the measurement category; 

• Inputs required to operationalize the measures; 

• Current use of measures in Ontario, including the proportion of measures in current use in Ontario 

(where possible); 

• Theoretical operation of the measures in Ontario; 

• Desirability, usefulness and benefits of using the measures; 

• Challenges identified in using the measures; and, 

• Linkages with other built environment measures. 

Data sources and sets were organized and described using the following categories: 

• Data source/set; 

• Topic area covered by data source/set; 

• Current use of data source/set in Ontario PHUs; 

• Desirability, usefulness and benefits of using the data source/set; 

• Access and availability of the data source/set in Ontario; and, 

• Barriers, challenges and limitations of data source/set. 

Following completion of the gap analysis, a set of guiding principles and recommendations were developed 

by the project team. After a series of meetings and feedback shared via electronic correspondence 

from project team members, consensus on the final principles and recommendations was 

achieved. 


WALKABILITY 

BUILT ENVIRONMENT MEASURES & DATA USED IN THE ASSESSMENT OF URBAN WALKABILITY 

BACKGROUND LITERATURE REVIEW KEY INFORMANT INTERVIEWS SUMMARY OF SURVEY RESULTS GAP ANALYSIS 

An Environmental Scan of Built Environment Data Related to Walkability & Environmental Exposures in Urban Ontario2


35 

BUILT ENVIRONMENT MEASURES & DATA USED IN THE ASSESSMENT OF URBAN WALKABILITY 

CHAPTER 2: WALKABILITY 

BACKGROUND 

WALKABILITY AND HEALTH 

Walking is an acceptable form of physical activity among the broadest cross section of society regardless 

of age, sex, ethnic group, education or income level. 37 It involves little expense, does not require 

learning new skills and can be used for transportation purposes as well as exercise. Additionally, walking 

is an activity that can be done close to home, making it a practical option for people who do not 

have time to commute to a gym or are unable to afford the expense of a car, gym membership or home 

equipment. 37;38 

Walking represents a more sustainable form of transportation than the automobile. By reducing reliance 

on the automobile, walking contributes to reductions in air pollution and has the potential to reduce the 

rates of respiratory diseases associated with air pollution. 39;40 A study linking land use, transportation, air 

quality and health in the Atlanta Region, Georgia, found that walkable neighbourhoods were linked to 

fewer per capita air pollutants and greenhouse gases. Travel patterns of residents in the least walkable 

neighbourhoods generated about twenty percent higher carbon dioxide emissions than travel by those 

who lived in the most walkable neighbourhoods. 41 


36 

WALKABILITY 

Walking is about more than simply getting from one place to another. The public health benefits of walkfriendly 

environments are extensive and far reaching. For example: 

• In Canada, the prevalence of obesity has more than doubled over the last twenty years. Thus, 

creating more walkable, less automobile dependent communities can contribute to decreasing 

the incidence of obese and overweight Canadians. 1 

• Physical inactivity is a leading risk factor attributable to a number of health problems such 

as heart attacks, strokes, hypertension and diabetes. Thus, increasing the rate of active 

transportation has potential to lead to broader public health benefits. 1;42;43 

• A study by the Heart and Stroke Foundation found that Canadians living in compact, mixed use 

neighbourhoods were 2.4 times more likely to meet physical activity guidelines. 44 

• Walking promotes the goals of civic life by providing opportunities for face-to-face contact, 

casual interaction and public participation, all of which are closely tied to improved mental health 

and well-being. People on streets and in public places are an essential ingredient for vibrant, 

economically viable and safe communities as well. 45;46 

• Specific groups such as children and older people who are often more reliant on their local 

neighbourhoods can gain significant health benefits and independence through walking. 9;47 

• Increased physical activity among youth is likely to produce improvements to health and quality of 

life, such as preventing and reducing the prevalence of overweight and obese youth, increasing 

self-esteem, and enhancing scholastic success, in addition to the longer term health gains of 

preventing chronic diseases such as diabetes and cardiovascular disease. 48 


WALKABILITY 37 

WALKABILITY AND THE BUILT 

ENVIRONMENT 

NEIGHBOURHOOD DESIGN 

Using a socio-ecological lens, physical activity and walking behaviour 

can be examined at multiple levels; by individual factors 

(such as attitudes to physical activity), the microenvironment 

(conduciveness of the places where people live, learn and work 

for physical activity) and the macroenvironment (general socioeconomic, 

cultural and environmental conditions). Interventions 

that aim to create built environments that support physical activity 

are therefore suitable population-based approaches for 

tackling the rising population levels of physical inactivity and 

obesity. 43 

Neighbourhood design relates to travel patterns primarily by impacting 

proximity between destinations and directness of travel 

between these destinations. The design of neighbourhoods can 

be described in terms of: 

• Density and land use mix characteristics that work in 

tandem to determine how many activities are within a 

convenient distance. 

• Proximity as a function of both density (compactness) of 

development and the level of land use mix. 

• Connectivity which determines how directly one can travel 

between activities on a street or path network. 49 

There is a significant body of research related to the design 

and quality of the built environment and its effects on levels of 

physical activity. Studies have demonstrated that aspects of the 

built environment can both promote and discourage walking. 

People walk more in environments characterised by compact 

land development, interconnected city streets and blocks, and 

mixed land-uses (e.g. parks, commercial, residential). These 

characteristics are thought to promote pedestrian activity by 

making it more practical to reach destinations on foot. Features 

of the neighbourhood environment such as sidewalks and bike 

paths are related to increased utilitarian and leisure non-motorized 

trips. The importance of having access to suitable recreational 

facilities (e.g. parks and recreation centers), has also been 

established. 43;48;50;51 

Neighbourhood design can significantly 

influence the factors 

that contribute to peoples’ decision 

to walk. For instance, when 

everyday destinations such as 

grocery stores, schools, post offices 

and daycares are placed 

near or within residential areas, 

people are encouraged to adopt 

active transportation options 

(walking or cycling), rather than 

drive or be driven to their destination. 

Higher population densities 

can support shops and services 

in the neighbourhood, and build 

ridership for better quality transit 

which in turn encourages walking 

to and from transit stops. Likewise, 

when streets are designed 

to reduce traffic speeds and 

make routes pleasant for pedestrians 

(e.g. shorter blocks, sidewalks, 

and a grid street pattern), 

many people will adopt active 

forms of transportation. Together, 

these and other design features 

can contribute to the walkability 

of a neighbourhood. 1 


38 

WALKABILITY 

Several other urban design characteristics facilitate active transport. 

Residential density, a greater diversity of businesses and greater proximity 

to shopping centres in a neighbourhood are associated with increased 

walking. 15 Greater residential densities and street connectivity 

of walkable neighbourhoods also support higher levels of public 

transit service and ridership, including walking to and from transit. 3;52 

Research indicates that perceptions about neighbourhood safety, 

aesthetics and the location of recreational facilities influence walking 

activity. 15;50;53 Thus, an inviting pedestrian environment with access 

to commercial, leisure and other destinations is recognized as a key 

component of walkability. 9;54 

WALKABILITY & VULNERABLE POPULATIONS 

It is important to highlight that individuals may respond differently to their environment depending on their 

age, gender, education, income, ethnicity, physical health, attitudes or preferences. 34;46;55 Furthermore, 

there are important equity implications when contextualizing the walkability of neighbourhoods. For example, 

it is vital to understand the difference between an individual who chooses to walk as a result of 

living in a walkable neighbourhood and someone who, for financial constraints or other reasons, has 

no choice but to walk in a neighbourhood that may or may not be conducive to walking. 54 Research 

that focuses solely on built environment and land use characteristics has potential to misrepresent the 

neighbourhood-individual interaction. In Ontario, walking conditions vary greatly by socioeconomic status 

across neighbourhoods. For example: 

• Recent research conducted in the City of Ottawa found that lower socioeconomic status (SES) 

neighbourhoods suffer a greater burden of traffic, have more pedestrian vehicle collisions and 

have less green space than their higher SES counterparts. 46;53;56;57 

• In Toronto, rates of diabetes were higher in neighbourhoods with low walkability. These high risk 

neighbourhoods were comprised of lower average household incomes and higher concentrations 

of visible minority residents and immigrants; lower levels of walking and cycling; poor access or 

longer distances to healthy resources such as stores selling fresh fruits and vegetables, and fewer 

parks and recreation centres. 53;58 Furthermore, lower SES neighbourhoods had less access to 

public transit compared to higher SES neighbourhoods. 

Another Canadian study demonstrated that when opportunities exist to walk and cycle, low-income 

Canadians were more likely to make use of them. 59 Consequently, creating walkable environments has 

significant potential for addressing health disparities between socially advantaged and disadvantaged 

groups by providing supportive environments to those who rely on walking for transport, are unable to 

access other forms of physical activity, and who are most exposed to the health hazards linked to automobile 

emissions and other pollutants. 57 



ASSESSING WALKABILITY 

Measuring the physical environment characteristics of neighbourhoods is the first step to understanding 

the walkability of an area. 3 Interest in the connections between land use and health has introduced new 

methods and tools for describing health-related aspects of the built environment. 60 Yet to understand the 

impact of the built environment on physical activity, the development of high quality measures is essential. 

Several measures of the built environment have been developed over the years to examine multiple elements 

of the environment in relation to multiple modes and purposes of physical activity. 

Early research in the area of built environments and health relied on subjective measures of the environment 

collected through self-report instruments. Examinations of this complex relationship have been 

increasingly augmented by objective measures. Generally, three methods are being used in the assessment 

of walkability 61 : 

(i) 

(ii) 

(iii) 

Perceived measures obtained by telephone interview or self-administered questionnaires; 

Observational measures obtained using systematic observational methods (audits); and, 

Archival data sets that are often layered and analyzed with GIS. 

Common objective measures of walkability in an urban environment include measurements of density 

(e.g. residential density), diversity (e.g. land use mix as measured by retail floor area ratio, proximity 

measures), street connectivity (e.g. intersection density), and pedestrian oriented design (e.g. presence 

of cross-walks). These types of built environment measures, along with considerations of neighbourhood 

design and demographics, represent built environment factors that are strongly and consistently associated 

with physical activity, walking and health outcomes in the literature. These built environment metrics 

are also often correlated with one another which has motivated the use of composite indices to capture 

multiple aspects of the built environment at once. 2;3 By measuring both form and content of neighbourhoods, 

walkability indices are expected to measure the degree to which an area provides opportunities 

to walk to various destinations. 54 Such indices are thought to capture the inter-relatedness of many built 

environment characteristics, minimize the effect of spatial collinearity, and ease the communication of 

results. 60;61 

Increasingly, objective measures of the built environment are obtained using GIS technology. GIS integrates, 

analyzes, and maps data linked to time and location. GIS has become an invaluable tool for 

studying the relationship between the built environment and physical activity. It has been used to test for 

associations between a number of built environment features and physical activity. However, despite the 

advances in GIS measurements, there is wide variability in how GIS variables are constructed to represent 

built environment concepts. Overcoming this and other measurement challenges will require a concerted 

effort by all relevant disciplines. 2;5;61 


40 

WALKABILITY 

A 2011 report reviewing the evidence establishing 

connections between walking, municipal environments 

and health in Ontario offered that creating 

walkable places must be considered as part of 

a comprehensive strategy to improve conditions 

for human health, promote population levels of 

physical activity and reduce environmental damage 

caused by widespread automobile use. 57 

Until now, public health advocates, community 

leaders, and researchers across Ontario lack 

an empirically-based, actionable set of community 

level indicators that can be used to measure 

walkability in their jurisdictions. In the absence of 

consensus, municipalities and local communities 

are examining various indicators in silos. Thus, 

current approaches are disparate and limit the 

comparability of outcomes between communities 

across the province. 

Objectively measured, province-wide built environment 

indicators that clearly illustrate the scale 

of walkability in a community could help public 

health practitioners identify their needs, monitor 

health outcomes, facilitate research, guide possible 

interventions and inform planning and development 

decision making. Providing communities 

with the tools to gather reliable and valid information 

about their own physical environments 

would allow them to make more effective assertions 

and better advocate their needs to decision 

makers. With indicators in place to measure 

the quality of the built environment as it relates 

to walking, both decision-makers and the general 

public can make informed decisions about 

policy and priorities. High quality indicators can 

help convey complex built environment and land 

use information in a user-friendly approach to engage 

and empower the wider public in promoting 

and advocating for their quality of life. 38 




Understanding the impact of the built environment 

on walkability requires relevant, easy-tounderstand, 

and reliable measures to assess 

built environment features. 61-63 These measures 

assist in determining the status and progress of 

interventions and provide evidence of the state of 

population health within the context of the built 

environment. 63;64 

There has been considerable progress over the 

past decade in the use of multiple modes of assessment 

and diverse built environmental measures. 

61 Although measures used to assess walkability 

vary considerably, there are three main approaches 

used to operationalize environmental 

indicators related to walkability. The first method, 

obtained by interview or self-administered questionnaires, 

examines the extent to which individuals 

perceive various elements of the built environment. 

The second approach uses systematic 

observations, or audits, to quantify attributes 

of built environment characteristics. Finally, the 

third method uses geospatial databases and 

geographic information system (GIS) data in order 

to assess or develop indicators. 61;65;66 

PERCEPTIONS AND OBSERVATIONS 

OF THE BUILT ENVIRONMENT 

Numerous studies have examined physical activity 

behavior in relation to perceptions of the 

environment. Because of the slow speed and 

nature of walking, a pedestrian is typically much 

more aware of and exposed to the environment 

than a driver. The micro-features in the environment 

largely shape how accommodating an area 

is for pedestrian travel. Individuals’ perceptions 

of the built environment provide information on 

how a person experiences a neighbourhood and 

are measured by subjective indicators derived 

mostly by self-report data methods. These perceived 

measures are considered to be ‘subjective’ 

because two unique individuals in the same 

environment may perceive it differently. Generally, 

identifying perceptions of the built environment 

is an important approach in determining 

behavioral patterns. According to the literature, 

the most commonly assessed measures of perception 

include land use, traffic, aesthetics, and 

safety from crime at neighborhood or community 

levels. 1;11;34;61 

In addition to perceived measures of the built environment, 

researchers have developed methods 

to assess the actual physical environment as it is 

being directly observed. 61 Audit tools allow systematic 

observation of the physical environment, 

including the presence or absence of features hypothesized 

to affect physical activity. For example, 

the quality of existing sidewalks and the cleanliness 

of the walking environment. Audit tools are 

used for measuring physical features that are 

best assessed through direct observation (e.g. 

architectural character, landscape maintenance). 

These tools most commonly include one or more 

measures of: land use (e.g. commercial space); 

streets and traffic (e.g. pedestrian crosswalk); 

sidewalks (e.g. presence, width, and continuity 

of sidewalks); cycling features (e.g. presence of 

bike lanes); public space/amenities (e.g. presence 

of benches); architecture or building characteristics 

(e.g. building height); parking/driveways 

(e.g. presence of parking lot(s)); maintenance 

(e.g. presence of litter); and indicators related to 

safety (e.g. presence of graffiti). 13;61;67;68 Several 

audit tools have been developed over the years 

including: Pedestrian Environmental Data Scan 


42 

WALKABILITY 

(PEDS) which was designed to capture a range of 

elements of the built and natural environment efficiently 

and reliably 11;13;67 ; Systematic Pedestrian 

and Cycling Environmental Scan (SPACES), an 

audit instrument that subjectively evaluates the 

attractiveness and the degree of physical difficulty 

related to mobility and reflected in the pedestrian 

and cycling environment 11;69 ; and, both the Irvine- 

Minnesota inventory (I-M) and St. Louis University 

Analytic Audit Tool (SLU) have 150 to 200 measures 

and include questions with extensive detail 

about land uses. 11;62;67;70 

Audit tools have also been used to engage people 

in better understanding their neighbourhoods and 

communities, as well as to empower community 

members in shaping local political decisions related 

to their environment. In the Canadian context, 

the Spryfield community in Nova Scotia recognized 

that one of the ways people could make 

improvements to their community was by taking 

inventory. They developed an audit tool (“Walkabout 

Tool”) that would help bring their community 

together to assess the built environment, discover 

what improvements were desired and help 

people to work together to achieve them. The 

tool was based on “Placecheck”, a checklist tool 

widely and successfully used in many areas of 

the United Kingdom to assess built environment 

features related to physical activity. 63;71 Researchers 

also developed a neighbourhood level audit 

tool to measure and compare the active living potential 

of 112 Montreal neighbourhoods 55;67;68 

GEOGRAPHIC INFORMATION 

SYSTEMS (GIS) 

Early research on the relationship between built 

environments and health relied on subjective 

measures of the built environment collected 

through self-report instruments. 5;15 Today, GIS has 

become an invaluable tool that integrates, analyzes, 

and maps data linked to time and location. 

GIS integrates hardware, software, and data for 

capturing, managing, analyzing, and displaying 

all forms of geographically referenced information. 

72 It is rapidly becoming an essential part of 

health research and GIS techniques are increasingly 

being utilized by the public health sector. 58 

Combined with measures of individual physical 

activity behavior collected by accelerometry and 

global positioning system (GPS) devices, observation, 

or self-report instruments, GIS has been 

used to test for associations between a number 

of built environment features and physical activity 

or walking. 5 These spatial approaches provide 

the ability to create maps, measure distances 

and travel times, and define the extent and nature 

of spatial relationships. 58 They take into account 

the physical location of areas, boundaries, 

people, and services, as well as types of land 

use and natural features. Increased availability of 

high-resolution spatial data combined with the 

computational power of GIS translates into many 

options available to measure aspects of the built 

environment that influence walking. The use of 

such objective measures of the built environment 

are important to better understanding how land 

use may influence walking behaviours and also 

to identify specific dimensions that could be used 

by public health and land use planners and policy 

makers to increase walking. 15 

BUILT ENVIRONMENT 

MEASURES OF WALKABILITY 

Guiding research on built environments and 

physical activity propose that different domains 

of physical activity (e.g. leisure, transportation) 

are affected by different environmental attributes. 

Leisure physical activity may be most affected by 

access to, and characteristics of, public and pri- 



vate recreation facilities. Transportation physical 

activity may be most related to the proximity and 

directness of routes from home to destinations 

as well as characteristics of the walking and cycling 

infrastructure, including sidewalks, bicycle 

lanes, and trails. Therefore, to understand the influences 

of the built environment on walkability, a 

wide range of built environmental measures are 

required. 61 

Urban form refers to the layout of metropolitan 

areas and consists of many different components. 

Common components or metrics of walkability in 

an urban environment include density, land use 

mix (diversity), and street connectivity. These built 

environment measures, along with considerations 

of neighbourhood design and demographics, represent 

built environment factors that are strongly 

and consistently associated with physical activity, 

walking and health outcomes in the literature. 

DENSITY AND DIVERSITY 

Density (compactness) is a measure of the amount 

of activity found in an area and can be defined in 

terms of population, housing unit, or employment 

density. High density represents compact land development, 

reduced travel distances between trip 

origin and destination, and reduced dependence 

on motorized transportation. 2 Therefore, density 

is an important correlate of walking. Population 

density is one of the most common measures 

included in studies of the built environment and 

transportation-based physical activity, primarily 

because the data are readily available (e.g. census 

data), it is easy to compute, and it has been 

consistently associated with walking for transportation. 

Gross population density (population 

per total land area) and net residential density 

(e.g. residential units per residential acre) are also 

commonly used measures of density from the 

reviewed literature. 61 It has been recommended 

that where possible, density measures should be 

measures of net density (as opposed to gross 

density) because it excludes other land uses. 

That being stated, residential density is important 

because it serves as a proxy for other urban form 

factors, and is of particular importance at larger 

geographic scales of measurement or in cases 

where there is insufficient data. 49 

The retail floor area ratio (FAR), also known as 

commercial density, is a diverse measure that 

can be applied not only as a density indicator but 

also as an indicator of pedestrian-oriented design 

and used in conjunction with land use mix (LUM). 

When applying FAR (ratio of building square footage 

to land square footage or total parcel area), 

higher numbers indicate that the building is using 

most or all of the land, and lower ratios suggest 

much of the land is used for parking; factors 

thought to impact pedestrian access. It is often 

used in composite measures assessing walkability 

and is a standard planning measure that is frequently 

used in development regulations. 10;49;61;73 

Diversity refers to the spatial arrangement of 

land use that influences the distance and mode 

of travel. Mixed land use brings different and 

necessary uses into relative proximity, thereby 

shortening trip distances and encouraging active 

modes of transport. 2 Mixed use is also thought 

to provide more visual variety and interest for 

pedestrians. 70 Although land use mix is a rather 

abstract measure, it is considered to be a crucial 

factor to include when assessing physical activity 

and walkability. It is theorized that multifunctional 

environments can improve proximity and reduce 

travel times between departure sites (e.g. home) 

and destination sites (e.g. work) and consequently 

encourages active transportation and physical 

activity. 65 Mixed land uses also have been associated 

with lower automobile ownership, use and 

emissions. 74 


44 

WALKABILITY 

Measures of land use mix (LUM) can be categorized 

as accessibility (degree to which mixedland 

activities are easy to reach by residents), 

intensity (volume or magnitude of mixed-land 

uses present in an area), and pattern measures 

(degree of evenness of various land-use types in 

an area). 61;74 Given the appropriate parcel data, 

the quantitative calculation of land use mix is 

possible through a GIS or database interface. 49 

The most frequently used LUM measure is the 

entropy index representing how land is divided 

between different uses, most frequently residential, 

commercial (retail, entertainment), office, and 

institutional uses. Since the entropy index does 

not consider the type or intensity of mixing, the 

dissimilarity index provides a useful complement. 

The index measures dissimilarity based on 

predominant use of neighbouring squares (areas 

are divided into one hectare squares and a predominant 

land use is assigned to each square). 38 

However, data availability is often a limiting factor 

in using these measures since parcel-level 

data are required to compute many land-use mix 

measures. These data are typically obtained from 

land ownership records but parcel-level data may 

be unavailable in some locations and in others 

may lack detail about land use. For business locations, 

alternative sources of data include the 

Yellow/White Pages or employment records. 61 

Several studies have opted to use survey items to 

approximate land use mix (e.g. ‘‘Are there shops 

where you live?’’) but such approaches do not 

allow between-study comparisons because of 

unspecified definitions of place. 2 Song and colleagues 

74 proposed the following considerations 

for choosing which land use measures to implement: 

(i) the extent to which a measure captures 

the presence or configuration of land uses; (ii) 

practical considerations including data collection, 

amount of computation and ease of communicability; 

and (iii) connection between the measures 

and the purpose of the investigation. 

Proximity describes the number and variety of 

destinations within a specified distance of any location. 

It is a function of both density of development 

and the level of land use mix. As proximity 

and directness between destinations increases, 

distance between destinations decreases. As 

the distance between destinations decreases, so 

does travel by car. When distances between destinations 

are sufficiently short (1km or less), walking 

trips will more likely substitute for some driving 

trips. 49;60 Close proximity to parks (e.g. hectares 

of parks and playgrounds per/capita; proportion 

of 1-km buffer covered by parks), trails (e.g. distance 

to closest trail; km of trail per 1,000 residents; 

number of people in a 2.5 km radius of a 

trailhead; number of trail/hiking clubs per 1,000 

residents.), recreational facilities (e.g. total public 

community activity centres per/capita), pathways 

(e.g. walking path length (km)), and schools have 

been consistently correlated with physical activity, 

particularly in children. 34;38;48;49;65;75 One study 

found that residing in a neighbourhood with nearby 

parks and open spaces doubled the chances 

of an adult walking for a home-based discretionary 

trip (e.g. shopping, recreation, etc.). 73 Additionally, 

Tucker and colleagues 48 showed that 

greater access to recreational opportunities were 

essential to facilitating healthy levels of physical 

activity in youth. This was likely the first Canadian 

study of its kind and one of the only studies set 

in a mid-sized North American city (London, Ontario), 

as the literature is dominated by studies set 

in larger U.S. cities. 

Distance to retail activity is important in creating 

inviting pedestrian environments and in predicting 

levels of walking in cities or metropolitan 

areas, and small distances matter. 76 . Krizek and 

Johnson 76 defined neighborhood retail establishments 

as those having 200 or fewer workers in 

the following North American Industrial Classification 

System (NAICS) categories: 



• Food and beverage stores (e.g. grocery, 

convenience, meat, fish, specialty, alcohol); 

• Health and personal care stores (e.g. 

pharmacy); 

• Clothing and clothing accessory stores (e.g. 

shoes, jewelry, luggage); 

• Sporting goods, hobby, book, and music 

stores; general merchandise stores (includes 

department stores); 

• Miscellaneous stores (e.g. florists, novelty, 

used merchandise, pet, art, tobacco); and, 

• Food services and drinking places (e.g. 

restaurants). 

Distance from home to the nearest neighborhood 

retail establishment is a common proximity measure, 

and distances commonly used to analyze 

walking behavior include: less than 200 meters, 

200 to 399 meters, 400 to 599 meters, and 600 

meters or more. 76 Other research proposed the 

use of the following proximity indicators to measure 

diversity: percent of residential dwellings within 

2.5 km (walking distance) of retail sales, food and 

beverage, business/office, and neighbourhood 

grocery stores. 38 Using metrics such as mean 

distance to supermarket (metres) and number of 

supermarkets within 1,000 metres, Larsen and 

Gilliland 77 used network-based GIS accessibility 

measures to determine the extent to which food 

deserts exist in London, Ontario, between 1961 

and 2005. They concluded that residents living in 

inner-city neighbourhoods of low socioeconomic 

status had the poorest access to supermarkets 

and that spatial inequalities in access to supermarkets 

have increased over time, particularly 

in certain neighbourhoods where distinct urban 

food deserts now exist. Other studies have determined 

the number of retailers within a certain distance, 

using either circular buffers or road network 

buffers. When Seliske 51 examined the relationship 

between the built environment and obesityrelated 

behaviours in Ontario youth, circular and 

road network buffers were used to identify that 

the presence of food retailers near schools was 

strongly associated with students regularly eating 

their lunch at snack-bars, fast-food restaurants 

or cafés. At 100 metres, students with 3 or more 

food retailers near their schools had 3.42 greater 

odds of eating their lunchtime meal at a food retailer 

compared to students with no food retailers 

near their schools. In order to minimize fast-food 

consumption among Ontario youth, this same 

study also determined that 1,000 metres was the 

optimal buffer size of the food retail environment 

surrounding schools. 51 

Additionally, built environments that are more walkable 

tend to support higher levels of public transit 

service and ridership. 3;78;79 The provision of public 

transit use in turn reduces auto dependence and 

plays a major role in meeting energy consumption, 

greenhouse gas emission, and air pollution 

reductions. 3;52 Lachapelle and colleagues 52 found 

a positive association between the frequency of 

commuting by transit and physical activity when 

controlling for sociodemographic characteristics, 

car availability, and neighborhood income and 

walkability. Other research found that adults living 

in the top twenty-five percent of the most walkable 

areas in Vancouver, were between 2 and 3 

times more likely to walk or take transit for any 

home-based trip compared to those in the least 

walkable neighbourhoods. 3 Measures of access 

to public transit stops (e.g. distance to nearest 

transit stop; transit stop density; number of transit 

stops; service areas and routes; percent population 

with bus access) are commonly used in testing 

for associations with transportation physical 

activity. 1;5;60;70;72;73;77;80 Not only are a higher number 

of transit stops associated with higher levels 

of physical activity, 5 Ryan and Frank’s 81 research 

identified that higher land-use density near sta- 


46 

WALKABILITY 

tion areas placed greater opportunities near the 

transit system, which increased the propensity 

to use transit for accessing those opportunities. 

In this study, land-use density near station areas 

was measured in terms of net residential density 

in the station area buffer, and average retail floorarea-ratio 

(FAR) in the station area buffer. 81 

STREET PATTERN CONNECTIVITY 

Connectivity affects the ease of travel between 

places and represents the degree to which roads, 

pedestrian walkways, trails and cycling paths are 

connected so that moving from point A to point B 

is relatively easy. 82 Street networks that are more 

connected are thought to increase walkability by 

offering shorter and many alternate routes. The 

grid pattern, where streets cross at right angles 

and form small blocks and numerous intersections, 

is the archetypal high connectivity network; 

shorter blocks in a street grid system result in 

more intersections and better connectivity. On 

the other hand, neighbourhoods that include fewer 

intersections, blocks or sidewalks, as well as 

more dead-ends and cul-de-sacs, are argued to 

be less conducive to walking. 2;83 Diverse studies 

have examined the association between various 

measures of street connectivity. Many, but not all 

of these studies find positive associations between 

measures of connectivity and walkability. 

Measures of connectivity are related to the physical 

design and the layout of transportation infrastructure. 

Connectivity measures quantify the network 

connections between trips and describe directness 

of possible paths and the number of mobility 

options available. 60 The most common measures 

of connectivity include: block size and length 

(e.g. block density; average or median block area), 

intersection density (e.g. number of intersections 

per unit of area), street density, connected node 

ratio (e.g. percentage of 4-way intersections), 

segment/intersections ratio, and the number 

of intersections per length of street network. 

The easiest way to operationalize street network 

connectivity is by measuring the number of intersections. 

This is an important measure because a 

higher density of intersections corresponds with a 

more direct path between destinations. It is often 

more straight forward to measure the number of 

intersections than block size or block length because 

the latter measures are difficult to assess in 

suburban areas where the street layout may not 

match the traditional definition of a block. Derived 

indices such as the alpha and gamma index are 

also measures that are reported and analyzed in 

relation to pedestrian behavior and mode choice. 

The alpha index uses the concept of a circuit (a 

finite, closed path starting and ending at a single 

node) and is the ratio of the number of actual 

circuits to the maximum number of circuits. The 

gamma index represents the ratio of the number 

of links in the network to the maximum possible 

number of links between nodes. 2;3;49;61;65;83;83;83 

PEDESTRIAN ORIENTED DESIGN 

In addition to the characteristics of dwelling 

density, mixed land use and street pattern 

connectivity, several studies have documented the 

importance of street design and physical activity 

in relation to safety, pleasant surroundings, sidewalks 

and accessible recreational facilities such 

as parks and walking trails. 53 For instance, higher 

levels of objectively measured safety and comfort 

are positively associated with total physical activity 

behavior. Street design refers to the scale and 

design of sidewalks and roads and how they are 

managed for various uses (e.g. narrower streets, 

traffic signalling and calming designs that regulate 

speed and volume); street networks that support 

and balance a variety of transport modes 

(e.g. public transit, walking, cycling and motor- 



ized vehicles); street-specific bicycle-friendly design; 

and street lights that reduce night-time glare, 

uplight and light trespass (e.g. reduce night-light 

pollution in rural and urban areas). 82 

With respect to leisure-time physical activity, higher 

neighborhood safety, higher traffic safety, and 

aesthetically appealing communities are positively 

associated with physical activity engagement. 5;70 

Aesthetics are often assessed by observation and 

include the presence of grass, flowers, and trees 

(shade); presence of public art, interesting natural, 

architectural, and historical features. 50 Measures 

of neighborhood comfort, cleanliness and safety 

include the following (noting that some measures 

could appear in multiple categories): 

• Comfort: presence of cross-walks, sidewalk 

buffers; number of traffic lanes; street width; 

block length; sidewalk width; traffic circles; 

curb bulb-outs; speed bumps/humps; 

pavement treatments; posted speed limits. 

• Cleanliness: percentage of street segments 

with visible litter, graffiti or dumpsters. 

• Safety: crime rates; presence of graffiti, 

windows facing street, street lighting, 

abandoned or vacant buildings, and rundown 

buildings; indicators of loitering, alcohol or 

drugs, and gang activity. 5;50;58;70;84;85 

In a California research study evaluating indices of 

accessibility, safety and comfort, safety emerged 

as the most important built environment characteristic 

related to both destination and recreational 

walking. 84 Areas with a higher percentage of 

design elements related to perceived crime safety 

had higher adult walking rates compared to areas 

in which these features were absent. In another 

study measuring walkability to schools among 

elementary-aged children across Ontario, nearly 

seventy-five percent of students preferred to walk 

or cycle to school. However, schools and parents 

were less supportive of these modes of transport, 

identifying security (e.g. vandalism and theft of bicycles 

on school property) and safety (e.g. unsafe 

routes) concerns as barriers. 86 

It is important to mention the normalized difference 

vegetation index (NDVI), the most widely 

used vegetation health index. It has been used 

in numerous studies to estimate vegetation biomass, 

greenness, and dominant species. NDVI 

calculations are based on the principle that green 

plants absorb radiation in the visible region of the 

spectrum and reflect radiation in the near-infrared 

region. Values close to 1 represent strong vegetation 

cover, while low values indicate rock and 

bare soil. 65;75;87 A recent study investigated the 

impact of neighborhood greenness on physical 

activity levels, using satellite images to calculate 

the NDVI. 75 Among pre-school children, higher 

levels of neighborhood vegetation were associated 

with higher levels of physical activity after 

controlling for race/ethnicity, gender, parental 

education and body mass index (BMI), as well 

as parental support for physical activity. Another 

study showed that higher neighbourhood greenness, 

as measured by the NDVI, was associated 

with lower odds of children increasing their BMI 

scores over two years. 87 

DEMOGRAPHICS 

When assessing walkability, one must consider 

the people that live, work, and play in the built 

environment. People react in different ways to 

their surroundings depending on demographic 

characteristics such as age, gender, education, 

and income. Ross and colleagues 88 investigated 

the influence of neighborhood characteristics 

on BMI in urban Canada. Using measures of 

dwelling density, urban sprawl, and social characteristics 

(e.g. recent immigrant (≤ 5 years), 

educational attainment and income), the study 

concluded that BMI was strongly patterned by 


48 

WALKABILITY 

an individual’s social position in urban Canada, 

and the magnitude of the effect differed for men 

and women. For instance, living in a neighborhood 

with a high proportion of recent immigrants 

was associated with lower BMI for men, but not 

for women. 88 Using an activity-friendly index, another 

study found that high diabetes neighbourhoods 

in Toronto had lower average household 

incomes and higher concentrations of visible 

minority residents and immigrants. Meanwhile, 

higher income neighbourhoods had low rates 

of diabetes, even in parts of Toronto that scored 

low on activity-friendliness or had poor access to 

health care resources. The researchers hypothesized 

that a higher income may provide people 

with more options for exercise and healthy eating 

and make them less dependent on their immediate 

environment. 58 Butler and colleagues 59 

also examined socio-demographic factors (i.e. 

age, marital status, labour force status, student 

status, education level, yearly household income, 

immigrant status, region, urban/rural status, 

smoking status), geography, and physical activity 

(i.e. typical daily activity, leisure-time physical 

activity as measured by a Physical Activity Index) 

correlates of walking and cycling for non-leisure 

purposes (i.e. to work, school, or errands) across 

Canada. This study found that for both genders, 

and all models, lower household incomes were 

more highly associated with walking and cycling. 

They concluded that where opportunities exist to 

walk and cycle, low-income Canadians are more 

likely to make use of them. 59 Furthermore, money 

spent on pedestrian and cycling infrastructure 

was particularly important in supporting the mobility 

and participation of lower-income individuals 

in work and community life. 59 Another study by 

Larsen and colleagues 77 found that residents of 

inner-city neighbourhoods of low socioeconomic 

status had the poorest access to supermarkets. 

They concluded that policies aimed at improving 

public health must recognize not only the spatial, 

but also socioeconomic inequities with respect to 

access to healthy and affordable food. 

COMPOSITE MEASURES 

Built environment metrics are often correlated 

with one another. For example, grid patterns, 

sidewalks, and public transit are usually found 

together in old parts of cities or traditional pre- 

World War II neighborhoods which also have high 

density and diversity. This, along with the multitude 

of built environment factors that influence 

transportation mode choice, has motivated the 

use of composite indices to capture many aspects 

of the built environment at once. 2;73 By measuring 

both form and content of neighbourhoods, walkability 

indices are expected to measure the degree 

to which an area provides opportunities to 

walk to various destinations. 54 Such indices are 

thought to capture the inter-relatedness of many 

built environment characteristics thereby minimizing 

the effect of spatial collinearity, and ease 

the communication of results. 60;61 It has also been 

suggested that composite measures of walkability 

are more consistent predictors of walking behavior 

than single component measures. 60 

The earliest work undertaken by Frank and 

colleagues, 89 used a walkability index with three 

sub-components (residential density; connectivity; 

and land use mix) and found significant associations 

with physical activity. In attempting to 

replicate this original walkability index, modifications 

are often required due to differences in both 

the structure and availability of secondary data for 

a study area. For instance, subsequent versions 

of indices have modified the land use mix (LUM) 

computation by varying or adding new categories 

such as retail floor area ratio (FAR) as an indicator 

of pedestrian-oriented design. 10;61 In 2010, Frank 

and colleagues 73 modified the index to account for 



FAR in a composite measure that also included: 

net residential density (ratio of residential units to 

the land area devoted to residential use per block 

group); retail floor area (retail building floor area 

footprint divided by retail land floor area footprint); 

intersection density (ratio between the number of 

true intersections (three or more legs) to the land 

area of the block group in acres); and land use 

mix or entropy score (residential, retail, entertainment, 

office and institutions). Composite indices 

vary by the components they include, the scale at 

which they are measured, and the methods used 

in computation but the literature suggests that 

they are suitable measures of walkability. 10;73 

Both the Vancouver study and the Atlanta-based 

SMARTRAQ study found that walkable neighbourhoods 

were associated with increased 

physical activity when measured using an index 

that included land use mix, residential/commercial 

density and street connectivity (intersection 

density). In Vancouver, the walkability index 

turned out to be a highly significant predictor 

of travel patterns as well as obesity. 3;78 Toronto 

researchers assessed relationships between 

neighbourhood design and diabetes using an 

Activity-Friendly Index (AFI) that measured how 

conducive individual neighbourhoods were to 

walking, bicycling and other types of physical 

activity. The index included measurements such 

as population density, density of and access to 

retail services, car ownership rates, and rates of 

drug-related and violent crime. They found that 

people living in “activity-friendly” neighbourhoods 

reported walking and bicycling more often and 

were less dependent on cars for travel. Activityfriendly 

neighbourhoods also had lower rates of 

diabetes. 58 

Another study conducted in Montreal by Manaugh 

and colleagues 54 examined four existing walkability 

measures and indices at multiple geographic 

scales in order to understand how these measures 

were related to actual observed travel behavior. 

All of the examined walkability indices and 

individual measures performed well in describing 

pedestrian behavior. It was also recognized that 

socio-demographic characteristics played a key 

role in walkability outcomes. The study results include 

the following: 

• Indices were all highly correlated with 

walking trips for most non-work trip 

purposes; 

• The highest level of correlation was 

observed for home-based shopping trips. 

For example, the free online Walk Score 

index explained as much, if not more, of 

the variation in walking trips to shopping 

compared to the other walkability indices 

being studied; and, 

90 

• The simple Pedshed method was found 

to be the best walkability index when it 

came to explaining the odds of walking to 

school. 54 

Web-based geospatial technologies that estimate 

neighborhood walkability have emerged as 

potential tools to be used in public health. The 

Walk Score tool has become increasingly recognized 

in the study of walkability due to its accessibility, 

international scale and use of up-to-date 

data. This popular tool allows a user to enter any 

query location into the online interface on the 

Walk Score website (publicly available at www. 

walkscore.com) and receive the Walk Score (0 

to 100) assigned to that location, free of charge. 

Duncan and colleagues 66 evaluated the validity of 

Walk Score for assessing neighborhood walkability 

based on several GIS indicators of neighborhood 

walkability in four U.S. metropolitan areas 

with several street network buffer distances (i.e. 

400-, 800-, and 1600-meter buffers). This study 

confirmed previous findings demonstrating that 


50 

WALKABILITY 

Walk Score is a valid measure for estimating 

neighborhood walkability in multiple geographic 

locations and at multiple spatial scales. 66 Similarly, 

other researchers have used free, publicly 

available data to develop composite measures 

of walkability. Vargo and colleagues 60 used data 

that was readily available from Google and found 

these measures to be nearly as effective in predicting 

walking outcomes as walkability measures 

derived without such publicly and nationally 

available data. 

Other researchers and practitioners have applied 

composite indices to evaluating urban sprawl. Indicative 

features of sprawl are ‘‘leapfrog’’ development 

pattern, low density, homogeneous and segregated 

land uses, and an extensive disconnected, 

hierarchical road network, making motorized 

travel a necessity and active transport unsafe and 

impractical. 2 Areas of urban sprawl are associated 

with higher levels of automobile dependence 

and overweight populations compared to more 

compact neighbourhoods. 57 For instance, one 

Canadian study showed that metropolitan sprawl 

was associated with higher BMI for men, but the 

effect was not significant for women. 88 Based on 

an index developed by Razin and Rosentraub in 

2000, the urban sprawl index used in this study 

was composed of three equally weighted dimensions: 

(i) proportion of Census Metropolitan Area 

(CMA) dwellings that are single or detached units, 

(ii) dwelling density, and (iii) percentage of CMA 

population living in the urban core (an urban area 

around which a CMA is delineated and contains 

a minimum of 100,000 residents). 88;91 Another 

urban sprawl index created in collaboration with 

Smart Growth America was developed to assess 

urban development patterns based on 4 factors 

(comprised of twenty-two measures): residential 

density; neighborhood mix of homes, jobs, and 

services; strength of activity centers and downtowns; 

and, accessibility of the street network. 

They applied this index to 83 metropolitan areas 

across the United States (representing nearly half 

of the nation’s population) and found that people 

living in areas with more sprawl tended to drive 

greater distances, own more cars, breathe more 

polluted air, face a greater risk of traffic fatalities 

as well as walk and use transit less. Although 

this study was not designed to prove that landuse 

patterns cause these mentioned conditions, 

sprawl and its component factors were found 

to be a greater predictor than numerous demographic 

control variables that were also tested. 92 

LIMITATIONS OF MEASURES 

AND METHODOLOGIES 

Metrics of the built environment can be created 

with a variety of data sources, computational 

methods, and characterizations of the environment. 

In 2006, Handy and colleagues 93 found 

little consistency in measures of the built environment 

and walking, making it difficult to compare 

results from one research study to another. Four 

years later, Feng and colleagues 2 concluded that 

the most striking feature of their research was the 

absence of agreement on how the built environment 

should be measured and modeled. Spatial 

extent, source of data, and the number and 

range of places compared across studies were 

so variable, that virtually no two studies evaluated 

built environment metrics regarding these three 

features the same way. 2 Choice of methodology 

is further complicated by variations in terminology. 

In 2011, Butler and colleagues 5 stated that the 

lack of standardization among built environment 

definitions creates a challenge for public health 

researchers and practitioners and hampers their 

ability to synthesize evidence and identify environmental 

features that may explain physical activity 

behavior. 



GIS data is increasingly being used to evaluate 

neighborhood walkability. 66 Although GIS measurements 

have evolved and improved over the 

years, there is still wide variability in how GIS 

variables are constructed to represent built environment 

concepts. 2;5 Typically one of three 

GIS methods is used to define spatial units to 

measure aspects of the built environment. The 

first method is to use pre-defined spatial units 

such as census tracts to construct measures of 

the built environment. However, 

to enhance accuracy, 

some studies have begun to 

use location information (e.g. 

addresses, postal codes) to 

define unique areas for each 

individual. Another common 

method to define spatial units 

has been to establish a circular 

buffer around individuals’ 

geocoded location at a given 

radius. While likely providing a 

more representative assessment 

of the built environment 

that may influence walking, a 

circle may not accurately represent 

the spatial area that 

influences walking. 15 Barriers 

(e.g. fences, walls, buildings, 

large or busy roadways, rivers, 

cliffs, railways) frequently exist 

on the landscape and prevent 

walking along the shortest 

path. Information on these types of barriers is 

not normally included in network databases and 

the time and expense required to collect such information 

is prohibitive. Given that these barriers 

are not included in network databases and the 

time and expense required to collect such information 

is prohibitive, some way of recognizing 

the increased walking distances created by such 

barriers must be included in a model designed to 

Increasingly, 

objective 

measures 

of the built 

environment 

are obtained 

using GIS. 

measure accessibility. 80 Thus, a polygon-based 

road network buffer can be used to define one 

kilometre areas around respondent locations, accounting 

for the increased walking distances created 

by these barriers. 80 While this method may 

provide a more accurate assessment of the actual 

land area that influences walking, joining road 

vertices using straight lines may lead to inaccuracies 

in areas that do not have dense, regular 

grid street patterns 15 It is therefore apparent that 

researchers and practitioners 

need to carefully consider the 

most appropriate measurements 

with which to calculate 

built environment characteristics 

and the associated 

methodological limitations. 

While GIS data can be useful 

for measuring neighborhood 

walkability, there are challenges 

in the measurement, 

analysis and interpretation 

of data collected using GIS 

methods and tools that may 

require specialized expertise. 

Furthermore, GIS data layers 

might not be readily accessible 

for certain geographic regions 

and can be expensive to 

acquire. 61;66 

As mentioned earlier, metrics 

used to assess neighborhood 

walkability include use of self-reported information 

and use of systematic field observation (audits). 

Yet self-reported measures of neighborhood 

walkability can be implicated in same-source bias, 

and there are other noteworthy problems including 

issues with reliability, validity, low response 

rates and a biased sample of respondents. In relation 

to systematic field observations, they can be 

very laborious (i.e. time-intensive and have mul- 


52 

WALKABILITY 

tiple logistical constraints), often require significant 

specialized training and are notorious for being 

very costly. 66 Plus, the relationships observed 

cannot be interpreted as definitely causal (due to 

a lack of longitudinal studies for example). 43 Available 

evidence establishes correlations between 

the built environment and walking, but correlation 

does not mean causality. This has led researchers 

to debate whether self-selection explains the 

observed correlations; that is, do residents who 

prefer to walk choose to live in more walkable 

neighborhoods? 93 Therefore, the results from built 

environment and walking studies with cross-sectional 

designs have been criticized on the issue 

of self-selection and the bias it introduces into 

studies. 8 Additional criticisms highlight that built 

environment and walking studies do not account 

for the possibility that walking, particularly transportation 

walking, substitutes for other forms of 

physical activity. Several research reviews stress 

the importance of matching specific measures of 

the built environment to specific types of physical 

activity. 8 

in the development of walkability indices) and 

their association with walking behaviour. Different 

computations of LUM were found to be relevant 

for different types and amounts of walking, suggesting 

the need to explore alternative or complimentary 

measures of the environment. 10 

Although limitations have been identified for all 

types of built environment measures, existing 

measures have stimulated rapid advancements 

in understanding environmental correlates of 

physical activity in a variety of populations and 

settings. Most measures of the built environment 

are considered to be first-generation measures, 

so further development is needed. In particular, 

further work is required to standardize measures 

and terminology, improve the technical quality of 

measures, ensure relevance for diverse populations, 

and integrate built environment measures 

into public health and planning systems. 5;61 Moving 

forward, cross- and inter-disciplinary collaborations 

will be critical to making these measurement 

improvements. 

A composite measure of walkability is useful to 

avoid problems of multi-collinearity between 

variables that are highly correlated with one another. 

For example, areas with higher residential 

densities often have more mixed use and connected 

streets. However, the use of composite 

measures introduces methodological concerns 

regarding validity, reliability, and generalizability, 

as many of these indices are developed for a 

specific setting. 2;73 In addition, composite indices 

may be less useful for assessing the impact of an 

intervention, as one cannot identify the specific 

component that should be the highest priority for 

change. 2 One particular area where increased 

transparency is needed is in the measurement 

and computation of land use mix (LUM) variables. 

Christian and colleagues 10 examined different 

entropy based computations of LUM (used 



KEY INFORMANT INTERVIEWS SUMMARY: 

WALKABILITY 

Twelve key informant interviews were conducted between April and June 2012 to explore built environment 

data availability, quality and gaps as well as capacity for the assessment of urban walkability in Ontario. 

Key informants represented a diverse cross-section of disciplines including public health, land use 

planning, academia, as well as local, provincial, and federal agencies. For the most part, key informants 

represented the most established organizations in terms of the assessment of urban walkability in Ontario. 

The results of the key informant interviews were subsequently used to inform the development of a survey 

that was administered to Public Health Units across Ontario. 

HIGHLIGHTS 

• Walkability indices have been applied in major urban areas across Ontario. 

• Public health organizations with the most established walkability assessment programs were 

using a walkability index: 

o There was, however, limited collaboration between organizations in the development and 

implementation of indices. That said, the indices in current use had similar key components: 

density, land use mix, and connectivity. 

• Similar data sources were being used to assess walkability among organizations, with a 

predominant use of secondary data sources. 

• GIS was commonly used in the assessment of urban walkability. 

• Several challenges were identified, including human resource and financial capacity challenges 

as well as numerous data limitations (lack of data-sharing agreements, lack of standardization, 

availability, accessibility, cost, and quality of data). 

• Recommendations for data infrastructure improvements included the need for data sharing 

agreements, improvements to core built environment data sets, collaboration between 

public health and land use planning departments, and standardization of terminology and 

methodologies used in the assessment of urban walkability. 

BUILT ENVIRONMENT MEASURES 

The most commonly used measures in the assessment of urban walkability as identified by the key 

informants, included land-use mix (measure of diversity), density (population, residential, employment), 

retail floor area ratio (commercial density), street connectivity, and proximity (to community focal points). 

Less often, streetscape factors (such as aesthetics) were included in the assessment of walkability. 


54 

WALKABILITY 

For the most part, organizations with the most established walkability assessment programs in Ontario 

were using an index. Table 2 represents five walkability indices used across the province of Ontario; 

check marks indicate the individual components that comprise each index. Several of these walkability 

indices incorporated similar measures, namely: measures of density, diversity (e.g. land use mix), and 

connectivity (e.g. intersection density). 

For most organizations, land use planning and public health departments worked closely together to 

operationalize these indices. Five organizations were assessing walkability using an index with similar 

components, however there was limited collaboration with one another in relation to the development 

and implementation of these indices. One organization was able to operationalize their walkability index 

in multiple jurisdictions across Ontario using core data sets that are widely accessible. However, this was 

accomplished independently, with limited collaboration and input from the jurisdictions studied. Another 

organization relied on the built environment indicators established by APHEO to help operationalize their 

index. 



Table 2: Built environment measures that comprise walkability indices in Ontario, 2012 (n=5) 

Measures 

Organization 

1 

Organization 

2 

Index 

Organization 

3 

Organization 

4 

Organization 

5 

Density 

Residential density • • • • • 

Population density • 

Diversity 

Mixed land use • • • 

Retail floor area 

ration (FAR) or 

Commercial density 

Employment 

density 

Proximity 

(e.g. to services, 

employment) 

Connectivity 

Street connectivity 

(e.g. Intersection 

density) 

• • • 

• 

• • 

• • • • • 

Trail availability • 

Bicycle path 

availability 

Parking • 

• 

Paedestrian oriented design 

Aesthetics • 

Sidewalk 

characteristics 

• 


56 

WALKABILITY 

DATA INFRASTRUCTURE 

The methodologies used for the computation of walkability measures (e.g. formulas, individual components), 

including the level of granularity (i.e. geographic scale) for analysis, varied by organization. However, 

most organizations were accessing secondary data sources in order to assess urban walkability in 

Ontario and GIS was the most commonly used tool in these assessments. Primary data collection was 

uncommon, with only one organization using the Pedestrian Environmental Data Scan (PEDS) audit tool 

to assess urban walkability. 

Key informants identified commonly used data sources in the assessment of urban walkability, 

as follows: 

• Canadian Community Health Survey (CCHS; Statistics Canada) 

• Census (Statistics Canada) 

• DMTI Spatial 

• Google Maps 

• Land Information Ontario (LIO; Ministry of Natural Resources) 

• Municipal Property Assessment Corporation (MPAC) 

• National Resources and Values Information (NRVIS; Ministry of Natural Resources) 

• Street Network File (Statistics Canada) 

• Rapid Risk Factor Surveillance System (RRFSS) 

• Transportation Tomorrow Survey (TTS; Ministry of Transportation) 

When asked for suggestions in relation to where to get the most appropriate sources of built environment 

data to measure walkability across Ontario, key informants identified the larger core data sets (presented 

above). Most notably, the Census, Street Network files from Statistics Canada, LIO, the Transportation 

Tomorrow Survey (TTS) from the Ministry of Transportation, DMTI and MPAC were recommended as 

core data sources and sets for the assessment of urban walkability. Several key informants highlighted 

the need for improvement and enhancement of these core data sets in order to capture walkability 

more completely and accurately. For example, it was suggested that TTS could potentially incorporate a 

specific section on walkability. 

DATA AVAILABILITY AND ACCESSIBILITY 

The core data sets are readily available, but many of the data sets that are considered of high value to key 

informants are financially expensive (e.g. MPAC, DMTI, RRFSS). The cost of purchasing data from these 

existing sources was identified by many key informants as a barrier to undertaking walkability studies. 



One key informant stated the following: 

“Certainly cost is a barrier to purchasing some of the retail data sets 

such as the Dunn and Bradstreet as well as MPAC. It would be fantastic 

if MPAC data was available for a reasonable cost.” 

Moreover, some municipalities may not be willing or able (based on existing legal agreements) to share 

data already purchased or local parcel-level zoning files, and thus, data availability across jurisdictions 

often depends on existing sharing agreements and relationships. Creating and fostering sharing agreements 

across jurisdictions was identified by many key informants as a prominent data need. 

In addition, key informants expressed that existing data is often created for other land use and planning 

functions, so data specific to walkability is not often accessible. Some zoning files from smaller municipalities 

or cities are not always created, or the quality of files are questionable given the GIS capacity 

and resources of smaller communities, and thus data for these locales was considered inaccessible by 

several key informants. 

The availability, quality and usability of metadata was also a concern for some key informants. Often 

metadata that describes who created and/or edited the file, when and how the file was created, and how 

data were collected was missing or incomplete. 

One key informant indicated that one of the greatest challenges was related to knowing what data are 

available. 

“...the challenge is knowing what’s there. Because we don’t hold the 

data, we’re not the data custodians, it’s difficult for a program area 

to obviously know all the files that belong to another agency...it’s 

impossible for me to be aware of everything that the City has unless I 

go out and ask for a catalogue...a catalogue to me is calling colleagues 

in the Planning Department, the Engineering Department that I happen 

to know” 

Two key informants indicated that they were fortunate to not have overly prohibitive data access and 

availability challenges. One of these key informants came from a well-resourced organization (i.e. in terms 

of human resource and financial capacity) where for the most part, appropriate data sharing agreements 

were in place. Another specified the benefits of being part of an upper-tier municipality as it relates to 

data accessibility: 


58 

WALKABILITY 

“We’re in a lucky position being an upper-tier municipality that actually 

holds this data and partners well with our local municipality so we 

used existing data sets that are used for other land use and planning 

functions” 

DATA QUALITY 

Key informants indicated that data quality varies depending on the data source and its intended use. In 

general, they were confident in the data quality of existing data sources. Key informants were most confident 

in data that were collected at the city or regional level. Key informants felt quality control mechanisms 

are most rigorous at the city or regional level compared to larger-level data sets that produce provincial 

or national data. The larger the city, the more resources and capacity to collect data and conduct quality 

control, and thus the more confident some key informants were in the data submission for larger cities 

and regions. Key informants often voiced that smaller more rural areas do not have the human resource 

and financial capacity to implement the necessary quality control processes for local data sources. 

Crude land-use, population, and retail data is widely available across Ontario using such sources as the 

Statistics Canada, DMTI, and LIO. However, key informants voiced concern and suspicion about the 

quality and coverage of land-use data from these data sources for rural or smaller communities, most 

notably from the Road Network and Trail Network Files (LIO) and Street Network File (Statistics Canada). 

There was concern that smaller cities and communities do not have the GIS capacity or data to accurately 

feed these files, and that existing data may not be properly validated given large geographic areas 

of more rural areas. Parcel-level or planning files either do not exist for some smaller communities, or are 

not readily available to researchers across jurisdictions to supplement existing data sets. Most inconsistencies 

in data coverage affected the capacity of smaller municipalities to obtain detailed GIS data to 

supplement existing standardized data sources. 

By using existing data sets such as the census and MPAC, the quality of an organization’s built environment 

data was described as “really good” by one key informant. However, most key informants commented 

on the lack of consistency between municipalities, from the way data is collected to the way it is 

applied in the assessment of walkability. One key informant stated: 



“the biggest issues with data quality are probably around consistency 

between municipalities in terms of the way that they are collecting 

data and then within the data sets that are available at the provincial 

level, the classification (e.g. in the retail-type data) is often not suited 

to the types of businesses that we are looking for...or there are often 

many duplicates if you’re trying to go through and clean records which 

can be quite time consuming as well.” 

The geographic scale of data for existing sources was also a concern. It is difficult to disentangle data 

or conduct analyses for smaller, more defined geographic scales such as communities/neighbourhoods 

within larger municipalities. Key informants noted RRFSS and CCHS as particularly problematic in this 

regard. 

In relation to the frequency of data submission, key informants reported that it is often difficult to track 

trends because of the frequency of data submission for existing sources and the temporality of data 

that is needed. Concern was voiced about the temporality of data even within newer versions of existing 

data sources. 

Another data gap identified by some key informants was the lack of complete and/or usable sidewalk 

data. Sidewalk data from existing sources were difficult to integrate into broader databases, such as into 

road network databases. Key informants voiced that sidewalk data also required a lot of cleaning and 

was subsequently unusable and impractical in several instances. 

“integrating that [sidewalks] into the road network is still a challenge 

technically and time wise.” 

“it would be nice to have...sidewalk data which we don’t have and nobody 

really collects that information as far as we know in the format 

that would be useful for us.” 


60 

WALKABILITY 

STANDARDIZATION 

Key informants identified the need for standardization, from standard terminology to standards for operationalizing 

built environment measures. They noted inconsistencies in operationalization and variations 

in the protocols and tools used to collect, manage, and analyze walkability data. 

“the data is collected differently, the explanations are different” 

Standardized data collection and analysis protocols and software would enable cross-jurisdiction comparability 

of walkability findings. It must be noted, however, that some key informants voiced their concern 

about a standardized set of walkability indicators that would be applied regardless of context. Because 

of the differences between the size and the level of development of communities, it was suggested by 

one key informant that it may be necessary to have a scoring system based on different types of urban 

contexts. 

One key informant pointed out how context matters especially as it relates to the dissemination of results 

for their walkability assessment: 

“I think we have to be ready for the conversation that’s going to come 

up. The interesting thing about the built environment is that context 

matters immensely and so once it’s about me and where I live, I have 

things to say about that. You know, if you’re talking about the walkability 

in my Region or you’re talking about my neighbourhood, well yeah, but 

what about this path. Yeah okay, I know you can walk there, but it’s 

a scary place to walk. You know there’s a discussion and a visceral 

response which we expect [in discussing the results of a walkability 

assessment].” 

DATA SHARING AGREEMENTS 

A major challenge identified with the current data infrastructure was the data licensing and sharing agreements 

that prohibit sharing across municipalities or jurisdictions. Many key informants reported that lack 

of cross-municipal data sharing agreements and collaboration was a major data infrastructure challenge 

as it relates to walkability assessments. 

A recommendation was to set up a small unit that is responsible for collecting and updating information 

that is available for all municipalities across the province, even suggesting a central and open data site 

that is easily accessible by all municipalities. This was identified as particularly important for smaller municipalities 

that may not have the expertise to collect and maintain appropriate data. Similarly, key inform- 



ants also recommended better means to collaborate with urban planners, whom many believed have a 

greater understanding of appropriate data sources for walkability analyses. They also felt that planners 

would benefit from such partnerships, as public health practitioners have a better understanding of how 

to translate walkability findings into practical health intervention practices. 

CAPACITY 

HUMAN RESOURCE CAPACITY 

Staff shortages and competing priorities were identified as internal organizational capacity challenges 

with regards to walkability assessments. It was noted that smaller municipalities have even greater capacity 

challenges as they often lacked the financial and human resources to conduct analyses or hire 

independent consultants. 

“There is a huge learning curve around this [assessing walkability]. So, 

the concepts are simple in terms of mixed use, residential density, 

connectivity. The ‘floor-area retail ones’ take a little bit to get your head 

around, but the concepts are simple. The application of them, the 

techniques that are involved, the specific questions that come up and 

how to handle them were difficult even for our experienced group” 

Additionally, the translation of evidence for practical policy or intervention purposes was identified as a 

prominent internal capacity challenge. Collaboration between public health units and urban planning departments 

was recommended to conduct scientifically sound walkability studies that can be translated 

into policy and have practical use. 

GIS EXPERTISE 

The majority of key informants interviewed work with existing sources of data to evaluate and describe 

walkability, as opposed to primary data collection in the field. The most prominent internal capacity challenge 

public health practitioners experienced when using objective walkability data was the lack of technical 

GIS expertise to interpret, clean, and enhance data, as well as conduct geographic analyses. It was 

noted that Public Health Units generally do not have an in-house GIS expert, and often Epidemiologists 

do not have the GIS expertise to conduct the appropriate analyses. Some public health units also lacked 

the appropriate GIS programs and licenses. One key informant stated the following: 


62 

WALKABILITY 

“What I know is that as in many public health units, the Epidemiologist, 

because he or she is good with computers and good with numbers, is 

given a GIS bundle...[but] it is it’s own discipline and what happens is 

that people from other disciplines are attempting or trying to take on 

this role that requires a great deal of attention.” 

Even a well-resourced organization (i.e. in terms of human resource and financial capacity) commented on 

the lack of GIS expertise available to meet their needs in relation to the assessment of urban walkability: 

“we have issues with having funded technical staff to do GIS analysis. I 

have some staff who have bits and pieces of that strength, but there’s 

not a GIS expert per se.” 

CHALLENGES 

Several challenges were identified by key informants in relation to the current walkability data infrastructure, 

many of which have already been mentioned in the sections above. Lack of data standardization 

was identified as a challenge, with reference to walkability indicators that are operationalized in multiple 

ways and various protocols and software programs that are being used to collect, manage, and analyze 

walkability data. 

Other data limitations included data coverage (for smaller communities), scale, frequency of submission 

(temporality), accessibility (cost, sharing), and overall quality of data including lack of quality control 

mechanisms. Lack of complete metadata was identified as a significant data gap in conducting walkability 

studies. 

Capacity challenges included lack of technical GIS expertise, staff shortages, competing priorities as well 

as limited capacity in relation to translating walkability evidence. 


Collaboration between public health and urban planning was recommended, including the need for education 

and a greater understanding of respective roles and responsibilities. One key informant stated: 

“we’ve been trying to build those bridges between Planning and Engineering 

[and Public Health] and really helping everybody understand, 

but they are really different languages...Public Health needs to understand 

that world to be able to work with that world and integrate best 

into that world as possible.” 



Several key informants suggested a provincial approach such as a Public Health Ontario providing a repository 

of key indicators and a core data set to be used as a building block. Creating and fostering data 

sharing agreements across Ontario jurisdictions was identified by many key informants as a prominent 

data need as well. One recommendation was to set up a small unit that is responsible for collecting and 

updating information that is available for all municipalities across the province, even suggesting a central 

and open data site that is easily accessible by all municipalities. 

Further collaboration among municipalities was recognized as a necessary and important next step. One 

key informant specifically stated that the Ontario Federation of Municipalities should be on board for any 

provincial approach. While another suggested that municipalities will be an important linkage for data 

sources moving forward. 

“if municipalities were able to have some sort of a round table where 

they got together and discussed how they were collecting their data 

and tried to come up with consistent protocols for data collection for 

these types of measures and then implement those protocols, I think 

that would be very useful” 

Several key informants mentioned that some of the core built environment data sets should be improved 

and enhanced to capture walkability more completely and accurately (e.g. TTS survey, as mentioned 

earlier). 

Several key informants recognized the need for a reduced list of core standardized walkability measures 

and/or a standardized walkability measurement tool that can be applied or manipulated for various contexts. 

However, they recognized that a “one size fits all” approach was not necessarily the solution and 

that ultimately, context matters. 

Finally, one key informant emphatically stated that there needs to be political will (it’s “not on public or 

political radar”) in order to thrust assessments of urban walkability in Ontario forward. 


64 

WALKABILITY 

SUMMARY OF SURVEY RESULTS: 

WALKABILITY 

This section represents a summary of the built environment survey results specific to walkability measures 

and data sources. The survey was administered to Ontario Public Health Units (PHU) in July 2012. 

The number of organizations responding to each question varies due to the structure (skip pattern) of 

the survey. For instance, if a PHU indicated that they assess walkabiltiy using an index, then they were 

prompted to provide further details about the specific components of the index. Whereas a PHU that did 

not report using an index was prompted to skip forward to other survey questions. Overall, this impacted 

the number of PHUs (i.e. denominator) that responded to each survey question. 

HIGHLIGHTS 

• 25 Ontario PHUs completed the walkability section of the survey 

• 72% of PHUs surveyed assess walkability in urban environments in Ontario 

• Most PHUs (65%) have been assessing urban walkability for 1 to 5 years 

• The most common methods of assessing urban walkability in Ontario include self-administered 

survey (66.7%) and systematic observation (61.1%) 

• 38.9% of PHUs use Geographic Information Systems (spatial analytics) to assess walkability 

• Street network (44%) is the most common geographic data used to assess urban walkability 

• 44% (8 PHUs) assess urban walkability using an index (or composite measure) 

• Of the 3 PHUs that have been assessing urban walkability for the longest period of time (6 to 10 

years), all three were using an index to assess urban walkability. 

• 44% (8 PHUs) assess urban walkability using individual measures 

• Most PHUs have access to the following data: location of schools (92%), parks (92%), trails 

(92%), and recreation centres (88%) 

• The most common challenges identified by PHUs included (i) human resource capacity (84%), (ii) 

variations between municipalities (64%), and (iii) data availability (60%) 

The walkability survey response rate was 69% (25/36), with 25 Ontario PHUs completing the survey. 

Overall, 72% (18/25) of PHUs that responded to the survey, assess walkability in urban environments in 

Ontario. 



Overall, 18% have been assessing walkability for less than one year, 65% for 1 to 5 years, and 18% for 

6 to 10 years (Figure 2). 

Figure 2: Number of years PHUs assessed urban walkability in Ontario, 2012 (n=17) 

18% 

18% 

66 

WALKABILITY 

Ontario PHUs were asked to identify which method(s) they use to assess urban walkability – the following 

methods were identified: self-administered survey (assessing perceptions) (66.7%), systematic observation 

(i.e. audit tool) (61.1%), Geographic Information System (spatial analytics) (38.9%), interview (e.g. 

RRFSS) (27.8%), and accelerometers (5.6%) (Figure 3). PHUs also identified other methods including 

focus groups, public consultation, and desktop mapping/visualization. 

Figure 3: Method(s) used by PHUs to assess urban walkability in Ontario, 2012 (n=18) 

Method 

Self-administered survey 

Systematic observation 

Geographic Information System 

Interview 

Other 

22% 

28% 

39% 

61% 

67% 

Accelerometers 

6% 

0 2 4 6 8 10 12 14 

No. of Public Health Units 



Of the 3 PHUs that have been assessing urban walkability for the longest time (6 to 10 years), all three 

were using self-administered surveys and systematic observation to assess urban walkability. Two of 

these PHUs were also using GIS to assess urban walkability. 

Of the 11 PHUs that have been assessing urban walkability for 1 to 5 years, 73% (8 PHUs) use selfadministered 

surveys. A small portion (36%; 4 PHUs) use GIS to assess walkability. 

When asked about geographic scale most commonly used to assess urban walkability, street network, 

dissemination area, census track, dissemination block, census subdivision, and census division were 

identified (Figure 4). Other geographic scales were identified including postal code, neighbourhood, city, 

and ward level as well as school catchment area. 

Figure 4: 

Geographic scale most commonly used by PHUs to assess urban walkability in 

Ontario, 2012 (n=18) 

Geographic Scale 

Street Network 

Dissemination Area 

Census Tract 

Dissemination Block 

Census Subdivision 

Census Division 

Other 

6% 

11% 

22% 

28% 

33% 

44% 

50% 

0 1 2 3 4 5 6 7 8 9 10 



68 

WALKABILITY 

WALKABILITY INDEX 

Of the PHUs that assess urban walkability, 44% (8 public health units) assess urban walkability using 

an index (or composite measure). Of the 3 PHUs that have been assessing urban walkability for the 

longest period of time (6 to 10 years), all three were using an index to assess urban walkability. 

Of the 8 PHUs that use a walkability index, 3 (38%) PHUs identified using density and retail floor 

area as part of the index. For density, the census was identified as the primary data source. One PHU 

indicated that density is integrated into the model but it is not significant because of high correlation with 

connectivity. For retail floor area, MPAC and commercial land use (just the footprint not the height of the 

building) were identified as data sources. 

Five (63%) PHUs use land use mix (LUM) as part of their walkability index. Data sources and 

methodologies to calculate LUM include MPAC, commercial land use as a diversity measure, community 

walkabout, surveys, focus groups, GIS inventory, and indirect measures (observation) for a ‘Canadian 

Master Walking Class’ and ‘Stepping It Up’ pilot. 

Seven (88%) PHUs use connectivity measures as part of their walkability index. Data sources used 

to assess connectivity included the following: community walkabout, surveys, focus groups and a GIS 

inventory for one PHU; streets, sidewalks, multiuse paths and sometimes parks for another PHU; number 

and connectivity of sidewalks surrounding school areas through observation; road network GIS layer; 

finally, sidewalks, recreation trails, routes, and perceived risk at one PHU. 

When PHUs were asked about other elements included in their walkability indices, they reported the 

following: pathways and trails network map, and quality of urban environment (e.g. street trees). 

One PHU indicated that their team has been utilizing a GIS-based tool developed as part of the Coalitions 

Linking Action & Science for Prevention (CLASP) research project to assess walkability, utilizing MPAC 

parcel data, census data, Canadian Community Health Survey (CCHS) measures and other health 

data that is accessible to the PHU. Another PHU noted that they do not specifically do the work but 

have contracted universities or consultants to assist in the assessment of walkability in their health 

jurisdiction. 

INDIVIDUAL MEASURES 

Of the PHUs that assess urban walkability, 44% (8 PHUs) assess individual measures. 

None of the 3 PHUs that have been assessing urban walkability for the longest time (6 to 10 years) 

indicated using individual measures to assess urban walkability. 



The following individual connectivity measures are used by PHUs to assess urban walkability: 

• Block size and length (4 PHUs) 

• Intersection density (3) 

• Street density (2) 

• Connected node ratio (1) 

One health unit uses the ‘I can walk’ tool to assess connectivity at the neighbourhood level (www.icanwalk.ca). 

The following individual density measures are used by PHUs to assess urban walkability: 

• Population density (6 PHUs) 

• Residential density (3) 

The following individual diversity measures are used by PHUs to assess urban walkability: 

• Proximity (6 PHUs), including to: 

o Schools 

o Food outlets 

o Parks 

o Trails 

• Land use mix (LUM) (3) 

• Retail Floor Area (FAR) (1) 

The following are individual pedestrian oriented design measures used by PHUs to assess urban walkability: 

• Crime rates (4 PHUs) 

• Street lighting (4) 

• Canopy coverage (trees) (4) 

• Posted speed limits (3) 

• Presence of street furniture (3) 

• Sidewalk width (2) 

• Proximity to sources of air pollution (2) 

• Presence of traffic circles (1) 

• No. of traffic lanes (1) 

• Presence of graffiti (1) 

• Pedestrian and cyclist collision rates (Police Dept.) 

• Accessible transit stops (YRT/Viva) 


70 

WALKABILITY 

Public health units reported having access to several data (sources) related to walkability, most commonly 

having access to location of schools (92%), parks (92%), trails (92%), and recreation centres (88%) 

(Figure 5). Even PHUs that do not assess urban walkability, have access to these data (schools, parks, 

trails, recreation centres). 

Figure 5: Public Health Unit access to data sources in Ontario, 2012 (n=25) 

Data Source 

Location of schools 

Location of parks 

Location of trails 

Location of recreation centres 

Census boundary files 

Presence of sidewalks 

Public transit data 

Street network files 

Speed limits per street 

Rapid Risk Factor Surveillance System (RRFSS) 

Traffic counts 

Municipal Property Assessment Corporation (MPAC) 

Crime statistics 

Street lighting standards 

Digital elevation model (topography) 

Location of controlled cross walks 

Location of traffic controls (e.g. speed bumps) 

Other 

DMTI Spatial 

Business registrations 

76% 

72% 

68% 

64% 

60% 

60% 

56% 

56% 

52% 

48% 

48% 

44% 

40% 

36% 

32% 

32% 

92% 

92% 

92% 

88% 

0 5 10 15 20 25 




Public Health Units also identified having access to data for collisions, urban forests, litter containers, 

walking to work, and transit use. Other data sources include Teranet; Transportation Tomorrow Survey 

(TTS); Metrolinx; and the Vulnerable Persons Registry. 

The results for data access presented in Figure 5 were reviewed based on the number of years PHUs 

have been assessing urban walkability, The majority of PHUs had access to the following data regardless 

of the number of years they had been assessing walkability or whether or not they were assessing urban 

walkability at all: MPAC, RRFSS, public transit data, and presence of sidewalks. 

Most PHUs that had been assessing urban walkability for 1 to 5 years or 6 to 10 years, had access to 

census boundary files, street network files, speed limits per street, street lighting standards, and traffic 

counts. 

All or most PHUs who had been assessing urban walkability for the longest period of time (6 to 10 years) 

had access to DMTI Spatial, location of traffic controls and location of controlled cross walks. For those 

PHUs assessing walkability for less than 6 years, a small proportion had access to these data. 

More PHUs with 1 to 5 years of experience had access to digital elevation model (topography) compared 

to PHUs that had been assessing walkability for less than one year or more than 6 years. 

MEASURES CURRENTLY OF MOST VALUE 

When PHUs were asked which measures (in current use) are of most value in assessing urban walkability, 

responses varied as follows: 

• 3 PHUs reported connectivity (bike paths, multi-use paths, trails, sidewalks, and streets) 

• 2 PHUs mentioned proximity 

(residential areas to parks and amenities; public transit access points) 

• 2 PHUs reported sidewalk information, including condition affected by seasonal variances 

• 1 PHUs reported their walkability index 

• 1 PHU reported land use mix 

One PHU also listed the following measures of importance: shade availability; location of parks, schools, 

recreation centres, farmers markets, businesses etc.; availability of crosswalks/safe crossing for 

pedestrians; accessibility of neighbourhoods; and availability of benches/seating areas. 

The following methods were identified as being of most value in assessing urban walkability: self 

administered surveys (1 PHU) and Rapid Risk Factor Surveillance System (RRFSS) (1 PHU). 


72 

WALKABILITY 

MEASURES THAT WOULD BE OF MOST VALUE 

PHUs (that do not currently assess walkability) were asked about measures that would be of most value 

in assessing walkability. Although several PHUs indicated that they were not prepared to comment on 

this question (e.g. “has not been assessed at this time.”), three PHUs identified the following measures: 

• Land use 

• Connectivity 

• Sidewalk data 

• Rates of school aged children walking to and from school 

• Barriers for residents walking (to work, school, retail, transit) 

• Enablers for walking 

• Census section that goes beyond or adds to the “Mode of transportation to work” 

One PHU suggested that a standardized assessment tool that could communicate the same information 

throughout the province would be of most value in assessing urban walkability. 

CHALLENGES 

Public Health Units identified the major challenges their organization’s face in assessing urban walkability. 

The most common challenges identified by PHUs included human resource capacity (84%), variations 

between municipalities (64%), and data availability (60%) (Figure 6). 

In relation to PHUs that have been assessing urban walkability for less than one year, all three PHUs 

reported lack of human resource capacity as a challenge. 

For PHUs assessing urban walkability for 1 to 5 years, most PHUs (9) reported lack of human resource 

capacity as a challenge. Most (8 PHUs) also reported data availability and variations between municipalities 

as challenges. 

Of the 3 PHUs that have been assessing urban walkability for the longest period of time (6 to 10 years), 

2 cited human resource capacity and lack of GIS technical support as challenges in assessing urban 

walkability. The third PHU indicated that there were no major challenges to report. 

Most of the 8 PHUs that do not assess urban walkability, cited human resource capacity (88%; 7 PHUs) 

and variations between municipalities (75%; 6 PHUs) as the most common challenges in assessing 

urban walkability. 



Figure 6: Major challenges faced by Ontario PHUs in assessing urban walkability, 2012 (n=25) 

Challenge 

Human resource capacity 

Variations between municipalities 

Data availability 

Financial capacity 

Data accessibility 

Lack of GIS technical support 

Data quality 

Other 

No challenges to report 

4% 

20% 

48% 

44% 

40% 

40% 

64% 

60% 

84% 

0 5 10 15 20 25 


Several PHUs commented further on the capacity challenges they face: 

“All of this work is being done at the University at the moment. We do 

not have the resources to do the work in-house.” 

“Capacity is a major challenge for our health unit. We are very interested 

in learning more from this report. We look forward to implementing 

more practices in the area of walkability and the built environment.” 


74 

WALKABILITY 

“Currently we do not have a dedicated person or department that 

focus specifically on walkability or the built environment. We have a 

limited capacity within our agency and municipality to do this work 

but are currently working on bridging the gap between civic work and 

public health to advise departments on considering health including 

walkability when creating plans for the built environment.” 

One PHU commented on GIS capacity as follows: 

“We do not have GIS internally and our municipalities all have various 

levels of GIS capabilities. The urban centre has GIS but not all the files 

we would like, and they do not have the time to create reports based 

on multiple factors (i.e. walkability).” 

One PHU commented on lack of political interest, while another stated that it is not a priority issue. Lack 

of standards, related to methodologies and analyses, was also identified as a challenge. 



GAP ANALYSIS: WALKABILITY 

Insights gathered from the comprehensive literature review, key informant interviews and survey administered 

to Ontario Public Health Units were compiled to identify gaps between the necessary and available 

built environment data for the assessment of urban walkability in Ontario. The gap analysis for the assessment 

of urban walkability in Ontario was conducted for the following measures and data sources: 

• Measures of density, connectivity, diversity, pedestrian oriented design and indices. 

• Data sources from MPAC / Teranet / OMNR; DMTI, Environics Analytics; GeoBase; and, Ministry 

of Natural Resources. 

OVERALL FINDINGS 

The overall findings from the walkability gap analysis, including gaps identified for measures, methodologies 

and data sources, are presented below (Tables 3 – 8). 

MEASURES 

• Several organizations are using similar types of measures (e.g. density, diversity, connectivity, 

pedestrian oriented design), but they are using different computational methods and a variety of 

data sources. It is challenging to draw comparisons between health jurisdictions, especially for 

organizations that are predominantly using local data sources. 

• Lack of standard built environment terminology, definitions, and measures creates a challenge for 

public health researchers and practitioners and hampers their ability to synthesize evidence and 

identify environmental features that may explain walkability. 

• Public health organizations with the most established walkability assessment programs in the 

province were using a walkability index. However, there was limited collaboration with one another 

in the development and implementation of these indices. 

o Walkability indices may perform well in describing pedestrian behavior, but they may be less 

useful for intervention, as one cannot identify the specific component that should be the 

highest priority for change. 


76 

WALKABILITY 

METHODOLOGY 

• According to the survey, PHUs were predominantly using self-administered survey (assessing 

perceptions) and systematic observation (i.e. audit tool) to assess urban walkability. Yet selfreported 

measures of neighborhood walkability can be implicated in same-source bias, and there 

are other noteworthy limitations including issues with reliability, validity, low response rates and a 

biased sample of respondents. 

• GIS methods are commonly used in the assessment of urban walkability and are required to 

operationalize several measures, however 40% of PHUs indicated that lack of GIS technical 

support was a challenge for their organization. 

• Although GIS measurements have evolved and improved over the years, there is still wide 

variability in how GIS variables are constructed to represent built environment concepts (e.g. 

circular buffers versus polygon-based road network buffers). 

• GIS data layers might not be readily accessible for certain geographic regions and can be 

expensive to acquire. 

• Walkability is being assessed at different scales (namely street network and dissemination area) 

depending on the needs and requirements of an organization. 

• Simplicity of computation, readily available data, and ease of interpretation make density a 

commonly used metric; however there are many ways to calculate densities using different units 

of measurement. 

• Web-based geospatial technologies that estimate neighborhood walkability have emerged 

as potential tools to be used in public health, however their utility in rural environments and 

specifically in the Ontario context has not been adequately assessed. 

• People react in different ways to their surroundings depending on demographic characteristics 

such as age, gender, education, and income yet many assessments of urban walkability in 

Ontario do not account for these factors. 



DATA SOURCES AND SETS 

• Core data sets were readily available, but many of the data sets that were considered of 

high value by key informants were cost prohibitive (e.g. MPAC, DMTI, RRFSS). The cost of 

purchasing data from these existing sources was identified by many key informants as a barrier to 

undertaking walkability studies. 

• Creating and fostering sharing agreements across health jurisdictions was identified by many key 

informants as a prominent data need. 

• Existing data is often created for other land use and planning functions, so data specific to 

walkability is not often accessible. 

• Data quality varies depending on the data source and its intended use. 

• The larger the city/region, the more resources and capacity to collect data and implement quality 

control mechanisms; thus the more confident some key informants were in the data submission 

for larger cities and regions. 

• Key informants voiced concern and suspicion about the quality and coverage of land-use data 

for rural or smaller communities, most notably from the Road Network and Trail Network Files 

(LIO) and Street Network File (StatsCan). There was concern that smaller cities and communities 

do not have the GIS capacity or data to accurately feed these files, resulting in unreliable data for 

these areas. 

• It is difficult to track trends because of the frequency of data submission for existing sources and 

the temporality of data that is needed. 

• Lack of cross-municipal data sharing agreements and collaboration was identified as a major 

data infrastructure challenge. 

OTHER GAPS 

• One of the most common challenges identified by PHUs was the lack of human resource capacity. 

Even organizations with the most experience in the assessment of urban walkability identified 

human resource capacity as a challenge. 

• Variations between municipalities were identified as a major challenge among PHUs. For instance, 

a region may be comprised of several municipalities, each operating at varying levels of human 

resource and financial capacity. The methods of collection and application as well as data 

sources being applied also varied by municipality (even within the same region). 

• For some more rural public health units, the assessment of urban walkability is not 

considered a priority. 


78 

WALKABILITY 

Table 3: Gap analysis for walkability indices, 2012 

AIR QUALITY 


Measure Description Inputs †‡ Current Use in 

Ontario 

Measures 

Count ¥ 

Theoretical Op. 

Ontario 

Desirability 

Challenges 

Link bw 

Measurement 

Approaches ¥ 

Walkability 

Indices 

(Also known 

as Composite 

Measures) 

Walkability indices 

are derived from 

a combination 

of individual built 

environment 

measures 

(mostly using GIS 

methods). †‡¥ 

Indirect measure of 

walkability ¥ 

Urban sprawl 

indices: features 

of sprawl include 

‘‘leapfrog’’ 

development 

pattern, low density, 

homogeneous 

and segregated 

land uses, and 

an extensive 

disconnected, 

hierarchical road 

network, making 

motorized travel a 

necessity and active 

transport unsafe 

and impractical. 

Indices vary by the 

components they 

include, the scale 

at which they are 

measured, and the 

methods used in 

computation. 

Indices often 

include measures of 

density, diversity & 

connectivity. 

The relative 

importance of each 

measure depends 

on the specific 

formula employed. 

All indices require 

a reference 

geographic area. 

Data sources 

include: Census, 

MPAC, DMTI, 

Environic Analytics, 

Ontario Road 

Network or National 

Road Network .†‡¥ 

10 PHUs reported 

using an index, 

out of the 19 that 

currently assess 

walkability. †‡ 

4 PHUs are more 

advanced in 

implementation; 

modifications have 

been made to 

make the index 

specific to their 

jurisdiction .†‡ 

1 research 

institution has 

implemented an 

index in multiple 

jurisdictions across 

Ontario (generalized 

index) ‡ 

Some PHUs work 

in conjunction with 

external consultants 

to implement 

and calculate the 

index. †‡ 

2 PHUs are using 

a GIS based tool 

developed by Larry 

Frank. †‡ 

All PHUs use spatial 

measures and 

methods (i.e. GIS). †‡ 

Data sources and 

methodologies 

vary by health 

jurisdiction. †‡¥ 

13 different 

walkability indices 

were identified 

in the literature 

review (50 articles 

reviewed), created 

by various 

academics and 

using a variety 

of data sources; 

5 applied in the 

Canadian context 

(ON, BC, QC), 1 

in Australia and all 

others applied in 

the USA. 

> 15 different 


identified in the 

literature review. 

Several PHUs have 

implemented a 

walkability index 

but they differ in the 

components used 

and application, 

with some 

overlap. †‡ 

1 Ontario research 

institute has 

implemented a 

walkability index in 

multiple jurisdictions 

across Ontario 

(generalized index )‡ 


methodologies 

vary, making 

comparisons 

between health 

jurisdictions 

challenging .†‡¥ 


have been successful 

in describing the 

walking environment 

in several 

jurisdictions. †‡¥ 

Strongly and 

consistently 

associated with 

physical activity, 

walking and health 

outcomes in the 

literature. ¥ 

Built environment 

metrics are often 

correlated with 

one another & thus 

has motivated the 

use of composite 

indices to capture 

many aspects of the 

built environment at 

once.¥ 

Research has 

suggested that 

composite measures 

of walkability are 

more consistent 

predictors of walking 

behavior than 

single component 

measures. ¥ 

Urban sprawl is 


higher levels of 

car dependence 

& overweight 

populations than 

cities with more 

compact buildings. ¥ 

Indices vary in 

both structure 

& availability of 

data. Thus, lack 

of consistency in 

measurements 

(including bw 

municipalities within 

same jurisdiction). †‡¥ 

Methodological 

concerns regarding 

validity, reliability, 

and generalizability. ¥ 

Less useful for 

intervention, as one 

cannot identify the 

specific component 

that should be the 

highest priority for 

change. ¥ 

Human resource 

capacity, data 

availability, financial 

capacity & lack of 

GIS expertise .† 

4/5 of most 

advanced orgs in 

Ontario are working 

independently from 

one another; limited 

collaboration. ‡ 

Captures interrelatedness 

of many built 

environment 

characteristics. 

Minimize the 

effect of spatial 

collinearity. 

Eases the 

communication of 

results. 

†Survey ‡Key informant interview ¥Literature review 

An Environmental Scan of Built Environment Data Related to Walkability & Environmental Exposures in Urban Ontario 

Click to open the full table. 



Table 4: Gap analysis for density measures used to assess urban walkability, 2012 

AIR QUALITY 


Measure Description ¥ Inputs †‡¥ Current Use in 

Ontario 

Measures 

Count ¥ 


Ontario 

Desirability ¥ 

Challenges 

Link bw 

Measurement 

Approaches ¥ 

Density 

(Population and 

Land-Use) 

Density is a measure 

of the amount of 

activity found in an 

area and can be 

defined in terms of 

population, housing 

unit, or employment 

density. 

Important correlate 

of walking. 

High density 

represents compact 

land development 

and reduces travel 

distances between 

trip origin and 

destination; reduces 

dependence 

on motorized 

transportation; 

supports higher 

levels of public 

transit service and 

ridership, including 

walking to and from 

transit. 

Density is a ratio in 

which a measure of 

population or built 

form serves as the 

numerator and a 

measure of land area 

(e.g. per unit area) as 

the denominator .†‡¥ 

The denominator can 

be either total land 

area (as in “gross 

density”), or a pared 

down measure of 

usable land area (as in 

“net density”). 

Density measures 

include: ¥ 

• Population density 

• Residential 

(household) 

density (e.g. ratio 

of residential 

units to the land 

area devoted to 

residential use per 

block group) 

• Employment 

density 

Recommend 

measures of net 

density (as opposed 

to gross density) 

because it excludes 

other land uses. ¥ 

Data sources include: 

census, MPAC, DMTI, 


Ontario Road 


Road Network †‡¥ 

PHUs use 

these individual 

measures: † 

• Population 

density (6 PHUs) 


density (3) 

5 (of 10) PHUs 

identified using 

density as part of 

their index. †‡ 


institute uses 

population density 

(per square 

kilometre of 

residential area) 

as part of their 

walkability index. ‡¥ 


methodologies 



40 (80%) studies 


density measures 



reviewed); 24 

applied in the 


(ON, BC, QC), 1 


others applied in the 

USA. 

~ 25 different 





Simplemented 

density measures, 

namely population 

and residential 

density. †‡ 

1 Ontario 

research institute 

implemented 


in several health 

jurisdictions 

across Ontario, 

thus applicability 

in more than one 

health jurisdicion 

is possible as part 

of a generalizable 

index. ‡ 


methodologies 

vary, making 

comparisons 

challenging. †‡¥ 

Simplicity of 

computation, 

readily available 

data, and ease of 

interpretation make 

density a commonly 

used metric. ¥ 

Strongly and 

consistently 






Consistency in 

data sources 

for calculating 

population 

density in multiple 

jurisdictions in 

Ontario census 

and parcel-level 

data available 

from government 

sources) .†‡¥ 

Census data 

(Statistics Canada) 

is comprehensive 

for the entire 

population and is 

produced every 

five years, allowing 

changes in density 

to be monitored 

over time. ¥ 

Many ways to 

calculate densities 

using different units 


Scale of density 

measurement is 

another challenge to 

measuring density 

(e.g. calculations 

of parcel density, 

block density, 

neighbourhood 

density, and gross 

density for the 

same area will each 

produce distinct 

results). 

Census data 

has limitations 

including it’s focus 

on residential 

population counts 

thereby being less 

useful for examining 

employment 

density. It also 

uses predefined 

geographic units for 

measurement that 

may not capture the 

types of changes 

that are of most 

interest. 

To measure change 

before 2001, only 

Census Tracts (CTs) 

are available. 

Integrated into 

most walkability 

and sprawl indices 

(composite 

measures); namely 

residential density 

measures. †‡¥ 

Proximity is 

a function of 

both density 

(compactness) of 

development and 

the level of land 

use mix. ¥ 

Density and land 

use mix work 

in tandem to 

determine how 

many activities are 

within a convenient 

distance. ¥ 

Residential density 

is important 

because it serves 

as a proxy for other 

urban form factors, 

and is of particular 

importance at 

larger geographic 

scales of 

measurement or in 

cases where data 

is missing. ¥ 





80 

WALKABILITY 

Table 5: Gap analysis for connectivity measures used to assess urban walkability, 2012 

AIR QUALITY 



Ontario 

Measures 

Count ¥ 


Ontario 


Challenges 

Link bw 

Measurement 

Approaches ¥ 

Connectivity 

(related to street 

pattern) 

Connectivity affects 

the ease of travel 

between places 

& represents the 

degree to which 

roads, pedestrian 

walkways, trails, 

etc. are connected 

so that moving from 

point A to point B is 

relatively easy. 

Measures quantify 

the network 

connections 

between trips to 

describe directness 

of possible paths 

& no. of mobility 

options available. 

The denser the 

street network 

is in terms of 

intersections and 

blocks, the higher its 

connectivity will be. 


walkability. 

Measures are related 

to the physical 

design & layout 

of transportation 

infrastructure. 

Measures of 

connectivity include: 

block size and length; 

intersection density; 

street density; 

connected node ratio; 

segment/intersections 

ratio; number of 

intersections per 

length of street 

network; alpha index; 

gamma index. 

Easiest way to 

operationalize street 

network connectivity 

in a GIS environment 

is by measuring 

the number of 

intersections. 


Local database, 

DMTI, Ontario Road 


Road Network. 

PHUs use these 

individual measures † : 

• Block size and 

length (4 PHUs) 

• Intersection 

density (3) 


• Connected node 

ratio (1) 


use connectivity 

measures as part 

of their walkability 

index; 1 research 

institution uses 

connectivity as 

well .†‡ 

1 PHU uses 

the ‘I can walk’ 

tool to assess 

connectivity at the 

neighbourhood level 

(icanwalk.ca). † 

Most PHUs use 

spatial methods for 

measurement (i.e. 

GIS). †‡ 

When asked which 

measures (in current 

use) are of most 

value in assessing 

urban walkability, 


connectivity (bike 

paths, multi-use 

paths, trails, 

sidewalks, and 

streets). † 



connectivity 

measures in the 

literature review (50 

articles reviewed) 





(ON, BC, QC), 1 



USA. 

> 30 connectivity 

measures identified 


review. 

Several PHUs 

have implemented 

connectivity 

measures, namely 

the intersection 

density measure. †‡ 

1 Ontario 


implemented 

connectivity 

measures in several 

health jurisdictions 


(as part of a 

generalized index )‡ 

Data sources vary 

considerable by 

PHU: community 

walkabout; surveys; 

focus groups and 

GIS inventory for 

one PHU; includes 

streets, sidewalks, 

multiuse paths and 

sometimes parks; 

Road Network GIS 

Layer .† 

Street networks 

that are more 

connected 

are thought to 

increase walkability 

by offering 

shorter and many 

alternate routes. ¥ 

Several 

studies have 

found positive 

associations 

between measures 

of connectivity and 

walkability. ¥ 

Greater street 

connectivity 





walking to and 

from transit. ¥ 


considerably by 

health jurisdiction. †¥ 

No standard 

methods for 

operationalizing 


Different tools are 

used to characterize 

road network 

configuration in 

relation to physical 

activity. †‡¥ 

Not all studies 

have found positive 

associations 




Most measures 

use data from the 

street network, 

but omitting 

pedestrian networks 

(e.g., sidewalks, 

park paths) may 

appreciably 

underestimate 

connectivity.¥ 

Determining how to 

handle freeways or 

other limited-access 

roads is another 

methodological 

issue. ¥ 



indices (composite 



density measure. 






Table 6: Gap analysis for diversity measures used to assess urban walkability, 2012 

AIR QUALITY 



Ontario 

Measures 

Count ¥ 


Ontario 


Challenges 

Link bw 

Measurement 

Approaches ¥ 

Diversity 

(Land Use Mix 

and Proximity) 

Diversity refers 

to the spatial 

arrangement of 

land use that 

influences the 

distance and mode 

of travel. 

Mixed land use 

brings different and 

necessary uses into 

relative proximity, 

thereby shortening 

trip distances and 

encouraging active 

modes of transport. 

Proximity describes 

the no. & variety 

of destinations 

within a specified 

distance of any 

location; function 

of both density of 

development & 

level of land use 

mix. 

As proximity and 

directness between 

destinations 

increases, 

distance between 

destinations 

decreases. 

The entropy index 

(entropy-based 

measure) is frequently 

used; land use types 

include: residential, 

retail, entertainment, 

office and institutional. ¥ 

Dissimilarity index 

measures dissimilarity 

based on predominant 

use of neighbouring 

squares. ¥ 

With appropriate parcel 

data, the calculation 

of land use mix is 

possible through a GIS 

or database interface. ¥ 

LUM data are typically 

obtained from land 

ownership records. ¥ 

Proximity is often 

calculated using 

circular or road 

network buffers. ¥ 

Distances (e.g. 400m, 

800m) commonly used 

to analyze walking 

distance vary. ¥ 


Census, MPAC, DMTI, 


Ontario Road Network 

or National Road 

Network .†‡¥ 



• Land Use Mix (3 

PHUs) 

• Proximity 

(schools, food 

outlets, parks, 

trails) (6) 

studies identified 

using diversity 


literature review 

(50 articles 

reviewed); 

21 applied in 

the Canadian 

context (ON, 

BC, QC), 1 in 

Australia and all 


the USA. 

43 (86%) 



LUM as part of their 

index. †‡ 


institute uses LUM 




methodologies to 

calculate LUM vary 

considerably. †‡¥ 

PHUs did not identify 

using dissimilarity 

indices. †‡ 







proximity (2 PHUs) 

and LUM (1). † 


methodologies 



> 80 diversity 

measures 



Most PHUs are 


measures (either 

individually or as 

part of an index) 

to assess urban 


1 Ontario 


implemented 

diveristy measures 


jurisdictions 




health jurisdiction 



index. ‡ 


methodologies 

vary, making 

comparisons 


jurisdictions 










Strongly and 

consistently 





literature. 

Parks, trails, 

recreational 

facilities, pathways, 

and schools 

within walking 

distance have 

been consistently 



particularly in 

children. 

Distance to retail 

activity is important 

in creating inviting 

pedestrian 

environments and in 

predicting levels of 

walking in cities. 

Distances used to 

analyze walking 

distance vary, 

making comparisons 

between jurisdictions 

challenging. 


does not consider 

the type or intensity 

of mixing. ¥ 


methodologies 

vary, making 

comparisons 



is often a limiting 

factor since parcellevel 

data are 

required to compute 

many land-use mix 

measures. Parcellevel 

data may be 

unavailable in some 

locations and in 

others may lack 

detail about land 

use. ¥ 

Many studies 

have opted to use 

survey items to 

approximate land 

use mix (e.g. ‘‘Are 

there shops where 

you live?’’) but such 

approaches do not 

allow betweenstudy 

comparisons 

because of 

unspecified 

definitions of place. ¥ 

The retail floor area 

ratio (FAR) is often 

used in conjunction 

with LUM. 

Proximity is 


both density of 



use mix. 





82 

WALKABILITY 

Table 7: Gap analysis for pedestrian oriented design measures used to assess urban walkability, AIR QUALITY2012 

Table 7: Gap analysis for pedestrian oriented design measures used to assess urban walkability, 2012 

Measure Description ¥ Inputs †‡¥ Current Use 

in Ontario 

Measures 

Count ¥ 


Ontario 


Challenges 

Link bw 

Measurement 

Approaches ¥ 

Street design refers 

to the scale & 

design of sidewalks 

and roads, and how 

they are managed 

for various uses. 

POD includes 

measures of 

neighborhood 

comfort (including 

aesthetics), 

cleanliness and 

safety. 


ratio (FAR) is used 

as an indicator of 

POD measuring 

retail density and 

site design. Low 

ratio indicates a 

low density retail 

development likely 

surrounded by 

substantial parking; 

high ratio indicates 

smaller setbacks 

& less surface 

parking; two factors 

thought to facilitate 

pedestrian access. 

The normalized 

difference 

vegetation index 

(NDVI) is used 

to estimate 

vegetation biomass, 

greenness, and 

dominant species. 

Various methods 

can be used to 

assess street design 

(audit, survey of 

perceptions, GIS); 

method depends on 

specific measures 

being assessed. 

Aesthetics and 

cleanliness are 

often assessed by 

observation (e.g. 

audit) or survey (of 

perceptions). 

FAR = retail building 

floor area footprint 

divided by retail land 

floor area footprint; 

included in walkability 

indices. Data 

source: DMTI (Route 

Logistics); 

Environic Analytics 

(Directory of 

Shopping Centres). 

NDVI: ratio between 

measured reflectivity 

in the red and near 

infrared band, in 

satellite images 

(DEM/SRTM). 

Safety and 

crime statistics 

obtained from 

police department; 

otherwise, through 

observation (audit) or 

survey. 

PHUs use individual 


comfort and safety, 

including: † 

• Crime rates (4 

PHUs) 


• Canopy coverage 

(trees) (4) 

• Posted speed 

limits (3) 

• Presence of street 

furniture (3) 



FAR as part of their 

index. †‡ 






using NDVI indices. †‡ 







sidewalk information 

(including condition 

affected by seasonal 

variances). † 



POD measures 


review (50 

articles reviewed); 



(ON, BC, QC) 

and all others 


USA. 


measures 



Several PHUs 

are using POD 






1 Ontario 


implemented safety 









index. ‡ 


methodologies 

vary considerably, 

making 

comparisons 


jurisdictions 

extremely 


Higher levels 

of objectively 

measured safety 

(e.g. traffic safetly) 

and comfort 

(e.g. aesthetically 

appealing 

communities) are 

positively associated 

with physical activity 

engagement. 

Since building 

setbacks are 

important predictors 

of walking and POD, 

FAR is introduced 

to increase the 

sensitivity to retail 

use believed to 

stimulate pedestrian 

activity. 

Higher levels of 

neighborhood 

vegetation have 

been associated 

with higher levels of 

physical activity. 

FAR is a standard 

planning 

measure and is 

frequently used 

in development 

regulations - and 

therefore is useful 

to apply to policy or 

existing regulations. 

Higher levels 




and comfort 


appealing 

communities) 

are positively 


physical activity 

engagement. 


setbacks are 

important 

predictors of 

walking and POD, 






activity. 


neighborhood 






planning 









methodologies 

vary (especially 

for comfort, 

aesthetics and 

safety measures), 



Self-reported 

measures implicated 

in same-source 

bias and issues with 

reliability, validity, low 

response rates and 

a biased sample of 

respondents. ¥ 

Systematic field 

observations can 

be very laborious 

(i.e. time-intensive 

and have multiple 

logistical constraints), 

often require 

significant specialized 

training and are 

notorious for being 

very costly. ¥ 


ratio (FAR), 

also known as 

commercial 

density, is a 

diverse measure 

that can be 

applied not only 

as a density 

indicator but also 

as an indicator 

of pedestrianoriented 

design 

and used in 

conjunction with 

land use mix 

(LUM). 






Table 8: Gap analysis for data sources and sets used in the assessment of urban walkability, 2012 

WALKABILITY 


Data Source Dataset Topic Area Current Use in Ontario PHUs Desirability Access/Availability Barriers/Challenges/Limitations 

MPAC / 

Teranet / 

OMNR 

Ontario Parcel 

database 

3 components: 

• Digital Assessment 

Parcel Fabric 

(MPAC) 

• Digital Ownership 

Parcel Fabric 

(Teranet) 

• Digital Crown Parcel 

Fabric (OMNR) 

LUM 

Retail Density 

56% of 25 PHUs that responded to the survey 

report having access to MPAC data † 

7 (of 10) PHUs identified using LUM as part of their 

walkability index. †‡ 

5 (of 10) PHUs identified using retail floor area as 

part of their walkability index †‡ 

PHUs identified using the following as individual 

measures † : 

• Land Use Mix (3 PHUs) 

• Retai floor area (1) 

Long list of Property Codes including detailed Residential, Commercial and 

Industrial codes 

Updated quarterly 

3 mapping specifications dictate supporting spatial data— POLARIS (Province 

of Ontario Land Registration Information System) mapping, Basic Index 

Mapping (BIM) and Pre-Basic Index Mapping (Pre-BIM): 

• Where POLARIS, features related to survey plans (including reference & 

subdivision), roads, major easements, township fabric, railways and major 

water bodies are captured. 

• Where BIM, features relate to survey plan text, roads, major easements, and 

geographic township fabric, railways and major water bodies. 

• Where Pre-BIM much less extensive. Features relate primarily to road text, 

geographic township fabric and major water bodies. 

Available at no cost through MNR’s LIO Warehouse to Ontario 

municipalities, conservation authorities, and provincial ministries. Eligible 

organizations must enter into the relevant license agreement(s) and 

become members of the Ontario Geospatial Data Exchange (ODGE) to 

access. 

Municipalities wishing to access the ownership data must be licensed 

through Teranet. 

Through Teranet the only costs incurred by municipalities for standard 

deliveries is a modest delivery and support charge. 

Variable licensing fees depending on derived products. 

Internal use for walkability does not appear to be subject to fee/royalty 

Some municipalities report quality issues with the spatial component 

of Ontario Parcel database. They use their own digital files and merge 

with MPAC data. 

Accuracy variable from location to location and depends on the source 

data available and the build procedure employed. 

• Where POLARIS standards were used to assemble the ownership 

mapping and where good control and legal or cadastral surveys 

were available, the data has better accuracy than other types of 

Ontario Parcel mapping. 

• Where the product is assembled upon 1:10,000 (or smaller) scale 

topographic mapping, and where control and surveys are not 

available, or not used, the data is much less accurate. http://www. 

ontarioparcel.ca/ 

http://www.ontarioparcel.ca/ 


Good coverage of the entire province. ‡ 

DMTI 

CanMap Route 

Logistics 

Network Analysis (to 

measure proximity or to 

model population affected) 

Connectivity 

Posted Speed Limits 

DMTI data used to assess connectivity measures. †‡ 

Excellent level of detail, appropriate for assessing walkability. £ 

The dataset is GIS-ready and requires minimal, if any, massaging prior to 

analysis. 

Vendor offers dataset in multiple formats. 

The dataset is designed for location-based service (LBS) applications and so is 

already topologically clean. 

Fee ‡£ 

Preferential pricing may be available to public sector clients. 

Presents a continuing expense to acquire annual updates (subscription 

model). 

Depending on new residential development activity, point-in-time 

extracts may weaken the representativeness of local calculations. Note 

also that not all streets are bordered by sidewalks. 

Enhanced Points of 

Interest (EPOI) 

Business Facilities & 

Amenities 

(for measures that capture 

proximity and accessibility to 

destinations) 


Offers provincial coverage and enhanced locational precision. Includes SIC and 

NAICS codes to identify business types (e.g., restaurants, retail, etc.). 


Fee £ 



model). 

Depending on business churn, point-in-time extracts may weaken the 

representativeness of local calculations. 

Environics 

Analytics 

BusinessWhere 


Amenities 



destinations) 



NAICS codes to identify business types (e.g. restaurants, retail, etc.). 

Businesses are verified annually. 


Fee £ 



model). 



GeoBase 

National Road Network 

(NRN) 




Connectivity 

Walkability Indices 

Density 

Diversity 

Unknown use across Public Health Units. 

No specific example of NRN usage. 

68% of PHUs indicated having access to street 

network files. † 

44% of PHUs indicated that Street networks (44%) 

are the most common geographic scale used to 

assess urban walkability. † 

Good coverage at both national and provincial scales. The Ontario Road 

Network (ORN) is used to populate the NRN’s Ontario level file. £ 


The dataset is GIS-ready and requires minimal, if any, manipulation prior to 

analysis. 

Vendor offers dataset in multiple spatial formats. £ 

Detailed Metadata records with online access and retrieval. 

Free with no cost user registration. £ 

Excludes select street attributes that could be required to compute 

specific calculations. £ 


extracts may weaken the representativeness of local calculations. 

Public Health unit road authority or “Local” Road Network file may 

override NRN usage due to technical specifications (i.e. Accuracy, 

Update Frequency, Maintenance etc.). 

Ministry 

of Natural 

Resources 


(ORN) 




Connectivity 


Density 

Diversity 


No specific example of ORN usage. 

68% of PHUs indicate having access to street 


44% of PHUs indicated that street networks (44%) 



Standardized provincial dataset with detailed documentation and regular 

update frequency. ORN is the authoritative source of roads data for the Ontario 

Government. £ 



analysis. 








† Survey ‡ Key Informant Interview ¥ Literature Review £ GIS Metadata 




3AIR QUALITY 

MEASURES & DATA USED IN THE ASSESSMENT OF AIR QUALITY 

BACKGROUND LITERATURE REVIEW KEY INFORMANT INTERVIEWS SUMMARY OF SURVEY RESULTS GAP ANALYSIS


87 

MEASURES & DATA USED IN THE ASSESSMENT OF AIR QUALITY 

CHAPTER 3: AIR QUALITY 

BACKGROUND 

AIR QUALITY AND HEALTH 

Hundreds of studies conducted in communities around the world have consistently demonstrated that 

short-term increases in levels of the five common air pollutants are: ground-level ozone (O 3 

), fine and 

coarse particulate matter (PM 2.5/10 

), sulphur dioxide (SO 2 

), nitrogen dioxide (NO 2 

) and carbon monoxide 

(CO) - are associated with a broad range of acute health effects including 94-98 : 

• Reduced lung function; 

• Increased frequency and severity of asthmatic symptoms; 

• Increased emergency room visits and hospital admissions for respiratory and cardiovascular 

conditions including respiratory infections, asthma; and 

• Increased non-traumatic deaths for respiratory and cardiovascular conditions. 


88 

AIR QUALITY 

Several studies have also demonstrated that long-term exposures to elevated levels of PM 2.5 

/ 10 

are 

associated with the development of chronic heart and lung diseases, including lung cancer, among 

adults. 97;99;100 An American Heart Association comprehensive review of the relationship between health 

and fine particulate matter (PM 2.5 

), the air pollutant most strongly linked to chronic health effects concluded 

that: 

• There is a causal relationship between exposure to PM , cardiovascular disease and death; 

2.5 

• Long term exposure (i.e. a few years) to elevated levels of PM increases the risk for 

2.5 

cardiovascular mortality and reduces life expectancy; and 

• Reductions in air levels of PM can decrease cardiovascular mortality within a few years.98 

2.5 

AIR POLLUTION BURDEN OF ILLNESS IN ONTARIO 

Several studies suggest that air pollution has a significant impact on health in Ontario. In 2005, the Ontario 

Medical Association (OMA) used risk coefficients from epidemiological studies, health statistics from 

Ontario communities, and air quality data from air monitoring stations across Ontario, to estimate the 

burden of illness from air pollution in Ontario. The OMA estimated that air pollution is responsible for approximately 

5,900 non-traumatic deaths, 16,800 hospital admissions, and 59,700 emergency room visits 

each year in Ontario. 94 Researchers from Health Canada and Environment Canada conducted a study 

to estimate the burden of illness associated with air pollution in several cities across the country. They 

concluded that the five common air pollutants – ozone, PM 2.5 

/ PM 10 

, SO 2 

, NO 2 

and CO, were responsible 

for approximately 2,900 non-traumatic deaths each year in Windsor, Hamilton, Toronto and Ottawa. They 

attributed one third of those deaths to acute health impacts associated with the mix of the five air pollutants 

and two thirds to the chronic health impacts associated with PM 2.5 

alone. They concluded that air 

pollution is responsible for between 7% and 10% of all non-traumatic deaths in cities across Ontario. 19 

AIR QUALITY & VULNERABLE POPULATIONS 

Many studies have shown that air pollution increases the risk of death and illness due to heart disease, 

stroke, and respiratory disease through both short term and long term exposures. While everyone 

faces increased health risks due to air pollution, the risk is greater for: 

• People with cardiovascular conditions such as angina, congestive heart failure, heart rhythm 

problems; those who have suffered a previous heart attack; 

• People with respiratory conditions such as asthma and chronic obstructive lung disease; 

• People with diabetes and/or those who are obese; 

94;98;101;102 

• The elderly, women (especially those that are pregnant), and young children. 


AIR QUALITY 89 

AIR QUALITY AND THE BUILT ENVIRONMENT 

The five common air pollutants measured in Ontario are ground-level ozone (O 3 

), fine particular matter 

(PM 2.5 

), nitrogen dioxide (NO 2 

), carbon monoxide (CO) and sulphur dioxide (SO 2 

).* Many of those activities 

involve the combustion of fossil fuels (e.g. natural gas, coal, oil, gasoline, diesel fuel, and jet fuel) in 

industrial processes, utilities and transportation. These five air pollutants have also been associated with 

the greatest burden of illness in modern society. 

Ground-level ozone is a secondary air pollutant that is formed in the atmosphere from reactions between 

other air pollutants; primarily nitrogen oxides (NO x 

) and volatile organic compounds (VOCs) in the presence 

of sunlight. 103 Because these reactions are affected by meteorological conditions, elevated concentrations 

of ground-level ozone are typically recorded on hot and sunny days from May to September 

between noon and early evening. 103 The transportation sector is the largest source of both NO x 

(73%) 

and VOCs (37%) in Ontario (Figures 7 and 8). 

Whenever a fuel (e.g. gasoline, diesel, natural gas, wood or coal) is combusted, nitrogen oxides are 

emitted. Major sources of NO x 

emissions include the transportation sector, industrial processes and 

utilities. 103 

Figure 7: Ontario nitrogen oxides emissions by sector (emissions from point/area/transportation sources, 

2009 estimates) 

7% 4% 6% 8% 

2% 

Smelters/Primary Metals 

Other Transportation 

Road Vehicles 

Other Industrial Processes 

46% 

Cement and Concrete 

Utilities 

27% 

Miscellaneous 

Source: Ministry of Environment 103 . Reproduced with permission 

*These air pollutants are similar to the criteria air contaminants assessed by the MOE, which include the 5 common air pollutants and total reduced sulphur (TRS). 


90 

AIR QUALITY 

Figure 8: Ontario volatile organic compounds emissions by sector (emissions from point/area/ 

transportation sources, 2009 estimates) 

12% 

3% 8% 

General Solvent Use 


13% 

Road Vehicles 

14% 


Miscellaneous 

24% 

26% 

Residential 

Printing/Surface Coating 


Airborne particulate matter (PM) is the general term used to describe a mixture of microscopic solid particles 

and liquid droplets that are suspended in air. Particulate matter is classified according to its size because 

of the different health effects associated with particles of different diameters. Fine particulate matter 

(PM 2.5 

) is less than 2.5 microns in diameter and can penetrate deep into the respiratory system. 103 

PM can be emitted directly from a source or formed in the atmosphere by the transformation of gaseous 

emissions. It includes aerosols, smoke, fumes, dust, fly ash and pollen. 103 The three major sources of 

directly emitted PM 2.5 

are residential (40%), the transportation sector (25%) and industrial sources (28%) 

(Figure 9). Under certain meteorological conditions, emissions from US based coal-fired power plants 

account for a significant share of the PM 2.5 

in southern Ontario. 103 



Figure 9: Ontario PM2.5 emissions by sector (emissions from point/area/transportation sources, 

2009 estimates) 

22% 

3% 

Smelters/Primary Metals 

9% 


Road Vehicles 

15% 

7% 

4% 


Cement and Concrete 

Residential 

40% 

26% 

Miscellaneous 


The transportation sector accounted for 88% of all CO emissions. Electric utilities and smelters are the 

major contributors to SO 2 

emissions in Ontario, accounting for approximately 54 per cent of the provincial 

SO 2 

emissions.(MOE, 2010). 104 

URBAN FORM, AIR QUALITY AND EXPOSURE 

While there are a number of articles which suggest that compact or walkable communities are associated 

with lower vehicle-related emissions of air pollutants, it is much more complicated to say how urban 

form is related to air quality and exposure to air pollutants. For example: 

• One study suggested that, while compact neighbourhoods can reduce per capita vehicle-related 

emissions, they can also concentrate the emissions particularly if the neighbourhoods do not 

have a strong mix of land uses and an efficient public transit system 105 ; 


92 

AIR QUALITY 

• Another study found that population centrality and population density were the strongest urban 

form predictors for air quality. Population centrality correlated with lower concentrations of ozone, 

PM 2.5 

and aggregate pollutant levels, and population density correlated with higher concentrations 

of PM 2.5 

. This study also found that transit supply was correlated with lower concentrations of 

PM 2.5 

. The researchers noted that the impact of urban form on air quality, particularly PM 2.5 

, was 

comparable to the impact of climate, and therefore significant from a public health perspective 106 ; 

• A third study conducted in southern California used population density, intersection density and 

land use mix as indicators of neighbourhood walkability. They found that concentrations of NO x 

and PM 2.5 

were highest near the city centre and major roadways, while the concentrations of 

ozone were higher in the outer-lying areas. This study found that when comparing estimated 

ischemic heart disease mortality rates among neighbourhoods, differences attributable to physical 

inactivity were comparable to differences attributable to individual air pollutants. The researchers 

suggest that the study demonstrates the importance of considering the impact of urban form on 

both physical activity and air pollution exposures. 107 

AIR QUALITY AND INCOMPATIBLE LAND USES 

Air quality can vary substantially across a community as local emission sources such as highways, industrial 

facilities, and truck depots add to background levels of air pollution that includes transboundary air 

pollution. 108 In Ontario, the Ministry of the Environment (MOE) has responsibility for permitting industrial 

facilities and other emission sources with certificates of approval (CofAs) based on the emissions from 

a single facility or operation and, sometimes, on a single source within a facility. This approach does 

not take into consideration background levels of air pollution or the cumulative impacts of a variety of 

emission sources in a local area. Consequently, while the CofA process ensures that individual emission 

sources do not exceed air standards, it does not ensure that air levels within a community stay within 

health-based air standards. 109 

Historically, these shortcomings in regulatory control have been mitigated, to some extent, by recommending 

separation distances to keep industrial facilities separate from sensitive land uses such as 

homes, daycares, schools and hospitals. 109 However, separation distances between sensitive land uses 

and industrial point sources have not always been preserved as development pressure on communities 

grows. In addition, in Ontario, high volume traffic corridors have not been included in the Land Use Compatibility 

Guidelines developed by the MOE. 109 

URBAN FORM AND VEHICLE-RELATED EMISSIONS 

The relationships between land use patterns, vehicle emissions, air quality, and human health are complex. 

A number of studies have demonstrated that variations in air quality within a community can be 

as great as the variation between communities. Millar and colleagues as discussed by Hankey 107 , and 

other researchers 110 , suggest that the built environment, including neighbourhood location, urban design 

and proximity to roads, can impact exposures. Air pollution is made up of a variety of substances, 



each with different sources, patterns of distribution, 

chemical reactions and health impacts. 

Each pollutant therefore has a different association 

with land use patterns and transportation, 

making it difficult to determine how a particular 

land use policy will affect levels of air pollution 

and exposures to air pollution. 

Several studies have demonstrated that the walkability 

of communities can have a significant impact 

on emissions from the transportation sector 

by influencing the extent to which people depend 

upon automobiles and other modes of transportation. 

For example: 

• The California Air Resources Board found that 

“complete” neighbourhoods (i.e. compact 

neighbourhoods built around public transit 

with a variety of services within a five minute 

walk) can reduce vehicle-related air emissions 

by up to 20% relative to more typical 

suburban neighbourhoods 108 ; 

• A study conducted by Frank and Chapman, 

as described by Frank and colleagues, 49 in 

Atlanta found that people who lived in the 

most auto-oriented neighbourhoods drove 

30% more than those who lived in the most 

walkable neighbourhoods. It found that with 

each step up a five-part walkability scale, 

vehicle related emissions of NO x 

and VOCs 

decreased by 6% and 3.6% respectively, and, 

• In King County, Washington, a 5% increase in 

the walkability of a neighbourhood, using land 

use mix, street connectivity, net residential 

density and retail floor area ratios as the 

indicators of walkability, was associated with 

6.5% reduction in vehicle miles travelled and 

a 5.6% and 5.5% reduction in vehicle-related 

emissions of NO x 

and VOCs respectively. 111 

ALTERNATIVE MODES OF 

TRANSPORTATION AND AIR QUALITY 

Several studies have suggested that public transit and 

active transportation can have a significant impact on 

local air quality and/or human health. Using air modelling 

and road count data, a Toronto study estimated 

that 190 premature deaths could be avoided, and $900 

million in health benefits could be realized, each year, 

if vehicle emissions in Toronto were reduced by 30 per 

cent by shifting to other modes of travel. 112 

A US study modelled the impact of eliminating all short 

automobile trips (≤ 8km) and replacing 50% of them 

with cycling trips in 11 metropolitan areas. Assuming a 

20% reduction in vehicle use they found that: 

• Air levels of PM in most of the metropolitan 

2.5 

urban centres would be reduced by 0.08 to 0.15 

g/m³ (1 to 2%) and in upwind states by about 

0.05 μg/m³; 

• Exceedances of the annual air standard for PM2.5 

would be reduced by 5 to 25% in the urban grids; 

• Air levels of ozone in the metropolitan urban 

centres would increase while the air levels of 

ozone in the suburbs, downwind states, and small 

urban centres would be reduced; 

• 433 deaths, 2,000 asthma attacks, 75 chronic 

obstructive pulmonary disease cases, 93,607 

emergency room visits or hospital admissions, 

and 660 heart attacks and related hospital 

admissions could be avoided each year; and 

• The net health benefits were valued at $3.6 billion 

per year. 113 


94 

AIR QUALITY 

AIR QUALITY AND TRAFFIC CORRIDORS 

The principal source of variation in air quality within many communities is vehicle-related air pollution associated 

with high volume traffic corridors. 114 A review of 15 different studies conducted by the World 

Health Organization (WHO) found that concentrations of air pollutants along traffic corridors were 1.2 to 

2.3 times higher than background levels in those urban areas. 102 In 2010, the Health Effects Institute (HEI) 

released a special report on traffic-related air pollution which concluded that: 

• Traffic emissions are the principal source of variation in the levels of air pollutants within many 

cities; 

• Traffic emissions have an impact on air quality at an urban and regional scale as well as a local 

scale; and 

• Air pollution associated with traffic corridors can extend up to 300 to 500 metres from a highway 

depending upon factors such as traffic volume, wind direction and wind speed. 

Furthermore, Brugge and colleagues 115 reviewed the air quality and health studies directed at high volume 

traffic corridors and they concluded that: 

• Air levels of ultra-fine particles (UFP), black carbon (BC), CO and NO are all elevated along high 

x 

volume traffic corridors with greater than 30,000 vehicles per day; 

• People living beside these highways are likely to receive much higher exposures to traffic-related 

air pollutants than people living more than 200 metres from highways; 



• Evidence from a variety of sources suggests that vehicle-related air pollutants have adverse 

effects on cardiovascular systems; and 

• There is strong evidence linking traffic corridors to the development of asthma and reduced lung 

function among children. 115 

Other researchers found statistically significant associations between residents who live in close proximity 

to traffic corridors and increases in asthma and other respiratory diseases, reduced lung function, 

adverse birth outcomes, childhood cancer, and premature death. 116 Although there are a number of 

uncertainties making it difficult to prove that traffic corridors are causing these health effects, the study 

authors concluded that enhanced precautionary land use, smart growth and transportation polices could 

better protect the public’s health. 116 

Recent studies have also linked air pollution to other health concerns. A Montreal study’s findings 

suggested that traffic-related air pollution might be associated with an increased incidence of postmenopausal 

breast cancer, 117 and a Danish study based on 53,000 participants in the Danish Diet Cancer 

and Health cohort, identified that traffic-related air pollution might increase the risks of cervical and 

brain cancer. 118 

SEPARATION DISTANCES FROM TRAFFIC CORRIDORS 

Proximity to high traffic areas has been a key area of academic research as well as a focus for public 

health units. Kim and colleagues 119 found traffic density and annual averages of daily traffic were significantly 

correlated with NO x 

and NO, accounting for 35 to 60% of the variation. Elemental carbon, VOCs, 

CO, and fine particulate matter are also expected to be higher in close proximity to traffic buffers. 110;120 

Approximately 32% of Canadians reside within 500 metres (m) from a highway or within 100 m from a 

major road. 121 Amram and colleagues 122 found that for 10 of Canada’s largest cities, 16.3% of schools 

were within 75 metres and 36.1% were within 200 metres of a major road. 

Brauer and colleagues 121 evaluated research in Canada on traffic related air pollutants, finding similar results 

to other international studies. Air pollutants flow towards adjacent land areas, with higher pollutant 

levels noted approximately 50 to 100 m from major roads, and approximately 150 m from highways. 114;121 

Pollutant gradients could be as high as 500 m to 800 m from the road. Determining distribution becomes 


96 

AIR QUALITY 

more complex when meteorological conditions 

are considered. The impact of wind speed and 

direction has been noted to increase the distance 

downwind from roadways by two or three 

fold, and to be dependent on the reactivity of the 

pollutant. 114;121 

Traffic buffers with a specific distance from residences 

have previously been used as a measure 

of exposure to air pollutants in the Hamilton 

area 110 and in Vancouver. 120 A Vancouver-based 

study found a higher risk for new born infants being 

small for gestational age (SGA) and having a 

low birth weight (LBW) in pregnant women residing 

within 50 m from a major highway. 120 However, 

there was no significant effect for pregnant 

women within 150 m from a major highway or 

within 50 m from a major roadway. 120 Morgenstern 

and colleagues 123 noted a dose-response 

relationship with distances of 50 m or less from 

major roads having the highest effect on allergen 

sensitization in children. 

While road distance is a simple and easy measure 

of potential pollutant exposure, it does not 

accurately estimate pollutant levels or sources 

of pollution other than traffic. Furthermore simple 

road distance measures might not consider other 

factors influencing exposure such as topography, 

land use, wind patterns, and traffic levels. 124 

AIR QUALITY AND EXTREME HEAT 

While a better understanding is required for air 

quality in the built environment, it is also important 

to consider how air quality relates to other environmental 

health issues such as extreme heat. 

The relationship between extreme heat and air 

quality is complex with various influencing factors, 

in addition to the interaction between the two. 

Solar radiation is one meteorological variable of 

potential interest to extreme heat which also impacts 

the distribution of air pollutants. 121 



The built environment also plays an important role in linking heat with pollutant exposure, as temperature 

is positively correlated with ozone levels. 106 One study noted that temperature differences within urban 

areas were greater than or equal to urban-rural differences and contributed to the intra-urban differences 

of ozone. 125 Ren and colleagues 126 noted how ambient air pollution, in particular ozone, can interact with 

temperature and impact heart rate variability. Another study using regression modelling highlighted the 

impact of vegetation and pavements on explaining spatial and temporal differences in surface temperature, 

which in turn contributed to ozone levels. 125 

ASSESSING AIR QUALITY 

The Environmental Monitoring and Reporting Branch (EMBR) of the MOE measures ambient air quality 

across the province manages the Air Quality Index (AQI), issues smog advisories, and prepares the province’s 

annual air quality report. The EMBR is responsible for air quality monitoring at the regional scale. 

It conducts ambient air monitoring to assess background conditions representative of rural and urban 

settings, including transboundary assessments. 

The Air Quality Index (AQI) is a single indicator of air quality that is used to communicate the quality of 

the air to the public. It is based on the six criteria air pollutants, which the MOE describes as air pollutants 

that have adverse effects on human health and the environment. They include ground-level ozone, PM 2.5 

, 

NO 2 

, SO 2 

, CO and total reduced sulphur (TRS). 104 The AQI readings are collected by an air monitoring 

network composed of 40 air monitoring stations located across the province. In Perrotta’s 127 review of air 

monitoring in Ontario, the Environmental Monitoring and Reporting Branch (EMRB) reported that these 

stations continuously measure a combination of the six criteria air contaminants. 

The Air Quality Health Index (AQHI) is a new single indicator for air quality that has been developed 

over many years by a collaborative process led by the federal government. It was developed to better 

reflect the health impacts associated with the different air pollutants. Within Ontario, this new index is 

being piloted in the Greater Toronto Area (GTA) and in the National Capital Region (NCR) in a project led 

by Environment Canada. The EMRB is supporting this project by providing air monitoring data, AQHI 

forecasting, and technical advice. 127 To date, monitoring for the AQHI is being done from the same locations 

and with the same air monitoring equipment that is used for the AQI in the GTA and the NCR. 


98 

AIR QUALITY 


The growth of urban communities has important 

implications for local air quality issues. Through 

increased population sizes and increased demand 

for vehicle transportation and other services 

which contribute to emissions, there is the 

potential for higher concentrations of air pollutants 

as well as greater risk of exposure to the 

public. 

While there is a need for data on the built 

environment relevant to air quality issues, 

establishing the link between the two areas is 

difficult for multiple reasons: 

• The distribution of pollutants is impacted 

by multiple meteorological variables such 

as wind speed and direction, atmospheric 

stability and mixing, precipitation, 

temperature, etc. 

• Physical and chemical properties of pollutants 

differ, and these individual properties can 

impact the life cycle of the pollutant, their 

distribution in the built environment, and 

interaction with the atmosphere and other 

pollutants 

• The diverse range of urban structures 

influences air quality through the creation of 

emissions, altered flow patterns of pollutants, 

and risk of exposure to the public 

• There is a greater need for understanding 

emission sources as well as population 

exposure in the built environment 

The complex relationship between the built environment 

and air quality illustrates the need for 

more research on the subject before accurate 

measures can be developed. Certain measures 

may have the potential to be generalized to a 

wide range of urban communities. At the same 

time, air quality issues can differ significantly between 

Public Health Unit (PHU) jurisdictions. This 

would require assessments of pollution sources 

and exposure within local communities. 

To manage health risks associated with air pollution, 

PHUs and environmental agencies have 

adopted various measurement approaches to 

better assess air quality in the built environment. 

These methods include surveillance of ambient 

air pollutant levels through monitoring stations, 

maintaining an inventory of emissions levels, and 

developing detailed spatial models for air pollution. 

Each method has their own strengths and 

weaknesses in assessing air quality issues and 

requires different data sources and measures. 

The purpose of the literature review is to capture 

research on the link between the built environment 

and air quality. The literature review does 

not focus in detail on any specific aspect of air 

quality in the built environment, such as traffic 

corridors. Rather the approach is to provide an 

overview of a variety of ways to assess air quality 

related to the built environment. This includes 

reviewing currently available data sources as well 

as measurement approaches to fill present data 

gaps on air quality in the built environment. The 

following sections will discuss: 

• Common built environment measures relevant 

to air pollution; 

• Air quality indices used to monitor pollutant 

levels in Ontario; 

• Emissions estimates data noted in the 

literature review; 

• Modelling methods relevant to the built 

environment and urban areas. 



COMMON BUILT 

ENVIRONMENT MEASURES 

AND AIR POLLUTION 

Built environment variables commonly investigated 

in the literature for their association with 

air pollutants are listed below. In general these 

variables were mostly extracted from Digital Mapping 

Technologies Inc. (DMTI) sources, census 

data, other Statistics Canada data sets, as well 

as municipal level data. 

• Road distance/density 

• Transit supply 

• Topography (e.g. elevation, terrain) 

• Population and or dwelling (i.e. measured as 

counts or density) 

• Land use type (e.g. industrial, commercial, 

residential counts) 

• Mixed land use 

• Centrality (e.g. residential, job) 

• Urban sprawl 

• Traffic volume (e.g. vehicle kilometres travelled, 

annual average daily traffic) 

Through modelling techniques, other built environment 

variables have been evaluated. Some 

studies have used municipal zoning categories 

and investigated the relationship with air pollutant 

levels. For example, Parenteau and Sawadra 128 

noted how green spaces and their potential linkages 

to lower level of pollutants need to be further 

assessed, Recent research has provided some 

insight onto the relationship between the built environment 

variables and exposure to pollutants. 

In a study by Atari and colleagues (2009) dwelling 

count and industrialized areas were determinants 

of intra urban variation in BTEX levels (benzene, 

toluene, ethylbenzene and xylene). Rosenlund 

and colleagues 129 found the most predictive variables 

of measured NO 2 

were distance from busy 

roads, the area of the city, altitude, inverse population 

density, and the size of the census block. In 

another study, Cesaroni and colleagues 130 evaluated 

the linkages of three health outcomes (rhinitis, 

chronic bronchitis, and asthma) with various 

indices measuring and estimating air pollution in 

Rome, Italy. The study found that only rhinitis was 

associated with traffic exposure, and that selfreported 

indices were only not strongly or moderately 

correlated to objective indices. 

Nonetheless, determining how the built environment 

influences air pollutants is only one aspect 

in understanding the distribution of air pollutants 

in urban areas. Differences between pollutants 

as well as meteorological conditions need to be 

further considered. Not all pollutants share the 

same spatial trends in the built environment, creating 

greater challenges to assessing exposure. 

Some pollutants, such as ozone (O 3 

) compared 

to PM 2.5 

, differ more significantly within a city. 110 

For O 3 

, the most important predictors were found 

to be altitude and sprawling. 131 Furthermore, the 

impact of pollutants is not isolated. They can 

undergo chemical reactions and have synergistic 

effects that can alter the spatial pattern of pollutant 

exposure. Ozone concentrations may differ 

from other pollutants due to O 3 

scavenging 

by NO close to roadways. 110 This spatial heterogeneity 

can have important implications for health 

outcomes and illustrates the need for assessing 

multiple pollutants and their relationships to the 

built environment. 

AIR POLLUTANTS AND INDICES 

An air quality index (AQI) is a common tool used 

for monitoring and communicating pollutant levels 

that pose health risks to the public. Indices 

convert complex data involving time, spatial distribution, 

and pollutant concentrations into values 


100 

AIR QUALITY 

or terms that are better understood by the general 

public. Rather than stating pollutant levels in 

µg/m 3 , an index communicates air quality using 

an aggregated value relative to a scale. 132 

The majority of research in the literature review 

focused on monitoring station networks. Table 9 

summarizes data sources relevant to Ontario for 

individual pollutants and indices. These networks 

created by government agencies are a favourable 

source of pollutant data since the information is 

available at no cost and can provide high temporal 

detail (hourly data on pollutant levels). Forty 

monitoring stations are currently located within 

Ontario, mostly in urban communities. Presently, 

the MOE publishes data on the number of smog 

advisories at a provincial level, historical AQI readings, 

and air pollutant levels for PM 2.5 

, NO 2 

, NO, 

NO x 

, O 3 

, SO 2 

, and CO. 104 The monitoring stations 

managed by MOE feeds into the National Air Pollution 

Surveillance (NAPS) network for monitoring 

air quality. 

AIR QUALITY INDEX 

At the provincial level, the Ontario Ministry of the 

Environment (MOE) has developed its own AQI 

using a network of 40 monitoring stations, providing 

air quality readings at a municipal level 

across the province. AQI pollutant levels are converted 

to an index scale, where the pollutant with 

the highest value for the hour determines the AQI 

reading for the hour. 104 The AQI focuses on six 

pollutants of concern in Ontario (Table 9). However, 

the ability of the Ontario AQI to prevent adverse 

health outcomes had been questioned. A 

Toronto Public Health study 133 found that 92% of 

premature mortality and hospitalizations relating 

to respiratory and cardiac admissions occurred 

during a good or very good AQI rating. 

AIR QUALITY HEALTH INDEX 

At the federal level, Environment Canada and 

Health Canada created the AQHI. The AQHI differs 

from the AQI by weighing the impact of multiple 

pollutants and incorporating findings from 

epidemiological studies. 134 Within Ontario, this 

AQHI is being piloted in the Greater Toronto Area 

(GTA) and in the National Capital Region (NCR) 

in a project led by Environment Canada. According 

to Perrotta’s review 127 , support for the project 

came from the EMRB through the provision of air 

monitoring data, AQHI forecasting, and technical 

advice. Monitoring levels of PM 2.5 

, NO 2 

and O 3 

, 

the AQHI places a greater weight on NO 2 

levels 

followed by O 3 

and PM 2.5 

. At a municipal level, the 

AQHI may be less reflective of air pollutant health 

risks due to higher levels of PM 2.5 

. Furthermore, 

the AQHI focuses on regional air quality levels 

and does not monitor other pollutants, such as 

SO 2 

and CO, which may contribute to local air 

quality health concerns. 

To date, AQHI monitoring is done from the same 

location and equipment that is used for the AQI. 

The pilot project has demonstrated that the AQHI 

system tends to be more sensitive for urban centres 

where health impacts are dominated by air 

levels of NO 2 

. By contrast, the Ontario AQI system 

tends to be more sensitive for rural areas where 

health impacts tend to be dominated by air levels 

of ozone and PM 2.5 

and trans-boundary impacts. 

Perrotta 127 highlight’s the EMRB’s acknowledgment 

that pending the completion of additional 

analyses, decisions will have to be made about 

how to move forward with the AQHI and the AQ. 



DATA ON POINT SOURCE AIR 

POLLUTANT LEVELS 

With a few exceptions, the MOE Regional Offices 

no longer own, operate or maintain the fixed station 

air monitoring equipment that is used for ongoing 

air monitoring of point sources in Ontario. 

Responsibility for these monitoring programs 

was transferred to the organizations responsible 

for the point sources under a program called 

Source Emissions Monitoring in May 2003. Under 

this program, the organizations (i.e. industrial facilities, 

generating stations, etc.) are required to 

monitor air quality on an on-going basis, because 

of the volume of their emissions or their potential 

to negatively impact human health or the environment. 

The organizations are responsible for 

funding, operating, and maintaining the air monitoring 

equipment. Under this program, the MOE 

Regional Offices continue to have direct access 

to real-time data, receive regular reports on air 

monitoring results, and audit the air monitoring 

programs. 127 

RESEARCH-BASED AIR 

MONITORING AND DATA 

When the fixed stations were transferred, MOE 

Regional Offices enhanced their capability to 

undertake flexible air monitoring studies by purchasing 

more portable equipment. These instruments 

can be used to identify air emissions and 

support compliance and air emission reduction 

activities. 127 

CURRENT AIR QUALITY DATA IS 

LIMITED FOR LOCAL AIRSHEDS 

While AQI/AQHI readings are available for most 

public health units in Ontario, these readings 

provide indicators of ambient or background air 

quality only. For the most part, the air monitoring 

stations used to produce these readings have 

been sited intentionally at a distance from local 

emissions sources to ensure that they reflect ambient 

or background air quality for the community. 

There are a few exceptions to this rule; several of 

these air monitoring stations have been located 

adjacent to high volume traffic corridors to provide 

a better understanding of air quality as it is 

impacted by mobile sources of air pollution. 

The MOE and Environment Canada have indicated 

that their organizations use their air monitoring 

stations to assess ambient air quality or regional 

air quality. The MOE reports that it does not, as 

a rule, get involved in air monitoring projects that 

are directed at understanding how air quality varies 

across a community. This means that most 

PHUs in Ontario have only one or two air monitoring 

stations in their communities providing data 

on local air quality. 

A few PHUs will have access to data collected 

by organizations that are required to monitor air 

quality under the Source Emissions Monitoring 

program. This usually applies to large point 

sources such as steel or nickel-processing facilities, 

oil refineries or coal-fired generating stations. 

These monitoring stations are usually directed at 

air pollutants that are associated with the point 

sources. As a rule, these stations are not sited to 

assess air quality across the community from all 

sources; nor are they measuring air pollutants beyond 

those associated with their own operations. 


102 

AIR QUALITY 

EMISSIONS ESTIMATES DATA 

In addition to monitoring stations data, the literature 

review noted some important sources of 

information derived from emissions estimates. In 

many studies, air pollutant levels in the built environment 

were derived by using emissions estimates 

from mobile sources (e.g. vehicle and 

other transportation sources) and point sources 

(e.g. industrial facilities). Table 9 summarizes the 

data sources of emissions estimates noted in the 


TRAFFIC EMISSIONS ESTIMATES 

In the literature review the most common data 

source for emissions obtained at a Municipal level 

was traffic volume estimates. Data on traffic flow 

rates or industrial activities can be converted to 

emissions rates using an emissions factor, which 

converts the known input energy and raw materials 

to emissions. The variables in calculating 

emissions involve data on activity rates, the emissions 

factor, and composition of traffic vehicles. 135 

Emissions data along with dispersion modelling 

has commonly been utilized by the MOE to determine 

ambient concentration levels. These techniques 

have also been used by PHUs in Halton 

and Toronto. 

As part of Ontario Regulation 127/01, all facilities 

based in Ontario that emit specific substances 

at certain quantities are required to provide information 

to the government, which is also publicly 

available. At the federal level, Environment Canada 

developed the National Pollutant Release 

Inventory (NPRI) which collects information on facilities 

emitting specific substances across Canada. 

Presently, the requirements for emissions 

reporting in Ontario have been harmonized with 

NPRI, with the data being made publically available 

on the NPRI website. 104 Environment Canada 

has data available at a facility level and aggregated 

data at the provincial level. The provincial 

information includes estimates for 17 air pollutants 

organized by sector. In addition to industrial 

sources, NPRI estimates emissions from various 

other sectors such as transportation, agriculture, 

landfills, natural sources, etc. 134 

While NPRI is a useful resource for point source 

emissions, it does not capture smaller point 

sources existing in urban communities such as 

printing and automotive service facilities. The 

data collection for smaller point source will be 

discussed further in the case studies of Halton 

and Toronto. 

INDUSTRIAL EMISSIONS ESTIMATES 

DATA 



Table 9: Summary of Air Quality and Air Pollutant Data Sources at Municipal, Provincial and Federal levels 

Measure 

Pollutant 

Components 

Data Source Data Format Frequency 

Municipal level: 

Traffic emissions 

Traffic related 

pollutants 

Traffic counts (e.g. 

average annual daily 

traffic) 

Varies between 

municipalities 

Varies between 

municipalities 

Provincial level: 

Ontario Air Quality 

Index 

NO 2 

, O 3 

, SO 2 

, CO, 

PM 2.5, 

and total 

reduced sulphur 

compounds 

Monitoring stations 

from Ministry of the 

Environment Ontario 

Air Quality Information 

System (AQUIS) 

Aggregated 

index 

Hourly 

(AQI reading: 

2007 to 

present) 

Individual 

Pollutants 

CO, NO 2 

, NO, NO x 

, 

O 3 

, SO 2 

, and PM 2.5 

, 


from Ministry of the 

Environment Ontario 

Air Quality Information 

System (AQUIS) 

Ambient 

concentrations 

Air Quality 

Information 

System (1953- 

2003) 

Federal level: 

Air Quality Health 

Index 

O 3 

, PM 2.5 

, NO 2 


from National Air 

Pollution Surveillance 

(NAPS) 

Integrates AQUIS 

Composite 

measure 

Hourly 

Canada 

Environmental 

Sustainability 

Indicators 

O 3 

, PM 2.5 

, SO 2 

, NO 2 

, 

VOCs 


from National Air 

Pollution Surveillance 

(NAPS) and Canadian 

Air and Precipitation 

Monitoring Network 

(CAPMoN) 

Ambient 

concentrations 

Hourly average 

2 year lag 

period for data 

O 3 

( (1990 to 

2010)PM 2.5 

(2000-2010) 

SO 2 

, NO 2 

, VOC 

(1996-2010) 

National Pollutant 

Release Inventory 

(NPRI) 

Over 300 pollutants 

Emissions (reporting, 

stations, surveys, 

and estimates) from 


NPRI database 

Emissions 

estimates 

(Does not 

provide 

atmospheric 

concentrations) 

Yearly (1 to 2 

year lag for 

most current 

data) 


104 

AIR QUALITY 

MODELLING AIR QUALITY 

Recent research highlights the value of more 

detailed exposure estimates to accurately determine 

health risks and for effective public health 

programming related to the built environment. To 

assess air quality issues in the built environment, 

it is important to consider the spatial scale of pollutant 

levels. The distribution of air pollutants is 

complex, ranging from characteristics of vehicle 

emission flow patterns to trans-boundary movements 

of pollutants. For local air quality issues 

relevant to public health, the most common approaches 

have been to evaluate traffic corridors 

and measure pollutant levels at a neighbourhood 

level. 

To accurately map the spatial distribution and 

temporal changes of air pollutants a large number 

of air sampling equipment may be required. This 

may not be feasible for most public health units. 

To overcome this challenge, modelling tools have 

been developed and utilized to predict pollutant 

levels at a high spatial resolution. 

The literature review identified a variety of models 

that were utilized by researchers to estimate pollutant 

exposure (Table 10). These range from more 

basic models such as distance from air monitors 

to more complex models such as Kriging, land 

use regression, dispersion modelling, and the application 

of satellite technologies. While various 

modelling methods are provided, some of these 

methods can be combined with other modelling 

techniques to further assess air quality. For more 

detail on options for assessing air quality refer to 

the “Air Quality Assessment Tools: A guide for 

Public Health Practitioners” developed by the 

National Collaborative Centre for Environmental 

Health. 135 

BASIC MODELS AND INVERSE 

DISTANCE WEIGHING 

The simplest models determine air pollutant exposure 

by the point of interest at a certain distance 

from an air monitoring station. Some stations 

are over 10 km away from the point of interest. 

136 Using inverse distance weighing (IDW), 

studies determined the air pollutant levels at a 

specific location by averaging the three nearest 

monitoring sites. Pollutant levels at monitoring 

stations closer to the location of interest were 

given a greater weighting. 120 The use of IDW has 

also been applied to determine concentrations 

close to pollutant point sources as well as ambient 

concentrations for sensitive receptors such as 

residential areas. These methods take advantage 

of detailed temporal information and low cost 

data from monitoring stations. However, without 

considering meteorological or built environment 

effects such as wind direction or mixed land use, 

the use of IDW provides limited accuracy in mapping 

the spatial trends of pollutant levels. 

To gain greater spatial detail on pollutant exposures, 

especially for long term exposure and chronic 

health outcomes, more sophisticated models 

have been developed. The output of these models 

commonly involves generating spatial maps 

which could be used either for pollutant exposure 

risk assessments or in air quality monitoring. 

Geographic information systems (GIS) are often 

used as a tool in producing pollutant exposure 

maps. 



Figure 10: Operational land use regression 

predicted surface for Toronto. 140p.208 

KRIGING 

Kriging is a method used to model air pollutant 

exposure in urban environments. The model assumes 

that the measure of interest can be described 

by trends of pollutants at various spatial 

scales. Based on the association of the variable 

(e.g. location, land use, etc.) with the measured 

pollutant concentration, the model then predicts 

what the concentration will be at unmeasured 

sites. 131 In the Ottawa air study, Kriging was used 

with satellite information to assess the spatial distribution 

of pollutants at a neighborhood level. 

©. Taylor & Francis, 2007. Reproduced with permission. 

DISPERSION MODELLING 

Another option for mapping the spatial distribution 

of air pollutants is dispersion modelling. 

These models have been commonly used by 

MOE and local municipalities in risk assessments 

and evaluating air quality management of point 

sources emissions. 137 Dispersion modelling is 

able to consider a wide range of issues including 

wind patterns, deposition of pollutants, filtration 

from coniferous trees, and turbulent air flow 

caused by urban land cover. In general the model 

requires data from traffic volume, composition of 

traffic vehicles, point source emissions (e.g. industrial 

sources), and local weather information. 

Numerous dispersion models are approved by 

the Ontario MOE including ASHRAE, SCREEN3, 

CALPUFF/CALMET, ISCPRIME, CALINE4, and 

AERMOD. 104 Through the MOE’s Certificate of 

Approval Process, industries could be required to 

perform dispersion modelling for an emitting facility.This 

data may be available and useful for public 

health decision making. In Ontario CALMET/ 

CALPUFF has been used by Halton Region and 

City of Toronto to model the spatial distribution 

of pollutants at a resolution of one to two kilometres. 

127 

LAND USE REGRESSION 

Another commonly cited model in the literature 

was land use regression (LUR). The focus 

of LUR is to develop predictors of pollutant 

concentration by showing strong linkages to 

certain variables such as road density or land 

use type. The level of detail can be substantial 

with a resolution of 10 meters for spatial 

maps. 135;137 The approach involves sampling 

of pollutants at multiple sites. Linear regression 

statistics are used to correlate measured 

pollutant concentrations with the strongest 

predictive variable. This correlation can be 

linked to a variety of geographic attributes 

relating to the built environment. In particular 

LUR uses four main variables: population 

density, traffic density, road length, and landuse. 

The data sources noted for LUR include 

DMTI files on roads and land use, local traffic 

data, population data, and municipal zoning 

data. With various subcategories of each 

variable, there are a total of 55 possible predictors 

of environmental exposure. 137 These 

models have previously been developed in 

Sarnia, 138 Hamilton, 139 Toronto (Figure 10), 140 

Windsor, 141 and Ottawa. 128 Refer to Brauer and 

colleagues 121 for examples of LUR models 

conducted in Canadian cities. 


106 

AIR QUALITY 

WHICH MODEL IS MOST EFFECTIVE? 

The research has been mixed on which model most 

accurately estimates air pollution levels. Ryan and 

LeMasters 142 state that LUR models can provide 

equal or better spatial information than dispersion 

models. The precision of LUR modelling was better 

for certain pollutants such as total suspended 

particulates (TSP) 143 and NO 2 

, 120 but poorer than 

nearer monitoring stations for PM 2.5 

and black 

carbon. 120 Nitrogen dioxide is strongly correlated 

with the land use variables, but SO 2 

is not 

as strongly correlated with all land use variables 

used in LUR. 141 Beelen and colleagues 131 noted 

that universal Kriging had outperformed LUR and 

ordinary Kriging for NO 2 

, PM 10 

, and O 3 

when an 

exposure map (1km resolution) for Europe was 

developed. Brauer and colleagues 137 notes how 

research projects have found dispersion modelling 

to have similar predictive power to LUR, but 

with LUR being more valuable when emissions 

data is unavailable. Despite noted weaknesses in 

LUR predicting pollutant exposure, others have 

argued that LUR provides the most reliable results 

in comparison to distance from road ways 

and questionnaires regarding perceived traffic 

density. 129 

SATELLITE TECHNOLOGIES 

The use of remote sensing, particularly satellite 

imagery was not commonly cited in the literature 

review, but has been a tool used by municipalities 

such as Ottawa. Satellite monitoring systems 

have been developed to monitor a variety 

of pollutants including NO 2 

, SO 2 

, CO, O 3 

, and 

PM through indirect measures such as assessing 

the number of aerosols in a vertical column of 

the earth’s atmosphere and hydrocarbons. 144 The 

spatial resolution can vary significantly from 250 

m to 320 km depending on the instrument used. 

While satellite technologies have shown to be a 

useful tool in monitoring the built environment, 

other issues need to be considered such as cost, 

the quality of images, and the accuracy of pollutant 

concentration through measurements of 

infrared and light scattering. The example of Ottawa 

and use of satellite technologies in air quality 

assessments is discussed later. 

Generally, these sophisticated models have 

shown strong predictive value for pollutants. The 

choice of model depends on multiple factors. Key 

questions need to be answered before modelling 

begins: What is the objective of the monitoring 

program or study? Is spatial and/or temporal detail 

required? Is the required data available? What 

financial and logistical issues are involved? What 

pollutants and health outcomes are of special 

concern? Brauer and colleagues 120;137 highlight 

that air monitoring stations are a good predictor 

of urban levels of particulate matter but in contrast 

ozone, nitric oxide, and black carbon differ 

more significantly within urban environments and 

can benefit from modelling to gain better spatial 

exposure maps. 120 



Table 10: Methods of modelling air quality and pollution levels noted in the literature review 

Proximity Models 

(e.g. IDW) 

Interpolation 

models, Kriging 

Land Use 

Regression 

Dispersion Modelling 

Description 

Distance from 

emission source 

or from ambient 

measuring station. 

Inverse Distance 

Weighing formula 

calculates average 

exposure levels based 

on distance from 

monitoring stations. 

Mathematical 

formula predicting 

pollutant levels 

in unknown area 

based on data near 

area. 

Estimates air 

pollution levels 

which correlate 

well with land cover 

type, traffic, road 

type, elevation, 

and other possible 

variables 

Commonly used for 

point and area sources. 

Using emissions data, the 

model calculates ambient 

air pollutant levels. The 

model takes into account 

low wind speeds, timevariation 

from pollution 

sources, and various land 

types. 

Benefits 

• Simple method of 

calculating exposure 

• Low financial and 

logistics cost 

• Can be applied 

for point source 

emissions 

• Models spatial 

distribution of air 

pollutants 

• Universal Kriging 

considers large 

scale factors (e.g. 

regional or global 

levels) 

• At a resolution of 

10 meters 

• Allows for flexible 

sampling in terms 

of location and 

time period 

• Better for 

between 

participant 

variability in 

epidemiology 

studies 

• Accounts for 

meteorological impacts 

(mainly wind) and 

temporal changes in 

pollutant levels 

• Can estimate pollutant 

levels from different 

scenarios 

• Does not require air 

monitoring data 

• At a resolution of 10s of 

meters 

• Provides details at 

regional and urban level 

Challenges 

• Does not account 

for landscape and 

meteorological 

features 

• Greater error range 

in estimates 

• Needs large 

number of 

observations 

• Initially requires 

air sampling or 

emissions factor 

• Do not take into 

account other 

variables such as 

terrain 

• May exaggerate 

variability 

• Limited 

predictability and 

generalization 

from one city to 

another 

• Initially requires 

air sampling or 

emissions factor 

• May produce 

higher risk 

estimates 

• Less developed 

for point sources 

• Costly, requiring 

powerful computers 

• Requires emissions, 

traffic density data 

• Mismatch in temporal 

data (e.g. derives hourly 

estimates from annual 

emissions data 

• More sophisticated 

models require a lot of 

data to be accurate. 

• Present applications in 

Ontario municipalities 

at 1-2 kilometres 

resolution 


108 

AIR QUALITY 

AIR MONITORING AND MODELLING STUDIES AT THE PUBLIC 

HEALTH UNIT LEVEL 

This section provides examples of air monitoring and modelling projects conducted by three municipalities: 

Toronto, Halton Region, and Ottawa. These examples highlight how various measurement approaches 

and data sources are combined to better assess ambient levels of pollution at a neighborhood 

level and to estimate associated health risks. 

ASSESSING AIR QUALITY IN TORONTO 

In the early 1990s, the MOE was operating five 

AQI air monitoring stations in what is today the 

amalgamated City of Toronto. While the MOE 

monitors provided an indicator of ambient air 

quality at those sites, they could not provide information 

about the quality of air between these 

stations. Therefore, they were inadequate in addressing 

community concerns about air quality in 

neighbourhoods that were located in close proximity 

to local emission sources. Furthermore, air 

monitors could not predict how air quality would 

be impacted by changes in municipal policies related 

to land use or development according to 

the TEO. 127 

To address these concerns, the City of Toronto 

began modelling air quality. They subsequently 

verified the results by comparing them against 

the data from the MOE AQI air monitoring stations, 

and by undertaking specific air monitoring 

projects. Accordingly, the TEO deemed air modelling 

to be useful for the City of Toronto as it indicates 

what comes from where and when. 127 



AIRSHED MODELLING – COMMON 

AIR POLLUTANTS 

Toronto staff began the process of building a Toronto-specific 

airshed model by using all available 

known emissions data; collecting data needed to 

estimate relevant emissions such as traffic count 

data and engine emissions standards; modifying 

federal estimates of fugitive emissions (particulate 

matter release from industrial and construction 

area sources) and other area emissions; and collecting 

federal meteorological data from across 

southern Ontario. After evaluating several modelling 

options, City of Toronto staff decided to use 

the CALMET/CALPUFF model suite that was developed 

by a private research corporation under 

contract to the California Air Resources Board 

(CARB) in the late 1980’s; a suite which was later 

adopted by the U.S. Environmental Protection 

Agency (EPA) and the MOE among others. 127 

Originally, the Toronto-specific airshed model 

work was used to estimate annual air levels of 

the common air pollutants, PM 10 

/PM 2.5 

, NO 2 

, SO 2 

, 

CO and VOCs, at a 2 km resolution for the entire 

City. Ozone has also been estimated but using 

satellite data. The airshed model was developed 

so that the air quality impacts associated with 

the four different sectors - point sources such as 

industrial facilities, area sources such as commercial 

and residential buildings, mobile sources 

such as cars, trucks and trains, and biogenic 

sources such as trees and gardens - could be 

viewed separately and in combination. 127 

AIRSHED MODELLING – AIR TOXICS 

The Toronto airshed model has been extended to 

examine air toxics as well. This effort began with 

a project directed at South Riverdale – Leslieville 

and the Beach neighbourhoods in Toronto. In this 

project, air modelling was used to estimate the air 

levels for the common air pollutants and all of the 

25 substances that are captured in Toronto’s new 

Reporting, Disclosure and Innovation By-law. The 

TEO indicates that for the entire city, emissions 

from adjacent municipalities, southern Ontario 

and the United States were modelled at a 1 km 

resolution using the CALMET/CALPUFF models 

plus the CMAC model. 127 

Both the TEO and Toronto Public Health indicate 

that for the targeted neighbourhoods, 30 air pollutants 

were modelled at a 100 metre resolution 

using emissions from all potential sources including 

mobile sources, residential sources, small 

area and point sources such as dry cleaners and 

autobody shops, as well as the large local and 

transboundary point sources that are included in 

the National Pollutant Release Inventory (NPRI) 

and the American Toxic Release Inventory (TRI). 127 

While this phase of the project focused on local 

air quality in these two neighbourhoods, the 

background air levels estimated for the entire City 

will be used when assessments are conducted in 

the future for other neighbourhoods in the City of 

Toronto. 127 

ASSESSING THE CUMULATIVE 

HEALTH IMPACTS ASSOCIATED WITH 

MULTIPLE POLLUTANTS & SOURCES 

Toronto Public Health assessed the cumulative 

health impacts associated with multiple air pollutants 

for two neighbourhoods in downtown 

Toronto over several years. The common air pollutants 

(ozone, PM 2.5 

, NO 2 

, CO and SO 2 

) and 25 

priority substances identified for the City’s new 

Environmental Reporting and Disclosure Bylaw 

were assessed. The 25 priority substances were 


110 

AIR QUALITY 

Figure 11: Distribution of risks associated with airborne non-carcinogencic toxics for two 

neighbourhoods in Toronto, 2011 145;146 

selected by comparing air quality data for many 

different air pollutants, that had been collected 

by Environment Canada and the Ontario MOE, 

against health benchmarks established for these 

air pollutants by organizations such as the California 

Environmental Protection Agency, the Ontario 

MOE, Environment 

Canada, and the 

New Jersey Department 

of Environmental 

Protection. 

As indicated above, 

the common air pollutants 

and priority 

substances were 

all modelled using 

emissions data directly 

available from 

sources such as 

NPRI and from estimates 

of emissions 

derived for mobile, 

residential and commercial 

sources in 

the two neighbourhoods. 

The estimated 

levels of the air 

pollutants were then 

compared against 

health benchmarks 

for each air pollutant 

individually and cumulatively with other air 

pollutants. 145;146 

For non-carcinogenic substances, the average, 

maximum and minimum hazard ratio values were 

estimated for each non-carcinogenic substance 

using the average annual air concentrations from 

the 551 receptor sites modelled in the two neighbourhoods. 

These hazard ratios were used to 

determine relative risk of each compound, and 

were then added together to create a cumulative 

hazard ratio. 

Using this approach, 

a cumulative hazard 

ratio less than 

1 indicates that the 

cumulative impact 

of exposure to all 

of the non-carcinogens 

in combination, 

does not present a 

health concern. A 

cumulative hazard 

ratio greater than 1 

indicates that the 

combined exposures 

might present a 

health concern and 

require a more detailed 

analysis. 

In the study directed 

at specific City of 

Toronto neighbourhoods, 

the cumulative 

hazard ratio was 

0.31. The conclusion 

was that the 

Reproduced with permission 

non-carcinogenic contaminants are present in 

the air at levels below levels of concern to health, 

even when the combined exposure is taken into 

account 145;146 (Figure 11). 



Figure 12: Distribution of risks associated with airborne carcinogens for two neighbourhoods in 

Toronto, 2011 145;146 

For carcinogenic substances, the average, 

maximum and minimum cancer risk values were 

estimated for each carcinogenic substance 

using the average annual air concentrations 

from the 551 receptor sites modelled in the two 

neighbourhoods. 

These cancer risk 

values were then 

added together to 

create a cumulative 

cancer risk value 

which was compared 

against a risk level of 

1 in a million excess 

cancer cases in a 

lifetime. 145;146 

In this study, the 

cumulative cancer 

risk for all of the 

carcinogenic air 

pollutants combined 

was 83 in one million. 

The study found that 

most carcinogens 

in these two 

n e i g h b o u r h o o d s 

are present at 

c o n c e n t r a t i o n s 

below the one in 

a million excess 

cancer risk benchmark. However, benzene 

and Polycyclic Aromatic Hydrocarbons (PAHs) 

exceed their annual ambient air quality criteria in 

some areas, on occasion. It also found that the 

cumulative risk from airborne carcinogens is very 

low when compared to the total incidence rate of 

cancer in Toronto 145;146 (Figure 12). 

For the common or criteria air pollutants, the 

average, maximum and minimum excess per 

capita risk for premature death values were estimated 

for each substance using the average 

annual air concentrations from the 551 receptor 

sites modelled in 

the two neighbourhoods 

(Figure 13). 

These per capita risk 

values were then 

added together to 

create a cumulative 

increased risk 

of premature death 

value. In this study, 

the cumulative per 

capita risk of premature 

death was 

8.9%. This study 

also found that 24- 

hour ambient air 

quality criteria for 

PM 10 

and NO x 

are 

exceeded on occasion 

in these neighbourhoods. 

The 

study found that the 

criteria air contaminants 

such as ozone, 

Reproduced with permission 

nitrogen dioxide 

and particulate matter, 

are responsible for the largest burden of illness 

associated with air pollution in these two 

neighbourhoods 145;146 


112 

AIR QUALITY 

Figure 13: Distribution of risks associated with criteria air pollutants for two neighbourhoods in Toronto, 

2011 145;146 Reproduced with permission 

CHEMTRAC EMISSIONS DATA FOR 

POINT SOURCES ON A FINER SCALE 

Toronto is establishing the ChemTRAC system to 

implement its Environmental Reporting and Disclosure 

Bylaw which requires that certain facilities 

report, on an annual 

basis, if they use or 

release any of the 

25 priority substances 

above certain 

thresholds. While all 

25 chemicals in Toronto’s 

ChemTRAC 

system are included 

in NPRI, Toronto’s 

ChemTRAC system 

has: removed the 

employee exemption 

which excludes facilities 

with 10 or fewer 

employees from reporting 

under NPRI; 

reduced the reporting 

mass exemption to 

about one tenth of 

the mass required 

for exemption under 

NPRI; and applies 

to the use of chemicals 

as well as to 

emission releases, while NPRI requires reporting 

on releases only. In 2010, with the first phase of 

ChemTRAC rolled out, Toronto had 272 facilities 

reporting on their use and emissions, 152 of 

which were not reporting under either NPRI or 

TRI (Toxic Release Inventory; a US based data 

source). 147 

ASSESSING AIR QUALITY IN 

HALTON 

In June 2007, Halton Regional Council adopted 

a 5-part air quality program that included airshed 

modelling, stationary air monitoring, portable 

air monitoring, 

policy development 

directed at the land 

use planning process, 

and a health 

promotion program 

directed at air 

quality and climate 

change. 148 

The airshed model 

was based on detailed 

emissions 

data for the industrial, 

transportation, 

commercial, residential 

and agricultural 

sectors as well 

as biogenic sources 

(i.e., trees and other 

natural sources). 

This Halton-specific 

model was supposed 

to be developed 

for the five common 

air pollutants 

(i.e. ozone, PM 2.5 

, NO 2 

, SO 2 

and CO) with a 2 km 

resolution using the CALMET/CALPUFF model 

adopted by the U.S. Environmental Protection 

Agency. The Regional Municipality of Halton, as 

cited by Perrotta 127 , suggests the model would 

be used to: 



• Characterize air quality across the Region; 

• Demonstrate the contribution of different 

sectors to air quality across the region; 

• Inform the broad land use and transportation 

planning processes; 

• Identify how a new major point source, such 

as a new generating station, might contribute 

to air quality across the community; 

• Inform the review of certificates of approval 

by providing better information about 

background air levels across the community; 

and 

• Inform public education and social marketing. 

STATIONARY AIR MONITORING 

STATION 

The Health Department was authorized to establish 

a stationary air monitoring station capable of 

monitoring the five common air pollutants - ozone, 

PM 2.5 

, NO 2 

, SO 2 

and CO – on a continuous basis 

in the Town of Milton in 2007. This station was 

sited in Milton in response to concerns about 

how air quality was being affected by the rapid 

growth being experienced in the town. 148 

PORTABLE AIR MONITORING 

In 2007, Halton purchased two Airpointer® portable 

air monitors capable of monitoring the five 

common air pollutants (Ozone, PM 2.5 

, NO/NO 2 

, 

SO 2 

and CO). These instruments are compact, 

relatively light-weight and capable of matching 

the detection limits and accuracy achieved with 

the air monitoring equipment used by the MOE. 

They come in their own weather-proof and climate 

controlled housing so they do not need to 

be housed in trailers or buildings like traditional 

equipment. Once power is available, the machine 

can simply be plugged in to a 120 V circuit. 148;149 

COSTS FOR AIR MONITORING & AIR 

MODELLING 

Halton Region’s air monitoring and airshed modelling 

program cost 148-150 : 

• $286,000 to purchase the two portable air 

monitors and all of the equipment for the 

stationary air monitoring station in Milton. 

This includes the equipment needed to store, 

transfer, and process air monitoring data. 

• $76,000 per year to an air monitoring firm to 

install, operate and maintain its stationary air 

monitoring station and to relocate, operate 

and maintain its two portable air monitors. For 

this fee, the firm also manages the Region’s 

air monitoring data, generates the AQHI on 

an on-going basis, and ensures that the AQHI 

readings and continuous air monitoring data 

are current for uploading on the Region’s 

website on a 24-hour basis. 

• Up to $50,000 per year for the development 

and management of the Halton-specific 

airshed model 


114 

AIR QUALITY 

ASSESSING AIR QUALITY IN THE NATIONAL CAPITAL REGION (OTTAWA) 

In 2005, there was only one MOE AQI air monitoring 

station to provide indicators for air quality 

for the National Capital Region, an area of 2,700 

square kilometres. A second MOE AQI air monitoring 

station for the NCR was granted in addition 

to the development of a pilot project that aimed 

to provide a better spatial picture of air quality 

across the area. 

In February 2007, the City of Ottawa submitted 

a proposal to GeoConnections, a Natural Resources 

Canada (NRCan) Program, to characterize 

air quality in the NCR using satellite data as 

well as air monitoring data. Air monitoring data 

were collected with mobile air monitoring stations 

provided by the Transport Canada, Environment 

Canada and the MOE, and passive monitors on 

loan from Health Canada and analyzed by Environment 

Canada. This project allowed for the 

testing and calibration of a new satellite-based 

monitoring technology and provided data to support 

research in the air quality field. 127 

The information used to generate maps included: 

• Air monitoring data from four NAPS and 

Quebec sites and the mobile monitoring units 

that were rotating between eight sites; 

• Satellite-based air quality data; 

• Meteorological data and geographic 

characteristics; and 

• Road counts and emissions data for 

stationary emission sources. 

Although the pilot project only ran for one year, 

maps were generated for an additional year. City 

staff plan on developing a database of maps for 

the purposes of: 

• Compilation of baseline data for 

environmental assessments; 

• Educational strategies to promote walking, 

cycling and public transit; 

• To inform land use planning processes (e.g. 

community design, land use compatibility); 



Figure 14: Airpointer® belonging to the City of Ottawa 151 

• To inform transportation planning (e.g. 

Transportation Master Plan); and 

• To support corporate projects such as the 

Fleet Emissions Reduction Strategy. 127 

Costs for the Project 

This pilot project was led by the Air and Energy 

team of the Environmental Sustainability Division 

of the Environmental Services Department within 

the City of Ottawa. This project cost approximately 

$340,000, with GeoConnections providing 

$149,000 in funding, Ottawa’s Public Health 

Department providing $20,000, and the Environmental 

Sustainability Division providing $10,000 

in funding, with in-kind funding provided by 

Transport Canada, Environment Canada, and the 

MOE. The Ottawa International Airport also provided 

air traffic data and financially supported the 

purchase of a roadside monitor (Airpointer®). 127 

LIMITATIONS AND 

CHALLENGES OF MEASURES, 

MODELS & DATA 

There is a clear need for more research linking 

built environment characteristics to air pollutant 

exposure. The previous sections discussed various 

measures, data sources, and models used 

to gain a better understanding of pollutant exposure 

in the built environment. The studies in the 

current literature review mainly focused on finding 

relationships between various pollutants and 

health outcomes, as well as their geographic and 

temporal distribution. Research based in Ontario 

did not provide specific recommendations about 

the air quality indexes presently used by Health 

Canada or MOE; it focused on new methods 

available for public health units to measure air 

pollutant exposure. 

Portable Air Monitors 

©Urquizo, N. 2009. Reproduced with permission. 

In 2009, Ottawa City Council authorized staff to 

move to a second phase in its project by approving 

the purchase of a second Airpointer® (Figure 

14) equipped with modules to monitor ozone, 

PM 2.5 

, and NO 2 

/NO at a cost of $90,000. 127 The 

two monitors have been used to acquire street 

level air pollution data to refine the satellite monitoring 

technology, support car free roads and 

satisfy public concerns. 

A European Space Agency sponsored project used 

this data, and incorporated health risk assessment 

modelling to demonstrate the health effects of 

pollution from traffic corridors. The resulting tool 

translates pollution levels into real health impacts 

both in monetary terms and in terms of morbidity 

and mortalit. 127 

The next steps will be to use more updated transportation 

data and more detailed health data in 

the mapping process. If funding permits, staff plan 

to incorporate wood burning emissions data into 

the mapping process (as one more local emission 

source that contributes to pollution). 127 


116 

AIR QUALITY 

Direct comparisons between studies in the literature 

review were challenging since they differed 

in the pollutants studied, the health outcomes of 

interest, models used, and populations of interest. 

While air monitoring stations can be useful 

sources of data, they are limited in determining air 

quality conditions and risk areas between neighborhoods. 

Various models have been developed 

to estimate urban pollutants more accurately. 

More sophisticated models evaluate how pollutant 

distribution is impacted by the distance from 

and density of specific built environment traits 

(e.g. roads, elevation, land-use type, etc). The 

majority of studies focused on urban environments, 

and noted the benefits and limitations of 

present assessments of pollutant exposure. The 

use of spatial models (e.g. land-use regression), 

developed for one city may not translate well to 

other urban centres. 140;141 

Epidemiology research challenges 

A majority of the epidemiology studies assessed 

correlation between specific pollutant levels and 

health outcomes. Causation from specific pollutants 

is difficult to ascertain due to the high correlation 

between pollutants. 152 Nonetheless, the 

review of literature by Brauer and colleagues 121 

identified significant evidence from Canadian 

studies relating traffic pollutants to health outcomes. 

Linking outdoor air quality to indoor air 

quality 

In order to better assess the public’s exposure 

to pollutants, further development is required 

to understand the relationship between outdoor 

air quality and indoor air quality. Behavioural 

characteristics such as the time individuals 

choose to spend outside versus inside,, is 

a factor that present models have generally not 

considered. 110;137;153;154 Exposure to air pollutants 

is commonly defined at residential addresses 

and does not consider other key areas where individuals 

may be present in a day. 155 Furthermore, 

measurements of infiltration (outdoor pollutant 

exposure entering indoor environments) can enhance 

the use of modelling and datasets in calculating 

health risks. 137;156 Both behaviour and 

infiltration measurements have the potential to 

increase the accuracy of exposure modelling and 

in the assessments of risk for acute and chronic 

health outcomes. 

Temporal limitations 

Exposure maps are commonly used for annual 

estimates, and may not take into account temporal 

changes in pollutant levels. Studies varied 

in their use of temporal information, ranging from 

pollutant readings every minute 157 to annual averages. 

120 Seasonal trends were noted for some 

pollutants as well, 110;141 and need to be considered 

when developing exposure maps. 

The temporal scale can play an important role in 

evaluating different health risks from air pollutants. 

Land use regression focuses on chronic exposure 

using annual averages to evaluate the model. 

Nonetheless, acute exposure studies can benefit 

from a detailed map, that highlights pollutant 

levels at different times of day (e.g. during rush 

hour). 



Development of key indicators for air 

pollutants in the built environment 

A specific pollutant or land variable may be used 

as a marker for predicting exposure and distribution 

of other pollutants. The most commonly 

cited example is NO 2 

, which has been used as a 

marker for vehicle/traffic emissions 129;158 and potentially 

for benzene, toluene, and SO 2 

. 141 Proximity 

to traffic corridors is commonly used to determine 

exposure to vehicle emissions and is a 

useful measure for land use planning. 120;123 More 

research is needed to better understand how 

various pollutants relate to one another as well as 

any synergistic effects. 159 

Studies have also shown the need for greater 

geographic detail on pollutant exposure in urban 

areas. This requires an accurate understanding of 

how the built environment contributes to sources 

of pollutants as well as its distribution. Determining 

exposure from air pollutants can be challenging 

as researchers and public health units try to 

capture all relevant pollutants and optimize the 

temporal and spatial detail. While traffic and industrial 

emissions are key sources of pollution, 

there are other sources of pollution that require 

more research. 160 More research is needed to 

meet these challenges in order to optimize both 

spatial and temporal detail and to address both 

acute and chronic health outcomes resulting 

from air pollutants in the built environment. Nonetheless, 

significant strides have been made in developing 

more detailed exposure estimates, with 

recent research emphasizing the role of the built 

environment. 


118 

AIR QUALITY 


AIR QUALITY 

In total, six individuals were interviewed on issues pertaining to air quality and the built environment. They 

represented federal and provincial stakeholders as well as Public Health Units involved in air quality assessments. 

Some of the information provided in the interviews overlapped with findings from the literature 

review and survey data conducted for this project. As a result the following information focuses on 

new information not found in other components of this research project. 

HIGHLIGHTS 

• In Ontario, the network of air monitoring stations is an important resource for monitoring air 

pollutants, but is not designed for evaluating air quality at the neighborhood level. 

• Key informants identified current methods used to further assess and collect data on air quality in 

the built environment including additional monitoring, modeling, and use of satellite technologies. 

o There are various air monitoring equipment tools available to better assess pollutant levels at a 

neighborhood levels and to assess the impact of the built environment 

o Previous projects by public health units highlight the value of using modeling and satellite 

technologies 

• NO and PM were identified as two key pollutants that can be used as indicators of local air 

x 2.5 

quality 

• While local emission sources may be captured from data sources such as the NPRI, smaller 

emission sources are not considered. 

• There is a vast amount of research present on pollutant gradients from traffic corridors, which 

could be used to develop indicators and measures for mobile/transportation sources in the built 

environment 

AIR QUALITY DATA FROM THE MINISTRY OF ENVIRONMENT (MOE) AND 

ENVIRONMENT CANADA 

As previously mentioned, the MOE has an established network of 40 permanent air monitoring stations 

across Ontario. The majority of the stations are located in urban areas in southern Ontario around the 

Greater Toronto/Hamilton area, and are focused on ambient air pollutant concentrations not impacted 

from local point emissions or traffic corridors. However, the National Air Pollutant Surveillance (NAPS) unit 

at Environment Canada has provided new criteria to include monitoring stations close to traffic corridors. 

Currently, MOE distinguishes between urban and rural air monitoring stations as well as roadside loca- 



tions (within 100 meters from the road) for their AQI sites. Ten stations fall into the roadside monitoring 

criteria including Windsor downtown, Hamilton downtown, Toronto downtown, Toronto East, Toronto 

North, Toronto West, Oshawa, Thunder Bay and North Bay. 

There are also five research-grade air quality stations that are not public, but are for the MOE, and other 

agencies’ academic research. Of the five research stations, three of them are permanent, and two are 

mobile. The stations are used to test equipment and for short-term studies. These stations typically monitor 

AQI pollutants, and have additional equipment to monitor organic and inorganic compounds, black 

carbon, ultrafine particle matter, and BTEX (benzene, toluene, ethyl benzene and xylene). 

These research stations are managed in collaboration with Environment Canada. Presently, Health Canada, 

MOE, and Environment Canada are working on research projects to evaluate traffic corridors and 

pollutant levels. The MOE also monitors VOCs as part of the NAPS program. These sites include Toronto 

West and Hamilton downtown. While such data can be useful to other communities with traffic corridors, 

they need additional data such as traffic volumes to determine the relationship of the built environment 

with pollutant levels. 

STRATEGIES TO PRODUCE LOCAL AIR QUALITY DATA 

There are a number of different approaches that can be used to assess air quality across a community. 

The following approaches were identified: 

• Conduct air dispersion modelling using an inventory of existing emissions and validate and fine 

tune the results with air monitoring using the MOE air monitoring stations and portable monitors; 

• Conduct monitoring with an array of electro-chemical sensors for NO that are spatially dispersed, 

add the results from the MOE/NAPS air monitoring stations, and combine the results with land 

use regression modelling tools; and, 

• Use satellite based systems, combine them with air monitoring results conducted with mobile and 

portable devices, and model the results along with sector-specific emissions estimates data such 

as traffic volume. 

MONITORING EQUIPMENT RECOMMENDED FOR ASSESSING LOCAL AIR 

QUALITY 

Key informants were questioned on potential instruments that can be utilized to better understand local 

air quality issues. Two different methods to approach air monitoring for spatial variation were noted. The 

first involves developing a regular grid of the community and trying to cover the grids as best as one can 

with a limited budget. The second option involves focusing monitoring on particular types of land uses 

and key micro-environments that are of concern, such as traffic corridors, or local small point source 


120 

AIR QUALITY 

emissions. The following instruments for air monitoring were noted by key informants: 

• Airpointers are good portable monitors that can provide data not only for NO but also particulate 

x 

matter, sulphur compounds, O 3 

, and CO. Airpointers can also be useful in measuring microenvironments 

as the instrument can placed in areas with limited space within urban communities. 

While the Airpointer equipment was recommended by most interviewees, this equipment can be 

quite expensive ($100,000). 

• Inexpensive NO / NO electro-chemical sensors (i.e. about $100 /sensor) can be used in multiple 

x 

locations with relatively little investment. These sensors could be used to measure pollutant levels 

at multiple distances and heights to identify the vertical and horizontal reach of the air pollutants 

associated with traffic corridors. These sensors can be connected wirelessly, collecting results on 

an instantaneous basis, and eventually be incorporated into modelling. 

• Passive NO and NO samplers, which are very inexpensive, can be used to collect weekly 

2 x 

air quality results with a greater spatial density, but require a link with an accredited laboratory 

to analyze the samples. One organization has been doing some studies on variations across 

communities using a combination of NAPS stations and passive NO 2 

monitors. They are 

combining these results with land use regression modelling in different communities across 

Canada. This strategy holds some promise as a method that could be used more broadly in the 

future. 

• There are no inexpensive sensors for PM . There are less expensive portable monitors that cost 

2.5 

about $500 that can be used for micro-environments or that could be used with mobile sources 

(e.g., on a police car or on city bus) to see how air quality varies across an area. 

MEASUREMENTS FOR LOCAL AIR QUALITY 

POLLUTANTS RELEVANT TO THE BUILT ENVIRONMENT 

When we asked four air quality key informants about the two of three air pollutants that could be used as 

indicators of local air quality from a built environment perspective, all four recommended: 

• NO : It is easy to measure, it varies substantially across a community, it is strongly linked to 

x 

combustion processes in buildings (furnaces) and vehicles, it is a good indicator of the fine scale 

for air quality, and it is strongly correlated with health impacts.; and 

• PM : It is a very good indicator of “dirty air”, it builds up when air stagnates, its mass varies 

2.5 

substantially across a community, it is affected by a variety of emission sources in the built 

environment including fireplaces, vehicles, industry, and fugitive dust (particulate matter from 

industry and the construction sector), and it is strongly correlated with health impacts. 

Other air pollutants identified as potential indicators of local air quality included: 

BTEX, UFP, BC, SO 2 

, PM 2.5 -10. 



MEASURES OF LOCAL POINT SOURCES 

When asked about two or three air pollutants that can be used as indicators of local point sources in a 

community, the air quality key informants had varied responses. In Toronto, where a cumulative risk assessment 

was conducted on a variety of different air pollutants from all sources, it was suggested that 

one should select the point source pollutants to be used as indicators using both, the background levels 

for air pollutants in the community derived from nearby NAPS stations, and emissions data for the point 

sources in the community. 

When key informants were asked about indirect indicators that might be used for local point sources, 

emissions data from NPRI and TRI were identified as imperfect tools that can be used to identify large 

emitters. It was noted by a few key informants that smaller facilities that are exempted from NPRI and TRI 

can be important sources of air pollution at a localized level, particularly if the air pollutants are emitted 

at ground level. This could be remedied using a a system similar to Toronto’s ChemTRAC system that 

would capture smaller facilities and could have a substantial impact on air quality at the local level. One 

key informant noted that dispersion modelling conducted for CofAs might provide information (e.g., setback 

distances) that could be used as indirect indicators for local point sources. 

Estimates of greenhouse gas emissions would be one way of using inventories as indirect indicators to 

monitor potential exposure to air pollution from point sources. Greenhouse gas and source emissions 

can be used to determine residential natural gas consumption due to residential heating. This indirect 

indicator, again, would only give a limited sense of emissions. 

MEASURES OF MOBILE SOURCES 

When we asked the air quality key informants about the two or three air pollutants that could be used as 

indicators for mobile sources, they all agreed that NO x 

(i.e., NO and NO 2 

) are the best indicators for mobile 

source because they are so strongly associated with vehicles and are fairly cheap to monitor. Other 

air pollutants identified included UFP, VOCs (i.e., BTEX), and BC. 

When we asked about indirect indicators that could be used for air quality associated with vehicles, it 

was suggested that set-back distances could be used based on modelling, traffic volume and/or fleet 

demographics. While the air quality key informants agreed that researchers are still not clear which air 

pollutants in the traffic-related air pollutant mix are responsible for negative health impacts, they agreed 

that research has demonstrated gradients for levels of air pollutants such as NO x 

and UFP beside traffic 

corridors, which are associated with traffic volume, fleet demographics and meteorology. 


122 

AIR QUALITY 

HUMAN RESOURCE CAPACITY AT THE PUBLIC HEALTH UNIT LEVEL 

In Ontario, a few municipalities have funded additional work for the development of an air quality monitoring 

program within their jurisdiction. While costs may vary for each program, key informants identified the 

human resource requirements for the development and maintenance of these programs. For two municipal 

level organizations, one full-time employee is responsible to manage the air quality program. One key 

informant noted additional in-house support may be required for data analysis. Consultants were hired for 

more technical aspects of the air quality programs, such as modeling, calibration of equipment, quality 

assurance and quality control issues for monitoring, and maintenance of air monitoring equipment 

. 




AIR QUALITY 

This section represents a summary of the survey results specific to air quality measures and data sources. 

The survey was administered to Ontario Public Health Units (PHU) in July 2012. 

Note that the number of organizations responding to each question varies due to the structure (skip pattern) 

of the survey. For instance, if a PHU indicated that they assess air quality using monitoring stations, 

then they were prompted to provide further details about specific air pollutants. Whereas a PHU that did 

not report using an air monitoring station to assess air quality was prompted to skip forward to other 

survey questions. Overall, this impacted the number of PHUs (i.e. denominator) that responded to each 

survey question. 

HIGHLIGHTS 

• 28 Ontario PHUs completed the air quality section of the survey 

• 14 (50%) PHUs assess urban air quality in Ontario 

• Most of the 14 PHUs have been assessing urban air quality for 1 to 5 years (36%) and 6 to 10 

years (36%) 

• PHUs which have been assessing air quality for greater than ten years were in larger cities, or in 

areas with industries of concern 

• Of the 14 PHUs assessing urban air quality using an index, 93% are using the Ontario Air Quality 

Index (AQI) and 50% are using the Air Quality Health Index (AQHI) 

• Of the 14 PHUs assessing urban air quality, 79% of PHUs use meteorological data from 

Environment Canada weather stations, 50% use air modelling estimates, 43% use data from 

portable monitors, 43% use emission estimates, 29% use data from air monitoring stations not 

collected by the MOE, and 7% use satellite data 

• Built environment data relevant to air quality were predominantly acquired from city, regional or 

municipal departments. For most of the built environment data collected, PHUs indicated that 

they had limited information describing the data. Less than half of PHUs indicated that the data 

was available as a spatial map. 

• Of the 28 PHUs responding to the survey, 43% said they had access to transit stops per square 

kilometre and traffic volumes, and 36% had access to modal share (commuting choice) in their 

communities. In addition, 29% said they had access to data on: vehicle kilometres traveled per 

capita in their community; proximity of populations to industrial emission sources; and residents 

and sensitive populations living in close proximity to high volume traffic corridors. 

• The most common challenges identified by PHUs included (i) human resource capacity (79%), 

(ii) data availability (61%) and (iii) financial capacity (61%) 


124 

AIR QUALITY 

The air quality survey response rate was 78% (28/36), with 28 Ontario PHUs completing the survey. Overall, 

50% (14/28) of PHUs assess air quality in urban environments in Ontario. 

Of the 14 PHUs that assess air quality, 36% have been assessing air quality for 1 to 5 years, 36% for 6 to 10 

years, and 29% for over 11 years (Figure 15). 

Figure 15: Number of years Public Health Units have been assessing urban air quality in Ontario, 2012 (n=14) 

36% 

36% 

1-5 years 

6-10 years 

11+ years 

29% 



All 14 PHUs use an index to assess air quality in urban environments. More specifically, of the 14 PHUs 

that assess urban air quality in Ontario, 93% (13 PHUs) use an Air Quality Index (AQI), 50% (6 PHUs) 

use an Air Quality Health Index (AQHI), and 14% (2 PHUs) use other indices (Figure 16). Seven of these 

PHUs are using AQI alone, 6 PHUs are using both AQI and AQHI, and 2 PHUs are using AQI, AQHI and 

another index to assess air quality. 

Figure 16: Type of index used to assess urban air quality in Ontario, 2012 (n=14) 


14 

12 

10 

8 

6 

93% 

50% 

4 

2 

14% 

0 

AQI AQHI Other 

Type of Air Quality Index 

Most of the 14 (79%) PHUs that assess urban air quality use readings for specific air pollutants from 

nearby Ministry of Environment (MOE) air monitoring stations. Most PHUs track fine particulate matter 

(PM 2.5 

) (71%), ground level ozone (O 3 

) (71%), nitrogen dioxide (NO 2 

) (71%), and sulphur dioxide (SO 2 

) 

(64%) using general MOE monitoring stations (regardless of the number of years they have been assessing 

air quality). A smaller proportion of PHUs track carbon monoxide (CO) from general MOE monitoring 

stations (Figure 17). All PHUs that assessed urban air quality for 11 or more years monitored CO levels. 

Fewer PHUs track air pollutants using MOE stations directed at local industrial sources: 29% are tracking 


126 

AIR QUALITY 

PM 2.5, 

NO 2, 

and SO 2 

while 21% are tracking O 3 

and CO in this manner. Several PHUs further commented 

on the specific air pollutants that are tracked and methods of data collection: 

• In addition to the above noted pollutants, one PHU tracks total reduced sulphur (TRS), nitric 

oxides (NO), oxides of nitrogen (NOx), and inhalable particulate matter (PM 10 

). As well, they utilize 

non-continuous air-monitoring samplers that collect data on pollutants such as total suspended 

particulates (TSP), volatile organic compounds (VOC’s), polycyclic aromatic hydrocarbons (PAH), 

and metals. 

• One PHU tracks 25 priority chemicals that are in the environment at levels that are of concern to 

public health. 

• One PHU added that they will soon be tracking Ammonia (NH ) and a suite of Volatile Organic 

3 

Compounds (VOCs). 

• One PHU further commented that they monitor the above mentioned pollutants using satellite 

data in conjunction with MOE and roadside monitors that belong to the city. 

Figure 17: Air pollutants measured by type of Ministry of Environment stations in Ontario, 2012 (n=12) 

MOE stations directed at local industrial sources 

Both General MOE and MOE stations directed at local sources 


12 

10 

8 

6 

4 

General MOE air monitoring stations 

2 

0 

PM2.5 O3 NO2 SO2 CO 

Air Pollutant 



The 14 PHUs use several types of data in their assessment of air quality including: meteorological 

data from Environment Canada weather stations (e.g. temperature, humidex, wind speed) 

(79%); air modelling estimates (e.g. dispersion modelling, land use regression) (50%); data from 

portable or mobile air monitoring equipment (43%); emission estimates (e.g. traffic, industrial, 

etc.) (43%); data from stationary air monitoring equipment not collected under the MOE (29%); 

and, data from remote sensing technologies (i.e. satellite based) (7%) (Figure 18). 

Seven PHUs use data derived from air modelling; one from northern Ontario; one from eastern 

Ontario; and five from in and around the Greater Toronto Area; and six have access to this data 

in spatial maps. Six PHUs use data collected with portable air monitoring equipment; two in 

eastern Ontario and four in the Greater Toronto Area and Hamilton area; and four indicated that 

they have spatial maps for this data. Four PHUs use data collected from stationary air monitoring 

equipment that does not belong to the MOE; three from in and around the Greater Toronto 

Area and one from northern Ontario; and three indicated that the data is available in spatial 

maps. 

Spatial mapping of data 

With the exception of meteorological data, less than half of PHUs indicated that other air quality 

data and estimates are available as a spatial map. Data were available as spatial maps for: all 

PHUs using emission estimates (6 in total); 86% of PHUs using modelling (6/7); 67% using portable 

equipment (4/6), and for the one PHU using satellite/remote sensing technologies (Ottawa). 

Of the PHUs using various methods of data collection, 43% to 83% had information describing 

the data, depending on the approach. 

Only the 4 PHUs that had assessed urban air quality for 11 or more years, had access to data 

and estimates from stationary air monitoring equipment not collected under the MOE. All 4 of 

these PHUs also used air modelling estimates to assess air quality compared to 3 PHUs with 

less experience using air modelling estimates. Overall, only one PHU had access to data from 

remote sensing technologies. 


128 

AIR QUALITY 

Figure 18: Data and estimates used to assess urban air quality in Ontario, 2012 (n=14) 

Data and estimates 

Meteorological data from 


weather stations 

Air modelling estimates 

Emission estimates 

Data from portable or mobile air 

monitoring equipment 

Data from stationary air 

monitoring equipment 

not collected under MOE 

Data from remote 

sensing technologies 

7% 

29% 

43% 

43% 

54% 

79% 

0 2 4 6 8 10 12 


Built environment data access 

All PHUs (28), regardless of whether or not they assessed urban air quality, were asked if they had access 

to the following data (Figure 19), including: 

• Number of transit stops per square kilometre (43%); 

• Volume of traffic on regional or municipal roads (43%); 

• Modal share (i.e., car, transit, cycling, walking split) in communities (36%); 

• Vehicle kilometres travelled (per capita) in communities (29%); 

• Residents and sensitive populations living within pre-determined distance from high volume roads 

(29%); and, 

• Proximity of population(s) to emission sources that report through the National Pollutant Release 

Inventory (NPRI) (29%). 



The majority of PHUs that were assessing urban air quality for 6 to 10 years and 11 or more years, had 

access to the above data. On the other hand, PHUs with 1 to 5 years of experience had limited access 

to these data. 

For most of the built environment data collected, PHUs had limited information describing the data. Less 

than half of PHUs indicated that the data are available as a spatial map. Data were mostly acquired from 

city, regional or municipal departments (e.g. Transportation department). Other commonly used data 

sources included the census to assess data such as residents and sensitive populations living within 

pre-determined distance from high volume road. 

The Census, Transportation Tomorrow Survey, Smartmoves Transportation Survey, and transportation 

departments were used by PHUs to assess vehicle kilometres travelled (per capita) in communities and 

modal share. 

Figure 19: Access to data used to assess urban air quality in Ontario, 2012 (n=28) 

Air Quality Data 

Number of transit stops 

per square kilometre 

Volume of traffic on regional 

or municipal roads 

Model share in your communities 

Vehicle kilometres travelled 

(per capita) in your communities 

Proximity of population(s) to emission 

sources that report through NPRI 

Residents and sensitive populations 

living within pre-determined distance 

from high volume 

43% 

43% 

36% 

29% 

29% 

29% 

0 2 4 6 8 10 12 14 



130 

AIR QUALITY 

Challenges 

PHUs identified major challenges their organization’s face in assessing air quality in urban environments. 

The most common challenges identified by PHUs included human resource capacity (79%), data availability 

(61%), and financial capacity (61%) (Figure 20); these challenges were most commonly reported 

regardless of the length of time a PHU had been assessing urban air quality and whether or not a PHU 

was assessing air quality. In fact, all 4 PHUs, with 11 or more years of experience in assessing urban 

air quality, identified human resource capacity as a challenge; 3 of these PHUs also identified financial 

capacity as a challenge. While all 5 PHUs with 6 to 10 years of experience, identified data availability as 

a challenge; 4 of these PHUs also identified data accessibility as a challenge. 

Several predominantly rural PHUs commented that air quality monitoring is not a priority: 

“The municipality does not assess air quality. Because it is not an issue 

here, there is little interest in tracking it.” 

“This is not a priority issue for us in a predominantly rural area.” 

“Air quality...is rarely an issue except in a couple small municipalities 

where operating pulp and paper mills or other large industrial mills are 

located very close to the residential areas. MOE has some AQ monitors 

in these circumstances. We have access to their results if required to 

follow-up on a complaint.” 



Figure 20: Challenges faced by PHUs in assessing air quality in Ontario, 2012 (n=28) 

Challenge 








Other 


4% 

11% 

46% 

39% 

36% 

29% 

61% 

61% 

79% 

0 5 10 15 20 25 


Five PHUs commented about the limited coverage offered by MOE air monitoring stations in their 

communities: 

“Limited number of MOE stations/geography covered, stations are purposely 

located away from sources of emissions (e.g. highway) and needs 

to be reflective of community exposure, outdated air quality standards 

(not reflective of current health-based evidence), lack data/methodology 

to assess cumulative air impacts, lack access to technical expertise (e.g. 

for air modelling), limited resources for assessing micro-environments.” 


132 

AIR QUALITY 

“Regarding data quality: the data from the local MOE monitoring site is 

not representative of conditions throughout the health unit area.” 

One PHU commented that there is no public health mandate from the province. While another PHU indicated 

that access to data within their own organization is challenging: 

“Our counties don’t necessarily share the same viewpoint on sharing their 

data with us. Especially spatial data as one county employs a private 

sector GIS provider that is less keen on data sharing.” 



GAP ANALYSIS: AIR QUALITY 

Insights gathered from the comprehensive literature review, key informant interviews and survey administered 

to Ontario’s Public Health Units were compiled to identify gaps between the necessary and available 

built environment data to assess urban air quality in Ontario. A gap analysis for the assessment of air 

quality in Ontario was conducted for the following measurement approaches and data sources: 

• Measurement approaches: air quality indices, monitoring individual pollutants, satellite based 

technologies, emissions estimates, modeling (Table 11). 

• Data sources from Census, Ministry of the Environment, National Pollutant Release Inventory, Ontario 

Ministry of Transportation, and local road data (Table 12). 

OVERALL FINDINGS OF GAP ANALYSIS 

MEASUREMENT APPROACHES 

Challenges in assessing air quality 

• The project noted the following challenges in assessing air quality in the built environment: 

o The movement of pollutants is influenced by various meteorological factors such as wind 

speed and direction, atmospheric stability and mixing, temperature, etc. 

o Pollutants differ in their physical and chemical properties which in turn impacts the spatial 

pattern of pollutants in the built environment and the pollutant lifecycle. This is further 

complicated by meteorological conditions as well as interactions between pollutants. 

o Urban structures impact air quality through altering flow patterns of pollutants, the creation 

of emissions, and exposure to the public. 

Pollutant sources 

• In addition to transportation and industrial sources, the research noted specific c sources of pollut- 

ants in the built environment including: 

o Wood fireplaces (PM 

2.5 

, BC, and UFP) 

o Traffic emissions (BC, UFP, NO ) x 

o Fugitive dust from industry and construction sites (PM ) 2.5 


134 

AIR QUALITY 

Pollution gradients 

• There is a significant amount of research on pollutant gradients from traffic corridors from academic 

and government agencies that can be further developed recommend separation distances 

between sources of emissions and sensitive populations. 

Modelling approaches 

• There are various modelling approaches that can be taken to better capture pollutant levels in the 

built environment. The appropriate approach will depend on the needs for PHU units, financial 

costs, and data availability for each modelling approach. 

Monitoring approaches 

• The literature review and key informant interviews noted that additional sampling of pollutants 

can be done through a grid system or by focusing on specific areas of interest (e.g. small point 

sources, traffic corridors, sensitive populations). 

o To gain additional data on air quality in the built environment, various instruments were identified 

by the key informant interviews including passive NO/ NO x 

samplers, electrochemical 

NO/ NO x 

sensors, and Airpointer. Passive samplers and electrochemical sensors are noted 

as more affordable instruments, but sensors for PM 2.5 

are more expensive. 

Remote sensing 

• Remote sensing, especially satellite based technologies, is a noted measurement approach with 

potential for better assessing pollutant distribution at a neighborhood level. More work is needed 

in assessing its application and feasibility in Ontario for looking at the built environment and air 

quality. 

Emission estimates 

• The use of emissions data from NPRI is limited as it does not capture smaller sources of emissions 

existing in urban areas. 

Cumulative impacts 

• Currently, MOE permitting does not take into consideration cumulative impacts or traffic 


AIR QUALITY 135 155 135 

PHU capacity 

• There are limited resources available for measuring pollutant levels (e.g. modelling, satellite technologies, 

and portable monitoring) by PHUs. PHUs with more resources have generally been in 

higher density urban areas around the southern Ontario. 

o For a small number of rural PHUs, the concern for air quality is not that high. 

DATA SETS AND SOURCES 

Pollutants of concern 

• While the five common pollutants (ground level ozone, PM , NO , CO, and SO ) are those which 

2.5 2 2 

have been associated with the greatest burden of illness, other pollutants of concern in the built 

environment have been noted such as UFP and BC. 

Neighbourhood-level air quality data 

• While the air quality indexes used in Ontario are a valuable resource for monitoring air pollution for 

PHUs, this data source is not designed to capture pollutant levels at a neighborhood level. 

Traffic data 

• PHUs report their traffic data were based on municipal data sets. While provincial data sets on 

traffic volumes exist from MTO, the data is limited to provincial highways. 

Census data 

• The use of census data was noted in looking at traffic related information as well as population 

information for modelling 

Local air quality data needs 

• PHUs have indicated that they need information on local air quality to: 

o Assess the impacts of air quality on health within their health units; 

o Inform land use and transportation planning decisions for the protection of human health; 

o Assess the impacts of public policy on air quality and human health; and 

o Monitor local air quality over time to assess the impact of policies and programs on human 

health as it is impacted by local air quality. 


136 

AIR QUALITY 


review, key informant interviews, survey and GIS metadata). 

AIR QUALITY 

Table 11: Measurement approaches and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata). 

Measurement 

Approach 

Description Inputs Current Use (SU) Measures of interest 

Theoretical 

Operationalization 

Ontario 

Demand / Prioritization/ Desirability 

Challenges 

Link between 

Measurement 

Approaches 

Individual 

Pollutants 

Direct 

Measurement of ambient air 

concentration of common 

pollutants (e.g. NO 2 , NO, SO 2 , 

CO, TSR, O 3 , PM 2.5 , PM 10 , etc.) 

• Individual pollutant data 

• Protocol for determining 

averaging time period (LR) 

Of 11 PHUs that assess specific 

pollutants (SU) 

• 83% assess ozone, fine 

particulate matter, and nitrogen 

dioxide 

• 75% assess sulfur dioxide 

• 50% assess carbon monoxide 

Of the 11 PHUs assessing individual 

pollutants, 4 PHUs look at individual 

pollutants from air stations directed 

at industrial sources 

Of 14 PHUs, that assess air quality 

6 use portable air monitoring 

equipment 

Hundreds of pollutants have been identified and 

are monitored at the federal level. A select few 

criteria air pollutants are monitored provincially 

due to their links to health and knowledge of 

sources. 

Sources of pollutants in the built environment 

include: (LR) 

• Wood fireplaces (PM , BC, and UFP 

2.5 

• Traffic emissions (BC, UFP, NO ) x 

• Fugitive dust from industry and construction 

sites (PM 2.5 ) 

The development of pollutant measures needs 

further research to better understand the 

relationship with the built environment. 

Spatial level 

• geocoded reference 

for monitoring stations 

(regional level coverage) 

(GM) 

Certain monitoring stations 

do not measure all criteria air 

contaminants (LR) 

NO 2 is noted as a good measure of traffic pollution that is cost 

effective and easy to monitor. Nonetheless more research is 

needed in developing NO 2 as an indicator (KI, SU) 

Black Carbon, UFP, PM 2.5 , PM 10 and NO x have been identified as 

pollutant measures of health. BTEX is a useful built environment 

pollutant to measure for its use in combustion fuels and in 

solvents, but is costly to measure (KII, LR). 

Mobile Equipment: 

Portable instruments can provide better detail of air pollution 

through high spatial resolution, 

selection of specific target areas, and by focusing on vulnerable 

populations or household level (LR, KII) 

Inexpensive NO/ NO x sensors, passive NO x /NO 2 samplers, or 

Airpointer equipment were noted as useful monitoring tools for 

traffic related pollutants (KII,LR) 

Pollutant sources and their impact on air quality differs for 

each region/PHU (SU, LR) 

Additional air monitoring stations would be expensive and 

resource intensive. Specific pollutants require expensive 

monitoring equipment, For instance, PM 2.5 sensors start at 

$500 CAD (KII, LR) 


Costs for sampling and equipment may be high (LR) 

Limited sampling my not capture seasonal trends (LR) 

Required as data inputs 

for developing air quality 

indexes and models 

Air Quality 

Indexes 

(composite 

measures) 

Indexes are a direct measure of 

air quality based on individual or 

multiple pollutants (LR) 

• The Ontario Air Quality Index 

(AQI) involves data on the 

following pollutants O 3 , NO 2 , 

PM 2.5 , SO 2 , CO, and TRS (LR) 

• The Air Quality Health Index 

(AQHI) involves data on three 

pollutants: PM 2.5 , O 3 , NO 2 

(LR) 

• Pollutant data which can be 

collected through mobile 

or permanent monitoring 

equipment (LR) 

• Formula calculation (e.g. 

weighting for specific 

pollutant) (LR) 


averaging time period and 

threshold levels (LR) 

• Use of epidemiological data to 

determine health based levels 

of interest (LR) 

Of 14 PHUs assessing air quality 

(SU): 

• 93% use the AQI 

• 53% use the AQHI 

While many air quality indexes have been 

developed internationally, only 2 indexes are 

currently used in Ontario (LR, SU) 

Indexes were developed at the provincial and 

federal level. 

Spatial level 




(GM) 

• AQI: available across 

Ontario 

• AQHI: available in Southern 

Ontario 

Built environment attributes that could be targeted include 

fireplaces and old diesel vehicles (KII, LR) 

Historical and temporal data available at no cost. 

Information provided with high temporal resolution 

Availability of monitors throughout province 

No cost for data 

The Ontario AQI may not capture health risks (LR) 

The current composite measures do not capture the built 

environment and its relationship to the distribution of 

pollutants. (KII, LR) 

While O 3 and PM 2.5 are monitored at most stations across 

Ontario, not all key air pollutants are monitored by the MOE 

(LR) 

The concern for air quality issues is lower in less populated 

cities situated far from urban areas. However, concern 

remains for specific sources from industrial emissions (KII, 

SU) 

Noted as an important 

component for 

PHUs evaluating 

meteorological data. 

A composite measure of 

individual pollutant data 

Remote 

Sensing 

Indirect measure 

Satellite images provide detail 

on air pollutant levels. Resolution 

varies from 250 m to 320 km. 

Images taken from every 1-7 

days. 

Satellites with information on 

pollutants. 

• OMI, TES, CALIOP & GOME 

(for NO 2 and hydrocarbons) 

• MODIS, OMI, PARASOL, & 

MISR (for PM) 

• MOPITT, AIRS, PARASOL, 

IASI& SCIAMACHY (for CO) 

• GOME, SCIAMACHY, OMI, 

TES, IASI, & GOME-2 (for O 3 ) 

Only used in one PHU in 

conjunction with air monitoring and 

modelling 

Spatial level 

• need further evaluation of 

what maps or datasets 

exist that can be applied to 

Ontario (LR) 

• Need further development of satellite systems and recognition 

as a promising area (KII, LR) 

• Potential for greater spatial coverage (LR, KII) 

Technical expertise required to create air quality maps from 

satellite images (LR, KII) 

Spatial resolution can vary significantly from 250m to 320km 

(LR) 

Some satellites do not capture air pollutant concentrations at 

a resolution relevant to the built environment (LR) 

Cost for imagery (LR) 

Quality of images can vary due to cloud cover and other 

weather conditions (LR) 

Limitations in determining ground level estimates from upper 

atmosphere estimates (LR) 

Relevant to pollutant 

monitoring 

Emissions 

Estimates 

Emissions data can be used 

to estimate air pollutant 

concentrations and as an indirect 

indicator of potential exposure 

The data required for emission 

estimates were cited from 

various sources: 

• NPRI 

• TURI 

• MPAC 

• Municipal traffic volumes 

• Emissions factors formula 

to convert traffic volumes to 

emission rates 

• Fleet demographics to 

determine composition of 

traffic sources 

• Geocoding of point source and 

area emissions 

Of 14 PHUs assessing air quality, 6 

use emissions estimates 

Of the 28 PHUs that responded to 

the survey, 43% have access to 

traffic volume data from regional or 

municipal roads 

Traffic counts can be an indirect measure of 

traffic related air pollution and may translate to 

other traffic corridors with similar counts (KII) 

Can be incorporated 

into more detailed 

assessments of air 

quality in the built 

Greater need for smaller emissions sources for community environment and 

based modelling (KII) 

with meteorological 

Provides useful information on sources of air pollutants in the built 

environment (LR, KII) 

Potential estimation errors in pollutant levels and geographic modelling 

distribution (LR) 

Important in proximity 

May not require air monitoring data depending on purpose of air 

quality assessment (LR) 

Requires dispersion modelling and meteorological 

measures 

information for ambient air pollutant estimates (LR) 

Need more detailed 

Can be applied to point sources and traffic (LR) 

Spatial detail not sufficient for assessment at a municipal evaluation to determine 

level (LR) 

geographic range of 

impact of emissions. 

This is done with the 

support of modelling 

approaches 

Modelling 


• Used to provide additional 

spatial and temporal detail of 

air pollutant levels 

Modelling techniques used to 

capture pollutant information in 

urban areas included: 

• Land Use Regression 

• Dispersion Modelling 

• Kriging 

• Basic proximity or 

interpolation models (LR) 

Inputs vary by model, but can 

include: 

• Emissions data 

• Pollutant level measurements 

• Meteorological data (e.g. wind 

speed, direction, temperature) 

• GIS map files related to land 

use and surface characteristics 

54% of PHUs assessing air quality 

use some form of modelling 

Identified as being in previous and/ 

or current use in select few PHUs 

(e.g. Toronto, Halton, Ottawa) 

Modelling approaches are an important tool for 

assessing neighbourhood level differences in 

pollutants. 

Common variables considered include: 

• Road networks (e.g. road length and traffic 

density) 

• Land use 


• Topography 

• Meteorological conditions 

Various spatial models have 

been developed at the 

municipal level (LR) 

• Present work from Health 

Canada is using NO 2 

measurements and Land 

Use Regression modelling 

for various communities 

across Canada, and may 

have potential to be applied 

more broadly (KII) 

Modelling is noted as an important or necessary tool to gain 

greater spatial detail of air pollution distribution in urban 

communities (KII, SU) 

Can incorporate modelling approaches with built environment 

indicators (LR) 

Technical and human resources needed for modelling (LR, 

KII) 

Higher cost for more detailed maps (LR) 

• Important tool for 

mapping pollutant 

distribution from 

emission estimates 

and air monitoring 

Proximity 


Distance of sensitive populations 

• Distance of major roadways 

to areas with high pollutant levels 

and emitting facilities from 

Determination of a safe distance sensitive populations 

from major roadways or other 

sources of air pollutants 

29% of 28 PHUs that answered 

the survey have access to data 

regarding 

• Proximity of population to 

emission sources reported in 

NPRI 

• Proximity of population to high 

traffic volume roads 

While much research has shown how pollutant 

levels decline from a source, the setback 

distance for areas of high risk is not conclusive 

and depends on meteorological conditions, 

traffic volumes, and built environment attributes. 

Care should be taken in determining safe 

heights as well as distance (SU). 

• Need to better understand which pollutants are appropriate 

indicator for proximity to traffic sources (KII, LR) 

• Need to balance need for setback distances with designing 

compact communities (LR) 

• Still need to clarify which pollutants are causing most 

harm near high traffic areas (KII, SU) 

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 


Table 12: Data sources for Air Quality and Policy-Relevant Information as Identified in the Literature 

Review, Key Informant Interviews, Survey and GIS Metadata Exercise. 

AIR QUALITY 

Table 12: Data sources for Air Quality and Policy-Relevant Information as Identified in the Literature Review, Key Informant Interviews, Survey and GIS Metadata Exercise. 

Data Source Topic Area Utility in Outcomes Current Use in Ontario PHUs Desirability Cost Challenges 

93% of PHUs assessing air 

quality use the AQI (SU) 

Monitoring stations are geocoded, but no standard exists for 

geographic area represented by monitoring station 

Ministry of the Environment 

Air Quality Information System 

(AQUIS) 

Air quality 

Incorporated into National Air 

Pollutant Surveillance program 

(NAPS) Environment Canada Data 

One of two air quality indexes applicable to Ontario 

is produced by the MOE (Air Quality Index) (LR) 

Data on individual pollutants (PM 2.5 , NO 2 , NO, NO x , 

O 3 , SO 2 , CO) (LR) 




particulate matter, and 



Depends on local air quality 

issues 

Three PHUs which noted 

challenges in assessing air 

quality, stated that demand 

for monitoring pollutants was 

not a high priority (SU) 

Free (GM) 

Limited relevance to small populations. Strong focus on high 

population areas in Southern and South-eastern Ontario (KI, LR) 

Need for more air monitoring stations and for modelling to provide 

greater spatial detail to current data (SU, LR, KI) 

The density of monitoring stations can differ, between municipalities 

(KI, LR, SU) 

• 50% evaluate carbon 

monoxide 

Of PHUs assessing air quality 5 PHUs commented on the limited 

spatial representation of the present air monitoring stations 

Canada Environmental 

Sustainability Indicators 

Hourly data on O 3 , PM 2.5 , SO 2 , NO 2 , VOCs Was not evaluated Free Two year lag period for data. 

National Pollutant Release 

Inventory 

Air Quality 

Emissions data for Ontario prior 

to 2005 available from Ministry of 

Environment Historical OnAIR Data 

2001-2004 (MOE) 

The NPRI collects data on pollutant emissions from 

industrial and non-industrial sources 

Data available at a facility level, as well as 

aggregated data at the provincial level. 

The provincial information includes estimates for 17 

air pollutants organized by sector. 

In addition to industrial sources, NPRI estimates 

emissions from various other sectors such as 

transportation, agriculture, landfills, natural sources, 

etc. 

Of 28 PHUs who responded to 

the survey, 29% had access to 

data on proximity of population 

to emission sources reported 

through NPRI (SU) 

For large emission sources 

which meet the NPRI 

requirements for reporting, 

the data is of good quality 

and useful for PHU (KII) 

Provides maps on emissions 

density at low resolution (LR) 

Data available for specific 

pollutants 

Useful for traffic or point 

sources pollution (LR) 

Free 

Estimates only available for annual emission levels (LR) 

Limited information on day to day variation between communities 

(LR, KII) 

A lag period of 1 or 2 years exists for the data (LR) 

Many commercial and industrial sources within a city can be 

exempt from reporting to NPRI (LR, KII) 

Ontario Ministry of 

Transportation 

Air Quality (Traffic volume) 

Ontario Ministry of Transportation annual publication 

on traffic volume and accident rates for provincial 

highways in Ontario 

No PHU reported the use or 

access to MTO data. However, 

such information may be obtained 

indirectly through the municipal 

transportation and planning 

departments. 

A useful resource for 

high traffic volume 

highways situated in urban 

communities 

Free 

To translate into emission estimates, need information on fleet 

demographics (LR) 

Focus strictly on provincial highways (GM) 

Local Road Data 

(collected by 

municipalities /regions) 

Network Analysis (to measure 

proximity or to model population 

affected) 

Traffic data (e.g. vehicle kilometres 

travelled, modal share, traffic 

volume) 

12 of 28 PHUs that answered the 

survey stated they have access to 

municipal level traffic data (SU). 

Traffic related information is 

commonly gathered from 

municipal sources (SU). 

Free 

May require geocoding for GIS use. 

Coverage and update frequency will vary – data quality assessment 

is required. 

Dependent on municipalities to compile a local inventory of road 

and traffic data. 



4EXTREME HEAT 

MEASURES & DATA USED IN THE ASSESSMENT OF EXTREME HEAT 

BACKGROUND LITERATURE REVIEW KEY INFORMANT INTERVIEWS SUMMARY OF SURVEY RESULTS GAP ANALYSIS 



139 

MEASURES & DATA USED IN THE ASSESSMENT OF EXTREME HEAT 

CHAPTER 4: 

EXTREME HEAT 

BACKGROUND 

EXTREME HEAT AND HEALTH 

Urban heat islands (UHIs), the phenomenon where cities are warmer than their surrounding non-urban 

areas, exacerbate the intensity of heat waves and are thus considerably more problematic for urban 

dwellers when compared to rural residents. Heat related mortality peaks in urban centres, 161 where the 

effects of extreme heat events can be manifested both directly and indirectly. 

Exposure to high temperatures over prolonged periods can result in heat-related morbidity and mortality. 

Heat exhaustion is the most widespread heat-related illness 7 and occurs as a result of extreme salt 

and water loss. Associated symptoms include heavy sweating, weakness, dizziness, nausea, headache, 

diarrhoea and muscle cramps. 162 Heat exhaustion can exacerbate conditions such as cardiovascular illnesses, 

diabetes and respiratory diseases, 163 and if untreated, heat exhaustion can result in heat stroke. 

Heat stroke is the most serious heat-related illness, and occurs when core-body temperature exceeds 


140 

EXTREME HEAT 

40°C/140°F. This results in loss of consciousness and delirium, convulsions, coma or reduced mental 

ability. 7;164 Classic heat stroke is most common in vulnerable groups such as children, seniors, and those 

with chronic illness. 162 Excessive perspiration does not generally happen with classic heat stroke. Exertional 

heat stroke is accompanied by perspiration, and is more common in otherwise healthy individuals 

during strenuous exercise or occupational exposure during heat events. 36 More common but less serious, 

heat-related illnesses include fainting, heat cramps and heat rash. Fainting occurs due to lower blood 

pressure as a result of excessive water loss due to perspiration. Heat cramps are caused by salt imbalance. 

Heat rash is a result of clogged sweat glands. 162 

Deaths during a heat event more commonly occur as a result of indirect effects exacerbated by heat 

stress and include: cardiovascular disease, cerebrovascular accidents and vascular lesions, respiratory 

diseases, and increased susceptibility to infectious diseases. 165;166 Secondary pollutants including ground 

level ozone (O 3 

) and smog are extremely harmful to human health, and are also elevated during heat 

events. 103 This leads to acute and chronic damage to the respiratory system, of particular concern for 

individuals with cardiovascular or pulmonary disease. 23 

VULNERABLE POPULATIONS & EXTREME HEAT 

Two unique sets of factors contribute to vulnerability to extreme heat; individual and community factors. 

Individual factors determine how a person will respond to extreme heat and can include health status, 

degree of social isolation, air conditioner availability, income level, place of work characteristics, place 

of residence characteristics, and personal behavioral characteristics such as levels of strenuous activity, 

clothing type etc. 36 Previous long-term exposure to heat results in individual acclimatization to extreme 

heat. Short-term exposure also plays a part, and it is for this reason that the most deadly extreme 

heat events occur early in the summer before the body’s systems can acclimatize. Older adults and 

young children are less capable of this acclimatization. 7;29;30 Because individual factors are such important 

indicators of vulnerability to extreme heat, estimating the impact of specific temperature increments 

on population health is challenging. However, research has shown that cardiovascular and respiratory 

deaths increase considerably with each 1°C increase in temperature above a specified critical value 

specific to a location. 167;168 

The second group of factors that contribute to vulnerability are community factors. These factors include 

the local climate, community design and availability of services for the community to cope with extreme 

heat. 


EXTREME HEAT 141 

EXTREME HEAT EVENTS IN CANADA 

Extreme heat events have increased significantly in Canada 

over the last century. 169 While these events are not a natural 

hazard of major concern to most Canadians, 170 the resultant 

death tolls are substantially higher than those of other natural 

phenomena. Precise numbers of deaths attributed to heat 

events vary greatly due to inconsistencies in reporting. 170 

Large numbers of deaths in Canada are suspected to be 

heat-related but are coded as heart attack, stroke etc., 7 likely 

resulting in an underestimate of heat related morbidity and 

mortality. 

CLIMATE CHANGE, URBANIZATION AND 

DEMOGRAPHIC CHANGE 

Heat-related mortality could double in Southern Ontario by 

the 2050s, while rates of mortality due to worsening air pollution 

compounded by these temperature increases, could 

increase by about 15-25%. 21 Many researchers confirm 

this, predicting that heat-related illnesses and deaths will increase 

in the future, worsened due to the urban heat island 

effect related to increasing urbanization and Canada’s aging 

population. 6;7 Using modest assumptions about greenhouse 

gas (GHG) reductions, temperature increases for Ontario are 

in the range of 2.5°C to 3.7°C compared to 1961-1990 and 

lower emissions reductions raise this projection to between 

3.0°C and 4.0°C. 21 Researchers project that the number of 

days with average temperatures above 30˚C will increase nationally, 

particularly in the Windsor-Quebec corridor. 22 The intensity 

and duration of extreme heat events in Canada are also 

expected to rise. 24;25 Over eighty percent of Canadians reside 

in urban areas, and this number is increasing. 26 Canada’s 

population is aging, with the number of seniors expected to 

double by 2035, 27;28 dramatically increasing the size of Canada’s 

population vulnerable to extreme heat events. 

Prolonged heat events are not 

uncommon in Canada. In 1936, 

for 12 days in July, temperatures 

reached 44˚C contributing to 

the 1,180 deaths, 458 in Ontario 

alone. 171;172 In 2005, Toronto had 

41 days with temperatures above 

30˚C and Montreal had 23 days. 171 

Due to geographic variability in 

climatic thresholds, what constitutes 

a heat wave is defined neither 

regionally nor nationally. Environment 

Canada defines a meteorological 

heat wave as three or 

more consecutive days where the 

maximum temperature is greater 

than or equal to 32°C. Environment 

Canada may also issue 

extreme heat warnings when air 

temperature exceeds 30°C and 

the humidex exceeds 40. Municipalities 

and public health units 

also issue extreme heat warnings, 

however the criteria for 

issuing these warnings varies by 

jurisdiction. 173 


142 

EXTREME HEAT 

EXTREME HEAT AND THE BUILT ENVIRONMENT 

The urban heat island (UHI) effect is the phenomenon where cities are warmer than their surrounding 

non-urban areas. The ability of an urban area to generate a heat island is the observed temperature 

difference between urban and non-urban areas, or ΔT u 

- r 

. 174 Oke 175 developed the following equation to 

summarize the radiant energy balance in a city; Q* + Q F 

= Q H 

+ Q E 

+ ∆Q S 

+ ∆Q A 

, where Q* is net allwave 

radiation (the difference between incoming and out-going short and long wave radiation captured 

by an area); Q F 

is the sum of all anthropogenic heat generated by the buildings, vehicles and people; Q H 

and Q E 

represent the heat fluxes in the area; and ∆Q S 

and ∆Q A 

are the sensible heat storage and net heat 

advection respectively. 176 The temperature difference can be attributed to the replacement of vegetated 

surfaces and surface waters with urban construction materials that retain heat, and anthropogenic heat 

generation processes such as industry or vehicle use. 

The urban form results in increased surface areas. The three dimensional geometry of a city leads to the 

absorption and storage of solar energy through the vertical faces of buildings. These geometric features 

restrict horizontal air flow due to frictional drag created by the urban surface resulting in reduced windspeeds, 

and less convective cooling, trapping radiation in urban ‘’canyons’’. Reduced sky-view factors 

concurrently reduce opportunities for the re-emission of radiant heat into the atmosphere, producing air 

temperatures up to 5˚C warmer than surrounding non-urban areas. 177 

The UHI is influenced by thermal properties including albedo and emissivity. The albedo of a surface is 

defined by Taha 178 as its hemispherically and wavelength-integrated reflectivity. Measured between 0 and 

1.0, a perfectly reflective body would have an albedo of 1 while a perfectly black body would have an albedo 

of 0. Cities have large areas covered by low albedo materials (concrete, asphalt and dark coloured 

roofs) and average albedos tend to be low, generally no more than 0.2. 178 Vancouver, BC, for example 

has an average albedo of 0.13-0.15 179 and Hamilton, Ontario an albedo of 0.12-0.13. 180 Solar energy 

that is not reflected by a surface is absorbed. Emissivity determines the ability of a surface to release heat 

which was not reflected and was instead absorbed. Also measured on a scale of 0 (no emittance) to 1 

(full emittance), a surface material with a high emissivity remains cooler when exposed to solar energy, 

because it emits more energy per unit area and comes to thermal equilibrium at a lower temperature. 181 

Anthropogenic heating adds an additional supply of energy, where heating and cooling systems, machinery 

and vehicles, electricity use and fossil fuel combustion all contribute to the anthropogenic component 

of a city’s energy balance equation. The resultant air pollution from these activities leads to increased 

long wave radiation being created, and less re-emission of this energy, more commonly known as the 

‘’greenhouse effect‘’. Taha 178 notes that anthropogenic heat sources in the urban core can contribute 

2-3°C to the ambient air temperature in that area. 

ASSESSING EXTREME HEAT 

Various techniques to quantify the relationship between land use and surface temperature have been employed. 

Thermal remote sensing is the most common method. 182-185 Landsat Thematic Mapper (TM) and 



Enhanced Thematic Mapper (ETM) are the most commonly used and widely available remotely sensed 

imagery. Operated by the National Aeronautics and Space Administration (NASA), Band 6 of this satellite 

captures thermal infrared radiation (heat) at resolutions of 60m (ETM) and 120m every 16 days. Images 

are captured at approximately 10:00 local time. Together with the 120m pixel size and thermal anisotropy 

(i.e. what is actually being captured at a single angle by the sensor), this time of acquisition has been 

noted as a limitation of Landsat imagery (given the sun is not at its apex). 

Lo and Quattrochi 186 used Landsat imagery to examine relationships between land-use / land-cover 

change and urban heat islands over the metropolitan area of Atlanta, Georgia. Heat islands were concentrated 

in the inner city urban area, spreading out along highways where higher density urban uses were 

focused. Between 1987 and 1997, several of these heat islands were found to have merged into one 

larger concentration due to urbanization. These findings were confirmed for Toronto by Maloley. 182 . Lower 

density suburban development around the city’s peri-urban fringe was found to have lower surface temperatures 

than downtown areas. Rinner and Hussain 184 also examined the land-use – surface temperature 

relationships, finding commercial and resource/industrial land-uses were hottest, averaging 29.1˚C 

as compared to parks/recreational uses (25.1˚C), water bodies (23.1˚C) and residential and open areas 

(27.5˚C). This study also found larger single use zones positively correlated with surface temperature for 

both commercial (r=0.405) and resource/industrial uses (r=0.259) at the 0.01 level of significance. 

Applying a similar methodology to the Pearl River Delta in Guangdong, China, Chen and colleagues 187 

used Landsat 5 and Landsat 7 imagery were used to examine the relationship between urban heat 

islands and land use/cover change. They found that expansions of built-up areas were the main contributor 

to the regional temperature rise witnessed between 1990 and 2000. 187 Weng and Yang 183 examined 

relationships between land-use and heat in the same study area, finding that in 1989, industrial areas 

were hottest, followed by commercial, residential and rural. At this time, hotspots were scattered in an 

archipelago, but by 1997, many of the “islands” in the archipelago had merged into one heat island due 

to sprawling urban development. 

It should be noted that these cited studies sought to examine the relationship between surface temperature 

and land-use, however the relationship between surface and air temperature is more complex. 

Air temperature is measured between 1.5m and 2m above ground. Much of the heat transfer at these 

heights is not from the immediate surface below, but from surfaces several hundred metres away on a 

horizontal plane, with wind as a vector. Over more uniform surfaces, with lower wind speeds, surface 

temperatures inform air temperatures more accurately. 182 Urban areas rarely have such uniform surfaces. 

Although air temperature measurements inform human comfort levels and are desirable, it would be an 

onerous if not impossible task to establish in-situ air temperature monitoring sites at a density that would 

provide adequate coverages over urban areas. Modelling has been attempted using several methodologies 

including regression functions, objective hysteresis models and through high resolution urban surface 

models. However, these processes have limited successes. 185 While weather stations provide this 

data to a high degree of accuracy, the spatial resolutions obtained through such sparse station densities 

are not adequate to infer measurements over larger geographies such as municipalities. 


144 

EXTREME HEAT 


The purpose of this literature review is to capture research 

on the linkages between the built environment and extreme 

heat. The literature search identified a number of measures 

and approaches to assess extreme heat. The measurement 

approaches identified in the literature review have been categorized 

as follows: (i) Measures using meteorological variables; 

(ii) Measures of the built environment; (iii) Measures of 

community vulnerability. 

MEASURES USING 

METEOROLOGICAL VARIABLES 

MEASURES USING INDIVIDUAL 


Meteorological variables are commonly used to measure 

extreme heat. Variables identified included temperature, 

relative humidity, dew point temperature, wind speed and 

direction and solar radiation. These variables are usually directly 

measured and monitored at local airports and weather 

stations by government agencies. 

Environment Canada’s Meteorological Service of Canada 

(Weather Office) provides public meteorological information 

and weather forecasts for Ontario and across the country. 

Table 13 lists select meteorological variables available hourly 

from Environment Canada weather stations. Environment 

Canada weather stations are known to have the most complete 

data and include a large number of meteorological 

parameters. 23 However, these weather stations are often located 

at airports and are limited in number for a given jurisdiction. 

Meteorological information is therefore not always 

representative of conditions experienced by a community 

since they can be located some distance from the majority 

of the population. The nature of the airport environment 

can also influence readings. Airports may not be reflective 

of the community due to reduced tree cover or vegetation, 

reduced building density and increased pavement area. 



Table 13: Select hourly meteorological variables available from Environment Canada weather stations 188 

Measure 

Data format 

Temperature °C 

Dew point temperature °C 

Relative humidity % 

Wind speed 

Wind direction 

Total cloud cover 

(not available from aviation automatic weather stations) 

km/h 

Ten’s of degrees 

Clear (0 tenths) 

Mainly clear (1 to 4 tenths) 

Mostly cloudy (5 to 9 tenths) 

Cloudy (10 tenths) 

COMPOSITE MEASURES USING 


Based on the literature review, there are a number 

of meteorological variables that are also used 

in composite indices to determine environmental 

exposure to heat. Table 14 lists composite measures 

identified in the review of the literature. Although 

the application of these indices varies by 

user and purpose, common indices included the 

heat index (apparent temperature) and humidex. 

These indices both combine the effects of relative 

humidity and temperature to describe how hot 

weather feels to the average person. The humidex 

is used in Canada, whereas the heat index (apparent 

temperature) is used in the United States. 

These indices are pre-calculated and reported to 

the public as current weather conditions and in 

forecasts. 

on an ongoing basis due to their complex nature, 

specialized data requirements and availability. The 

utility of some indices may also be limited due to 

requirements for specialized equipment. For example, 

Environmental Heat Monitoring Systems 

(EHMS) are used by Health Canada for research 

projects across the country. EHMS units capture 

all four factors that comprise heat: ambient 

temperature; radiant solar load; humidity; and air 

velocity. EHMS units also calculate the Wet Bulb 

Globe Temperature (WBGT) based on the four 

factors. Without these monitors, calculating the 

WGBT would not be possible through standard 

Environment Canada data. Ontario PHUs would 

either have to purchase specialized equipment or 

partner with organizations who already own the 

equipment to be able to collect information on 

solar load and the WBGT. 

Many of the other indices identified in the review 

of the literature were used in academic studies 

and may not be realistic to calculate or collect 


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EXTREME HEAT 

Table 14: Composite measures of heat using meteorological variables, identified in the literature review 

Composite 

Heat Index/ 

Apparent 

Temperature 

Humidex 

Spatial Synoptic 

Classification 

Thom Index/ 

Discomfort Index 

Relative Stress 

Index 

Wet Bulb Globe 

Temperature 

Index 

Heat Exposure 

Index 

Net Effective 

Temperature/ 

Thermal Index 

Heat Load Index 

Heat Stress 

Index 

Perceived 

Temperature 

Human Thermal 

Comfort Index 

Description 

“The Heat Index or the ’Apparent Temperature’ is an accurate measure of how hot it really 

189(para. 7) 

feels when the relative humidity is added to the actual air temperature.” 

The humidex “describes how hot, humid weather feels to the average person. The 

humidex combines the temperature and humidity into one number to reflect the perceived 

190(para. 24) 

temperature.” 

“A series of meteorological variables (air temperature, dew point temperature, atmospheric 

pressure, wind speed and direction) are categorized into air masses using the spatial 

synoptic approach. Oppressive air masses are identified as those associated with an 

excess in mortality. The latter is calculated as the difference between observed and daily 

baseline values.” 191(p.2258) 

“Thom Index, also known as Discomfort Index, is useful to evaluate how current 

temperature and relative humidity can affect the sultriness or the discomfort 

sensation”. 192(p.648) 

The Relative Stress Index “calculates the ratio of sweat evaporation needed for comfort to 

193(para. 18) 

the amount of evaporation possible given ambient atmospheric conditions.” 

“The wet bulb globe temperature (WBGT) is calculated using a formula that takes into 

account air temperature, speed of air movement, radiant heat from hot objects, sunshine 

194(para. 4) 

and body cooling due to sweat evaporation.” 

A Poisson model is used to relate excess mortality during a heat wave to the 

meteorological indicators of a given geography. 195 

Net Effective Temperature (NET) “reflects the common perception that people tend to feel 

more stressful on hot and humid days with calm winds in summer. In summer, the higher 

the NET, the more stressful is the weather.” 196(p.401) 

Heat Load Index is “based on the Man–ENvironment heat EXchange model (MENEX 

model), [where] meteorological conditions as well as physiological parameters establish the 

heat gain or loss of the human body by radiation, convection, conduction, evaporation and 

respiration.” 197(p.154) 

Heat Stress Index is a relative comfort index. “The HSI has the ability to evaluate daily mean 

relative stress values for each first-order weather station in the United States. It includes 

important variables not used in previous indices, such as consideration of the impact of 

consecutive days of stressful weather, daily cloud cover (as a surrogate for solar load), and 

accumulation of heat through the day.” 198(p.504) 

Perceived Temperature (PT) is “derived from a heat budget model as a predictor for heatrelated 

health impact assessments. PT refers to a reference environment in which the 

perception of cold and/or heat would be the same as under the actual conditions.” 199(p.2) 

The Human Thermal Comfort Index (HTCI) “is based on energy balance of a hypothetical 

person given the weather data from a site and the site’s surrounding solar and thermal 

radiative environmental fluxes.” 200(p.2852) 



MEASURES OF THE BUILT ENVIRONMENT 

Evidence suggests that extreme heat is related 

to various built environment features and characteristics. 

In terms of assessing extreme heat, 

built environment measures differ from those 

using meteorological data, since they are indirect 

in nature. Built environment measures used in 

the assessment of extreme heat were identified 

in the literature review and can be categorized 

by community and residential characteristics, as 

well as natural and artificial surfaces (Table 15). 

Table 15: Examples of built environment measures for the assessment of extreme heat by category 

Community characteristics Natural & artificial surfaces Residential characteristics 

• Sprawl index 

• Density 

• Proximity 

• Land use mix 

• Green space 

• Open space 

• Non-green space 

• Soil Adjusted Vegetation Index 

(SAVI) 

• Single family detached homes 

• Homes without air conditioning 

• Swimming pools 

• Dwellings in high rise buildings 

REMOTE SENSING TECHNOLOGIES 

Environmental exposures to extreme heat can 

specifically be assessed by indirect monitoring 

through remote sensing technologies. Remote 

sensing is a way to acquire information about the 

Earth’s surface without being directly in contact 

with it. It involves processing and analyzing information 

from reflected or emitted energy. 201 Satellites 

are common platforms for remote sensing 

technology and this data can be used to quantify 

a number of measures that describe environmental 

exposures to extreme heat (Table 16). 

Thermal satellite imagery depicting land surface 

temperatures is commonly used to assess the 

urban heat island effect. Urban heat islands and 

hot spots can also be assessed by analyzing land 

cover and more specifically, vegetative cover and 

impervious surfaces (surfaces which water cannot 

penetrate). 

Vegetation is important in determining heat exposure 

because it absorbs solar energy and 

cools the air during the evaporation process. 202 

By contrast, impervious surfaces such as paved 

roads, concrete sidewalks and parking lots tend 

to absorb solar radiation which is released as 

heat into the surrounding environment. 202 Therefore 

the proportion of land use with vegetative 

cover compared to impervious surfaces can play 

an important role in the presence of urban heat 

islands. 


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EXTREME HEAT 

Table 16: Examples of remotely sensed measures of the built environment used to assess extreme heat, 

as identified in the literature review 

Measure 

Surface Temperatures 

Urban Heat Islands 

Land Cover 

Impervious surface 

Vegetation 

Bidirectional Reflectance Distribution Function 

Source 

Aqua - MODIS 

Landsat (5 TM & 7 ETM+) 

Terra – Aster 

Quickbird 

Landsat 5 TM 

MODIS 

Quickbird 

Landsat 

Infrared aerial imagery 

Landsat 7 ETM+ 

IKONOS 

Quickbird 

Aqua - MODIS 

Infrared aerial imagery 

Landsat (5 TM & 7 ETM+) 

Terra – Aster 

MODIS 

The quality and quantity of satellite data also varies 

by source. Properties of select satellites and 

sensors are outlined in Table 17. 203 For example, 

different satellites and sensors vary in terms of 

spatial resolution. This can limit the ability to precisely 

determine local level attributes. Another 

distinguishing feature of satellite data is the temporal 

resolution. This refers to the amount of time 

required to obtain an image at the exact same 

area for a second time. 204 Therefore, a given location 

could have access to coarse data daily 

through the MODIS sensor (TERRA/AQUA satellite) 

or have finer resolution data every 16 days 

with the Landsat satellite. 

It is also important to note that parameters such 

as land surface temperature do not capture, and 

therefore may underestimate indoor temperatures 

and only reflect data at one point in time. 205 Another 

limitation that should be considered is thermal 

anisotropy, 206 that is, what is actually being 

captured by the satellite-mounted sensor. Three 



Table 17: Select satellites and their sensors used in heat-related health studies 203(p.722)± 

Satellite 

Sensor 

Spatial Resolution 

(in metres) 

Temporal Resolution 

(in days) 

Landsat TM (5) 

TM Multispectral 30 16 

TM Thermal 120 16 

Landsat ETM+ (7) 

ETM+ Multispectral 30 16 

ETM+ Thermal 60 16 

TERRA ASTER 15 – 90 Variable (4 -16) 

TERRA/AQUA MODIS 250 - 1000 Twice Daily 

±© Wiley Publishing, 2011 Reproduced with permission. 

dimensional structures like buildings are viewed 

at a single angle by the satellite; therefore thermal 

properties of lateral walls are not captured. The 

satellite-mounted sensor only captures rooftop 

and street surface temperatures. 

Despite these limitations, remotely sensed image 

acquisition remains the best available tool for 

identifying and estimating heat islands and their 

intensities because of their complete coverage. 

Overall, these are common measurement approaches 

used in research-focused literature. 

Their use may be limited due to accessibility 

issues, associated costs (acquisition or processing) 

and the requirement for expertise to collect 

and analyze the data. GIS expertise in particular 

can be a requirement for the use of remotely 

sensed data. 


150 

EXTREME HEAT 

MEASURES OF COMMUNITY 

VULNERABILITY 

The final category explores community vulnerability 

to extreme heat. These measures combine 

environmental exposure to extreme heat, 

as well as individual and community vulnerability 

to determine overall risk. Although measures of 

community vulnerability may integrate measures 

previously described (e.g. land surface temperature), 

they also use data sets which speak to 

socio-economic and demographic variables of 

the populations of interest. Table 18 describes 

examples of community vulnerability measures 

related to extreme heat. Examples of socio-economic 

and demographic variables used to assess 

community vulnerability to heat include: 

• Education 

• Race 

• Age 

• Crime 

• Income 

• Social Isolation 

Toronto’s Heat Vulnerability Index combines exposure 

and sensitivity to heat to create a vulnerability 

index. The index uses GIS to identify community 

vulnerability to heat on a spatial scale. 

The exposure component of the index uses land 

surface temperature data derived from thermal 

imagery combined with other exposure variables 

from various data sources. 

Examples of exposure variables and their sources 

include 213 : 

• Surface temperature - Natural Resources 

Canada 

• Access to green space - City of Toronto, 

Forestry and Recreation 

• Percentage of tree canopy coverage by 

census tract - City of Toronto, Urban Forestry 

division 

• Percentage of dwelling units that are in high 

rises (5+storeys) - Statistics Canada 

• Percentage of all dwelling units that are renter 

high rise dwellings constructed before 1986 - 

Statistics Canada Census 

• Population density in persons/km2 over net 

area - Statistics Canada Census 

These measures require a variety of data sources, 

some of which vary in terms of their frequency 

and availability across Ontario. For example, 

many demographic and socio-economic variables 

are based on the Canadian census which 

occurs every 5 years. In order to advance the use 

of composite indices that incorporate a number 

of variables and sources, data availability issues 

must be reconciled. These measures would also 

require the expertise to develop the methodology, 

use satellite imagery, and create and analyze data 

on a spatial scale. It has also been suggested that 

the use of a composite index may over simplify a 

problem and is prone to misinterpretation. 208 



Table 18: Measures of community vulnerability to extreme heat, as identified in the literature review 


EXTREME HEAT 

Measure Data Component Location Applied 

Hazard layer 

• Satellite image of near surface air temperature 

Composite Heat 

Human vulnerability layer 

Vulnerability 

• Under 5 or over 65 years of age 

Index 207 

• Living on a low income 

• Being over 65 and living alone 

Exposure index (40%) 

• Mean surface temperature 

• Green space coverage 

• Accessibility to green space 

• Dwelling units in high rise buildings 

• Renter dwellings in older high rises 


Sensitivity index (60%) 

Heat Vulnerability • Children age =50% of income on housing 

• Recent immigrants (within 5 years or less) 

• Racialized groups 

• Emergency visits: respiratory or circulatory disease 

• Vulnerable seniors 

Coping resources 

• Social ties index 

• % air conditioned 

• % swimming pools 

• Roof reflectivity (% asphalt, % tile, % wood, % other) 

Population 

• Median income, % in poverty 

• Less than high school, College graduate 

Human Thermal 

• % minority 

Comfort Index 200 

• Median age 

• % ages 5 and under 

• % ages 65 and over 

Thermal environment 

• Distance from city center (km) 

• Population/km2 

• % open space 

• Vegetation abundance (SAVI) 

Land surface temperature 

Heat related mortality 

Vulnerability variables 

• Hispanic population 

• Black population 

• Asian population 

Risk from 

• Native American population 

extreme heat 209 

• Other race population 

• Age 65 and over 

• Age 65 and over living below poverty 

• Age 5 and under 

• Persons living below poverty 

• Low education (less than high school education) 

Social/environmental 

• % below the poverty line 

• % race other than white 

• % with less than a high school diploma 

• % of non-green space 

Social isolation 

Heat Vulnerability 

• % that live alone 

Index (HVI) 210;211 

• %> 65 years of age that live alone 

Air conditioning prevalence 

• % homes without central air conditioning 

• % homes with no air conditioning of any kind 

Preexisting health conditions 

• % population diagnosed with diabetes 

Hazard Layer: (50%) 

• Urban Heat Island magnitude (LST) 

Heat Health Exposure Layer (25%): 

Risk 212 • Household type (Experian MOSAIC data) 

Vulnerability Layer (25%): 

• Made up of vulnerable types extracted from the exposure layer (e.g. Old, Ill, Density, Flats) 

Thermal information (Landsat) 

Vulnerability indicators 

• Regional deprivation index 2006 


SUPREME 

• Age 

system 205 

• Housing conditions 

• Foreign language population 

• Dissemination areas inside heat Islands 

• Landed immigrants since 2001 

Montreal 

Toronto 

Phoenix, AZ 

Philadelphia, PA 

California, Massachusetts 

New Mexico 

Oregon 

Washington 

Birmingham, UK 

Quebec 

An Environmental Scan of Built Environment Data Related to Walkability & Environmental Click to Exposures open in Urban the Ontario full table. 


152 

EXTREME HEAT 


EXTREME HEAT 

Five key informant interviews were conducted to explore built environment data in Ontario related to 

the assessment of extreme heat. Key informants representation included public health, academia and 

federal agencies. Key informant interview questions focused on three specific data sources relevant to 

assessing heat in Ontario: Environment Canada’s weather data; Landsat imagery; and, syndromic surveillance 

systems. 

HIGHLIGHTS 

• Environment Canada weather station data may have limited usefulness in the assessment of 

cross-community heat variation 

• Temporal analysis using Environment Canada weather station data may also be limited depending 

on location and station history 

• Environment Canada weather data is free and undergoes a quality control process 

• Urban climate models that simulate weather conditions and estimate spatial variability in heat can 

be used to identify hot areas within a region 

• Surface temperatures can be used by PHUs as a basic indicator of relative hot and cool areas 

and neighbourhoods; however, a moderate level of expertise is required to interpret thermal 

imagery. For example, when using thermal imagery, there are benefits to normalizing several 

single shot images into an average single image 

• The historical Landsat data archive is completely open with no privacy restrictions 

• Heat data complimented and analyzed in conjunction with health outcome data (e.g. morbidity), 

provides a better approach for heat alert and response systems 

• A syndromic surveillance system has the capacity to monitor heat-related illnesses and Wet Bulb 

Globe Temperature (a composite measure of heat) 

• Locations for environmental heat sensors can reflect a variety of land uses in both large and small 

urban areas, as well as rural communities 

• Currently, data from the syndromic surveillance system and sensors is not available publicly 

• The need for more data was identified as a limitation and should be addressed to increase 

coverage and support evidence-informed decision making 



ENVIRONMENT CANADA WEATHER DATA 

Currently, Environment Canada maintains 110 weather monitoring stations across Ontario. These stations 

collect various weather-related data including ambient temperature, relative humidity, wind speed 

and direction (at 10m), pressure, precipitation rate and amount, and snow depth. Environment Canada 

also provides supplementary measures, including soil temperature and solar radiation at specific sites. 

One key informant suggested that: 

“[The] Meteorological Service of Canada (MSC) is an invaluable resource 

for climatological and weather data in Canada.” 

Temperature data is provided on an hourly basis, as well as maximum and minimum temperatures over 

each hourly period. Environment Canada offers its weather monitoring data feed online and is updated 

hourly. 

Weather data on the Environment Canada website is free but can be limited by incomplete datasets. 

Although weather stations are distributed across Ontario, the number of stations is limited. Not all communities 

are covered by stations and, therefore, may not have weather data specifically for their location. 

Moreover, some stations are not currently active or have been inactive for long periods of time. Therefore, 

temporal analysis using Environment Canada weather station data may be limited depending on location 

and station history. 

Environment Canada provides a limited amount of free metadata. Detailed metadata can be made available 

by Environment Canada through formal requests; however, data from a specific site, such as observer 

name, wind report related to a specific event, etc., have privacy restrictions and are not accessible. 

Weather data undergoes a quality control process. Hourly and daily data are subject to a manual quality 

assurance process. Temperature data is collected by a Campbell Scientific 44212EC temperature probe 

that is accurate to ±0.1°C. It can also measure air, soil, and water temperature in the -50° to +50°C temperature 

range. Humidity data is collected by a Campbell Scientific Vaisala HMP45C, with an accuracy of 

±2% over 10-90% Relative Humidity (RH) and ±3% over 90-100% RH. 

Weather station data may be limited in their usefulness to assess variation in heat across a community. 

Meteorological variables can vary substantially over short distances and may be influenced by natural 

and built environment features. Therefore, data from a weather station may not accurately reflect heat 

and humidity at a locale away from the station itself. One key informant provided advice on interpreting 

weather station temperature data and extrapolating it across a wider geography: 


154 

EXTREME HEAT 

“This would be best answered on a case by case basis, because of 

the location of a weather measuring site in relation to the locale of the 

initial request. For example: Temperature taken at Pearson International 

Airport may not be representative of downtown Toronto due to lake 

effect. Ottawa International Airport may not be representative of the City 

of Ottawa due to distance of the airport from the downtown core.” 

LANDSAT IMAGERY 

Natural Resources Canada (NRCan) has collected Landsat satellite imagery since 1975 and began collecting 

thermal imagery in the early 1980s. NRCan has images available from 1984 to 2011. Landsat 

has worldwide coverage and the images are collected in 16-day intervals at a resolution of 120 square 

meters. The thermal infrared band can be used to determine surface temperatures with a thermal accuracy 

of ±2°C. 

It was suggested that surface temperatures can be used by PHUs as a basic indicator of relative hot and 

cool areas and neighbourhoods. One key informant suggested that Landsat imagery: 

“… is probably the best indicator available of hot spots and areas of 

extreme heat, and a relative measure of where your coolest areas are 

also.” 

It was also suggested that there are benefits to normalizing several single shot images into an average 

single image. One key informant explained: 

“A single-shot of a single event is generally not representative of that 

week or that summer so it is better to have multiple dates and a 

normalization to capture average meteorological conditions.” 



This information could be used to target planning measures or cooling centres. However, the relationship 

between air and surface temperatures was identified as a limitation. One key informant explained that 

although air and surface temperature are closely related, they do not have a “direct, one-to-one-relationship”. 

There is also limited research on determining air temperature from surface temperature. One key 

informant mentioned that air temperature is difficult to characterize and that: 

“To measure air temperature at this scale [120 square metres], or even 

every kilometre, becomes expensive and complicated.” 

In addition, surface temperatures do not infer indoor temperatures. One key informant noted this as a 

limitation: 

“You can’t measure inside buildings, the surface temperature on the 

outside might be high, but inside it might be air-conditioned or 

well-insulated.” 

It was noted that a moderate level of expertise is required to interpret such imagery. Expertise may also 

be required to use calibration data to convert pixel values into degrees Celsius. 

One key informant explained: 

“One must be able to interpret what the measurements actually mean, 

noting that we are dealing with surface temperatures instead of 

air temperatures.” 

The historical Landsat data archive is completely open with no privacy restrictions. It is freely available 

online through a number of data centres. Data that has not been processed or geometrically corrected 

can also be requested. 

When asked about other sources of imagery, key informants noted MODIS and airborne photography. 

MODIS satellite data has a coarser resolution of one square kilometre. Airborne photography entails flying 

airborne passes over a given location. This can be customized by picking certain times and multiple 

acquisitions of different data sets, however it can be very expensive. 


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EXTREME HEAT 

SYNDROMIC SURVEILLANCE SYSTEMS 

The syndromic surveillance system is a pilot project conducted by Health Canada, Queen’s University 

and select public health units in Ontario. This system has been adapted to have the capacity to monitor 

heat related illness, as well as real time weather conditions. 

Early detection of heat-related illness and death through real time surveillance can be an integral component 

of a heat alert and response plan. One key informant highlighted the importance of identifying weather 

conditions that could affect vulnerable populations and increase heat-related deaths and illness: 

“Historical climate and health data can be used to understand the 

relationship between heat-related adverse outcomes but also to 

support the establishment of the community-based heat triggers.” 

Wet Bulb Globe Temperature (WBGT) is monitored through a network of 12 Environmental Heat Monitoring 

Systems (EHMS) across southeastern Ontario. In order to calculate the WBGT, the sensors measure 

temperature, humidity, wind and radiant heat. 

Key informants indicated that the accuracy of the sensors is provided by the manufacturer. Accuracy 

levels are provided below: 

• Temperature and globe temperature: ±5°C 

• Relative humidity: ±5% 

• Air probe: ±0.1 meters per second plus 4% of the measured value 

• All measures calculated together, expanded measurement uncertainty: ±1.1°C 

Sensor locations were identified to include both large and small built up areas and were evaluated based 

on space requirements, availability of partners and safety. The final locations reflected a variety of land 

uses in both large and small urban areas, as well as rural communities. Key informants provided insight 

on sensor location siting: 

“Sensors were distributed in urban settings and mainly away from the 

lake to adequately reflect any heat umbrella effects within an urban 

setting.” 



“In an ideal situation, sites would have been selected using a randomized 

point-selection process, but this would have neglected the requirement 

for a safe site.” 

Currently, data is not publicly available as the syndromic surveillance project is still in a pilot phase operating 

under research ethics board approval. Since data is limited to internal purposes, it has not yet been 

necessary to produce a metadata file; however, the sensors do provide a standardized data pocket as a 

text file. Key informants also highlighted the importance and need for data interpretation expertise, especially 

if data were to be made available to others. 

Key informants explained how this data could be used: 

“…to verify the Wet Bulb Globe Temperature and associated health 

outcomes, comparing it to our current method of declaring heat alerts 

and heat emergencies” 

“…[to] better inform urban planners and political stakeholders so that we 

can modify the built environment and encourage municipal and policy 

decision makers to start looking at how we make our built environment 

cooler.” 

Overall, in order to address the lack of data related to spatial variability of heat, one key informant suggested 

developing and using urban climate models to simulate weather conditions. Another suggested: 

“…. a limited number of units [EHMS] could be strategically installed over 

a region or a major urban centre to assess the spatial variability of heat. 

This obviously needs to be combined with other sources of vulnerability 

information such as the level of deprivation in the community, the size 

and design of the urban centre, the location of cooling centers or the 

effect of urban heat islands that exacerbate heat exposure.” 


158 

EXTREME HEAT 

Key informants highlighted the need for more data to increase coverage and support evidence-informed 

decision making: 

“More sensors are required to give a more complete spatial coverage 

and a more accurate picture of the relationship between heat and the 

built environment.” 

“Right now we do not have the data to be able to scientifically say when 

to modify your behaviour and when to react to a heat event.” 




EXTREME HEAT 

This section represents a summary of the built environment survey results specific to extreme heat measures 

and data sources. The survey was administered to Ontario Public Health Units (PHU) in July 2012. 

Please note that the number of organizations responding to each question varies due to the structure 

(i.e. skip pattern) of the survey. For instance, if PHUs indicated that they assess extreme heat using a 

model, then they were prompted to provide further details about the specific components of the model. 

Whereas PHUs that reported not using a model to assess extreme heat were prompted to skip forward 

to other survey questions. Overall, this impacted the number of PHUs (i.e. denominator) that responded 

to each survey question. 

HIGHLIGHTS 

• 29 Ontario PHUs completed the extreme heat section of the survey 

• 62% of PHUs surveyed assess extreme heat in urban environments in Ontario 

• Of the PHUs that assess extreme heat: 

o Most have been assessing extreme heat for 1 to 5 years (44%) and 6 to 10 years (33%) 

o Most reported using temperature (89%) and the humidex (89%) to assess extreme heat 

o One PHU uses a Heat Health Alert System to determine when a heat alert or extreme heat 

alert is declared. This Heat Health Alert System is based on a spatial synoptic classification 

system that compares forecasted data to historical meteorological conditions associated 

with increased mortality in their jurisdiction. 

o Only 2 PHUs identified using models to predict extreme heat events and/or health impacts 

related to extreme heat. 

• Most PHUs have access to meteorological data (79%) 

• Although 52% of PHUs identified having access to built environment data , very few PHUs 

specifically identified having access to data on: canopy cover; age stratification of the urban forest 

stock; urban sprawl index; average unit size for each land use type; building density data; 

data on building age; surface reflectivity /albedo by land use; and surface emissivity by land use 

• Only 2 PHUs identified having access to thermal imagery 

• One PHU also developed heat-related vulnerability indices to support heat response initiatives 

and climate change adaptation planning. 

• Almost half of PHUs (48%) reported using demographic data such as age, gender, income, and 

language to identify populations that are more vulnerable to extreme heat 

• The most common challenges identified by PHUs included (i) human resource capacity (79%), 

(ii) data availability (55%), and (iii) financial capacity (48%) 


160 

EXTREME HEAT 

The extreme heat survey response rate was 81% (29/36), with 29 Ontario PHUs completing the survey. 

Overall, 62% (18/29) of PHUs assess extreme heat in urban environments in Ontario. 

Of the 18 PHUs that assess extreme heat, 6% have been assessing extreme heat for less than one year, 

44% for 1 to 5 years, 33% for 6 to 10 years, and 17% for over 11 years (Figure 21). 

Figure 21: 

Number of years Public Health Units have been assessing extreme heat in Ontario, 

2012 (n=18) 

17% 

6% 


All 29 PHUs were asked about data accessibility related to extreme heat. They reported greater access 

to meteorological data (79%) compared to built environment data (52%) and thermal imagery (7%) (Table 

19). Most of the 8 PHUs (75%) who do not assess extreme heat in urban environments also reported 

having access to meteorological data. 

Of the 9 PHUs with 6 to 10 years and 11 or more years of experience in assessing extreme heat, a higher 

proportion (89% versus 22%) had access to built environment data compared to PHUs with less than 

one and 1 to 5 years of experience. 

Table 19: Accessibility of data used to assess extreme heat in Ontario, 2012 (n=29) 

Data Type No. of PHUs (%) 

Meteorological data 23 (79%) 

Built environment data 15 (52%) 

Thermal Imagery 2 (7%) 

METEOROLOGICAL DATA 

Most of the 29 PHUs accessed meteorological data through Environment Canada monitoring stations 

(91%; 21 PHUs). Three (13%) of these PHUs identified other temporary/mobile monitoring stations and 

one (4%) PHU identified other permanent monitoring stations as sources of data. Even most PHUs that 

were not assessing extreme heat identified having access to data from Environment Canada monitoring 

stations (63%; 5 PHUs). 

If Environment Canada monitoring stations were not being used to gain access to meteorological data, 

then PHUs were asked to identify the ownership of monitoring stations and data, as well as the specific 

variables measured from other data sources. Three PHUs identified Health Canada as owning the monitoring 

stations and data and reported the following variables being measured: globe temperature, wet 

bulb temperature, dry bulb temperature, air velocity, humidity, and solar radiation. One PHU identified the 

Weather Network as a source of heat and humidity information to measure the humidex. 

Of the 18 PHUs that assess extreme heat, most reported using temperature (89%) and the humidex 

(89%) to assess extreme heat, while four (22%) PHUs reported using wind speed to assess extreme 

heat. 


162 

EXTREME HEAT 

Respondents identified other meteorological data that was being used to assess extreme heat: 

• Air quality was used as one criterion (or trigger) in the issuing of a Heat Alert (i.e. smog advisory 

combined with humidex of 36°C or higher) 

• Tracking the UV index and Air Quality Index (AQI) 

• Weather Network heat and humidex readings provided hourly 

• Modeling the duration of heat 

One PHU stated that they monitor a Heat Health Alert System to determine when the Medical Officer of 

Health should declare a Heat or Extreme Heat Alert. Using spatial synoptic classification, the Heat Health 

Alert System compares forecast data to historical meteorological conditions which have in the past, lead 

to increased mortality in their respective jurisdiction. Forecast data includes weather conditions such as 

temperature, dew point, humidity, cloud cover, wind speed and direction. The system also considers the 

number of consecutive days oppressive conditions occurred. 

BUILT ENVIRONMENT DATA 

When asked about access to specific built environment measures, very few PHUs reported having access 

to specific built environment measures (as they relate to extreme heat) as follows: 

• Canopy cover 

• Age stratification of the urban forest stock 

• Urban sprawl index 

• Average unit size for each land use type 

• Building density data 

• Data on building age 

• Surface reflectivity/albedo by land use 

• Surface emissivity by land use 

None of the PHUs reported using any of the built environment measures to assess extreme heat. Two 

PHUs indicated that the following built environment measures related to extreme heat were in development: 

canopy cover; age stratification of the urban forest stock. One PHU indicated that data on building 

age was in development. 



THERMAL IMAGERY 

Two PHUs reported having access to thermal imagery data (Landsat and/or Infrared) to assess extreme 

heat. They gain access to thermal imagery data through federal, regional, local governments, or direct 

requests for information. 

EXTREME HEAT MODELS 

Two PHUs that assess extreme heat identified using models to predict extreme heat events and/or health 

impacts related to extreme heat. 

One PHU reported using an Environment Canada model for temperature/humidex projections. The inputs 

required are confirmed by local sensors in urban/rural settings which monitor Wet Bulb Globe Temperature 

(WBGT). While secondary systems monitor visits to energy departments, as well as admissions 

to hospital for heat-related illness syndromes. 

The other PHU reported that they developed two heat-related vulnerability indices that can be mapped 

spatially for their respective health jurisdiction. One index is for the general population, and the other is 

for seniors. Each composite index is composed of indicators for exposure and sensitivity to heat and is 

mapped at the census tract level. The intent is to enable community heat vulnerability to be visualized 

on a map, so that the most vulnerable areas of the city can be identified. The approach was developed 

to support planning and short-term response to hot weather, and long-term climate change adaptation 

efforts in the City. The heat vulnerability maps can also be used by a variety of stakeholders who are 

involved in heat response or long-term climate change adaptation planning.. 

The data inputs for the above mentioned model are as follows: 

Socio-demographic and health/physiological risk factors 

• Elderly population 

• Chronic respiratory and cardiovascular diseases 

• Mobility restrictions 

• Cognitive impairment 

• Infants and young children 

• Socially isolated persons (widowed, divorced; homeless) 

• Low-income households 

• Marginalized groups, new immigrants 

• Not English-speaking 


164 

EXTREME HEAT 

Behavioural Risk Factors 

• Workers engaged in heavy labour 

• People engaged in sport activities and recreational exertion 

• Lack of hydration 

Risk Factors Relating to the Physical, Built, and Social Environment 

• Urban heat island, local heat islands (e.g. industrialized areas) 

• Lack of tree canopy, green spaces 

• Lack of public shelters (cool places) 

• High-density dwellings without air conditioning 

• Lack of social infrastructure, service gaps 

• Living in high-crime rate areas 

DEMOGRAPHICS 

Almost half of the 29 PHUs (48%) who responded to the survey (whether they assessed extreme heat in 

urban environments or not) used demographic data to identify populations more vulnerable to extreme 

heat. For PHUs with more experience in assessing extreme heat (6 to 10 years; 11 or more years), a 

higher proportion used demographic data to identify vulnerable populations. 

Fourteen of the PHUs that assess extreme heat reported using demographic data such as age, gender, 

income, and language to identify populations that are more vulnerable to extreme heat (Figure 22). Age 

was used more than any of the other demographic indicators to identify vulnerable populations. 

Other demographic data used to identify vulnerable populations varied and included occupation, level 

of education, socio-demographic and health/physiological risk factors, immigration status, and type of 

dwelling (e.g. dwellings without air conditioning). One PHU indicated using a ‘Self Registered – Vulnerable 

Population Registry’ to identify populations that are more vulnerable to extreme heat. 



Figure 22: Demographics used to identify populations more vulnerable to extreme heat in Ontario, 2012 (n=14) 


16 

14 

12 

10 

8 

6 

100% 

64% 

43% 

4 

2 

0 

21% 14% 

Age Income Other Language Gender 

Demographics 

Demographic Variables 

CHALLENGES 

A total of 29 PHUs identified major challenges their organizations face in assessing extreme heat in urban 

environments. The most common challenges identified by PHUs included human resource capacity 

(79%), data availability (55%), and financial capacity (48%) (Figure 23). 

Most PHUs, regardless of the number of years they had been assessing extreme heat, identified human 

resource capacity and data availability as challenges in assessing extreme heat. Those PHUs that were 

not assessing extreme heat at the time of the survey also identified human resource capacity as their 

biggest challenge in assessing extreme heat. 


166 

EXTREME HEAT 

Figure 23: Challenges faced by PHUs in assessing extreme heat in Ontario, 2012 (n=29) 

Challenge 








Other 


14% 

10% 

31% 

31% 

28% 

28% 

55% 

48% 

79% 

0 5 10 15 20 25 


Several PHUs provided additional comments related to the challenges they face, as follows: 

“The municipality demonstrates little interest in assessing extreme heat, 

as it is not a major issue here.” 

“Not a priority issue for us in a predominantly rural area.” 

“The one issue we have is that the weather data is from the closest 

weather station which is about 30 minutes North of our location. We 

do subscribe to Environment Canada weather alert emails that provide 

relevant forecast conditions for our County.” 



One particular PHU brought forward several challenges they faced, including the following: 

“Lack standards/consistency for assessing heat (e.g. GIS methodologies, 

analysis, community thresholds); 

Limited access/availability of meteorological data at community level; 

Inconsistent methods use to assess heat in neighbouring PHUs; 

Lack access to models with high resolution (e.g. ability to forecast heat 

exposure at street level/micro-environments).” 


168 

EXTREME HEAT 

GAP ANALYSIS: EXTREME HEAT 

Insights gathered from the literature review, key informant interviews and survey administered to Ontario 

Public Health Units were compiled to identify gaps between the necessary and available data for the assessment 

of extreme heat in urban Ontario. The gap analysis for the assessment of extreme heat was 

conducted for the following measures and data sources: 

• Measurement categories include meteorological data, composite heat measures (using 

meteorological data), urban climate modelling, built environment data, and community 

vulnerability. 

• Data sources and sets include: Health Canada, Meteorological Service of Canada (Environment 

Canada), Natural Resources Canada, First Base Solutions, Statistics Canada and municipalities. 

OVERALL FINDINGS OF GAP ANALYSIS 

The overall findings from the extreme heat gap analysis, including gaps identified for measures, methodologies 

and data sources, are presented below (Tables 20 and 21). 

Measures 

• There are many varying measures of the built environment used to assess extreme heat 

• Many PHUs use meteorological data to assess heat and mostly report on temperature and 

humidex 

• Temporal analysis may be limited by the availability of local meteorological data 

• Theoretically, composite measures better reflect the relationship between multiple variables 

identified as important components of heat 

• Individual meteorological variables are limited in their interpretation in relation to extreme heat and 

health (as heat is comprised of 4 factors: temperature, humidity, wind and solar load) 

• Meteorological data is an important data set for composite measures and models of extreme 

heat, and is also used in assessing air quality 

• Some measures have complex data requirements and may not be appropriate for PHUs to 

assess heat exposure 

• Composite measures that incorporate the four components of heat would theoretically provide a 

more accurate depiction of exposure and better predict potential health impacts 

• A small number of PHUs are using models and Wet Bulb Globe Temperature. These 

measurement methods may require additional technical, human and financial resources. 



• Remotely sensed built environment data such as satellite-based thermal imagery has the potential 

for complete coverage, however it only represents one point in time and requires a level of 

expertise 

• While some PHUs have access to built environment data, most PHUs are not currently using built 

environment data to assess exposure to extreme heat 

• Many built environment measures used to assess exposure to extreme heat could also be used in 

the assessment of air quality and walkability 

• Many PHUs currently use age to identify populations more vulnerable to heat 

• There are a variety of social, built environment and demographic variables that could be combined 

with exposure variables to produce a measure of community vulnerability to heat 

• Community heat-vulnerability indices are not common in PHUs and it is likely unreasonable that 

each PHU undertake this work 

• Other private organizations or academia may be collecting relevant local data for their own 

purposes, however it may not be consistent or suitable for surveillance activities 

Data sets and sources 

• Limited availability of Environment Canada stations by region results in limited application for 

assessing heat within communities 

• Environmental Heat Monitoring System (EHMS) units are currently supplied by Health Canada to 

a number of PHUs to supplement meteorological data for their regions 

• EHMS units provide the benefit of assessing 4 parameters that comprise heat (and calculates the 

WBGT), as well as capturing heat exposure in areas outside typical weather stations 

• EHMS data is limited to study duration and information is not publicly available 

• Environment Canada is a free source of standardized meteorological data on a number of 

parameters; many PHUs have access to this data and most use information on humidex and 

temperature. 

• Solar load is not available at all Environment Canada stations, which limits the number of 

composite measures that can be calculated from the data set 

• There are various satellites and sensors which could provide information relevant to extreme heat 

exposure 

• Landsat imagery is available at no cost and at an appropriate resolution for all of Ontario 

• Any satellite data would still require processing and analysis, as well as a certain level of expertise 

• Aerial image acquisition has the potential to provide information on a variety of built environment 

data but may be unrealistic due to cost and time requirements 

• There is the potential for a lot of data to be available at the local level but this depends on the 

municipality and its capacity to collect, analyze and disseminate data 


170 

EXTREME HEAT 


review, key informant interviews, survey and GIS metadata 

EXTREME HEAT 

Table 20: Measurement approaches and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata 

Description (LR) Inputs Current Use in Ontario PHUs (SU) 

Measures 

(LR) 


Ontario (LR/GM) 

Desirability 

Challenges 

Link b/w Measurement 

Approaches 

Meteorological 

Data 

Direct measurements of Individual 

meteorological variables 

• Temperature 

• Dew point temperature 

• Relative humidity 

• Wind speed 

• Wind direction 

• Solar load (radiation) 

• Atmospheric pressure 

Individual met 

variables (LR) 

*May be real time or 

hourly (GM) 

Of 18 PHUs that assess heat 

• 16 PHUs have access to meteorological data (all 16 use temperature to asses heat, 4 use 

wind speed) 

23 PHUs have access to meteorological data 

• 21 PHUs use Environment Canada as source 

• 1 PHU uses Weather Network 

One PHU uses met data as input to a larger heat model to predict heat days/alerts 

7 variables 

6 of 7variables are 

available across Ontario 

Direct measurements 

Availability of meteorological variables may be 

limited by region (LR/KI) 

Limited local data (LR) 

Temporal analysis is limited (KI) 

Does not take into account effect of 4 variables 

that comprise heat. 

Main inputs for heat indices 

Used for AQ measures 

Used in conjunction with AQ info (e.g. 

AQHI) for calling heat alerts advisories 

Composite 

Heat Measures 

(using 


data) 

Composite measures of heat that combine 

individual meteorological variables based on 

given formulae 

• Apparent temp/heat Index 

• Humidex 

• Thom Index/Discomfort index 

• Relative stress index 

• WGBT index 

• Heat exposure index 

• Thermal Index (net effective temperature) 

• Heat Load Index 

• Heat Stress Index 

• Perceived temperature 

• Spatial synoptic classification 

• Human Thermal Comfort Index 



Scientific formulae 

(LR) 

Some require 

climate normal/ 

averages as 

reference periods 

(LR) 

Some require 

demographic/ 

morbidity/mortality 

data as reference 

for formula 

development (LR) 


• 16 PHUs have access to meteorological data (all 16 use humidex to assess heat) 

• 2 PHUs use models 

23 PHUs have access to meteorological data: 



• 3 PHUs get data from other temp/mobile monitoring stations (all from Health Canada) 

o All 3 measure WGBT 

11 measures 

Standard Environment 

Canada data would be 

sufficient to populate 

some of these 

measures. However, 

special equipment may 

be required for other 

measures (e.g. WGBT). 

(LR,KII). 

Some measures 

are not realistic for 

PHUs to calculate at a 

population level 

According to Health Canada, heat is 

comprised of 4 factors (temperature, 

humidity, wind and solar load). 

Composite heat measures better 

address this relationship (LR) 





Most require direct measurements of 

Individual meteorological variables 



Urban climate 

modelling 

Models that simulate local climates in urban 

environments 

Reasonably low spatial resolution (KII) 

Could be used to extrapolate met data 

across a wider geography(KII) 

Built environment feature and characteristics 


*Many of these measures have been identified 

as being collected through remote sensing. 

Individual features 

and characteristics 

Means of collecting 

data (e.g. satellite/ 

sensor) 

Expertise to collect 

and analyze data 

15 PHUs reported having access to built environment data (e.g. land use, forest cover etc.) 

Remotely sensed data: 

Potential for complete coverage (LR) 


Quality and quantity of satellite data varies by 

source (e.g. spatial and temporal resolution(LR) 

Parameters only reflect data at one point in time 

(LR/KII) 

Use may be limited due to accessibility issues, 

cost and expertise to collect and analyze data 

(LR/KII) 

Built 

environment 

data 

A. Temperature 

• Surface temperature 

• Urban heat Islands 

B. Land cover 

• Impervious Surfaces 

• Vegetation (NDVI, VCF, EVI, SAVI) 

• Albedo (BRDF) Open/green space 

C. Community characteristics 


• Density 

• Proximity 

• Building height 

• Land Use 

2 PHUs have access to thermal imagery 

1 PHU accesses thermal imagery through federal government 

1 PHU accesses thermal imagery by 1) directly requesting it from data source 2) through a 

regional or local government 

Of the 18 PHUs that assess heat: 

2 PHUS have identified the following as in development : canopy cover, age stratification of 

urban forest stock 

The PHUs that do not assess heat, identified having access to the following: canopy coverage 

(3), age stratification (3), surface reflectivity/albedo (1), surface emissivity (1) 


1 PHU identified the following as in development àbuilding age 

PHUs that do not assess heat identified having access to the following: urban sprawl (1), 

building density (2), building age (2), average size by land use (1) 

Unable to assess due to 

level of detail for each 

measure. 

Theoretically, should these 

measures be accessed 

through remote sensing, 

there is the potential 

for complete coverage 

in Ontario. However, 

processing would be 

required. 

Surface temperature can be used as a 

basic indicator of relative hot and cool 

areas and neighbourhoods (KII) 

Thermal anisotropy: 3D structures are viewed 

at a single angle so thermal properties of other 

sides are not captured. (LR) 

Unable to infer the temperature inside buildings 

(e.g. may be air conditioned). (KII) 

May be used in air quality assessments. 

Measures have also been used to 

assess walkability and air quality. 



D. Residential characteristics 

• Detached homes 

• Air conditioning 



Air 

Walkability 

Community 

vulnerability 

Measures that combine exposure to extreme 

heat and individual./community vulnerability to 

determine overall risk 

Socio-economic 

& demographic 

variables 

Heat exposure 

variables 

Platform/ 

methodology to 

combine the two 

to create overall 

vulnerability 

14 PHUs reported using demographic data to identify populations more vulnerable to heat (SU) 

Of these, the following are used: Age (14), gender (2), income (9), language (3) 

Other 

• Pre-existing medical conditions (4) 

• People without A/C (2) 


• Deprivation index 

• Occupation / employment (2) 

• Type of dwelling unit (2) 

• Education 



• Socially isolated persons 

• New immigrants 

• Urban heat island 

• Lack of tree canopy/green spaces 

• Lack of social infrastructure/ service gaps (2) 

• High-crime rate areas 

1 PHU has created a heat vulnerability index and maps (Toronto) (LR) 

7 

Measures of community 

vulnerably can vary in 

methodology. In Ontario, 

the Deprivation index and 

other census data could 

be used. Theoretically, 

thermal imagery for 

Ontario could be overlaid 

with a number of built 

environment, social and 

demographic data. 

Combines exposure and vulnerability 

which together to inform risk 

Likely unreasonable to expect every local health 

department to create its own heat vulnerability 

map – a national HVI created through freely 

available national data sets is useful (LR) 

BE data 

Composite measures using met data 





Table 21: Data sources and sets, and policy relevant information as identified from the literature 

review, key informant interviews, survey and GIS metadata. 

EXTREME HEAT 

Table 21: Data sources and sets, and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata 

Organization 

Data Source/ 

Set 

Topic Area Utility in Outcomes Current Use in Ontario PHUs Desirability Cost Challenges/Limitations 

Health Canada 


Heat Monitoring 

Systems 

Extreme Heat 

Air Quality 

Could potentially: 

• Contribute all met variables for 8 of 11 composite measures 

• Contribute to some met variables for 10 of 11 composite heat 

measures 

• Collect 4 of 7 individual meteorological variables 

Can contribute to syndromic surveillance 

South eastern environmental heat monitoring network: PHUs 

covered by network: Hastings & Prince Edward Counties; 

Leeds, Grenville & Lanark District; Peterborough County-City; 

and Kingston Frontenac and Lennox & Addington (KFL&A) 

(GM) 

3 PHUs identified using health Canada EHMS units (SU) (2 of 

these are separate from the south eastern heat network) 

Able to assess 4 parameters that comprise heat (LR) 

Useful for capturing heat exposure in various 

environments given the variation of meteorological 

conditions over distances (e.g. city centres to farm 

lands). (KII) 

Units can be placed in areas outside typical 

Environment Canada weather stations and 

strategically placed to assess spatial variability of heat 

over a region or major urban area (KII) 

No cost 

Funded 

by Health 

Canada (KI) 

Limited geographic coverage based on pilot projects 

Not publicly available (GM/KI) 

If data were to be released, it would require interpretation (KI) 


Service of 

Canada 

(Environment 

Canada) 

National Climate 

Archives 

Real time data 

from weather 

stations 

Extreme Heat 

Air Quality 

EC data 

integrated into 

syndromic 

surveillance 

system (KII) 

Could potentially 



measures 



• Of 18 PHUs that assess heat (SU) 

• 16 PHUs have access to meteorological data (all 16 use 

temperature and humidex to asses heat, 4 use wind 

speed) 

• 23 PHUs have access to meteorological data (SU) 


Undergoes quality control (KII) 

Data freely available and accessible across Ontario. 

(GM) 

Free 

GIS formatted data is not provided through the National Climate Archives; however, 

the database contains latitude and longitude of each observation (Degrees & minutes) 

that can be used to create a GIS shape file. (GM) 

Typically at local airports/weather stations (LR) 

Availability may be limited by region (LR/KI) 

Limited use to assess variation in heat across community due to distance from 

stations and sparse spatial coverage (KI, GM)Standard Environment Canada data 

would not be sufficient to populate some of the measures that address the four 

factors that comprise heat. 

Natural 

Resources 

Canada 

Landsat 

Heat 

Walkability 

Could contribute to BE data (LR) 


• Impervious surfaces 

• Vegetation 

• Urban heat islands 

• Land cover 

2 PHUs have access to Landsat (SU) 

Used Landsat imagery (from NRCan) for the heat exposure 

component for Toronto’s Heat Vulnerability Index (LR) 

Historical archive completely open and accessible 

online with no privacy restrictions (MD/KII) 

Canada wide coverage (MD) 

Includes metadata to convert the thermal infrared to 

temperature in Celsius (KII) 

Includes calibration data (KII) 

Free (MD) 

Satellite ending useful lifespan (MD) 

Update frequency is every 16 days 

Unspecified 

Heat 

One PHU identified NRCan as the source for data on surface 

reflectivity and surface emissivity (1) 

USGS Aster Heat 




No PHUs identified having access to ASTER (SU) 

Associated 

fees 


USGS MODIS Heat 



Update frequency is 2x a day (GM) Free Scale resolution limited (GM/KII) 

• Albedo 


First Base 

Solutions 

Custom Aerial 

Image Acquisition 

Heat 




One PHU identified using aerial photography but did not 

specify the source 

Customizable data set (GM) 

High resolution (GM) 

Associated 

fees (GM) 

Potentially expensive 



Statistics 

Canada 

Census 

Heat 

Air 

Walkability 

Could contribute to socio economic and demographic variables 

required for community vulnerability (LR) 

One PHU identified Statistics Canada as a source for data on 

building age = 

Source for dwelling units in high rises, dwelling units in high 

rises constructed before 1986 and population density in 


Standardized data available across Canada Free Updated every 5 years 

Municipalities Local data Heat 

Could contribute on built environment data. 

May be accessed through municipal departments 

• Forestry 

• GIS 

• Planning 


required for community vulnerability 

PHUs identified municipalities as sources for: (SU) 

• Canopy cover (5) 

• Age stratification of forests (4) 

• Unit size by land use (2) 

• Building density (3) 

• Building age (3) 

Source for public green space boundaries and land cover (for 

canopy coverage) in Toronto (LR) 

Provides local level data Unknown Data may not be consistently gathered or shared. 




5DISCUSSION

CHAPTER 5: DISCUSSION 

The built environment provides the setting and backdrop by which we live our lives, and undoubtedly 

impacts health. Creating more walkable and heat-resilient communities as well as reducing emissions 

and exposure to air pollutants, can contribute to decreasing the incidence of obesity, acute and chronic 

respiratory diseases. 1 Therefore, identifying the specific characteristics of the built environment that support 

or hinder people from living a healthy lifestyle is important. 

Given that an assessment of all built environment factors associated with health was beyond the scope of 

this study, the focus of this study was on the measures and data used for the assessment of walkability 

and environmental exposures (air quality and extreme heat) in urban environments. A comprehensive literature 

review, key informant interviews and a survey of Ontario Public Health Units (PHUs) helped inform 

these topic-specific assessments.

176 

DISCUSSION 

WALKABILITY 

Walkability describes those qualities of the built environment that encourage walking. Early research 

relied heavily on self-reported perceptions, rather than objectively measured characterisations of the built 

environment. While perceptions of the built environment are important, new developments in geographical 

information system (GIS) software and databases have enabled more sophisticated measurements 

of built environment variables at a variety of scales. Several built environment measures have been developed 

over the years to examine walkability including measures of density, diversity, street connectivity, 

and pedestrian-oriented design. These types of measures represent built environment factors that are 

strongly and consistently associated with physical activity, walking and health outcomes in the scientific 

literature. These built environment metrics are often correlated with one another as well, which has led to 

the adoption of composite indices to capture many aspects of the built environment at once. 2;73 

Evidenced informed decision making is hampered by the absence of agreement among public health 

researchers and practitioners on how the built environment should be measured and modeled. 2;5 Spatial 

extent, source of data, and the number and range of places compared across studies are so variable 

that virtually no two studies have evaluated built environment metrics the same way. 2 The choice of methodology 

is complicated further by variations in the terminology used to describe and measure the built 

environment. These challenges were highlighted in the key informant interviews and survey results. 

The gap analysis was informed by the literature review, key informant interviews, and survey results. Highlights 

from the gap analysis related to the assessment of urban walkability are provided below: 

• It is challenging to draw comparisons between the walkability of different PHUs. Although several 

organizations are using similar types of measures, they are using different computational methods, 

terminology and a variety of data sources. Comparisons become particularly challenging when 

organizations use predominantly local data sources. 

• The most common methods used by PHUs to assess urban walkability included self-administered 

survey and systematic observation; some PHUs assessed urban walkability using GIS software. 

• GIS is required to operationalize several walkability measures, yet many of Ontario’s PHUs 

identified lack of GIS technical support and capacity as a challenge. 

• Organizations with the most established walkability assessment programs in the province were 

using a walkability index. A walkability index shows the most promise in the application of a 

standardized assessment of urban walkability across PHUs in Ontario. 

• Human resource capacity, measurement variability between municipalities, and data availability 

are key challenges for Ontario’s PHUs in the assessment of urban walkability. 


DISCUSSION 177 

AIR QUALITY 

In order to manage health risks associated with air pollution, public health units and environmental agencies 

have adopted various measurement approaches to better assess air quality in the built environment. 

These methods include monitoring ambient air pollutant levels using the Air Quality Index (AQI) and Air 

Quality Health Index (AQHI) to communicate air quality; maintaining inventories on emissions levels like 

the National Pollutant Release Inventory (NPRI); and developing detailed spatial models. Each method 

has strengths and weaknesses in the assessment of air quality in the built environment and requires different 

measures and data sources. 

Although there have been several improvements in assessing air quality over the years, public health 

researchers and practitioners face several challenges in making evidence-informed decisions. Directly 

comparing different studies is challenging because of the different pollutants studied, health outcomes of 

interest, models used, and populations of interest. In addition, while air monitoring stations can be useful 

sources of data, they are limited in determining air quality between neighborhoods. Thus, more sophisticated 

spatial models have been developed to evaluate how pollutant distribution is impacted by the 

distance from and density of specific built environment traits. However, the use of these spatial models 

which are often developed for one city may not translate well to other urban centres. 

Highlights from the gap analysis related to the assessment of urban air quality exposure are provided 

below: 

• Most PHUs assessing air quality use an index, specifically the Air Quality Index (AQI) and the Air 

Quality Health Index (AQHI). 

• There are valuable modelling methods available to assess air quality in the built environment. 

• While important data sources from the municipal, provincial, and federal level were identified, 

there are limitations to assess air quality issues at a neighborhood scale. 

• NO and PM were noted as two key pollutants which can be used as indicators of local air 

x 2.5 

quality. While the five common pollutants (O 3 

, PM 2.5 

, NO 2 

, CO, and SO 2 

) are those which have 

been associated with the greatest burden of illness, other pollutants of concern in the built 

environment have been identified such as UFP and BC. 

• Public Health Units need information on local air quality to: 

o Assess the impacts of air quality on health within their jurisdictions; 

o Inform land use and transportation planning decisions for the protection of human health; 

and, 

o Assess and monitor the impacts of policies and programs on air quality and human health at 

the local level. 

• Human resources, financial capacity, and data availability are key challenges in the assessment of 

air quality and the built environment in Ontario. 


178 

DISCUSSION 

EXTREME HEAT 

Meteorological variables are commonly used to measure extreme heat including temperature, relative 

humidity, dew point temperature, wind speed and direction, and solar radiation. These variables are 

usually directly measured and monitored at local airports and weather stations by government agencies. 

There are a number of meteorological variables that are also used in composite indices to determine 

exposure to extreme heat; however, application of these indices varies. In addition to the use of direct 

measurements, indirect measures are also used to assess exposures to extreme heat. Measures of the 

built environment related to community and residential characteristics, as well as natural and artificial 

surfaces, provide key insights on exposures to extreme heat. Many of these measures employ remote 

sensing technologies to gather data. Lastly, composite measures have been developed which combine 

exposure with socio-economic, demographic and built environment data to map overall community vulnerability 

to extreme heat. 

Several limitations cited by the research literature were corroborated by the current study’s key informants 

and Ontario Public Health Unit survey results. Given the existing data infrastructure, some measures that 

are readily available and accessible without cost may not adequately cover the need to assess extreme 

heat within a community. Primarily, this extends to meteorological data provided by Environment Canada 

weather stations which do not provide adequate spatial resolution. While weather stations provide data 

to a high degree of accuracy, the spatial resolutions obtained through sparse station densities are not 

adequate to infer measurements within and across communities. This data gap is particularly important 

in urban areas where weather stations are not typically located and it would be unreasonable to establish 

monitoring sites at a higher density, especially in urban areas. In addition, standard weather monitoring 

stations do not collect information on solar radiation and therefore many composite measures would 

not be feasible without the use of additional equipment. Alternative measures have been identified in the 

literature, however they have not been widely adopted. This may be due to PHU capacity issues and the 

lack of research or consensus within Ontario on the best measures and approaches. 

Highlights from the gap analysis related to the assessment of extreme heat are provided below: 

• Many PHUs use meteorological data to assess heat and mostly report on temperature and 

humidex. 

• Meteorological data is an important data set for composite measures and models of extreme heat 

and most PHUs have access to meteorological data. 

• Environment Canada is a free source of standardized meteorological data on a number of 

extreme heat parameters. 

• There are many composite measures that are used to assess exposure to extreme heat but many 

may not be useful for local population health assessments due to complex data requirements. 



• Some PHUs have access to built environment data, however most PHUs are not currently using 

built environment data to assess exposure to extreme heat. 

• There are a limited number of PHUs with access to thermal imagery for the assessment of 

extreme heat. 

• Demographic data such as age, gender, income, and language are being used to identify 

populations that are more vulnerable to extreme heat. 

• Human resources, financial capacity, and data availability are the key challenges for Ontario PHUs 

in the assessment of extreme heat and the built environment. 

CROSS-CUTTING THEMES 

Researchers and public health practitioners trying to associate built environmental features with walking, 

air quality and extreme heat, have been hampered by several data limitations. Most striking were the 

commonalities in challenges reported for all three topic-areas. 

Lack of standardization in measurement approaches 

Lack of standardization was a common challenge in built environment assessments of walkability, air 

quality and extreme heat. For instance, several organizations were using similar types of measures, but 

they were using different terminology, measurement approaches and diverse data sources. Also, while 

similar measures are used across multiple jurisdictions, they are often collected at a different scale and 

frequency. As a result, it was challenging to draw comparisons between health jurisdictions, especially 

for organizations that are predominantly using local data sources. Undoubtedly, different communities will 

have different needs, however further research is required to identify which measures and approaches 

are most appropriate across the province. 

Variations between municipalities were reported as a prominent challenge in the assessment of urban 

walkability. Variations included a lack of consensus on standardized methods for measuring or cataloging 

GIS measures and no central provincial repository for such data. 


180 

DISCUSSION 

Availability and accessibility of data 

Data availability was reported as a major challenge in the assessment of the built environment. Access to 

and availability of high quality built environment data depends on the resources and policy priorities of the 

agencies that collect and warehouse the information. Some regions have excellent and accessible data, 

while others still have paper-based or nonexistent data. These observations echo other research findings 

that data sharing for the most part has been casual and opportunistic, impeded by lack of infrastructure, 

collaboration, and training. 2 


The primary challenge in the assessment of urban walkability and environmental exposures was human 

resource capacity. This challenge can be considered on two levels: (i) the need for staff dedicated to 

working on walkability, air quality and extreme heat assessments and (ii) the need for personnel to have 

specialized expertise (e.g. spatial analysis, epidemiology, satellite image processing). 


Financial capacity was identified as a major challenge in the assessment of air quality and extreme heat. 

For example, while some data may be freely available, other data must be purchased from government 

and/or private institutions. Some PHUs are collecting their own data (e.g. purchasing air monitoring 

equipment), but this expense may not be feasible for all PHUs. 

Competing public health priorities 

For some PHUs, walkability and environmental exposure assessments were not a key priority due to lack 

of political will and relevance to rural environments. While some PHUs identified these issues, there may 

be other reasons or competing priorities that were not identified via our environmental scan. 



HEALTH EQUITY AND THE BUILT ENVIRONMENT 

The built environment is closely tied to health equity. The built environment and health equity impacts may 

be linked to socioeconomic status (SES), age, health status, or cultural influences. While this topic was 

not identified as an area of focus for this project, some interesting findings bear mentioning. 

Walkability & Health Equity 

It is vital to understand the difference between an individual who chooses to walk as a result of living in a 

walkable neighbourhood and someone who, for financial constraints or other reasons, has no choice but 

to walk in a neighbourhood or circumstance that may or may not be conducive to walking. 54 The design 

of urban environments varies dramatically between high-income and lower income neighborhoods and 

the possible implications for physical activity are complex and multifaceted. For instance, lower income 

neighborhoods frequently have both features that are hypothesized to support walking for transportation 

(e.g. higher densities, grid streets that provide direct connections, transit access) and features that are 

hypothesized to deter walking (e.g. poor maintenance, few nearby destinations, and unpleasant walking 

environments.) Also, lower income residents may walk more irrespective of neighborhood design due to 

financial barriers to owning a car and incidentals like car insurance and a valid drivers’ license. Ultimately, 

sociodemographic characteristics warrant serious attention from urban designers and planners and from 

public health advocates who are interested in creating walkable urban environments. 214 

Air Quality & Health Equity 

Canada-wide research found that individuals and families living in low SES neighbourhoods are more 

likely to live close to a highway or industrial area that exposes them to higher levels of outdoor air pollution. 

37 While everyone faces increased health risks due to air pollution, the risk is greater for people with 

cardiovascular and respiratory conditions, people with diabetes and/or those who are obese, and the 

elderly, women (especially those that are pregnant), and young children. 

Several PHUs surveyed through this project reported that they had access to data on residents and 

sensitive populations living within a pre-determined distance from high volume roads; and that commonly 

used data sources, such as the census, were used to access this data. 


182 

DISCUSSION 

Extreme Heat & Health Equity 

Research in Montreal and Toronto found that neighbourhoods with the lowest SES are more likely to 

reach higher temperatures and less likely to have open green space than higher SES neighbourhoods. 37 

Some of the socio-demographic and health/physiological risk factors that have been used to assess 

heat-health impacts include elderly population, infants and young children, chronic respiratory and 

cardiovascular diseases, mobility restrictions, cognitive impairment, socially isolated persons (widowed, 

divorced, homeless), low-income households, marginalized groups, new immigrants and non-Englishspeaking 

populations. 

Almost half of PHUs surveyed through this project reported using demographic data such as age, gender, 

income, and language to identify populations that are more vulnerable to extreme heat. Age was used 

more than any of the other demographic indicators to identify vulnerable populations. Other demographic 

data used to identify vulnerable populations varied and included occupation, level of education, 

socio-demographic and health/physiological risk factors, immigration status, and type of dwelling (e.g. 

dwellings without air conditioning). 

LIMITATIONS 

As with any research endeavor, some methodological challenges were anticipated beforehand, while still, 

others arose as the project unfolded. Several caveats and limitations of the study are noted below. 

Early on, the project team confronted usual definition and terminology challenges. “An examination of 

data sources to characterize measures of the built environment in urban Ontario” may seem intuitive but 

what exactly constitutes ‘urban’ Ontario? Or, how is an ‘indicator,’ ‘measure,’ ‘metric,’ or ‘index’ defined? 

These questions challenged the project team at the outset and periodically throughout the project. Copious 

reading in the field of built environment research suggests similarly inexact language as these terms 

are used in the scholarly literature, grey literature, and popular press with some imprecision. Thus, the 

project team made a deliberate effort to explicitly define terms and to use them consistently throughout 

the project. 

Recognizing the scope of this study and the recent proliferation of built environment research, the keyword 

search criteria in the literature review were initially structured in order to practically limit the number 

of articles for review. The keyword lists were subsequently expanded and otherwise relaxed to a point 

where the team began to see saturation in the identified measures and data sources that researchers 

have used to study the built environment. No doubt, there are other novel and specialized measures that 

may have been missed, but the measures and data this review has revealed constitute the current locus 

of attention in built environment research. Though rigorous, given the scope of the project, an exhaustive 

search of all potentially relevant measures and data sources was not feasible. 



The project team, among its own collective expertise, identified a number of potential key informants. The 

key informants were selected from those who have made contributions to the study of the built environment, 

from those who are working at the nexus of the built environment and health, and from those who 

have considerable knowledge of relevant built environment data sources. Although critical insights were 

gleaned from the key informants, the project team makes no claims to having captured all strands of 

current thinking in the field of built environment research. The key informants selected represent more of 

a convenience sample rather than a rigorously vetted expert panel. 

Considerable thought and investment went into the development and testing of the survey instrument. 

However, to ensure the project met major milestones and key deadlines, the survey needed to be fielded 

during the summer and, thus, may have limited the number and quality of responses. Given the response 

rate and the lack of detail in some of the responses, there were insufficient data with which to generate 

jurisdictional profiles as planned. Comprehensively enumerating desired built environment indicators by 

public health unit was also not feasible. 

While the project has identified and documented key datasets (spatial and non-spatial) that can and are 

being leveraged to support built environment research programs, there may well be datasets that were 

missed. The ‘standard fare’ or ‘core’ datasets in the public domain were first identified, many of which 

are produced and/or disseminated by public agencies or government; secondly, the project team tried 

to enumerate key vendor datasets that can inform built environment research. It is recognized, however, 

that individual researchers, interest groups, or private sector firms may hold more proprietary datasets 

of value to built environment research. Moreover, it is important to mention that there exists considerable 

potential to construct derivative datasets (as either a transformation of a single core dataset or by 

innovatively combining two or more core datasets) that could greatly enhance the understanding of the 

relationship between the built environment and health outcomes. 

CONCLUSION 

In order to fully understand the impact of the built environment on health, the development of highquality 

measures is essential. Although several limitations have been cited in this report, existing built 

environment measures have stimulated rapid advancements in understanding environmental correlates 

of physical activity and environmental exposures in a variety of populations and settings. Many built environment 

measures are considered to be first-generation measures, so further development is needed. 

In particular, further work is required to standardize measures and terminology, improve the quality of 

measures, ensure relevance for diverse populations, and integrate measures into public health and planning 

systems. 5;61 Moving forward, cross- and inter-disciplinary collaborations will be critical to making 

these measurement improvements. By working together, our communities will reap the resulting health, 

environmental, and economic benefits. In the end, widespread implementation of effective policy interventions 

will be necessary to achieve these built environment and public health goals. 215 



185 

GUIDING PRINCIPLES & 


Several overarching principles were identified to help guide the development of the study recommendations. 

These principles are closely tied to the results of the literature review, key informant interview and survey 

results, and are applicable to the assessment of walkability, air quality, and extreme heat. 

Multiple sectors and disciplines are interested in the built environment including public health, geography, 

transportation, architecture, urban planning and design. Therefore the potential audience for our 

recommendations is large. The stakeholders for which our guiding principles and recommendations 

could apply to, include: 

• Ontario’s regional and municipal governments: public health, land use planning, transportation 

planning, public works, sustainability, and community services 

• Provincial government: Ministry of Health and Long Term Care, Public Health Ontario, Ministry 

of Transportation, Ministry of Environment, Ministry of Municipal Affairs and Housing Federal 

government: Statistics Canada, Environment Canada, Health Canada, Transport Canada, Public 

Health Agency of Canada, Natural Resources Canada 

• Non-governmental organizations: Ontario Public Health Association (OPHA), Association of Public 

Health Epidemiologists in Ontario (APHEO), Association of Local Public Health Agencies (alPHa), 

Association of Supervisors of Public Health Inspectors of Ontario (ASPHIO), Canadian Institute 

of Public Health Inspectors (CIPHI), Council of Ontario Medical Officers of Health (COMOH), 

Canadian Medical Association, Ontario Medical Association, Association of Municipalities of 

Ontario, Ontario Association of Architects, Ontario Association of Professional Engineers, Ontario 

Professional Planners Institute (OPPI) 

• Private organizations/agencies including planning, transportation and engineering firms 

• Academia 


186 

The guiding principles as well as the specific recommendations that relate to each principle are presented 

below. It is important to note that when ‘local agencies’ are referenced, these stakeholders include Public 

Health Units and municipal governments. 

1. STRENGTHEN MULTIDISCIPLINARY COOPERATION 

A recurring theme in all aspects of this project is the need for diversity in both expertise and skills in order 

to have an in-depth understanding of the built environment. More so than several other subject areas, 

assessing the impact of the built environment on the public’s health requires extensive collaboration. 

1.1 

Engage in multidisciplinary collaboration across all sectors, including government, 

academia and private sectors 

• Establish and enhance existing multidisciplinary built environment working groups and 

associations that are focused on assessing walkability, air quality and extreme heat 

through strengthened human and financial resource capacity. 

• Membership should include provincial and local built environment stakeholders (e.g. 

associations, Public Health Units, municipalities) and academia. 

• Preliminary areas of focus could include (but are not limited to): 

o 

o 

o 

Supporting the expansion of multidisciplinary groups with mandates to identify 

province-wide walkability indices and their operationalization. 

Creating a multidisciplinary committee to further research local air quality 

assessments as it relates to the built environment and to share information on: 

different technologies, methodologies and approaches; conducting research 

directed at air quality and traffic corridors; and research and assessments 

directed at air quality and point sources. 

Creating a multidisciplinary task force to further research variations in 

extreme heat across communities and community vulnerability, and to share 

information on: heat burden of illness and research directed at heat alert 

triggers, as well as heat alert and response systems. 


187 

2. PROVIDE METHODOLOGICAL GUIDANCE 

Currently, the assessments and research on public health and the built environment are comprised of 

a patchwork of different methodologies that are predominantly led by independent efforts. Providing 

guidance to local agencies on the assessment of the built environment, namely walkability, air quality and 

extreme heat, would help to promote comparability of results, reduce duplication of efforts, and hasten 

the inclusion of agencies that are new to assessing the built environment. 

2.1 Standardize built environment measures using a multidisciplinary approach 

across all sectors 

• Establish consistent built environment terminology across the province. 

• Develop a standard suite of measures (including indices) and methodologies that can be 

used to characterize the built environment in Ontario. 

• Develop guidelines or minimum requirements for built environment metadata. 


o 

o 

Developing protocols for the use and management of municipal traffic volume 

data for Public Health Units. 

Including extreme heat and air quality in the current development 

of standardized indicators for the built environment 

(e.g. Association of Public Health Epidemiologists in Ontario 

(APHEO) Built Environment Core Indicators Project). 

2.2 Put built environment research into practice 

• Identify, and use, best practices to determine which built environment measures and 

data sources are a priority in the assessment of air quality, extreme heat and walkability in 

Ontario. 


o Assessing the applicability of free and publicly available walkability software 

(e.g. Walkscore) in the Ontario context. 

o Exploring the use of thermal imagery to assess extreme heat within Ontario 

communities. 

o Providing complimentary monitoring programs to better understand air 

pollutant distribution (e.g. AirPointer, NO 2 

passive samplers). 

o Integrating socio-demographic characteristics into built environment and 

health assessments. 


188 

3. IMPROVE DATA AVAILABILITY AND ACCESSIBILITY 

Understanding the impact of the built environment on walkability, air quality and extreme heat requires 

relevant, easy-to-understand, and reliable measures and data to assess built environment features. Lack 

of data availability and accessibility was a recurring challenge identified in this study. 

3.1 Increase access to high quality data across the Province 

• Establish and maintain a centralized data repository for standardized built environment 

data relevant to health in Ontario. 


o 

o 

Developing consistent data sharing opportunities to use thermal imagery from Natural 

Resources Canada. 

Developing a mechanism for all Ontario Public Health Units to have access to the data 

repository at minimum cost. 

3.2 Empower local agencies to engage in built environment data initiatives 

• Develop data sharing agreement templates that make data sharing easier between Public 

Health Units and other stakeholders (e.g. to address privacy barriers). 

• Equip Public Health Units with valid tools, instruments and technology to collect local data 

at low or no cost. 


o 

o 

o 

Evaluating the ability to operationalize a similar walkability index in multiple Public 

Health Units across the province. 

Developing common methodology for assessing community vulnerability to 

extreme heat across Ontario. 

Expanding Health Canada’s use of NO sensors for use by Public Health Units in 

2 

conjunction with air modelling. 


189 

3.3 Identify and evaluate the use of current data sources and sets 


o 

o 

o 

o 

Acquiring data from research-based air monitoring stations and determine how 

such data could be used in assessing air quality and the built environment (e.g. 

emerging pollutants of concern; ultrafine particles, black carbon). 

Investigating remote sensing as an opportunity to complement and support 

Public Health Units in the assessment of the built environment. 

Creating a system or guide tool similar to Toronto’s ChemTrac, where Public 

Health Units can collect data and evaluate smaller emission sources not 

considered in the NPRI. 

Using data sources and data collection methods for air pollutant distribution 

modelling used by Ottawa, Halton, and Toronto municipalities in Public Health 

Units across the province. 

3.4 Explore the creation of new data sets 


o Investigating publicly available GIS maps for National Pollutant Release Inventory 

(NPRI) data sets. 

o Improving province-wide meteorological data by including the collection of solar load 

(radiation) at Environment Canada weather stations. 

3.5 Address the need for data auditing and validation 

• Develop strategies to ensure data auditing and validation becomes standard practice, 

with a priority on province-wide data sets. 

• Develop guidance documentation or protocols on built environment data auditing 

procedures. 


190 

4. ENGAGE IN SYSTEMATIC KNOWLEDGE TRANSFER AND EXCHANGE (KTE) 

Knowledge transfer and exchange activities related to the development, use and adoption of built 

environment measures and data sources are necessary to support evidence informed decision making. 

Networks are characterized by social interaction and knowledge-sharing related to a common goal within 

a specific domain of knowledge and practice. They are valuable in enhancing the management, sharing 

and co-creation of knowledge in public health, and augment professional and organizational capacity 

development, and system change. 216 

4.1 Facilitate engagement of expertise outside of the public health sector 

• Invest in the engagement of non-public health built environment stakeholders through 

KTE activities that demonstrate the value that public health can bring to built environment 

issues and vice versa. Audiences could include land use planners, police departments, 

engineers, conservations authorities, community groups, etc. 


o 

o 

o 

Providing logistical aid and capacity to foster networking and communication with 

other subject area experts (e.g. suggesting appropriate contacts, covering incidental 

costs, liaising between organizations, etc.). 

Creating an online portal for sharing data, methods for collection, and best practices 

on the built environment and health. 

Creating opportunities for public health to demonstrate the impact the built 

environment has on health to other organizations that have traditionally worked in the 

built environment (e.g. land use planning). 


191 

5. STRENGTHEN CAPACITY 

Human resource and financial capacity are challenges that Ontario’s Public Health Units face when 

assessing the urban built environment. Having a highly skilled and competent public health workforce 

will ultimately help strengthen capacity to protect and improve the public’s health and reduce pressures 

on the health-care system. 

5.1 Support Public Health Units in a technical capacity to assess the built environment 

• Develop the appropriate technical infrastructure at provincial and local levels to improve 

capacity to generate, manage, and communicate spatial and non-spatial information 

related to the built environment across all sectors. 


o 

o 

o 

Providing Public Health Units with technical support through pooling of resources 

(e.g. GIS analysts). 

Providing spatial modelling to complement current air monitoring networks and 

providing better spatial detail of pollutant distribution at the community level. 

Providing technical support and resources to those Public Health Units and/or 

municipalities that are doing air monitoring and/or air modelling studies of local 

airsheds and/or micro-environments. 

5.2 Enhance education and training for public health professionals 

• Strengthen post-secondary training of public health professionals to include the 

built environment-health relationships as part of the core curriculum and increase 

competencies around the use of spatial and non-spatial tools to evaluate these 

relationships. 

• Implement policies and programs to train local public health professionals on the roles 

and responsibilities, legislation, policy, standards, terminology/concepts, and utilization of 

spatial and non-spatial data related to land use planning and GIS (and vice versa for land 

use planners). 

• Strengthening Public Health Units and/or municipalities’ understanding of the air 

monitoring and modelling tools, technologies, and strategies that can be used to 

assess local airsheds and micro-environments, along with their strengths, limitations, 

and applications. 


192 

6. STRENGTHEN BUILT ENVIRONMENT AND HEALTH RESEARCH 

Although several advancements have been made in the measures and measurement approaches 

used in the assessment of urban walkability and environmental exposures, several gaps in the 

research have emerged. 

6.1 Increase research funding opportunities for exploring the relationship between the built 

environment and health 

Preliminary areas of focus could include (but are not limited to): 

o 

o 

o 

o 

o 

o 

o 

Providing additional data on pollutant distribution from key traffic sources to better 

understand variability in setback distances and heights. 

Evaluating elements of the built environment relevant to land use planning and air 

quality (e.g. impact of vegetation, noise walls, etc.). 

Exploring urban climate modelling for the assessment of extreme heat. 

Examining how meteorological conditions contribute to the distribution of air 

pollutants in communities in urban areas. 

Investigating how multiple pollutants contribute to health outcomes through 

additive or synergistic effects. 

Evaluating the South Eastern Ontario environmental extreme heat monitoring 

system to better understand the potential of syndromic surveillance. 

Determining how built environmental factors influence physical activity levels in 

youth, particularly to assist in the development of youth-oriented interventions that 

promote life-long healthy behaviors. 


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210 


APPENDICES 

211 

APPENDICES 


212 


APPENDIX A 

213 

APPENDIX A: 

WALKABILITY AND ENVIRONMENTAL EXPOSURES 

(AIR QUALITY AND EXTREME HEAT) LITERATURE REVIEW SUMMARY TABLES 

The literature review summary tables can be found at the following link: 

http://www.kflapublichealth.ca/files/research/AppendixA_LiteratureReviewSummaries.xls 


214 

APPENDIX B 

APPENDIX B: 

KEY INFORMANT INTERVIEW LETTER OF INVITATION (LOI) 

 

 

Re: Invitation to Participate in a Research Project 

Title of Project: An environmental scan of built environment data related to walkability and 

environmental exposure in urban Ontario. 

Dear 

On behalf of our investigation team, we would like to invite you to participate in a Public Health Ontario (PHO) 

funded study. Through PHO’s Locally Driven Collaborate Project (LDCP) initiative, several Ontario local public 

health agencies are working together on a project that will support the identification of standardized walkability 

and environmental exposure data and measures that can be used in the assessment of the urban built 

environment in Ontario, and to develop policy recommendations that would promote the development and use 

of such data and measures. 

We would like to gather the insights of those with relevant experience in the area of the urban built environment, 

specifically in relation to walkability and/or environmental exposures. At his time, would like to invite you or 

members of your team to participate in a key informant interview. We are interested in interview a person (or 

group) who possess knowledge related to areas of strength and weakness in the collection, availability, and 

comparability of built environment data within Ontario. We hope to briefly explore topics such as data availability, 

data quality, data gaps, internal capacity, suggestions for acquiring data, and ongoing challenges as it relates to 

data for built environment walkability and/or environmental exposures measures. 

The results of the interview will help inform the development of an electronic survey of built environment 

data sources that will be administered to public health departments, municipalities, the private sector and 

other agencies (e.g. provincial) across Ontario in May or June 2012. This information will later be applied in 

the development of policy recommendations related to the development and application of walkability and 

environmental exposure measures in Ontario. We expect to present our final results in report format on our 

project website (www.builtenvironment.ca). 

In the near future, you will be contacted to set up an appointment for a brief interview (about 30 minutes to 

one hour). We encourage you to participate or to indicate another time that is more convenient for you. Once 

an interview date is confirmed, we will share a copy of the interview questions and consent form prior to the 

interview. 

If you have any questions about this project, please contact our Project Coordinator, Popy Dimoulas-Graham, 

at 226-338-8004. If you have any concerns about your rights as a research participant please contact 

- Dr. Albert Clark, Chair of the Queen’s University and Affiliated Teaching Hospitals Research Ethics Board at 

(613)- 533-6081. 

We look forward to hearing from you and having you participate in this collaborative initiative. 

Sincerely, 

Paul Belanger, PhD 

GIS Services Manager 

KFL&A Public Health 

221 Portsmouth Ave 

Kingston, ON K7M 1V5 

613 549- 1232, ext. 1602 


APPENDIX B 

215 

APPENDIX B: 

KEY INFORMANT INTERVIEW LETTER OF INVITATION (LOI) 

INVESTIGATION TEAM 

Paul Belanger, PhD 

GIS Services 

Kingston, Frontenac and Lennox & 

Addington Public Health 

Helen Doyle 

Manager, Public Health 

York Region Public Health 

Deborah Moore 

Senior Epidemiologist 

Niagara Region Public Health 

Daphne Mayer, MPH 

Research Associate 



Mira Shnabel 

Environmental Health 

Program Coordinator 


Ryan Waterhouse 

GIS Analyst 


Novella Martinello, MSc 

Foundational Standard Specialist 



Asim Qasim 

Environmental Research 

and Policy Analyst 


Ahalya Mahendra 

Epidemiologist 

Public Health Agency of Canada 

Caitlyn Paget 




216 

APPENDIX C 

APPENDIX C: 

KEY INFORMANT INTERVIEW GUIDE – WALKABILITY 

Date of Interview: 

Name of Interviewer: 

DEMOGRAPHICS 

Name of key informant: 

Position: 

Organization: 

No. of years working on the built environment: 

Experience working on the built environment: 

QUESTIONS 

1. Can you tell me about the types of walkability measures your organization uses to measure, describe 

or evaluate walkability? 

The next set of questions will focus on data sources required to objectively measure walkability in 

Ontario: 


APPENDIX C 

217 

APPENDIX C: 

KEY INFORMANT INTERVIEW GUIDE – WALKABILITY 

DATA AVAILABILITY/QUALITY 

2. In your experience with these measures, can you comment on: 

(i) 

Data coverage (i.e. equally available across your jurisdiction); 

(ii) Frequency of data submission (i.e. how often is data collected; how current is the data); 

(iii) How data is collected (e.g. electronically, aerial images, specific databases) 

and at what level (geographic, population, political (e.g. municipal, provincial)); 

(iv) Data quality (e.g. reputable source, consistency, current); 

(v) Accessibility (e.g. readily available; cost). 

DATA GAPS 

3. What are the major challenges with the current walkability data infrastructure 

(i.e. collection, processing and dissemination)? 

CAPACITY 

4. What capacity challenges do you think public health practitioners (at the provincial or local levels) 

experience when using objective walkability data? (e.g. internal resources to analyze spatial data) 

NEXT STEPS 

5. Do you have any suggestions related to where to get the most appropriate sources of built 

environment data to measure walkability across Ontario? 

6. We would appreciate feedback on how best to communicate the results of this project so that it 

will useful for Built environment stakeholders in Ontario. Do you have any suggestions? 

Thank you for your time. If you have any questions or comments, please feel free to contact the project 

coordinator (contact information) at any time. 

-END- 


218 

APPENDIX D 

APPENDIX D: 

KEY INFORMANT INTERVIEW GUIDE – ENVIRONMENTAL EXPOSURES 

(AIR QUALITY AND EXTREME HEAT) 

Date of Interview: 

Name of Interviewer: 

DEMOGRAPHICS 

Name of key informant: 

Position: 

Organization: 

No. of years working on the built environment: 

Experience working on the built environment: 


APPENDIX D 

219 

APPENDIX D: 



AIR QUALITY KEY INFORMANTS 

Questions for Environment Canada and the Ministry of the Environment key informants 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

How many air monitoring stations do you operate in Ontario? 

What air pollutants are being monitored by these stations? 

Where are these air monitoring stations located? 

Are these stations monitoring regional or local air quality? 

Are these stations targeted at specific emission sources, and if so, what types of emission 

sources? 

Do you have any air modelling or air monitoring stations that are directed at local air quality related 

to non-industrial sources? If so, at which types of sources and where are they located? 

For public health units that are concerned about variations in air quality across their communities 

related to the built environment, what advice would you offer about: 

a. 

b. 

c. 

d. 

e. 

f. 

Which 2 or 3 air pollutants to target as indicators of local air quality as it is impacted by the 

built environment, and why? 

How to collect data that can be used as an indicator of exposure across the community (i.e., 

with air monitoring stations, local air monitoring devices, airshed modelling, other?) 

Are there indirect indicators that can be used to monitor potential exposure to air pollution from 

point sources such as set-back distances and/or NPRI or TURI reporting data? 

Which air pollutant(s) to target as indicators of traffic-related air pollution and why? 

How to collect data that can be used as indicators of exposure to traffic-related air pollution? 

(i.e., with portable air monitoring devices, air modelling, other)? 

Are there indirect indicators that can be used to monitor potential exposure to air pollution 

associated with traffic corridors such as set-back distances and/or traffic volumes ? 

8. We would appreciate feedback on how best to communicate the results of this project so that it 

will useful for Built environment stakeholders in Ontario. Do you have any suggestions? 



-END- 


220 

APPENDIX D 

APPENDIX D: 



Questions for air quality academics or research key informants 

1. 

For public health units that are concerned about variations in air quality across their communities, what 

advice would you offer about: 

a. 

b. 

c. 

d. 

e. 

f. 

Which 2 or 3 air pollutants to target as indicators of local point sources and why? 

How to collect data that can be used as indicators of local air quality and why (i.e., with air 

monitoring stations, local air monitoring devices, airshed modelling, other?) 

Are there indirect indicators that can be used for air pollutants associated with point sources such as 

set-back distances or NPRI or TURI data? 

Which air pollutants to target as indicators of traffic-related air pollution and why? 

How to collect data that can be used as indicators of traffic-related air pollution and why (i.e., with 

portable air monitors, air modelling, other). 

Are there indirect indicators that can be used for air pollutants associated with traffic corridors such 

as set-back distances and/or traffic volume? 

2. We would appreciate feedback on how best to communicate the results of this project so that it will 

useful for Built environment stakeholders in Ontario. Do you have any suggestions? 



-END- 


APPENDIX D 

221 

APPENDIX D: 



Questions for local public health agencies or municipalities key informants 

who collect their own air quality data 

1. 

Does the air modelling and/or monitoring program being run by your municipalities provide data that 

could be used as indicators for air quality as it is affected by the built environment? 

a. no (if no, skip to question 7) 

b. yes (if yes, continue to next question) 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

9. 

What air pollutants do you model and/or monitor? 

What modelling tools and/or monitoring equipment do you use and for what purpose? 

What emissions sources have you targeted with modelling? What was the source of data for point 

sources? What resolution do you get with the modelling being done? 

What types of emission sources have had a substantial impact on local air quality in your community? 

How frequently will you be repeating the modelling and/or monitoring under the same 

parameters if at all? 

What budget is directed towards air modelling/air monitoring? Capital and operating? 

How is the program managed (i.e., consultants? Internal staff?) 

How many FTEs per year are directed at this activity? 

10. How is this data currently being used? 

11. Are there plans to repeat the modelling or monitoring with the same parameters to 

track changes over time? 





-END- 


222 

APPENDIX D 

APPENDIX D: 



EXTREME HEAT KEY INFORMANTS 

Questions for Environment Canada key informants 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

9. 

How many weather stations do you operate in Ontario? 

What temperature measurements are obtained by these stations? (frequency, height of observation etc). 

Where are these stations located? 

What quality control procedures are in place for station temperature data? 

What is the accuracy of the temperature data? 

For public health units that are concerned about variation in heat across their communities, what advice 

would you offer regarding the interpretation of station temperature values and their extrapolation across 

a wider geography? 

What are the limitations to using this data? 

Does this data come with standardized metadata? 

Are there privacy restrictions around this data? 

10. How frequently is this data updated? 





-END- 


APPENDIX D 

223 

APPENDIX D: 



Questions for Natural Resources Canada key informants 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

9. 

When did NRCan start to collect LANDSAT thermal imagery? 

What years are currently available? 

Is there a fee for accessing the imagery? 

What level of end-user expertise is required to use/interpret this imagery? 

What geographic areas have been covered by these images? 

What is the resolution of this imagery? 

What is the accuracy? 

What benefits are there to normalizing several single shot images into an average seasonal image? 

How can public health workers use these images to assess exposure to heat? 

10. What are the limitations of using this data to assess exposure to heat? 

11. What are the benefits of using this data to assess exposure to heat? 

12. How often do you anticipate these images will be available in the future? 

13. Are there other sources from which this imagery can be obtained? 

14. Does this data come with standardized metadata? 

15. Are there privacy restrictions around this data? 

16. How frequently is this data updated? 





-END- 


224 

APPENDIX D 

APPENDIX D: 



Questions for public health research key informants 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

9. 

How many wet-bulb globe temperature sensors do you use in the syndromic surveillance system for 

extreme heat events? 

How were their locations decided? 

Do their locations reflect a variety of land uses? 

What is the accuracy of these sensors? 

What parameters are measured by these sensors? 

Does this data come with standardized metadata? 

Are there privacy restrictions around this data? 

How frequently is this data updated? 

Do you also incorporate environment Canada weather data in the system? 





-END- 


APPENDIX D 

225 

APPENDIX D: 



Questions for Health Canada key informant 

1. 

Could you describe the Environmental Heat Monitoring Systems? 

a. What parameters are collected? 

b. How do you determine the locations of the monitors? 

c. What is the accuracy of these monitors? 

d. What are the limitations of using /accessing data? 

e. How does this compare to data collected by Environment Canada weather stations? 

f. Are there plans to expand these monitors to make this data across the province? 

2. 

3. 

4. 

Based on your pilot projects for the heat & health resiliency initiative, what advice would you give to 

public health units in terms of using available heat data to build heat alert and response programs 

(e.g. triggers)? 

For public health units that are concerned about variations in heat across their communities, 

what advice would you offer regarding the interpretation of EHMS data and their extrapolation 

across a wider geography? 

We would appreciate feedback on how best to communicate the results of this project so 

that it will useful for Built environment stakeholders in Ontario. Do you have any suggestions? 

-END- 


226 

APPENDIX E 

APPENDIX E: 


AND DATA SOURCES SURVEY LETTER OF INVITATION (LOI) 

[Date] 

Re: Invitation to participate in a Public Health Ontario research project 

An environmental scan of built environment data sources related to walkability and environmental 

exposures in urban Ontario. 

Dear [Medical Officer of Health; Director or Environmental Health, and Director of Chronic Diseases] 

On behalf of our investigation team, we would like to invite your public health unit to participate in a Public Health 

Ontario (PHO) funded study. Through PHO’s Locally Driven Collaborative Project (LDCP) initiative, several Ontario 

public health agencies are working together on a project that will support the identification of walkability and 

environmental exposure indicators and related data sources. This information will be used in the assessment 

of the urban built environment in Ontario, subsequently leading to the development of policy recommendations 

based on our findings. 

We are gathering insights from public health units with relevant experience in the area of the urban built 

environment, specifically in relation to walkability and environmental exposure indicators and data sources/ 

infrastructure. We are inviting representatives from your public health unit to participate in this voluntary survey. 

Given the in-depth nature of this survey, your public health unit may be required to contact other departments 

and/or municipalities to accurately complete this survey. We kindly request that your public health unit complete 

the following survey by [date] [link to electronic survey]. Note, the questions for walkability and environmental 

exposures have been separated to facilitate survey completion; please complete both parts. 

The results of the survey will assist in the development of jurisdictional profiles of built environment data 

infrastructure across Ontario. In the reporting of our results, your public health unit may be identified in the 

jurisdictional profiles outlining data sources and infrastructure. This information will later be applied in the 

development of policy recommendations. All final results will be published in report format on our project website 

(www.builtenvironment.ca). 

If you have any questions about this project, then please do not hesitate to contact our Project Coordinator, 

Popy Dimoulas-Graham, at 226-338-8004. If you have any concerns about your rights as a research participant 

please contact - Dr. Albert Clark, Chair of the Queen’s University and Affiliated Teaching Hospitals Research 

Ethics Board at (613) - 533-6081. 

We look forward to your participation in this collaborative initiative. 

Sincerely, 

Paul Belanger and Daphne Mayer 

KFL&A Public Health 

221 Portsmouth Ave 

Kingston, ON K7M 1V5 

613 549- 1232 


APPENDIX E 

227 

APPENDIX E: 


AND DATA SOURCES SURVEY LETTER OF INVITATION (LOI) 

INVESTIGATION TEAM 

Paul Belanger (co-lead) 

GIS Services Manager 



Helen Doyle 

Manager, 

Health Protection Division 


Deborah Moore 

Senior Epidemiologist 


Daphne Mayer (co-lead) 

Research Associate 



Mira Shnabel 

Environmental Health 

Program Coordinator 


Ryan Waterhouse 

GIS Analyst 


Asim Qasim 

Environmental Research 

and Policy Analyst 


Bill Hunter 

Manager, Environmental Health 


Caitlyn Paget 



Ahalya Mahendra 




228 

APPENDIX F 

APPENDIX F: 

BUILT ENVIRONMENT MEASURES AND DATA SOURCES SURVEY 

Built Environment Measures and Data 

Sources Related to Walkability and 

Environmental Exposures in Ontario 

Background 

On behalf of our investigation team, we would like to invite your public health unit to participate 

in a Public Health Ontario (PHO) funded study. Through PHO’s Locally Driven Collaborative 

Project (LDCP) initiative, several public health organizations, including representatives 

from the Association of Public Health Epidemiologists in Ontario (APHEO) Built Environment 

Indicator Subgroup, are working together on a project that will support the identification of 

walkability and environmental exposure measures (or indicators) as well as related data sources. 

This information will be used in the assessment of the urban built environment in Ontario, 

and to subsequently develop policy recommendations based on our findings. 

We are collecting information from Ontario public health units in the area of the urban built 

environment, specifically in relation to walkability and environmental exposure (air quality and 

extreme heat) measures and data sources. 

Given the in-depth nature of this survey, your public health unit may need to include other 

departments (e.g. Geographic Information System (GIS); Transportation Planning; Land Use 

Planning) and/or municipalities, as well as employee(s) with knowledge of GIS to accurately 

complete this survey. 

We kindly request that your public health unit complete the following survey by July 31, 2012. 

Note, each survey page contains a "save and continue later" button. Click this button to save 

and continue the survey at a later time. 

Thank-you, 

Project investigation team: 

Kingston, Frontenac and Lennox Addington Public Health (Study Lead) 



Sudbury District Health Unit 

York Region Community and Health Services 


APPENDIX F 

229 

APPENDIX F: 


Survey Consent 

By submitting this survey electronically, your public health unit is agreeing to participate 

in this study and is aware of the following: This study aims to support the identification of 

walkability and environmental exposure measures and data sources that can be used in the 

assessment of the urban built environment in Ontario. The results of the survey will assist 

in the development of jurisdictional profiles of built environment data infrastructure across 

Ontario. In the reporting of study results, your public health unit may be identified in the 

jurisdictional profiles outlining data sources and data infrastructure. This information will later 

be applied in the development of policy recommendations. Participation in this survey is 

voluntary. At any time, you can refuse to answer certain questions or put an end to this survey 

without prejudice to your public health unit. All final results will be published in report format 

on our project website: www.builtenvironment.ca 

For any information about the study, you may contact the co-principal investigators, Paul 

Belanger (613 549-1232 ext. 1602) and Daphne Mayer (613 549-1232 ext. 1125); or the 

Project Coordinator, Popy Dimoulas-Graham (226-338-8004). 

If you have questions about your rights as a participant in this study, you can contact Dr. 

Albert Clark, Chair, Queen’s University Health Sciences 

and Affiliated Teaching Hospitals Research Ethics Board at 613 533-6061. 


230 

APPENDIX F 

APPENDIX F: 


Please identify which Ontario 

Public Health Unit you represent: 

Please select from the list below. 

Algoma Public Health Unit 

Brant County Health Unit 

Chatham-Kent Health Unit 

City of Hamilton - Public Health Services 

Durham Region Health Department 

Eastern Ontario Health Unit 

Elgin-St. Thomas Health Unit 

Grey Bruce Health Unit 

Haldimand-Norfolk Health Unit 

Haliburton, Kawartha, Pine Ridge District Health Unit 

... 15 additional choices hidden ... 

Region of Waterloo, Public Health 

Renfrew County and District Health Unit 

Simcoe Muskoka District Health Unit 

Sudbury and District Health Unit 

Thunder Bay District Health Unit 

Timiskaming Health Unit 

Toronto Public Health 

Wellington-Dufferin-Guelph Health Unit 

Windsor-Essex County Health Unit 

York Region Community and Health Services 

Please indicate which section(s) of the survey you are completing at this time: 

Walkability Measures and Data Sources 

Air Quality Measures and Data Sources 

Extreme Heat Measures and Data Sources 


APPENDIX F 

231 

APPENDIX F: 


Walkability: Measures and Data Sources 

Does your organization assess walkability in urban environments? 

Yes 

No 

Don't know 

How many years has your organization been assessing walkability in urban 

environments? 

1-5 years 

6-10 years 

11+ years 


232 

APPENDIX F 

APPENDIX F: 


Please identify which method(s) your 

organization uses to assess urban 

walkability: 

Please select all that apply. 

Systematic observation (i.e. audit tool) 

Self-administered survey (assessing perceptions) 

Interview (e.g. RRFSS) 

Geographic Information System (GIS) (spatial analytics) 

Accelerometers 

Other, please specify: __________________________ 

Does your organization use individual 

measures to assess urban walkability? 

Yes 

No 

Don't know 

Does your organization use an index (or 

composite measure) to assess urban 

walkability? 

Yes 

No 

Don't know 


APPENDIX F 

233 

APPENDIX F: 


Please identify which measures are 

being used in the walkability index 

(or composite measure) and specify 

respective data source(s) for each: 

Density, please specify: 

Land Use Mix, please specify: 

Retail Floor Area, please specify: 

Connectivity, please specify: 

Other (please specify indicator(s) and related data source(s) for each): 


234 

APPENDIX F 

APPENDIX F: 


Which individual Connectivity measures 

does your organization use? 

Please select from the list provided below and specify the data source(s) for each. 

Does your organization use this measure? If yes, please specify data source: 

(e.g. DMTI, CCHS, Census, etc.) 

Block size and length 

Yes 

Yes, in development 

No 

Don't know 

Intersection density 

Yes 


No 

Don't know 

Street density 

Yes 


No 

Don't know 

Connected node ratio 

Yes 


No 

Don't know 

Alpha index 

Yes 


No 

Don't know 

Gamma index 

Yes 


No 

Don't know 

Other, please specify measure(s) and related data source(s) for each: 


APPENDIX F 

235 

Which individual Density measures does 

your organization use? 


Does your organization use this measure? If yes, please specify data source: (e.g. 

DMTI, CCHS, Census, etc.) 

Population density 

Yes 


No 

Don't know 


Yes 


No 

Don't know 

Employment density 

Yes 


No 

Don't know 


Which individual Diversity measures does 


Please select from the list provided below and specify the data source(s) for each 



Land Use Mix (LUM) (or mixed land use) 

Yes 


No 

Don't know 

Proximity (e.g. to school, park, trail, etc.) 

Yes 


No 

Don't know 

Retail Floor Area (FAR) 

Yes 


No 

Don't know 



236 

APPENDIX F 

APPENDIX F: 


Which individual Pedestrian 

Oriented Design measures does 





Posted speed limits 

Yes 


No 

Don't know 

Sidewalk width 

Yes 


No 

Don't know 

Presence of traffic circles 

Yes 


No 

Don't know 

No. of traffic lanes 

Yes 


No 

Don't know 

Presence of street furniture 

Yes 


No 

Don't know 

Cleanliness 

Yes 


No 

Don't know 


APPENDIX F 

237 

APPENDIX F: 


Crime rates 

Yes 


No 

Don’t know 

Presence of graffiti 

Yes 


No 

Don’t know 

Street lighting 

Yes 


No 

Don’t know 

Presence of abandoned or vacant buildings 

Yes 


No 

Don’t know 

Proximity to sources of air pollution 

Yes 


No 

Don’t know 

Canopy coverage (trees) 

Yes 


No 

Don’t know 



238 

APPENDIX F 

APPENDIX F: 


At what geographic scale does your 

organization most commonly assess 

urban walkability? 


Street Network 

Dissemination Block (i.e. area equivalent to city block, bounded by intersecting streets) 

Dissemination Area (i.e. area composed of dissemination blocks with ~400 to 700 persons) 

Census Tract (i.e. area composed of dissemination area’s with ~2500 to 8000 persons) 

Census Division (Region) 

Census Subdivision (i.e. Municipality) 

Census Division (i.e. Region) 

Don't know 

Other, please specify (i.e. custom geography or street segment or road network) 


APPENDIX F 

239 

APPENDIX F: 


Of the measures currently in use by your 

organization, which are of most value in 

assessing urban walkability? 


240 

APPENDIX F 

APPENDIX F: 


In the future, which measures would 

be of most value to your organization in 

assessing urban walkability? 


APPENDIX F 

241 

APPENDIX F: 


Please identify the data (sources) your 

organization has access to: 

Please select all that apply. If necessary, please inquire about data (sources) from 

other departments, municipalities, and/or with employee(s) who have knowledge of 

GIS, in order to accurately complete this question. 

Business registrations 

Census boundary files 

Crime statistics 

Digital elevation model (topography) 

DMTI Spatial 

Location of trails 

Location of parks 

Location of schools 

Locations of recreation centres 

Location of traffic controls (e.g. speed bumps) 

Location of controlled cross walks 

Municipal Property Assessment Corporation (MPAC) 

Presence of sidewalks 

Public transit data 

Rapid Risk Factor Surveillance System (RRFSS) 

Speed limits per street 

Street network files 

Street lighting standards 

Traffic counts 

Other, please specify data source(s): 


242 

APPENDIX F 

APPENDIX F: 


What major challenges does your 

organization face in assessing urban 

walkability? 







Lack of Geographic Information Systems (GIS) technical support 



Other, please specify: 


APPENDIX F 

243 

APPENDIX F: 


Air Quality: Measures and Data Sources 

Does your organization assess air quality in urban environments? 

Yes 

No 

Don't know 

How many years has your organization been assessing air quality in urban environments? 

1-5 years 

6-10 years 

11+ years 


244 

APPENDIX F 

APPENDIX F: 


Does your organization use an index 

(or composite measure) to assess air 

quality? 

Yes, the Air Quality Index (AQI) 

Yes, the Air Quality Health Index (AQHI) 

Yes, a different index: 

No 

Don't know 

Does your organization use readings for 

specific air pollutants from nearby Ministry 

of Environment (MOE) air monitoring 

stations? 

Yes 

No 

Don't know 


APPENDIX F 

245 

APPENDIX F: 


Which air pollutants does your organization 

track? 

Please specify the specific air pollutants tracked at each type of air monitoring station: 

Fine particulate matter (PM2.5) 

Ground level ozone (O3) 

Nitrogen dioxide (NO2) 

Sulphur dioxide (SO2) 

Carbon Monoxide (CO) 

General MOE air 

monitoring stations 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

MOE stations directed 

at local industrial sources 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Other, please specify air pollutant(s) and type of air monitoring station (as above): 


246 

APPENDIX F 

APPENDIX F: 


Does your organization use any of the 

following to assess air quality? 

Please select from the list provided below and specify the data source(s) for each. If 

necessary, please inquire about data and relevant data sources from other departments 

and/or municipalities in order to accurately complete this question. Examples of 

"information describing the data": inventory describing the available data such as 

available time periods, frequency of updating, spatial scale, etc. 

Data from portable or mobile air 


Data from remote sensing 

technologies (i.e. satellite based) 

Emission estimates (e.g. traffic, 

industrial, etc.) 

Air modelling estimates (e.g. 

dispersion modelling, land use 

regression) 

Meteorological data from 


Do you use this data/ 

estimate? 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

If yes, is it available as a 

spatial map? 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

If yes, do you have information describing the data? 

Data from stationary air monitoring equipment not collected under the MOE 

Data from portable or mobile air monitoring equipment 

Data from remote sensing technologies (i.e. satellite based) 

Emission estimates (e.g. traffic, industrial, etc.) 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 


APPENDIX F 

247 

APPENDIX F: 


Air modelling estimates (e.g. dispersion 

modelling, land use regression) 

Meteorological data from Environment Canada 

weather stations (e.g. temperature, humidex, 

wind speed) 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Other, please specify data, access, availability of spatial map and information (as above): 


248 

APPENDIX F 

APPENDIX F: 


Does your organization have access to 

the following data? 

Examples of data sources: DMTI, CCHS, Census, organization's own research, etc 

Examples of "information describing the data": inventory describing the available 

data such as available time periods, frequency of updating, spatial scale, etc. 

Do you have access? 

If yes, specify data source: 

If yes, is it available 

as a spatial map? 

Volume of traffic on regional or 




from high volume roads 


sources that report through the National 

Pollutant Release Inventory (NPRI) 

Vehicle kilometres travelled (per capita) 

in your communities 

Modal share (i.e., car, transit, cycling, 

walking split) in your communities 

Number of transit stops per square 

kilometer 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Volume of traffic on regional or 




from high volume roads 


sources that report through the National 

Pollutant Release Inventory (NPRI) 

If yes, do you have information describing the data? 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 


APPENDIX F 

249 

APPENDIX F: 


Vehicle kilometres travelled (per capita) 

in your communities 

Modal share (i.e., car, transit, cycling, 

walking split) in your communities 

Number of transit stops per square 

kilometer 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Other, please specify data, data source, availability of spatial map and information (as above): 


250 

APPENDIX F 

APPENDIX F: 



organization face in assessing air quality? 

Please check all that apply. 











APPENDIX F 

251 

APPENDIX F: 


Extreme Heat: Measures and Data 

Sources 

Does your organization assess extreme heat in urban environments? 

Yes 

No 

Don't know 

How many years has your organization been assessing extreme heat in urban 

environments? 

< 1 year 

1-5 years 

6-10 years 

11+ years 


252 

APPENDIX F 

APPENDIX F: 


Does your organization have access to 

any of the following data types related to 

extreme heat? 

For each data source (row), please specify: 

Meteorological data 

Built environment data (e.g. land use, forest cover, etc.) 

Thermal Imagery 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Does your organization use models 

to predict extreme heat events and/or 

health impacts related to extreme heat? 

Yes 

No 

Don't know 

Does your organization use demographic 

data to identify populations more 

vulnerable to extreme heat? 

Yes 

No 

Don't know 


APPENDIX F 

253 

APPENDIX F: 


How does your organization gain access 

to meteorogical data? 

Environment Canada Monitoring Stations 

Other Permanent Monitoring Stations 

Other Temporary/Mobile Monitoring Stations 

Don't know 

For the data sources outside of Environment Canada, please specify: 

Who owns the monitoring stations and data? 

What variables are measured? 

What meteorological data does your 

organization use to assess extreme heat? 

Temperature 

Yes 

No 

Don’t Know 

Not available 

Humidex Yes 

Yes 

No 

Don’t Know 

Not available 

Wind Speed 

Yes 

No 

Don’t Know 

Not available 

Other, please specify data: 


254 

APPENDIX F 

APPENDIX F: 


Which built environment measures 

related to extreme heat does your 

organization have access to? 



and/or municipalities in order to accurately complete this question. Examples 

of data sources: DMTI, CCHS, Census, organization’s own research, etc. Examples of 

“information describing the data”: inventory describing the available data such as available 

time periods, frequency of updating, spatial scale, etc. 

If you have access to this 

data, please specify source: 

If you have access, is it 

available as a spatial map? 

Canopy cover 

Age stratification of the urban 

forest stock 

Urban sprawl index 

Average unit size for each land 

use type 

Building density data 

Data on building age 

Database of multiunit residential 

buildings without access to air 

conditioning or cool rooms 

Surface reflectivity /albedo by 

land use 

Surface emissivity by land use 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 


APPENDIX F 

255 

APPENDIX F: 


If you have access, do you have information 

describing the data? 

Canopy cover 





use type 







land use 


Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 



256 

APPENDIX F 

APPENDIX F: 


Which built environment measures 

related to extreme heat does your 

organization use? 



and/or municipalities in order to accurately complete this question. Examples 

of data sources: DMTI, CCHS, Census, organization's own research, etc. Examples of 

"information describing the data": inventory describing the available data such as available 

time periods, frequency of updating, spatial scale, etc. 

Do you use this measure? 

If you have access to this 

data, please specify source: 

Canopy cover 





use type 



Yes 


No 

Don’t know 

Yes 


No 

Don’t know 

Yes 


No 

Don’t know 

Yes 


No 

Don’t know 

Yes 


No 

Don’t know 

Yes 


No 

Don’t know 


APPENDIX F 

257 

APPENDIX F: 





Surface reflectivity 

/albedo by land use 


Yes 


No 

Don’t know 

Yes 


No 

Don’t know 

Yes 


No 

Don’t know 

If you have access, is it available as a spatial map? 

Canopy cover 





use type 






Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 


258 

APPENDIX F 

APPENDIX F: 


Surface reflectivity /albedo by land use 


Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

If you have access, do you have information describing the data? 

Canopy cover 





use type 







land use 


Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 

Yes 

No 

Don’t Know 


APPENDIX F 

259 

APPENDIX F: 




260 

APPENDIX F 

APPENDIX F: 


How does your organization gain access 

to thermal imagery? 


Information is accessed through a regional or local government 

Public health unit directly requests information from data source 

Information is accessed through the federal government 

Other, please specify: __________________________ 

What type(s) of thermal imagery is available to your organization? 

Landsat Y 

Yes 

No 

Don’t Know 

Modis 

Yes 

No 

Don’t Know 

Infrared (IR) 

Yes 

No 

Don’t Know 

Other, please specify type(s) of thermal imagery available: 


APPENDIX F 

261 

APPENDIX F: 


Please provide the following information 

as it relates to the extreme heat model(s) 

used by your organization: 

What is the name of the model? 

What outcome does the model predict? 

What inputs does the model require? 

What are the data sources for the inputs? 


262 

APPENDIX F 

APPENDIX F: 


What demographic considerations 

does your organization use to identify 

populations more vulnerable to extreme 

heat? 

Please select all that apply: 

Age 

Gender 

Income 

Language 



APPENDIX F 

263 

APPENDIX F: 



organization face in assessing extreme 

heat? 











Thank you for your time. Click the SUBMIT button below to complete this survey. 

Our final study results will be published in report format on our project website: 

http://builtenvironment.ca/ 

If you have any questions, please contact the co-principal investigators, 

Paul Belanger (613 549-1232 ext. 1602) and Daphne Mayer (613 549-1232 ext. 1125); 

or the Project Coordinator, Popy Dimoulas-Graham (226-338-8004). 


264 

APPENDIX G 

APPENDIX G: 

GIS META DATA – WALKABILITY 

1. Street Network Metadata (2012-07-03) 

a) National Road Network (NRN) 

Metadata Element 

Title / Short Name 

Description 

Rationale 

National Road Network (NRN) 

The National Road Network (NRN) product contains quality geospatial and 

aspatial data of Canadian road phenomena. 

Vintage Varies depending on Province/ Territory. Ontario release (2012-06) 

Update Frequency 

Cost 

Geographic Coverage 

Scale/Resolution 

Spatial referencing 

(projection and datum) 

Format 

Primary Contact and 

Contact Details 

Thematic Keywords 

Entity and Attribute 

Enumeration 

Online Resource: 

Suitability and 

comparability 

Yearly 

None. Registration required to download. 

National Coverage (i.e. Provinces and Territories) 

The planimetric accuracy aimed for the product is 10 meters or better. Under 

the data maintenance phase, no systematic validation of geometric and 

attributive accuracies is performed on all NRN datasets. 

Horizontal Datum Name: NAD83CSRS (North American Datum 1983 in 

Canadian Spatial Reference System); 

Ellipsoid Name: GRS80; 

ESRI, KML, GML and WMS 

Geobase Technical Support: 

Government of Canada, Natural Resources Canada, Earth Sciences Sector 

Email: supportGeobase@nrcan.gc.ca 

NRCan, Transportation Network, Infrastructure 

Major Entities include: Address Range, Alternate Name Link, Blocked 

Passage, Ferry Connection, Junction, Road Segment, Street And Place 

Names, Toll Points 

http://www.geobase.ca/geobase/en/data/nrn/index.html 

Standardized national dataset with detailed documentation and regular 

update frequency. Maintained from its national road network partners the NRN 

incorporates data from the ORN to build its Ontario level file. 


APPENDIX G 

265 

APPENDIX G: 


b) Statistics Canada Road Network File (RNF) 



Description 

Rationale 

Road Network File (RNF) 

The Road Network File is a digital representation of Canada’s national road 

network, containing information such as street names, types, directions and 

address ranges. 

Vintage 2005-2011 

Cost 






Format 





Enumeration 



comparability 

None. Free to download. 

Yearly 


No formal assessment of relative positional accuracy has been undertaken. 

In 2011, the ORN provided updates from six Census Divisions (i.e. Halton, 

Hamilton, Ottawa, Peel, Toronto and Waterloo) in Ontario. 

Horizontal Datum Name: 

NAD83 (North American Datum); Ellipsoid Name: GRS80; 

ESRI, GML,MapInfo 

Statistics Canada National Contact Centre. Phone: 1-800-263-1136 

Email: infostats@statcan.gc.ca 

Highways, Road transport, Street networks, Streets, Census. 

Major Attribute fields names include: Name, Type, Dir, Address Ranges, 

Csdname, Csdtype, Cmaname, Prname, Street Rank,Class 

http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=92- 

500-X 

Standardized national dataset with detailed documentation and irregular update 

frequency. Designed principally for the purposes of conducting Statistics 

Canada activities (i.e. census) it maintains that it is not suitable for route 

optimization and other critical or engineering applications. With the exception 

of the census areas that incorporated the ORN, visual comparison with the 

other reviewed datasets often produced inconsistent positional discrepancies. 

Additionally, it was found that segmentation also differed from that of the NRN, 

ORN and CanMap Street file. 


266 

APPENDIX G 

APPENDIX G: 


c) Ontario Road Network File (ORN) 



Description 

Rationale 

Ontario Road Network (ORN) 

A geospatial database of Ontario’s Road Network (ORN) and its 

associated attributes, e.g. its street name or road number, its address 

information, road classification, etc. 

Vintage 2010-02-01 

Cost 




Spatial referencing (projection and 

datum) 

Format 

Primary Contact and Contact 

Details 


Entity and Attribute Enumeration 


None. Registration or membership to OGDE required to download. 

On-going/Continuous 

Province of Ontario 

Within 10 meters (Average) 


NAD83 (North American Datum);Ellipsoid Name: GRS80 

ESRI, WMS 

William Millar, 

Ministry of Natural Resources, 

Land Information Ontario – Support, Email: william.millar@ontario.ca 

Transportation, Transport, Highways, Streets, Roads, Mapping, 

Emergency Services, Land Use Planning. 

Major Attribute fields names include: Street Name, Street Type, Street 

Dir, Address Ranges, Road Class, Surface Types, Direction Of Traffic 

Flow, Speed Limits . 

https://www.appliometadata.lrc.gov.on.ca:/ 

geonetwork?uuid=c7c7202d-942d-47dc-bb15-259eb71f2551 

http://www.mnr.gov.on.ca/en/Business/LIO/index.html 

Suitability and comparability 

Standardized provincial dataset with detailed documentation and 

regular update frequency. ORN is the authoritative source of roads 

data for the Ontario Government. 


APPENDIX G 

267 

APPENDIX G: 


d) Table 4.0 CanMap ® Street files (DMTI) 



Description 

Rationale 

CanMap ® Streetfiles 

Detailed geospatial database representing nationwide coverage of 

roads and highways. 

Vintage 2012 

Cost 





datum) 

Format 


Details 





Available at no cost through most University and College library’s via 

DLI. Interested users outside these institutions should contact DMTI 

Spatial as public sector pricing models vary with requirements etc. 

Quarterly, semi-annual, or annual maintenance. 


Ranging from the National Topographic Data Base (NTDB) standard 

to 

sub-meter accuracy. 


NAD83 (North American Datum); Ellipsoid Name: GRS80 

ESRI, MapInfo or Custom Format 

DMTI Spatial Inc. 15 Allstate Parkway, Suite 400 Markham, Ontario 

L3R 5B4 Canada 

Road, Line, Highway, Expressway, Major Road, Local Road, Trail, 

Transportation, Center Line, Centre-Line 

Street, Address Ranges, PreDir, PreType, Street Name, Street Suffix, 

CSDName, FSAName, ProvName & Additional Lookup Tables. 

http://www.dmtispatial.com/en/Products/DataManagement/ 

CanMapProductSuite/CanMapStreetfiles.aspx 

Standardized national dataset with detailed documentation and 

regular update frequency. Previous usage in walkability type projects. 

Fairly commonplace in research institutions but not all public sector 

health units have access. 


268 

APPENDIX G 

APPENDIX G: 


2. Provincial-Level GIS Datasets: Topography, Imagery, and Land Use Mix 

Topography 

a) Digital Elevation Model – Version 2.00 – Provincial Tiled Dataset 

Metadata Source: http://www.mnr.gov.on.ca/en/Business/LIO/index.html 


Title/Short Name 

Description 

Rationale 

Ontario Digital Elevation Model 

DEM, Provincial DEM v2.00 

Dataset of 3-dimensional raster data which captures terrain elevations. 

Available in vector. 

Vintage January 2001 to December 2002 


Cost 



Spatial Referencing (projection 

and datum) 


Details 




Updated as deemed necessary. 

None. 

Available from the Ontario Geospatial Data Exchange (OGDE), 


Covers the province of Ontario to the 51 st parallel. 

Scale: 10 m in southern Ontario and 20m in northern Ontario. 

Horizontal: +/- 10m 

Vertical: +/- 5m 

Projection: Transverse Mercator (UTM) 

Datum: NAD83 

Ken Todd, Ministry of Natural Resources, 705.755.5023 

Forest management, water, water management, environment, 

watersheds, digital topographic data base, elevation models, digital 

terrain model, water analysis, topography. 

Latitude, longitude, elevation. 

Additional attribute information is available with data acquisition. 

Coverage is available for a large part of Ontario. For a walkability index, 

the scale may be too coarse. 


APPENDIX G 

269 

APPENDIX G: 


b) Shuttle Radar Topography Mission (SRTM) 

Metadata Sources: 3-arc-second, http://dds.cr.usgs.gov/srtm/version2_1/Documentation/SRTM_Topo.pdf 

30 arc second metadata, http://library.mcmaster.ca/maps/SRTM30_Documentation.pdf 



Description 

Rationale 

Shuttle Radar Topography Mission, SRTM 

Near-global elevation data. Digital elevation data consists of an ordered 

array of ground elevations sampled at regular intervals over each grid 

area. Aside from estimating point elevations, digital elevation has a variety 

of applications, including terrain modeling, line of sight analysis, water 

flow and flooding analysis, and many others. 

Vintage 2000 


Cost 



Spatial referencing (projection 

and datum) 


Details 




Completed. 

None. 

Datasets available for free to the public. 

Full coverage of Canada. 

Resolution: 3 arc-second (90m) and 30 arc-second (1km). 

Projection: Universal Transverse Mercator (UTM) and geographic (lat/long) 

Datum: WGS84 

Available from the USGS, 

http://srtm.usgs.gov/index.php and http://earthexplorer.usgs.gov/. 

Water bodies, digital terrain elevation, digital elevation model, topography. 

Additional theme information available from data acquisition. 

Latitude, longitude, elevation. Additional theme information available from 

data acquisition. 

Available SRTM resolution is between 90m and 1km. SRTM resolution is 

coarser than the DEM scale. The SRTM resolution is less suitable for a 

walkability index than the DEM. 


270 

APPENDIX G 

APPENDIX G: 


Imagery 

a) Ontario SPOT Pansharpened Orthoimagery 2005-2010 (SPOT) 

Metadata Sources: 

http://www.geobase.ca/geobase/en/metadata.do?id=17FB0A31-D9FE-A35B-3BDD-B8AFB93BDB66 

http://www.geobase.ca/doc/specs/pdf/GeoBase_Orthoimage_2005-2010_specs_EN.pdf 



Description 

Rationale 

Pansharpened SPOT Imagery, SPOT 

Spot 4/5 was acquired and orthorectified for the entire 

country by the Natural Resources Canada. 

Raster and vector data available. 

Vintage 2005-2010 


Cost 



Spatial referencing (projection and datum) 

None planned. 

None. Available from GeoBase, www.geobase.ca and 

LIO, http://www.mnr.gov.on.ca/en/Business/LIO/ 

index.html. 

Available for entire country and entire coverage of the 

province of Ontario and orthorectified. 

Resolution: 10m for panchromatic band and 20 m for 

three multispectral bands and 10 m multispectral images 

covering Ontario. 

Horizontal: +/- 30m 

Projection: Transverse Mercator (UTM) 

Datum: NAD83 

Primary Contact and Contact Details Carey Gibson, Ministry Natural Resources, 705.755.2150 




Farming, Biota, Economy, Environment, 

ImageryBaseMapsEarthCover, IntelligenceMillitary, 

InlandWaters, Structure, Transportation, 

UtilitiesCommunication 

Attribute information available with dataset. 

Coverage is available for the entire province of Ontario. 

The resolution is finer than DEM and SRTM, but may still 

be too coarse for a walkability index. 


APPENDIX G 

271 

APPENDIX G: 


b) Digital Raster Acquisition Project Eastern Ontario (DRAPE) 

Metadata Source: 

http://www.library.carleton.ca/find/gis/geospatial-data/eastern-ontario-air-photos-drape-2008 




Description 

Rationale 

Digital Raster Acquisition Project Eastern Ontario (DRAPE) 

Digital imagery collected using DMC digital aerial sensors to 20 

cm resolution for eastern Ontario. Covering 54,000 km sq. Of 

Ontario. 

Off-leaf conditions. 

Raster data. 

Imagery available in 1km tiles or mosaiced 20km tiles. 

Vintage 2008-2009 


Cost 




datum) 

Primary Contact and Contact Details 




Updated as necessary. 

None. 

Available from the Ontario Geospatial Data Exchange (OGDE), 

http://www.mnr.gov.on.ca/en/Business/LIO/index.html. 

Approximately 54,000 sq km covering an area stretching from 

the City of Kawartha Lakes/Northumberland County, eastward 

along the St. Lawrence River to the Quebec border and north to 

the County of Renfrew. 

20cm and 50cm resolution available. 

Projection: Transverse Mercator 

Datum: NAD83 

Mike Robertson, Ontario Ministry of Natural Resources, 

705.755.1280 

Remote sensing, geographic imagery collections, orthoimage, 

elevation, contour, air photo, aerial photography, aerial photo, 

aerial colour photography, photography, raster, remote sensing, 

orthophoto. 


DRAPE provides fine resolution imagery for a walkability index. 

Coverage is only available for eastern Ontario. 


272 

APPENDIX G 

APPENDIX G: 


c) Landsat 7 

Metadata Source: http://library.mcmaster.ca/maps/ogde_ls7.htm 


Rationale 

Title/Short Name Landsat 7 

Description 

Vintage 


Cost 




datum) 





Satellite land coverage available for the province of Ontario. 

Orthorectified imagery over Canada. 

Images available are False Colour Infrared and Fused True Colour. 

Coverage is generated as mosaics. 

Raster format. 

1999 to 2001, +/- 3 years 

None planned. 

None. Available at no cost from the Ontario Geospatial Data 

Exchange (OGDE), 

http://www.mnr.gov.on.ca/en/Business/LIO/index.htm. 

A large amount of Ontario is covered, but some areas are missing. 

An index showing the available frames is available from McMaster 

University, 

http://library.mcmaster.ca/maps/images/ogde_ls7indx.pdf. 

Scale: 1:50,000 

Resolution: Panchromatic resolution is 15m and other bands are 

30m. 

Projection: UTM 

Datum: NAD83 

Customer Support Group, Natural Resources of Canada, 

800.661.2638 

Available from Ontario Geospatial Data Exchange (OGDE), 


Theme information is available with dataset. 

Attribute information is available with dataset. 

The geographic coverage of Ontario good, but not complete. The 

resolution is comparable to the Ontario DEM and SRTM and is 

coarse for a walkability index. 


APPENDIX G 

273 

APPENDIX G: 


d) Southwestern Ontario Orthophotography Project (SWOOP) 

Metadata Sources: 


http://www.lib.uoguelph.ca/resources/data_resource_centre/geospatial_data_resources/southwestern_ 

ontario_orthophotography_project.cfm 



Description 

Rationale 

Southwestern Ontario Orthophotography Project, 2010; SWOOP, 

2010 

The orthophotography provides high-resolution, true colour aerial 

coverage for Southwestern Ontario. This data serves as a valuable 

information resource, providing a spatial context to features 

existing on the landscape in 2010. 

Vintage 2010 


Cost 




datum) 

Not completed. Updates as necessary. 

None. 

Available at no cost through the Ontario Geospatial Data 

Exchange (OGDE), 

http://www.mnr.gov.on.ca/en/Business/LIO/index.html. 

45,000 sq m covering. Coverage includes: Brant, Bruce, Dufferin, 

Elgin, Essex and Chatham, Gray, Haldimand, Huron, Lambton, 

Middlesex, Norfolk, Oxford, Perth, Waterloo, Wellington. 

Resolution: 20 cm resolution panchromatic imagery and 40 cm 

multi spectral. 

Projection: Universal Transverse Mercator (UTM) 

Datum: NAD83 

Primary Contact and Contact Details Mike Robertson, Ministry of Natural Resources, 705.755.1280 




Geographic imagery collections, remote sensing. More information 

available with dataset. 


Geographic coverage is only available for southwestern Ontario. 

The resolution is fine and is appropriate for a walkability study. 


274 

APPENDIX G 

APPENDIX G: 


e) Light Detection And Ranging (LiDAR) 

Metadata Source: http://agrg.cogs.nscc.ca/projects/LiDAR_Metadata. See Appendix Note 3. 

http://www.lidarbasemaps.org/. See Appendix Note 3. 



Description 

Vintage 


Cost 








Rationale 

Light Detection And Ranging, LiDAR 

Type of remote sensing similar to radar. Uses light or laser 

beams to detect distance to surface and calculate elevation 

data (ie. DEMs). 

2006 to Present 

Updated as necessary or as new information becomes 

available. 

None through Applied Geomatics Research Group (AGRG), 

http://agrg.cogs.nscc.ca/projects/LiDAR_Metadata 

Coverage available for North America. Fragments of Ontario 

covered. 

Resolution: Differs depending on area. Resolution may be as 

fine as 0.80m. 

Projection: NAD83 

Ministry of Natural Resources and Ministry of Environment 

Thematic keywords are available with datasets. 

Elevation (x,y,z), Transportation, Boundaries, Hydrography, 

Orthoimagery, Land Cover. More information about attributes 

is available with individual datasets. 

LiDAR is subject to conditions and requires the area of 

interest to be well researched before using data. Coverage of 

Ontario is not comprehensive and is fragmented. Resolution is 

medium, possibly too coarse for a walkability study. 


APPENDIX G 

275 

APPENDIX G: 


Land Use Mix 

a) CanVec 

Metadata Source: http://geogratis.cgdi.gc.ca/geogratis/en/collection/5460AA9D-54CD-8349-C95E- 

1A4D03172FDF.html 



Description 

Rationale 

CanVec 

CanVec is a digital cartographic reference product produced by Natural 

Resources Canada. CanVec originates from the best available data sources 

(Landsat 7, NTDB, and Spot Imagery) covering Canadian territory and 

offers quality topographic information in vector format that complies with 

international geomatics standards. 

Vector data. 

Vintage 1945 to 2012 


Cost 


Bi-annually 

None to the public. Available for free through Geogratis. 

Coverage available for all of Canada by consolidating data from best available 

sources. 

Scale/Resolution Scale: 1:10,000 and 1:50,000 







Enumeration 


comparability 

Projection: Geographic (lat/long) 

Datum: NAD83CSRS 

Customer Support Group, Natural Resources Canada, Earth Sciences Sector, 

Centre for Topographic Information, 800.661.2638 

Available from Geogratis, http://geogratis.cgdi.gc.ca 

Administrative Boundaries, Buildings and Structures, Energy, Hydrography, 

Industrial and Commercial Areas, Places of Interest, Relief and Landforms, 

Toponymy, Transportation, Vegetation and Water Saturated Soils. 

89 entities organised into 11 distribution themes. 

See Appendix 4 for description of Feature Catalogue and link to document. 

Considered to be the most up-to-date source for land use mix data in 

Canada. CanVec is recommended to replace NTDB. Geographic coverage is 

extensive, but the scale is too coarse for a walkability index. 


276 

APPENDIX G 

APPENDIX G: 


b) National Topographic Data Base (NTDB) 

Metadata Source: http://geogratis.cgdi.gc.ca/geogratis/en/collection/F3D83500-2564-D61E-4F37- 

FEF860E6DDC0.html;jsessionid=4BB38783CA33505707E655049A394921 



Description 

Rationale 

National Topographic Data Base, NTDB 

Digital vector data sets that cover the entire Canadian landmass. 

Geomatics Canada has digitised and structured thousands of 

topographic maps, creating a complete and uniform product that 

can be highly useful in a broad range of industries. The NTDB 

includes features such as watercourses, urban areas, railways, 

roads, vegetation, and relief. 

Vintage 1944-2002 


Cost 


As of 2008 completed and no updates planned. 

None. 

Available for no cost to the public through Geogratis, 

http://geogratis.cgdi.gc.ca. 

Coverage available for all of Canada. 

Scale/Resolution 1:50,000 and 1:250,000 


datum) 





Datum: NAD83 

Projection: Geographic (lat/long) 

Customer Support Group, Ministry of Natural Resources, Earth 

Sciences Sector, Centre for Topographic Information 

800.661.2638 

Available from Geogratis, http://geogratis.cgdi.gc.ca. 

Designated Area, Roads, Manmade Features, Relief and 

Landform, General, Hydrology, Hypsography, Administrative 

Boundaries, Toponymy, Power Network, Rail network, Road 

network, Water saturated soils, and Vegetation. 

122 entities divided into 13 themes 

See user guide link in Appendix 5. 

http://library.mcmaster.ca/maps/ntdb250.htm 

NTDB is now static and users should transition to CanVec. 

Appendix 5 for link to Transition Guide. 


APPENDIX G 

277 

APPENDIX G: 


c) DMTI – CanMap Route Logistics 

Metadata Source: 

2010 User Manual, http://gsg.uottawa.ca/geo/metadata/dmti_doc/CanMap_RouteLogistics_v2010_3.pdf 



Description 

Rationale 

CanMap Route Logistics, DMTI 

CanMap RouteLogisitics produced by DMTI, can be used for location 

based service applications, routing and fleet management, market 

analysis, target marketing, site location analysis, customer service 

and asset management. Calculated gradient, distance and travel 

times based on the slope of each segment is also available. 

Vector data. 

Vintage Current to 2011 


Cost 




datum) 


Details 




Continuous 

Available at no cost to university students and faculty for educational 

purposes if educational institution is a subscriber. 

For all other purposes, must be purchased from the provider, DMTI 

Spatial Inc., http://www.dmtispatial.com/. 

Coverage available for all of Canada. 

Resolution: High 

Scales: Various 

Projection Unprojected latitude and longitude. 

Datum: NAD83 

DMTI Spatial Inc. 

Ivan Barron 

877.477.3684 x 2181 

Hydrology, Roads, Drainage, Political, Parks, Addresses, Postal 

Geography, Vegetation, Recreation, Streets – One way, Streets – 

Speed limits, Highways – Speed limits, Highways – Exits, Utilities, 

Place Names, Land Use, Boundaries – Political, Industrial, Food, Travel 

Time, Census, Services, Cultural, Emergency, Transportation, Train, 

Bus, Building – Points, Building – Footprints, Trails, Streets, Highways, 

Railroads, Hydrography. See User Guide link above for more details. 

Topographic, Point of Interest (POI), Streets. See 2010 User Guide link 

above for more details. 

Great detail and realism for a walkability index. Ability to optimise travel 

distances and time. Recommended to use the dataset with Canada 

Base Map (DMTI). 


278 

APPENDIX G 

APPENDIX G: 


d) Ontario Land Parcel - Teranet/Teraview and Municipal Property Assessment Corporation (MPAC) 

Metadata Source: http://publicdocs.mnr.gov.on.ca/View.asp?Document_ID=13873&Attachment_ID=43863 



Description 

Vintage 


Cost 


Rationale 

Ontario Land Parcel, MPAC, POLARIS, Teranet 

The Ontario Parcel database was brought together by the Ministry of Natural 

Resources, Teranet, and MPAC and consists of Ontario’s estimated 4 million 

parcels of land. It is an integrated set of assessment, Crown, and parcel data 

layers. The layers consist of polygons with unique identifiers and civic addressing 

linked to the assessment parcel. Can be used for land planning. Three structured 

layers are available: Digital Assessment Parcel Fabric (DAPF), Digital Ownership 

Parcel Fabric (DOPF), and Digital Crown Parcel Fabric (DCPF). 

DAPF is a digital index map describing the limits of parcels for property assessment 

purposes in Ontario. Contains 4,260,975 parcels with over 80,000 parcels inserted 

each year. 

DOPF data are compiled using legal information, including cadastral surveys, which 

are available in the land registry offices and contain graphical information describing 

the limits of the properties represented in the POLARIS title database. 

DCPF This is a digital index map describing the limits of Crown parcels within 

Ontario. Some examples include patented parcels, federal parks, First Nations 

lands and Crown land-use permits 

Vector. 

2002 to Present. 

Quarterly. 

Available at no cost through the Ontario Geospatial Data Exchange (OGDE), 

http://www.mnr.gov.on.ca/en/Business/LIO/index.htm. 

Coverage available for all of Ontario. 

Scale/Resolution Scale: 1:5,000 


(projection and 

datum) 





Enumeration 


comparability 

Datum: NAD83 

Projection: geographic (lat./long.) and UTM 

Ontario Geospatial Data Exchange (OGDE) 

http://www.mnr.gov.on.ca/en/Business/LIO/index.htm 

DAPF themes: Assessment Parcel, Parcel, assessment (estimation in general), land 

management, MPAC. 

DCPF themes: Crown parcel 

DOPF themes: Ownership parcel 

Additional theme information is available with data acquisition. See Appendix 6. 

See Metadata source link above for attributes contained in layers. Additional theme 

information is available with data acquisition. See Appendix 6. 

Geographic coverage is complete and frequently up-dated for the province of 

Ontario. Easily integrated with land data from other resources. 


APPENDIX G 

279 

APPENDIX G: 


3. Street Network Metadata (2012-07-03) 

a) Table 1.0 National Road Network (NRN) 



Description 

Vintage 


Cost 




Format 






Rationale 

National Road Network (NRN) 

The National Road Network (NRN) product contains quality 

geospatial and aspatial data of Canadian road phenomena. 

Varies depending on Province/ Territory. Ontario release 

(2012-06) 

Yearly 

None. Registration required to download. 

National Coverage(i.e. Provinces and Territories) 

The planimetric accuracy aimed for the product is 10 

meters or better. Under the data maintenance phase, no 

systematic validation of geometric and attributive accuracies 

is performed on all NRN datasets. 

Horizontal Datum Name: NAD83CSRS (North American 

Datum 1983 in Canadian Spatial Reference System); 

Ellipsoid Name: GRS80; 

ESRI, KML, GML and WMS 

Geobase Technical Support: 

Government of Canada, Natural Resources Canada, Earth 

Sciences Sector 

Email: supportGeobase@nrcan.gc.ca 

NRCan, Transportation Network, Infrastructure 

Major Entities include: Address Range, Alternate Name 

Link, Blocked Passage, Ferry Connection, Junction, Road 

Segment, Street And Place Names, Toll Points 

http://www.geobase.ca/geobase/en/data/nrn/index.html 

Standardized national dataset with detailed documentation 

and regular update frequency. Maintained from its national 

road network partners the NRN incorporates data from the 

ORN to build its Ontario level file. 


280 

APPENDIX G 

APPENDIX G: 


b) Statistics Canada Road Network File (RNF) 



Description 

Rationale 

Road Network File (RNF) 

The Road Network File is a digital representation of Canada’s national road 

network, containing information such as street names, types, directions and 

address ranges. 

Vintage 2005-2011 

Cost 






Format 





Enumeration 


None. Free to download. 

Yearly 


No formal assessment of relative positional accuracy has been undertaken. 

In 2011, the ORN provided updates from six Census Divisions (i.e. Halton, 

Hamilton, Ottawa, Peel, Toronto and Waterloo) in Ontario. 


NAD83 (North American Datum); Ellipsoid Name: GRS80; 

ESRI, GML,MapInfo 

Statistics Canada National Contact Centre. Phone: 1-800-263-1136 

Email: infostats@statcan.gc.ca 

Highways, Road transport, Street networks, Streets, Census. 

Major Attribute fields names include: Name, Type, Dir, Address Ranges, 

Csdname, Csdtype, Cmaname, Prname, Street Rank,Class 

http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=92- 

500-X 


Standardized national dataset with detailed documentation and irregular 

update frequency. Designed principally for the purposes of conducting 

Statistics Canada activities (i.e. census) it maintains that it is not suitable for 

route optimization and other critical or engineering applications. With the 

exception of the census areas that incorporated the ORN, visual comparison 

with the other reviewed datasets often produced inconsistent positional 

discrepancies. Additionally, it was found that segmentation also differed from 

that of the NRN, ORN and CanMap Street file. 


APPENDIX G 

281 

APPENDIX G: 


c) 3.0 Ontario Road Network File (ORN) 



Description 

Rationale 

Ontario Road Network (ORN) 

A geospatial database of Ontario’s Road Network (ORN) and its 

associated attributes, e.g. its street name or road number, its 

address information, road classification, etc. 

Vintage 2010-02-01 

Cost 





datum) 

Format 





None. Registration or membership to OGDE required to download. 

On-going/Continuous 

Province of Ontario 

Within 10 meters (Average) 


NAD83 (North American Datum);Ellipsoid Name: GRS80 

ESRI, WMS 

William Millar, 

Ministry of Natural Resources, 

Land Information Ontario – Support, 

Email: william.millar@ontario.ca 

Transportation, Transport, Highways, Streets, Roads, Mapping, 

Emergency Services, Land Use Planning. 

Major Attribute fields names include: Street Name, Street Type, 

Street Dir, Address Ranges, Road Class, Surface Types, Direction 

Of Traffic Flow, Speed Limits . 

https://www.appliometadata.lrc.gov.on.ca:/ 

geonetwork?uuid=c7c7202d-942d-47dc-bb15-259eb71f2551 



Standardized provincial dataset with detailed documentation and 

regular update frequency. ORN is the authoritative source of roads 

data for the Ontario Government. 


282 

APPENDIX G 

APPENDIX G: 


d) Table 4.0 CanMap® Street files (DMTI) 



Description 

Rationale 

CanMap ® Streetfiles 

Detailed geospatial database representing nationwide coverage of 

roads and highways. 

Vintage 2012 

Cost 





datum) 

Format 






Available at no cost through most University and College library’s 

via DLI. Interested users outside these institutions should 

contact DMTI Spatial as public sector pricing models vary with 

requirements etc. 

Quarterly, semi-annual, or annual maintenance. 


Ranging from the National Topographic Data Base (NTDB) 

standard to sub-meter accuracy. 


NAD83 (North American Datum); Ellipsoid Name: GRS80 

ESRI, MapInfo or Custom Format 

DMTI Spatial Inc. 15 Allstate Parkway, Suite 400 Markham, 

Ontario L3R 5B4 Canada 

Road, Line, Highway, Expressway, Major Road, Local Road, Trail, 

Transportation, Center Line, Centre-Line 

Street, Address Ranges, PreDir, PreType, Street Name, Street 

Suffix, CSDName, FSAName, ProvName & Additional Lookup 

Tables. 

http://www.dmtispatial.com/en/Products/DataManagement/ 

CanMapProductSuite/CanMapStreetfiles.aspx 

Standardized national dataset with detailed documentation and 

regular update frequency. Previous usage in walkability type 

projects. Fairly commonplace in research institutions but not all 

public sector health units have access. 


APPENDIX H 

283 

APPENDIX H: 

GIS META DATA – AIR QUALITY 

Metadata 

Element 

Air Quality 

Title / 

Short Name 

Description 

Ambient Air Quality 

Network 

The Ambient Air Quality 

Network provides AQI 

readings and hourly air 

pollutant concentrations 

measured from ambient air 

monitoring stations. 

Air Quality Health 

Index 

The AQHI is an index 

incorporating health 

outcomes with 

ambient pollutant 

levels 

National Pollutant 

Release Inventory 

The NPRI collects data 

on pollutant emissions 

from industrial and nonindustrial 

sources 

Vintage 2000-Present 1993-2010 1998-2008 

Update 

Frequency 

Geographic 

Coverage 

Hourly Hourly Annual emissions Annual 

Cost None None None None 

Ontario Canada (Ontario) Canada (Ontario) Ontario 

Scale/Resolution n/a n/a 

Spatial 

referencing 

(projection and 

datum) 

Primary Contact 

and Contact 

Details 

WG84 decimal degrees 

Publically available 

information 


information (not on 

website) 

Emission density (10km 

x 10km) 

WG84 decimal degrees 

(facility location), 

jpeg (aggregate of 

emission sources), Kmz 

spatial maps 


information 


1-877-877-8375 

inrp-npri@ec.gc.ca 

Provincial Highway Traffic 

volume 

Ontario Ministry of Transportation 

annual publication on traffic volume 

and accident rates of provincial 


n/a 

Highway segment defined by 

intersecting or proximate roads 

(e.g. W END GARDEN CITY 

SKYWAY BRIDGE) 

Publically available information 

Traffic Office (905)-704-2960 

Thematic 

Keywords 

Air quality index, AQI, 

pollutants, ozone, O3, 

nitrogen dioxide, NO2, nitric 

oxide, NO, nitrogen oxides, 

NOx, sulphur dioxide, SO2, 

particulate matter, PM2.5, 

carbon monoxide, CO, 

AQHI, ozone, O3, 

Nitrogen dioxide, 

NO2, particulate 

matter, PM2.5, 

PM10 

Industry, emissions, 

pollutants, emission 

density 

Traffic, road, highway, traffic 

volume, Annual Average Daily 

Traffic volume (AADT), Summer 

Average Daily Traffic volume 

(SADT), Summer Average Weekday 

Traffic volume 

(SAWDT), Winter Average Daily 

Traffic volume (WADT), Accident 

Rate (AR) 

Entity and 

Attribute 

Enumeration 

-Address location 

-AQI 

-Pollutant 

-Station type (urban/rural, 

etc) 

-Elevation 

-Air intake height 

-AQHI reading 

-Time and Date 

-Annual total emissions 

-Various facility 

descriptors 

-census location 

-Annual Average Daily Traffic 

-Summer Average Daily Traffic; 

-Summer Average Weekday Traffic; 

-Winter Average Daily Traffic; 

-Length of section (km) 

-Road pattern type 

-Accident rate 

-Location description 


comparability 

The data source is suitable 

as a general indicator of air 

quality. It provides limited 

spatial detail for the built 

environment. Some sites 

do not capture all pollutants 

of interest, and may have 

missing historical data. 

Data gaps exist for some 

years. Spatial detail on 

distribution of pollutant 

is limited. 

The information provided is suitable 

indicator of traffic volumes for 

significant sources of pollution. 

However, the information only 

covers provincial highways and 

does not provide additional 

information on city roads. 


284 


APPENDIX I 

285 

APPENDIX I: 

GIS META DATA – EXTREME HEAT 

APPENDIX I 

APPENDIX I:GIS META DATA – EXTREME HEAT 



Common name of the 

dataset 

Description 

More complete description 

Vintage 

Timeframe that the data 

refers to 

Update Frequency To 

assess how current the 

dataset may be at any 

given time 

Cost 

Cost to a public-benefit 

corporation and limits on 

redistribution of derived 

layers/ information 

products 


Relevant areal coverage 


Data Sources & Sets 

Southeastern Ontario 

Landsat Aster Modis Aerial Imagery Weather Stations 

environmental heat 

Census 

monitoring network 

Wet-bulb globe temperature 

sensors were installed over SE 

Advanced Spaceborne Thermal 

Information of 

Landsat 7 Orthorectified Imagery over 

Moderate Resolution Imaging 

Custom Aerial Image Acquisition Environment Canada Weather Stations / National Ontario to capture real-time heat 

Emission and Reflection 

socioeconomic and 

Canada 

Spectrometer 

(e.g. First Base Solutions) 

Climate Data 

data (air temperature, humidity, 

Radiometer 

demographic attributes 

wind, speed, solar load, and 

waterless wet bulb temperature). 

2000 - present (with First Base Hourly; Daily; Monthly; Almanac (Period of record varies 

1984 - present 1999 - present 1999 - present 

2009-Present Up to 2011 

Solutions) 

by station) 

Currently operating under 

a Queens REB so there are 

Every 16 days Varies by request, 4-16 day range 2x per day Varies by request Varies depending on station of interest 

Every 5 years 

restrictions and it has not been 

released to others. 

Free: Hourly (temp, dew point temp, relative humidity, 

None. Historical LANDSAT data archive 

wind speed, wind direction, station pressure, humidex, 

is completely open, accessed over the Varies, go to http://eros.usgs. 

Varies, go to http://www. 

weather); daily (max/ min/mean temp, cooling degree 

No cost, though data cannot yet No cost for standard data 

web through established data centres or gov/#/Find_Data/Products_and_ None 

firstbasesolutions.com/contact. days); monthly (max/ min/mean temp, extreme max 

be released. 

products 

USGS centres such as Earth Resources Data_Available/Aster 

php 

temp. Other observations not available online can only 

Observation System (EROS). 

be obtained by order of a cost-recovered customized 

dataset. 

SE Ontario (PHUs involved: 

Hastings & Prince Edward 

Canada Canada Canada Canada Canada (coverage is limited to station availability) 

Canada 

; Leeds, Grenville & Lanark; 

KLF&A, Peterborough) 

15m (VNIR) - 90m (TIR), 60km 

30m-60m, 185km swath 

250m - 1km, 10km swath 3cm - 65cm Individual stations Individual sensors Census block 

swath 



For GIS ingest 

NAD1983 NAD1983 NAD1983 Customizable 

Contains latitude and longitude (Degrees & minutes) 

that can be used to create a GIS shape file. This can 

be provided by ordering a customized dataset. 

WGS84 Geographic Coordinates 

of each station are available 

Census tract (larger) and 

dissemination area (smaller). 


Contact Details Who to 

contact to discuss access 

NRCan, Earth Sciences Sector, Centre 

for Topographic Information 010- 2144 

King Street West, Sherbrooke, Quebec 

J1J 2E8 

1-819-564-5600, 

geoginfo@NRCan.gc.ca 

USGS National Center 

12201 Sunrise Valley Dr 

Reston, VA 20192, USA 

703-648-5953 

custserv@usgs.gov 




703-648-5953 


First Base Solutions 

100-140 Renfrew Drive 

Markham, ON L3R 6B3 

Phone: 905.477.3600 

sales@firstbasesolutions.com 

EC -Ontario 

Tel: 1 900 565-1111 ($2.99/min) 

ontario.climate@ec.gc.ca 

Dr. Geoff Hall 

Department of Civil Engineering 

Department of Family Medicine 

Queen’s University 

Kingston, Ontario 

K7L 3N6 

Statistics Canada 

150 Tunney’s Pasture 

Driveway 

Ottawa, ON K1A 0T6 

1-800-263-1136 

infostats@statcan.gc.ca 


For searching 

Entity & Attribute 

Enumeration Besides 

georeferencing, what 

attribute data are 

captured? 


comparability 

How suitable is it for 

generating the metrics of 

interest and/or, how does it 

compare? 

Source 

Landsat, NRCan, thermal, IR, infrared, 

USGS, raster 

Pixel digital number (converted to 

degrees Celsius) 

Useful for heat primarily. Compares well 

to other data sources that estimate 

surface temperature. Satellite may be 

ending useful lifespan. 

Natural Resources Canada. Satellite 

Acquisition Services [Internet]. Ottawa 

(ON): Public Works and Government 

Services Canada; [updated 2009 Jul; 

cited 2012 Oct ]. Available from: http:// 

www.nrcan.gc.ca/earth-sciences/ 

products-services/satellitephotography-imagery/satelliteacquisition-services/2350. 

Aerial, orthophoto, image, IR, infra Heat, temperature, environment, Canada, station, Heat sensors, wet bulb globe, 

Aster, USGS 

MODIS, USGS 

Census, demographic 

red 

historical, weather, data 

heat stress 

Was not assessed Was not assessed Was not assessed Was not assessed Was not assessed Was not assessed 

Useful for obtaining thermal imagery 

at a high resolution, but this will only 

Useful for creation of 

Generates air temperatures, but sparse spatial 

Was not assessed 


be surface temperature, so again, 


community vulnerability 

coverage 

limited in its application given the 

indices 

cost of acquisition 

Earth Resources Observation and 

Belanger, P. (Department 

Science (EROS) Center. Advanced Earth Resources Observation and First Base Solutions. 

of Geography, Queen’s 

Statistics Canada. Census. 

Environment Canada. 

Spaceborne Thermal Emission and Science (EROS) Center. Moderate Aerial Imagery and 

University, Kingston, Frontenac, [Internet]. Ottawa (ON): 

National Climate Data and Information Archive 

Reflection Radiometer (ASTER) Resolution Imaging Spectroradiometer Orthophotography [Internet]. 

Lennox and Addington Public Statistics Canada. [ cited 

[Internet]. [Fredericton, (NB): Public Works and 

[Internet]. [Sioux Falls, (SD) ]: U.S. (MODIS) [Internet]. [Sioux Falls, (SD) ]: Markham (ON): First Base Solutions; 

Health Unit, Kingston, ON), 2012 Nov]. Available 

Government Services Canada; [updated 2012 Nov; 

Geological Survey; [ updated 2012 U.S. Geological Survey; [ updated 2012 c2001-2012 [cited 2012 Nov]. 

Conversation with: Geoff Hall ( from: http://www12. 

cited 2012 Nov]. Available from: 

Nov; cited 2012 Oct . Available Jul; cited 2012 Oct. Available from: Available from: 

Department of Civil Engineering, statcan.gc.ca/censusrecensement/index-eng. 

http://www.climate.weatheroffice.gc.ca/ 

from: http://eros.usgs.gov/#/ http://eros.usgs.gov/#/Find_Data/ http://www.firstbasesolutions. 

Department of Family Medicine, 


Find_Data/Products_and_Data_ Products_and_Data_Available/MODIS com/imagery.php 

Queen’s University, Kingston, cfm 

Available/Aster 

ON). 2012 Oct. 




APPENDIX I 

APPENDIX I:GIS META DATA – EXTREME HEAT 



Common name of the 

dataset 

Description 

More complete description 

Vintage 

Timeframe that the data 

refers to 

Update Frequency To 

assess how current the 

dataset may be at any 

given time 

Cost 

Cost to a public-benefit 

corporation and limits on 

redistribution of derived 

layers/ information 

products 


Relevant areal coverage 


Data Sources & Sets 

Landsat Aster Modis Aerial Imagery Weather Stations 

Landsat 7 Orthorectified Imagery over 

Canada 

Advanced Spaceborne Thermal 

Emission and Reflection 

Radiometer 

Moderate Resolution Imaging 

Spectrometer 

1984 - present 1999 - present 1999 - present 

Custom Aerial Image Acquisition 

(e.g. First Base Solutions) 

2000 - present (with First Base 

Solutions) 

Environment Canada Weather Stations / National 

Climate Data 

Hourly; Daily; Monthly; Almanac (Period of record varies 

by station) 

Every 16 days Varies by request, 4-16 day range 2x per day Varies by request Varies depending on station of interest 

None. Historical LANDSAT data archive 

is completely open, accessed over the 

web through established data centres or 

USGS centres such as Earth Resources 

Observation System (EROS). 

Varies, go to http://eros.usgs. 

gov/#/Find_Data/Products_and_ 

Data_Available/Aster 

None 

Varies, go to http://www. 

firstbasesolutions.com/contact. 

php 

Free: Hourly (temp, dew point temp, relative humidity, 

wind speed, wind direction, station pressure, humidex, 

weather); daily (max/ min/mean temp, cooling degree 

days); monthly (max/ min/mean temp, extreme max 

temp. Other observations not available online can only 

be obtained by order of a cost-recovered customized 

dataset. 

Canada Canada Canada Canada Canada (coverage is limited to station availability) 

30m-60m, 185km swath 

15m (VNIR) - 90m (TIR), 60km 

swath 

Southeastern Ontario 

environmental heat 

monitoring network 

Wet-bulb globe temperature 

sensors were installed over SE 

Ontario to capture real-time heat 

data (air temperature, humidity, 

wind, speed, solar load, and 

waterless wet bulb temperature). 

Census 

2009-Present Up to 2011 

Currently operating under 

a Queens REB so there are 

restrictions and it has not been 

released to others. 

No cost, though data cannot yet 

be released. 

SE Ontario (PHUs involved: 

Hastings & Prince Edward 

; Leeds, Grenville & Lanark; 

KLF&A, Peterborough) 

Information of 

socioeconomic and 

demographic attributes 

Every 5 years 

No cost for standard data 

products 

250m - 1km, 10km swath 3cm - 65cm Individual stations Individual sensors Census block 

Canada 



For GIS ingest 

NAD1983 NAD1983 NAD1983 Customizable 

Contains latitude and longitude (Degrees & minutes) 

that can be used to create a GIS shape file. This can 

be provided by ordering a customized dataset. 

WGS84 Geographic Coordinates 

of each station are available 

Census tract (larger) and 

dissemination area (smaller). 


Contact Details Who to 

contact to discuss access 

NRCan, Earth Sciences Sector, Centre 

for Topographic Information 010- 2144 

King Street West, Sherbrooke, Quebec 

J1J 2E8 

1-819-564-5600, 

geoginfo@NRCan.gc.ca 




703-648-5953 





703-648-5953 


First Base Solutions 

100-140 Renfrew Drive 

Markham, ON L3R 6B3 

Phone: 905.477.3600 

sales@firstbasesolutions.com 

EC -Ontario 

Tel: 1 900 565-1111 ($2.99/min) 

ontario.climate@ec.gc.ca 

Dr. Geoff Hall 

Department of Civil Engineering 

Department of Family Medicine 

Queen’s University 

Kingston, Ontario 

K7L 3N6 

Statistics Canada 

150 Tunney’s Pasture 

Driveway 

Ottawa, ON K1A 0T6 

1-800-263-1136 

infostats@statcan.gc.ca 


For searching 

Entity & Attribute 

Enumeration Besides 

georeferencing, what 

attribute data are 

captured? 


comparability 

How suitable is it for 

generating the metrics of 

interest and/or, how does it 

compare? 

Source 

Landsat, NRCan, thermal, IR, infrared, 

USGS, raster 

Pixel digital number (converted to 

degrees Celsius) 

Useful for heat primarily. Compares well 

to other data sources that estimate 

surface temperature. Satellite may be 

ending useful lifespan. 

Natural Resources Canada. Satellite 

Acquisition Services [Internet]. Ottawa 

(ON): Public Works and Government 

Services Canada; [updated 2009 Jul; 

cited 2012 Oct ]. Available from: http:// 

www.nrcan.gc.ca/earth-sciences/ 

products-services/satellitephotography-imagery/satelliteacquisition-services/2350. 

Aster, USGS 

MODIS, USGS 

Aerial, orthophoto, image, IR, infra 

red 

Heat, temperature, environment, Canada, station, 

historical, weather, data 

Heat sensors, wet bulb globe, 

heat stress 

Census, demographic 

Was not assessed Was not assessed Was not assessed Was not assessed Was not assessed Was not assessed 



Science (EROS) Center. Advanced 

Spaceborne Thermal Emission and 

Reflection Radiometer (ASTER) 

[Internet]. [Sioux Falls, (SD) ]: U.S. 

Geological Survey; [ updated 2012 

Nov; cited 2012 Oct . Available 

from: http://eros.usgs.gov/#/ 

Find_Data/Products_and_Data_ 

Available/Aster 



Science (EROS) Center. Moderate 

Resolution Imaging Spectroradiometer 

(MODIS) [Internet]. [Sioux Falls, (SD) ]: 

U.S. Geological Survey; [ updated 2012 

Jul; cited 2012 Oct. Available from: 

http://eros.usgs.gov/#/Find_Data/ 

Products_and_Data_Available/MODIS 

Useful for obtaining thermal imagery 

at a high resolution, but this will only 

be surface temperature, so again, 

limited in its application given the 

cost of acquisition 

First Base Solutions. 

Aerial Imagery and 

Orthophotography [Internet]. 

Markham (ON): First Base Solutions; 

c2001-2012 [cited 2012 Nov]. 

Available from: 

http://www.firstbasesolutions. 

com/imagery.php 

Generates air temperatures, but sparse spatial 

coverage 

Environment Canada. 

National Climate Data and Information Archive 

[Internet]. [Fredericton, (NB): Public Works and 

Government Services Canada; [updated 2012 Nov; 

cited 2012 Nov]. Available from: 

http://www.climate.weatheroffice.gc.ca/ 



Belanger, P. (Department 

of Geography, Queen’s 

University, Kingston, Frontenac, 

Lennox and Addington Public 

Health Unit, Kingston, ON), 

Conversation with: Geoff Hall ( 

Department of Civil Engineering, 

Department of Family Medicine, 

Queen’s University, Kingston, 

ON). 2012 Oct. 

Useful for creation of 

community vulnerability 

indices 

Statistics Canada. Census. 

[Internet]. Ottawa (ON): 

Statistics Canada. [ cited 

2012 Nov]. Available 

from: http://www12. 

statcan.gc.ca/censusrecensement/index-eng. 

cfm 


AIR QUALITY 


Measure Description Inputs †‡ Current Use in 

Ontario 

Measures 

Count ¥ 


Ontario 

Desirability 

Challenges 

Link bw 

Measurement 

Approaches ¥ 

Walkability 

Indices 

(Also known 

as Composite 

Measures) 


are derived from 

a combination 

of individual built 

environment 

measures 

(mostly using GIS 

methods). †‡¥ 


walkability ¥ 

Urban sprawl 

indices: features 

of sprawl include 

‘‘leapfrog’’ 

development 

pattern, low density, 

homogeneous 

and segregated 

land uses, and 

an extensive 

disconnected, 

hierarchical road 

network, making 

motorized travel a 

necessity and active 

transport unsafe 

and impractical. 

Indices vary by the 

components they 

include, the scale 

at which they are 

measured, and the 

methods used in 

computation. 

Indices often 

include measures of 

density, diversity & 

connectivity. 

The relative 

importance of each 

measure depends 

on the specific 

formula employed. 

All indices require 

a reference 

geographic area. 

Data sources 

include: Census, 

MPAC, DMTI, 


Ontario Road 


Road Network .†‡¥ 


using an index, 

out of the 19 that 

currently assess 


4 PHUs are more 

advanced in 

implementation; 

modifications have 

been made to 

make the index 

specific to their 

jurisdiction .†‡ 

1 research 

institution has 

implemented an 

index in multiple 

jurisdictions across 

Ontario (generalized 

index) ‡ 

Some PHUs work 

in conjunction with 

external consultants 

to implement 

and calculate the 

index. †‡ 

2 PHUs are using 

a GIS based tool 

developed by Larry 

Frank. †‡ 

All PHUs use spatial 

measures and 

methods (i.e. GIS). †‡ 


methodologies 



13 different 


were identified 



reviewed), created 

by various 

academics and 





(ON, BC, QC), 1 



the USA. 

> 15 different 





implemented a 

walkability index 

but they differ in the 

components used 

and application, 

with some 

overlap. †‡ 


institute has 

implemented a 

walkability index in 

multiple jurisdictions 


(generalized index )‡ 


methodologies 

vary, making 

comparisons 


jurisdictions 

challenging .†‡¥ 


have been successful 

in describing the 

walking environment 

in several 

jurisdictions. †‡¥ 

Strongly and 

consistently 






Built environment 

metrics are often 


one another & thus 

has motivated the 

use of composite 

indices to capture 

many aspects of the 

built environment at 

once.¥ 

Research has 

suggested that 

composite measures 

of walkability are 

more consistent 

predictors of walking 

behavior than 

single component 

measures. ¥ 

Urban sprawl is 


higher levels of 

car dependence 

& overweight 

populations than 

cities with more 

compact buildings. ¥ 

Indices vary in 

both structure 

& availability of 

data. Thus, lack 

of consistency in 

measurements 

(including bw 

municipalities within 

same jurisdiction). †‡¥ 

Methodological 

concerns regarding 

validity, reliability, 

and generalizability. ¥ 

Less useful for 

intervention, as one 

cannot identify the 

specific component 

that should be the 

highest priority for 

change. ¥ 

Human resource 

capacity, data 

availability, financial 

capacity & lack of 

GIS expertise .† 

4/5 of most 

advanced orgs in 

Ontario are working 

independently from 

one another; limited 

collaboration. ‡ 

Captures interrelatedness 

of many built 

environment 

characteristics. 

Minimize the 

effect of spatial 

collinearity. 

Eases the 

communication of 

results. 



AIR QUALITY 



Ontario 

Measures 

Count ¥ 


Ontario 


Challenges 

Link bw 

Measurement 

Approaches ¥ 

Density 

(Population and 

Land-Use) 

Density is a measure 

of the amount of 

activity found in an 

area and can be 

defined in terms of 

population, housing 

unit, or employment 

density. 

Important correlate 

of walking. 

High density 

represents compact 

land development 

and reduces travel 

distances between 

trip origin and 

destination; reduces 

dependence 

on motorized 

transportation; 





walking to and from 

transit. 

Density is a ratio in 

which a measure of 

population or built 

form serves as the 

numerator and a 

measure of land area 

(e.g. per unit area) as 

the denominator .†‡¥ 

The denominator can 

be either total land 

area (as in “gross 

density”), or a pared 

down measure of 

usable land area (as in 

“net density”). 

Density measures 

include: ¥ 



(household) 

density (e.g. ratio 

of residential 

units to the land 

area devoted to 

residential use per 

block group) 

• Employment 

density 

Recommend 

measures of net 

density (as opposed 

to gross density) 

because it excludes 

other land uses. ¥ 


census, MPAC, DMTI, 


Ontario Road 


Road Network †‡¥ 

PHUs use 

these individual 

measures: † 

• Population 

density (6 PHUs) 


density (3) 



density as part of 

their index. †‡ 


institute uses 

population density 

(per square 

kilometre of 

residential area) 




methodologies 








reviewed); 24 



(ON, BC, QC), 1 



USA. 

~ 25 different 





Simplemented 

density measures, 

namely population 

and residential 

density. †‡ 

1 Ontario 


implemented 



jurisdictions 







index. ‡ 


methodologies 

vary, making 

comparisons 


Simplicity of 

computation, 

readily available 

data, and ease of 

interpretation make 

density a commonly 

used metric. ¥ 

Strongly and 

consistently 






Consistency in 

data sources 

for calculating 

population 

density in multiple 

jurisdictions in 

Ontario census 

and parcel-level 

data available 

from government 

sources) .†‡¥ 

Census data 

(Statistics Canada) 

is comprehensive 

for the entire 

population and is 

produced every 

five years, allowing 

changes in density 

to be monitored 

over time. ¥ 

Many ways to 

calculate densities 

using different units 


Scale of density 

measurement is 

another challenge to 

measuring density 

(e.g. calculations 

of parcel density, 

block density, 

neighbourhood 

density, and gross 

density for the 

same area will each 

produce distinct 

results). 

Census data 

has limitations 

including it’s focus 

on residential 

population counts 

thereby being less 

useful for examining 

employment 

density. It also 

uses predefined 

geographic units for 

measurement that 

may not capture the 

types of changes 

that are of most 

interest. 

To measure change 

before 2001, only 

Census Tracts (CTs) 

are available. 



and sprawl indices 

(composite 


residential density 


Proximity is 


both density 

(compactness) of 



use mix. ¥ 

Density and land 

use mix work 

in tandem to 

determine how 

many activities are 

within a convenient 

distance. ¥ 


is important 

because it serves 

as a proxy for other 

urban form factors, 

and is of particular 

importance at 

larger geographic 

scales of 

measurement or in 

cases where data 

is missing. ¥ 



AIR QUALITY 



Ontario 

Measures 

Count ¥ 


Ontario 


Challenges 

Link bw 

Measurement 

Approaches ¥ 

Connectivity 

(related to street 

pattern) 

Connectivity affects 

the ease of travel 

between places 

& represents the 

degree to which 

roads, pedestrian 

walkways, trails, 

etc. are connected 

so that moving from 

point A to point B is 

relatively easy. 

Measures quantify 

the network 

connections 

between trips to 

describe directness 

of possible paths 

& no. of mobility 

options available. 

The denser the 

street network 

is in terms of 

intersections and 

blocks, the higher its 

connectivity will be. 


walkability. 

Measures are related 

to the physical 

design & layout 

of transportation 

infrastructure. 

Measures of 

connectivity include: 

block size and length; 

intersection density; 

street density; 

connected node ratio; 

segment/intersections 

ratio; number of 

intersections per 

length of street 

network; alpha index; 

gamma index. 

Easiest way to 

operationalize street 

network connectivity 

in a GIS environment 

is by measuring 

the number of 

intersections. 


Local database, 

DMTI, Ontario Road 


Road Network. 



• Block size and 

length (4 PHUs) 

• Intersection 

density (3) 


• Connected node 

ratio (1) 


use connectivity 

measures as part 

of their walkability 

index; 1 research 

institution uses 

connectivity as 

well .†‡ 

1 PHU uses 

the ‘I can walk’ 

tool to assess 

connectivity at the 

neighbourhood level 

(icanwalk.ca). † 

Most PHUs use 

spatial methods for 

measurement (i.e. 

GIS). †‡ 







connectivity (bike 

paths, multi-use 

paths, trails, 

sidewalks, and 

streets). † 



connectivity 


literature review (50 

articles reviewed) 





(ON, BC, QC), 1 



USA. 

> 30 connectivity 

measures identified 


review. 

Several PHUs 

have implemented 

connectivity 

measures, namely 


density measure. †‡ 

1 Ontario 


implemented 

connectivity 




(as part of a 

generalized index )‡ 


considerable by 

PHU: community 

walkabout; surveys; 

focus groups and 

GIS inventory for 

one PHU; includes 

streets, sidewalks, 

multiuse paths and 

sometimes parks; 

Road Network GIS 

Layer .† 

Street networks 

that are more 

connected 

are thought to 

increase walkability 

by offering 

shorter and many 

alternate routes. ¥ 

Several 

studies have 

found positive 

associations 




Greater street 

connectivity 





walking to and 

from transit. ¥ 


considerably by 

health jurisdiction. †¥ 

No standard 

methods for 

operationalizing 


Different tools are 

used to characterize 

road network 

configuration in 

relation to physical 

activity. †‡¥ 

Not all studies 

have found positive 

associations 




Most measures 

use data from the 

street network, 

but omitting 

pedestrian networks 

(e.g., sidewalks, 

park paths) may 

appreciably 

underestimate 

connectivity.¥ 

Determining how to 

handle freeways or 

other limited-access 

roads is another 

methodological 

issue. ¥ 



indices (composite 



density measure. 



AIR QUALITY 



Ontario 

Measures 

Count ¥ 


Ontario 


Challenges 

Link bw 

Measurement 

Approaches ¥ 

Diversity 

(Land Use Mix 

and Proximity) 

Diversity refers 

to the spatial 

arrangement of 

land use that 

influences the 

distance and mode 

of travel. 









Proximity describes 

the no. & variety 

of destinations 

within a specified 

distance of any 

location; function 

of both density of 

development & 

level of land use 

mix. 

As proximity and 

directness between 

destinations 

increases, 

distance between 

destinations 

decreases. 


(entropy-based 

measure) is frequently 

used; land use types 

include: residential, 

retail, entertainment, 

office and institutional. ¥ 

Dissimilarity index 

measures dissimilarity 

based on predominant 

use of neighbouring 

squares. ¥ 

With appropriate parcel 

data, the calculation 

of land use mix is 

possible through a GIS 

or database interface. ¥ 

LUM data are typically 

obtained from land 

ownership records. ¥ 

Proximity is often 

calculated using 

circular or road 

network buffers. ¥ 

Distances (e.g. 400m, 

800m) commonly used 

to analyze walking 

distance vary. ¥ 


Census, MPAC, DMTI, 



or National Road 

Network .†‡¥ 



• Land Use Mix (3 

PHUs) 

• Proximity 

(schools, food 

outlets, parks, 

trails) (6) 

studies identified 



literature review 

(50 articles 

reviewed); 

21 applied in 

the Canadian 

context (ON, 

BC, QC), 1 in 

Australia and all 


the USA. 

43 (86%) 



LUM as part of their 

index. †‡ 






methodologies to 

calculate LUM vary 

considerably. †‡¥ 


using dissimilarity 

indices. †‡ 







proximity (2 PHUs) 

and LUM (1). † 


methodologies 




measures 



Most PHUs are 







1 Ontario 


implemented 

diveristy measures 


jurisdictions 




health jurisdiction 



index. ‡ 


methodologies 

vary, making 

comparisons 


jurisdictions 










Strongly and 

consistently 





literature. 

Parks, trails, 

recreational 

facilities, pathways, 

and schools 

within walking 

distance have 

been consistently 



particularly in 

children. 

Distance to retail 

activity is important 

in creating inviting 

pedestrian 

environments and in 

predicting levels of 

walking in cities. 

Distances used to 

analyze walking 

distance vary, 


between jurisdictions 

challenging. 


does not consider 

the type or intensity 

of mixing. ¥ 


methodologies 

vary, making 

comparisons 



is often a limiting 

factor since parcellevel 

data are 

required to compute 

many land-use mix 

measures. Parcellevel 

data may be 

unavailable in some 

locations and in 

others may lack 

detail about land 

use. ¥ 

Many studies 

have opted to use 

survey items to 

approximate land 

use mix (e.g. ‘‘Are 

there shops where 

you live?’’) but such 

approaches do not 

allow betweenstudy 

comparisons 

because of 

unspecified 

definitions of place. ¥ 

The retail floor area 

ratio (FAR) is often 

used in conjunction 

with LUM. 

Proximity is 


both density of 



use mix. 



AIR QUALITY 

Table 7: Gap analysis for pedestrian oriented design measures used to assess urban walkability, 2012 

Measure Description ¥ Inputs †‡¥ Current Use 

in Ontario 

Measures 

Count ¥ 


Ontario 


Challenges 

Link bw 

Measurement 

Approaches ¥ 

Street design refers 

to the scale & 

design of sidewalks 

and roads, and how 

they are managed 

for various uses. 

POD includes 


neighborhood 

comfort (including 

aesthetics), 

cleanliness and 

safety. 


ratio (FAR) is used 

as an indicator of 

POD measuring 

retail density and 

site design. Low 

ratio indicates a 

low density retail 

development likely 

surrounded by 

substantial parking; 

high ratio indicates 

smaller setbacks 

& less surface 

parking; two factors 

thought to facilitate 

pedestrian access. 

The normalized 

difference 

vegetation index 

(NDVI) is used 

to estimate 

vegetation biomass, 

greenness, and 

dominant species. 

Various methods 

can be used to 

assess street design 

(audit, survey of 

perceptions, GIS); 

method depends on 

specific measures 

being assessed. 

Aesthetics and 

cleanliness are 

often assessed by 

observation (e.g. 

audit) or survey (of 

perceptions). 

FAR = retail building 

floor area footprint 

divided by retail land 

floor area footprint; 

included in walkability 

indices. Data 

source: DMTI (Route 

Logistics); 

Environic Analytics 

(Directory of 

Shopping Centres). 

NDVI: ratio between 

measured reflectivity 

in the red and near 

infrared band, in 

satellite images 

(DEM/SRTM). 

Safety and 

crime statistics 

obtained from 

police department; 

otherwise, through 

observation (audit) or 

survey. 

PHUs use individual 


comfort and safety, 

including: † 

• Crime rates (4 

PHUs) 


• Canopy coverage 

(trees) (4) 

• Posted speed 

limits (3) 

• Presence of street 

furniture (3) 



FAR as part of their 

index. †‡ 






using NDVI indices. †‡ 







sidewalk information 

(including condition 

affected by seasonal 

variances). † 



POD measures 


review (50 

articles reviewed); 



(ON, BC, QC) 

and all others 


USA. 


measures 



Several PHUs 

are using POD 






1 Ontario 


implemented safety 









index. ‡ 


methodologies 

vary considerably, 

making 

comparisons 


jurisdictions 

extremely 


Higher levels 




and comfort 


appealing 

communities) are 

positively associated 

with physical activity 

engagement. 


setbacks are 

important predictors 

of walking and POD, 






activity. 


neighborhood 






planning 








Higher levels 




and comfort 


appealing 

communities) 

are positively 


physical activity 

engagement. 


setbacks are 

important 

predictors of 

walking and POD, 






activity. 


neighborhood 






planning 









methodologies 

vary (especially 

for comfort, 

aesthetics and 

safety measures), 



Self-reported 

measures implicated 

in same-source 

bias and issues with 

reliability, validity, low 

response rates and 

a biased sample of 

respondents. ¥ 

Systematic field 

observations can 

be very laborious 

(i.e. time-intensive 

and have multiple 

logistical constraints), 

often require 

significant specialized 

training and are 

notorious for being 

very costly. ¥ 


ratio (FAR), 

also known as 

commercial 

density, is a 

diverse measure 

that can be 

applied not only 

as a density 

indicator but also 

as an indicator 

of pedestrianoriented 

design 

and used in 

conjunction with 

land use mix 

(LUM). 



WALKABILITY 


Data Source Dataset Topic Area Current Use in Ontario PHUs Desirability Access/Availability Barriers/Challenges/Limitations 

MPAC / 

Teranet / 

OMNR 

Ontario Parcel 

database 

3 components: 

• Digital Assessment 

Parcel Fabric 

(MPAC) 

• Digital Ownership 

Parcel Fabric 

(Teranet) 

• Digital Crown Parcel 

Fabric (OMNR) 

LUM 

Retail Density 

56% of 25 PHUs that responded to the survey 

report having access to MPAC data † 

7 (of 10) PHUs identified using LUM as part of their 

walkability index. †‡ 

5 (of 10) PHUs identified using retail floor area as 

part of their walkability index †‡ 

PHUs identified using the following as individual 

measures † : 

• Land Use Mix (3 PHUs) 

• Retai floor area (1) 

Long list of Property Codes including detailed Residential, Commercial and 

Industrial codes 

Updated quarterly 

3 mapping specifications dictate supporting spatial data— POLARIS (Province 

of Ontario Land Registration Information System) mapping, Basic Index 

Mapping (BIM) and Pre-Basic Index Mapping (Pre-BIM): 

• Where POLARIS, features related to survey plans (including reference & 

subdivision), roads, major easements, township fabric, railways and major 

water bodies are captured. 

• Where BIM, features relate to survey plan text, roads, major easements, and 

geographic township fabric, railways and major water bodies. 

• Where Pre-BIM much less extensive. Features relate primarily to road text, 

geographic township fabric and major water bodies. 

Available at no cost through MNR’s LIO Warehouse to Ontario 

municipalities, conservation authorities, and provincial ministries. Eligible 

organizations must enter into the relevant license agreement(s) and 

become members of the Ontario Geospatial Data Exchange (ODGE) to 

access. 

Municipalities wishing to access the ownership data must be licensed 

through Teranet. 

Through Teranet the only costs incurred by municipalities for standard 

deliveries is a modest delivery and support charge. 

Variable licensing fees depending on derived products. 

Internal use for walkability does not appear to be subject to fee/royalty 

Some municipalities report quality issues with the spatial component 

of Ontario Parcel database. They use their own digital files and merge 

with MPAC data. 

Accuracy variable from location to location and depends on the source 

data available and the build procedure employed. 

• Where POLARIS standards were used to assemble the ownership 

mapping and where good control and legal or cadastral surveys 

were available, the data has better accuracy than other types of 

Ontario Parcel mapping. 

• Where the product is assembled upon 1:10,000 (or smaller) scale 

topographic mapping, and where control and surveys are not 

available, or not used, the data is much less accurate. http://www. 

ontarioparcel.ca/ 



Good coverage of the entire province. ‡ 

DMTI 

CanMap Route 

Logistics 




Connectivity 

Posted Speed Limits 



The dataset is GIS-ready and requires minimal, if any, massaging prior to 

analysis. 


The dataset is designed for location-based service (LBS) applications and so is 

already topologically clean. 

Fee ‡£ 



model). 


extracts may weaken the representativeness of local calculations. Note 

also that not all streets are bordered by sidewalks. 

Enhanced Points of 

Interest (EPOI) 


Amenities 



destinations) 



NAICS codes to identify business types (e.g., restaurants, retail, etc.). 


Fee £ 



model). 



Environics 

Analytics 

BusinessWhere 


Amenities 



destinations) 



NAICS codes to identify business types (e.g. restaurants, retail, etc.). 

Businesses are verified annually. 


Fee £ 



model). 



GeoBase 

National Road Network 

(NRN) 




Connectivity 


Density 

Diversity 


No specific example of NRN usage. 

68% of PHUs indicated having access to street 


44% of PHUs indicated that Street networks (44%) 



Good coverage at both national and provincial scales. The Ontario Road 

Network (ORN) is used to populate the NRN’s Ontario level file. £ 



analysis. 

Vendor offers dataset in multiple spatial formats. £ 



Excludes select street attributes that could be required to compute 

specific calculations. £ 






Ministry 

of Natural 

Resources 


(ORN) 




Connectivity 


Density 

Diversity 


No specific example of ORN usage. 

68% of PHUs indicate having access to street 


44% of PHUs indicated that street networks (44%) 



Standardized provincial dataset with detailed documentation and regular 

update frequency. ORN is the authoritative source of roads data for the Ontario 

Government. £ 



analysis. 








† Survey ‡ Key Informant Interview ¥ Literature Review £ GIS Metadata 


AIR QUALITY 

Table 11: Measurement approaches and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata). 

Measurement 

Approach 

Description Inputs Current Use (SU) Measures of interest 

Theoretical 

Operationalization 

Ontario 

Demand / Prioritization/ Desirability 

Challenges 

Link between 

Measurement 

Approaches 

Individual 

Pollutants 

Direct 

Measurement of ambient air 

concentration of common 

pollutants (e.g. NO 2 

, NO, SO 2 

, 

CO, TSR, O 3 

, PM 2.5 

, PM 10 

, etc.) 

• Individual pollutant data 


averaging time period (LR) 




particulate matter, and nitrogen 

dioxide 


• 50% assess carbon monoxide 

Of the 11 PHUs assessing individual 

pollutants, 4 PHUs look at individual 

pollutants from air stations directed 

at industrial sources 

Of 14 PHUs, that assess air quality 

6 use portable air monitoring 

equipment 

Hundreds of pollutants have been identified and 

are monitored at the federal level. A select few 

criteria air pollutants are monitored provincially 

due to their links to health and knowledge of 

sources. 

Sources of pollutants in the built environment 

include: (LR) 

• Wood fireplaces (PM , BC, and UFP 

2.5 

• Traffic emissions (BC, UFP, NO ) x 

• Fugitive dust from industry and construction 

sites (PM 2.5 

) 

The development of pollutant measures needs 

further research to better understand the 

relationship with the built environment. 

Spatial level 




(GM) 

Certain monitoring stations 

do not measure all criteria air 

contaminants (LR) 

NO 2 

is noted as a good measure of traffic pollution that is cost 

effective and easy to monitor. Nonetheless more research is 

needed in developing NO 2 

as an indicator (KI, SU) 

Black Carbon, UFP, PM 2.5 

, PM 10 

and NO x 

have been identified as 

pollutant measures of health. BTEX is a useful built environment 

pollutant to measure for its use in combustion fuels and in 

solvents, but is costly to measure (KII, LR). 


Portable instruments can provide better detail of air pollution 

through high spatial resolution, 

selection of specific target areas, and by focusing on vulnerable 

populations or household level (LR, KII) 

Inexpensive NO/ NO x 

sensors, passive NO x 

/NO 2 

samplers, or 

Airpointer equipment were noted as useful monitoring tools for 

traffic related pollutants (KII,LR) 

Pollutant sources and their impact on air quality differs for 

each region/PHU (SU, LR) 

Additional air monitoring stations would be expensive and 

resource intensive. Specific pollutants require expensive 

monitoring equipment, For instance, PM 2.5 

sensors start at 

$500 CAD (KII, LR) 


Costs for sampling and equipment may be high (LR) 

Limited sampling my not capture seasonal trends (LR) 

Required as data inputs 

for developing air quality 

indexes and models 

Air Quality 

Indexes 

(composite 

measures) 

Indexes are a direct measure of 

air quality based on individual or 

multiple pollutants (LR) 

• The Ontario Air Quality Index 

(AQI) involves data on the 

following pollutants O 3 

, NO 2 

, 

PM 2.5 

, SO 2 

, CO, and TRS (LR) 

• The Air Quality Health Index 

(AQHI) involves data on three 

pollutants: PM 2.5 

, O 3 

, NO 2 

(LR) 

• Pollutant data which can be 

collected through mobile 

or permanent monitoring 

equipment (LR) 

• Formula calculation (e.g. 

weighting for specific 

pollutant) (LR) 


averaging time period and 

threshold levels (LR) 

• Use of epidemiological data to 

determine health based levels 

of interest (LR) 

Of 14 PHUs assessing air quality 

(SU): 

• 93% use the AQI 

• 53% use the AQHI 

While many air quality indexes have been 

developed internationally, only 2 indexes are 

currently used in Ontario (LR, SU) 

Indexes were developed at the provincial and 

federal level. 

Spatial level 




(GM) 

• AQI: available across 

Ontario 

• AQHI: available in Southern 

Ontario 

Built environment attributes that could be targeted include 

fireplaces and old diesel vehicles (KII, LR) 

Historical and temporal data available at no cost. 

Information provided with high temporal resolution 

Availability of monitors throughout province 

No cost for data 

The Ontario AQI may not capture health risks (LR) 

The current composite measures do not capture the built 

environment and its relationship to the distribution of 

pollutants. (KII, LR) 

While O 3 

and PM 2.5 

are monitored at most stations across 

Ontario, not all key air pollutants are monitored by the MOE 

(LR) 

The concern for air quality issues is lower in less populated 

cities situated far from urban areas. However, concern 

remains for specific sources from industrial emissions (KII, 

SU) 

Noted as an important 

component for 

PHUs evaluating 

meteorological data. 

A composite measure of 

individual pollutant data 

Remote 

Sensing 


Satellite images provide detail 

on air pollutant levels. Resolution 

varies from 250 m to 320 km. 

Images taken from every 1-7 

days. 

Satellites with information on 

pollutants. 

• OMI, TES, CALIOP & GOME 

(for NO 2 

and hydrocarbons) 

• MODIS, OMI, PARASOL, & 

MISR (for PM) 

• MOPITT, AIRS, PARASOL, 

IASI& SCIAMACHY (for CO) 

• GOME, SCIAMACHY, OMI, 

TES, IASI, & GOME-2 (for O 3 

) 

Only used in one PHU in 

conjunction with air monitoring and 

modelling 

Spatial level 

• need further evaluation of 

what maps or datasets 

exist that can be applied to 

Ontario (LR) 

• Need further development of satellite systems and recognition 

as a promising area (KII, LR) 

• Potential for greater spatial coverage (LR, KII) 

Technical expertise required to create air quality maps from 

satellite images (LR, KII) 

Spatial resolution can vary significantly from 250m to 320km 

(LR) 

Some satellites do not capture air pollutant concentrations at 

a resolution relevant to the built environment (LR) 

Cost for imagery (LR) 

Quality of images can vary due to cloud cover and other 

weather conditions (LR) 

Limitations in determining ground level estimates from upper 

atmosphere estimates (LR) 

Relevant to pollutant 


Emissions 

Estimates 

Emissions data can be used 

to estimate air pollutant 

concentrations and as an indirect 

indicator of potential exposure 

The data required for emission 

estimates were cited from 

various sources: 

• NPRI 

• TURI 

• MPAC 

• Municipal traffic volumes 

• Emissions factors formula 

to convert traffic volumes to 

emission rates 

• Fleet demographics to 

determine composition of 

traffic sources 

• Geocoding of point source and 

area emissions 

Of 14 PHUs assessing air quality, 6 

use emissions estimates 

Of the 28 PHUs that responded to 

the survey, 43% have access to 

traffic volume data from regional or 


Traffic counts can be an indirect measure of 

traffic related air pollution and may translate to 

other traffic corridors with similar counts (KII) 

Provides useful information on sources of air pollutants in the built 

environment (LR, KII) 

May not require air monitoring data depending on purpose of air 

quality assessment (LR) 

Can be applied to point sources and traffic (LR) 

Greater need for smaller emissions sources for community 

based modelling (KII) 

Potential estimation errors in pollutant levels and geographic 

distribution (LR) 

Requires dispersion modelling and meteorological 

information for ambient air pollutant estimates (LR) 

Spatial detail not sufficient for assessment at a municipal 

level (LR) 

Can be incorporated 

into more detailed 

assessments of air 

quality in the built 

environment and 

with meteorological 

modelling 

Important in proximity 

measures 

Need more detailed 

evaluation to determine 

geographic range of 

impact of emissions. 

This is done with the 

support of modelling 

approaches 

Modelling 


• Used to provide additional 

spatial and temporal detail of 

air pollutant levels 

Modelling techniques used to 

capture pollutant information in 

urban areas included: 

• Land Use Regression 

• Dispersion Modelling 

• Kriging 

• Basic proximity or 

interpolation models (LR) 

Inputs vary by model, but can 

include: 

• Emissions data 

• Pollutant level measurements 

• Meteorological data (e.g. wind 

speed, direction, temperature) 

• GIS map files related to land 

use and surface characteristics 

54% of PHUs assessing air quality 

use some form of modelling 

Identified as being in previous and/ 

or current use in select few PHUs 

(e.g. Toronto, Halton, Ottawa) 

Modelling approaches are an important tool for 

assessing neighbourhood level differences in 

pollutants. 

Common variables considered include: 

• Road networks (e.g. road length and traffic 

density) 

• Land use 


• Topography 

• Meteorological conditions 

Various spatial models have 

been developed at the 

municipal level (LR) 

• Present work from Health 

Canada is using NO 2 

measurements and Land 

Use Regression modelling 

for various communities 

across Canada, and may 

have potential to be applied 

more broadly (KII) 

Modelling is noted as an important or necessary tool to gain 

greater spatial detail of air pollution distribution in urban 

communities (KII, SU) 

Can incorporate modelling approaches with built environment 

indicators (LR) 

Technical and human resources needed for modelling (LR, 

KII) 

Higher cost for more detailed maps (LR) 

• Important tool for 

mapping pollutant 

distribution from 

emission estimates 

and air monitoring 

Proximity 


Distance of sensitive populations 

to areas with high pollutant levels 

Determination of a safe distance 

from major roadways or other 

sources of air pollutants 

• Distance of major roadways 

and emitting facilities from 

sensitive populations 

29% of 28 PHUs that answered 

the survey have access to data 

regarding 

• Proximity of population to 

emission sources reported in 

NPRI 

• Proximity of population to high 

traffic volume roads 

While much research has shown how pollutant 

levels decline from a source, the setback 

distance for areas of high risk is not conclusive 

and depends on meteorological conditions, 

traffic volumes, and built environment attributes. 

Care should be taken in determining safe 

heights as well as distance (SU). 

• Need to better understand which pollutants are appropriate 

indicator for proximity to traffic sources (KII, LR) 

• Need to balance need for setback distances with designing 

compact communities (LR) 

• Still need to clarify which pollutants are causing most 

harm near high traffic areas (KII, SU) 

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 

Table 12: Data sources for Air Quality and Policy-Relevant Information as Identified in the Literature Review, Key Informant Interviews, Survey and GIS Metadata Exercise. 

Data Source Topic Area Utility in Outcomes Current Use in Ontario PHUs Desirability Cost Challenges 

Ministry of the Environment 

Air Quality Information System 

(AQUIS) 

Air quality 

Incorporated into National Air 

Pollutant Surveillance program 

(NAPS) Environment Canada Data 

One of two air quality indexes applicable to Ontario 

is produced by the MOE (Air Quality Index) (LR) 

Data on individual pollutants (PM 2.5 

, NO 2 

, NO, NO x 

, 

O 3 

, SO 2 

, CO) (LR) 

93% of PHUs assessing air 

quality use the AQI (SU) 




particulate matter, and 



• 50% evaluate carbon 

monoxide 

Depends on local air quality 

issues 

Three PHUs which noted 

challenges in assessing air 

quality, stated that demand 

for monitoring pollutants was 

not a high priority (SU) 

Free (GM) 

Monitoring stations are geocoded, but no standard exists for 

geographic area represented by monitoring station 

Limited relevance to small populations. Strong focus on high 

population areas in Southern and South-eastern Ontario (KI, LR) 

Need for more air monitoring stations and for modelling to provide 

greater spatial detail to current data (SU, LR, KI) 

The density of monitoring stations can differ, between municipalities 

(KI, LR, SU) 

Of PHUs assessing air quality 5 PHUs commented on the limited 

spatial representation of the present air monitoring stations 

Canada Environmental 

Sustainability Indicators 

Hourly data on O 3 

, PM 2.5 

, SO 2 

, NO 2 

, VOCs Was not evaluated Free Two year lag period for data. 

National Pollutant Release 

Inventory 

Air Quality 

Emissions data for Ontario prior 

to 2005 available from Ministry of 

Environment Historical OnAIR Data 

2001-2004 (MOE) 

The NPRI collects data on pollutant emissions from 

industrial and non-industrial sources 

Data available at a facility level, as well as 

aggregated data at the provincial level. 

The provincial information includes estimates for 17 

air pollutants organized by sector. 

In addition to industrial sources, NPRI estimates 

emissions from various other sectors such as 

transportation, agriculture, landfills, natural sources, 

etc. 

Of 28 PHUs who responded to 

the survey, 29% had access to 

data on proximity of population 

to emission sources reported 

through NPRI (SU) 

For large emission sources 

which meet the NPRI 

requirements for reporting, 

the data is of good quality 

and useful for PHU (KII) 

Provides maps on emissions 

density at low resolution (LR) 

Data available for specific 

pollutants 

Useful for traffic or point 

sources pollution (LR) 

Free 

Estimates only available for annual emission levels (LR) 

Limited information on day to day variation between communities 

(LR, KII) 

A lag period of 1 or 2 years exists for the data (LR) 

Many commercial and industrial sources within a city can be 

exempt from reporting to NPRI (LR, KII) 

Ontario Ministry of 

Transportation 

Air Quality (Traffic volume) 

Ontario Ministry of Transportation annual publication 

on traffic volume and accident rates for provincial 


No PHU reported the use or 

access to MTO data. However, 

such information may be obtained 

indirectly through the municipal 

transportation and planning 

departments. 

A useful resource for 

high traffic volume 

highways situated in urban 

communities 

Free 

To translate into emission estimates, need information on fleet 

demographics (LR) 

Focus strictly on provincial highways (GM) 

Local Road Data 

(collected by 

municipalities /regions) 

Network Analysis (to measure 

proximity or to model population 

affected) 

Traffic data (e.g. vehicle kilometres 

travelled, modal share, traffic 

volume) 

12 of 28 PHUs that answered the 

survey stated they have access to 

municipal level traffic data (SU). 

Traffic related information is 

commonly gathered from 

municipal sources (SU). 

Free 

May require geocoding for GIS use. 

Coverage and update frequency will vary – data quality assessment 

is required. 

Dependent on municipalities to compile a local inventory of road 

and traffic data. 


EXTREME HEAT 


Measure Data Component Location Applied 

Composite Heat 

Vulnerability 

Index 207 

Hazard layer 

• Satellite image of near surface air temperature 

Human vulnerability layer 

• Under 5 or over 65 years of age 

• Living on a low income 

• Being over 65 and living alone 

Index 208 • Low income persons (after tax) 

Exposure index (40%) 

• Mean surface temperature 

• Green space coverage 

• Accessibility to green space 

• Dwelling units in high rise buildings 

• Renter dwellings in older high rises 


Sensitivity index (60%) 

Heat Vulnerability • Children age =50% of income on housing 

• Recent immigrants (within 5 years or less) 

• Racialized groups 

• Emergency visits: respiratory or circulatory disease 

• Vulnerable seniors 

Human Thermal 

Comfort Index 200 

Risk from 

extreme heat 209 

Heat Vulnerability 

Index (HVI) 210;211 

Coping resources 

• Social ties index 

• % air conditioned 

• % swimming pools 

• Roof reflectivity (% asphalt, % tile, % wood, % other) 

Population 

• Median income, % in poverty 

• Less than high school, College graduate 

• % minority 

• Median age 

• % ages 5 and under 

• % ages 65 and over 

Thermal environment 

• Distance from city center (km) 

• Population/km2 

• % open space 

• Vegetation abundance (SAVI) 

Land surface temperature 

Heat related mortality 

Vulnerability variables 

• Hispanic population 

• Black population 

• Asian population 

• Native American population 

• Other race population 

• Age 65 and over 

• Age 65 and over living below poverty 

• Age 5 and under 

• Persons living below poverty 

• Low education (less than high school education) 

Social/environmental 

• % below the poverty line 

• % race other than white 

• % with less than a high school diploma 

• % of non-green space 

Social isolation 

• % that live alone 

• %> 65 years of age that live alone 

Air conditioning prevalence 

• % homes without central air conditioning 

• % homes with no air conditioning of any kind 

Preexisting health conditions 

• % population diagnosed with diabetes 

Hazard Layer: (50%) 

• Urban Heat Island magnitude (LST) 

Heat Health Exposure Layer (25%): 

Risk 212 • Household type (Experian MOSAIC data) 

Vulnerability Layer (25%): 

• Made up of vulnerable types extracted from the exposure layer (e.g. Old, Ill, Density, Flats) 

SUPREME 

system 205 

Thermal information (Landsat) 

Vulnerability indicators 

• Regional deprivation index 2006 


• Age 

• Housing conditions 

• Foreign language population 

• Dissemination areas inside heat Islands 

• Landed immigrants since 2001 

Montreal 


Phoenix, AZ 

Philadelphia, PA 

California, Massachusetts 

New Mexico 

Oregon 

Washington 

Birmingham, UK 

Quebec 


EXTREME HEAT 

Table 20: Measurement approaches and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata 

Description (LR) Inputs Current Use in Ontario PHUs (SU) 

Measures 

(LR) 


Ontario (LR/GM) 

Desirability 

Challenges 

Link b/w Measurement 

Approaches 


Data 

Direct measurements of Individual 

meteorological variables 

• Temperature 

• Dew point temperature 

• Relative humidity 

• Wind speed 

• Wind direction 

• Solar load (radiation) 

• Atmospheric pressure 



*May be real time or 

hourly (GM) 


• 16 PHUs have access to meteorological data (all 16 use temperature to asses heat, 4 use 

wind speed) 

23 PHUs have access to meteorological data 



One PHU uses met data as input to a larger heat model to predict heat days/alerts 

7 variables 

6 of 7variables are 

available across Ontario 

Direct measurements 





Does not take into account effect of 4 variables 

that comprise heat. 

Main inputs for heat indices 

Used for AQ measures 



Composite 

Heat Measures 

(using 


data) 

Composite measures of heat that combine 

individual meteorological variables based on 

given formulae 

• Apparent temp/heat Index 

• Humidex 

• Thom Index/Discomfort index 

• Relative stress index 

• WGBT index 

• Heat exposure index 

• Thermal Index (net effective temperature) 

• Heat Load Index 

• Heat Stress Index 

• Perceived temperature 

• Spatial synoptic classification 

• Human Thermal Comfort Index 



Scientific formulae 

(LR) 

Some require 

climate normal/ 

averages as 

reference periods 

(LR) 

Some require 

demographic/ 

morbidity/mortality 

data as reference 

for formula 

development (LR) 


• 16 PHUs have access to meteorological data (all 16 use humidex to assess heat) 

• 2 PHUs use models 

23 PHUs have access to meteorological data: 



• 3 PHUs get data from other temp/mobile monitoring stations (all from Health Canada) 

o All 3 measure WGBT 

11 measures 

Standard Environment 

Canada data would be 

sufficient to populate 

some of these 

measures. However, 

special equipment may 

be required for other 

measures (e.g. WGBT). 

(LR,KII). 

Some measures 

are not realistic for 

PHUs to calculate at a 

population level 

According to Health Canada, heat is 

comprised of 4 factors (temperature, 

humidity, wind and solar load). 

Composite heat measures better 

address this relationship (LR) 





Most require direct measurements of 

Individual meteorological variables 



Urban climate 

modelling 

Models that simulate local climates in urban 

environments 

Reasonably low spatial resolution (KII) 

Could be used to extrapolate met data 

across a wider geography(KII) 

Built environment feature and characteristics 


*Many of these measures have been identified 

as being collected through remote sensing. 

Individual features 

and characteristics 

Means of collecting 

data (e.g. satellite/ 

sensor) 

Expertise to collect 

and analyze data 

15 PHUs reported having access to built environment data (e.g. land use, forest cover etc.) 


Potential for complete coverage (LR) 


Quality and quantity of satellite data varies by 

source (e.g. spatial and temporal resolution(LR) 

Parameters only reflect data at one point in time 

(LR/KII) 

Use may be limited due to accessibility issues, 

cost and expertise to collect and analyze data 

(LR/KII) 

Built 

environment 

data 

A. Temperature 


• Urban heat Islands 

B. Land cover 

• Impervious Surfaces 

• Vegetation (NDVI, VCF, EVI, SAVI) 

• Albedo (BRDF) Open/green space 

C. Community characteristics 


• Density 

• Proximity 

• Building height 

• Land Use 

2 PHUs have access to thermal imagery 

1 PHU accesses thermal imagery through federal government 

1 PHU accesses thermal imagery by 1) directly requesting it from data source 2) through a 

regional or local government 


2 PHUS have identified the following as in development : canopy cover, age stratification of 

urban forest stock 

The PHUs that do not assess heat, identified having access to the following: canopy coverage 

(3), age stratification (3), surface reflectivity/albedo (1), surface emissivity (1) 


1 PHU identified the following as in development àbuilding age 

PHUs that do not assess heat identified having access to the following: urban sprawl (1), 

building density (2), building age (2), average size by land use (1) 

Unable to assess due to 

level of detail for each 

measure. 

Theoretically, should these 

measures be accessed 

through remote sensing, 

there is the potential 

for complete coverage 

in Ontario. However, 

processing would be 

required. 

Surface temperature can be used as a 

basic indicator of relative hot and cool 

areas and neighbourhoods (KII) 

Thermal anisotropy: 3D structures are viewed 

at a single angle so thermal properties of other 

sides are not captured. (LR) 

Unable to infer the temperature inside buildings 

(e.g. may be air conditioned). (KII) 

May be used in air quality assessments. 





D. Residential characteristics 

• Detached homes 

• Air conditioning 



Air 

Walkability 

Community 

vulnerability 

Measures that combine exposure to extreme 

heat and individual./community vulnerability to 

determine overall risk 

Socio-economic 

& demographic 

variables 

Heat exposure 

variables 

Platform/ 

methodology to 

combine the two 

to create overall 

vulnerability 

14 PHUs reported using demographic data to identify populations more vulnerable to heat (SU) 

Of these, the following are used: Age (14), gender (2), income (9), language (3) 

Other 

• Pre-existing medical conditions (4) 

• People without A/C (2) 


• Deprivation index 

• Occupation / employment (2) 

• Type of dwelling unit (2) 

• Education 



• Socially isolated persons 

• New immigrants 

• Urban heat island 

• Lack of tree canopy/green spaces 

• Lack of social infrastructure/ service gaps (2) 

• High-crime rate areas 

1 PHU has created a heat vulnerability index and maps (Toronto) (LR) 

7 

Measures of community 

vulnerably can vary in 

methodology. In Ontario, 

the Deprivation index and 

other census data could 

be used. Theoretically, 

thermal imagery for 

Ontario could be overlaid 

with a number of built 

environment, social and 

demographic data. 

Combines exposure and vulnerability 

which together to inform risk 

Likely unreasonable to expect every local health 

department to create its own heat vulnerability 

map – a national HVI created through freely 

available national data sets is useful (LR) 

BE data 

Composite measures using met data 


EXTREME HEAT 

Table 21: Data sources and sets, and policy relevant information as identified from the literature review, key informant interviews, survey and GIS metadata 

Organization 

Data Source/ 

Set 

Topic Area Utility in Outcomes Current Use in Ontario PHUs Desirability Cost Challenges/Limitations 

Health Canada 


Heat Monitoring 

Systems 

Extreme Heat 

Air Quality 

Could potentially: 



measures 



South eastern environmental heat monitoring network: PHUs 

covered by network: Hastings & Prince Edward Counties; 

Leeds, Grenville & Lanark District; Peterborough County-City; 

and Kingston Frontenac and Lennox & Addington (KFL&A) 

(GM) 

3 PHUs identified using health Canada EHMS units (SU) (2 of 

these are separate from the south eastern heat network) 

Able to assess 4 parameters that comprise heat (LR) 

Useful for capturing heat exposure in various 

environments given the variation of meteorological 

conditions over distances (e.g. city centres to farm 

lands). (KII) 

Units can be placed in areas outside typical 

Environment Canada weather stations and 

strategically placed to assess spatial variability of heat 

over a region or major urban area (KII) 

No cost 

Funded 

by Health 

Canada (KI) 

Limited geographic coverage based on pilot projects 

Not publicly available (GM/KI) 

If data were to be released, it would require interpretation (KI) 


Service of 

Canada 

(Environment 

Canada) 

National Climate 

Archives 

Real time data 

from weather 

stations 

Extreme Heat 

Air Quality 

EC data 

integrated into 

syndromic 

surveillance 

system (KII) 

Could potentially 



measures 



• Of 18 PHUs that assess heat (SU) 

• 16 PHUs have access to meteorological data (all 16 use 

temperature and humidex to asses heat, 4 use wind 

speed) 

• 23 PHUs have access to meteorological data (SU) 


Undergoes quality control (KII) 

Data freely available and accessible across Ontario. 

(GM) 

Free 

GIS formatted data is not provided through the National Climate Archives; however, 

the database contains latitude and longitude of each observation (Degrees & minutes) 

that can be used to create a GIS shape file. (GM) 

Typically at local airports/weather stations (LR) 

Availability may be limited by region (LR/KI) 

Limited use to assess variation in heat across community due to distance from 

stations and sparse spatial coverage (KI, GM)Standard Environment Canada data 

would not be sufficient to populate some of the measures that address the four 

factors that comprise heat. 

Natural 

Resources 

Canada 

Landsat 

Heat 

Walkability 







2 PHUs have access to Landsat (SU) 

Used Landsat imagery (from NRCan) for the heat exposure 

component for Toronto’s Heat Vulnerability Index (LR) 

Historical archive completely open and accessible 

online with no privacy restrictions (MD/KII) 

Canada wide coverage (MD) 

Includes metadata to convert the thermal infrared to 

temperature in Celsius (KII) 

Includes calibration data (KII) 

Free (MD) 

Satellite ending useful lifespan (MD) 

Update frequency is every 16 days 

Unspecified 

Heat 

One PHU identified NRCan as the source for data on surface 

reflectivity and surface emissivity (1) 

USGS Aster Heat 




No PHUs identified having access to ASTER (SU) 

Associated 

fees 


USGS MODIS Heat 



Update frequency is 2x a day (GM) Free Scale resolution limited (GM/KII) 

• Albedo 


First Base 

Solutions 

Custom Aerial 

Image Acquisition 

Heat 




One PHU identified using aerial photography but did not 

specify the source 

Customizable data set (GM) 

High resolution (GM) 

Associated 

fees (GM) 

Potentially expensive 



Statistics 

Canada 

Census 

Heat 

Air 

Walkability 


required for community vulnerability (LR) 

One PHU identified Statistics Canada as a source for data on 

building age = 

Source for dwelling units in high rises, dwelling units in high 

rises constructed before 1986 and population density in 


Standardized data available across Canada Free Updated every 5 years 

Municipalities Local data Heat 

Could contribute on built environment data. 

May be accessed through municipal departments 

• Forestry 

• GIS 

• Planning 


required for community vulnerability 

PHUs identified municipalities as sources for: (SU) 

• Canopy cover (5) 

• Age stratification of forests (4) 

• Unit size by land use (2) 

• Building density (3) 

• Building age (3) 

Source for public green space boundaries and land cover (for 

canopy coverage) in Toronto (LR) 

Provides local level data Unknown Data may not be consistently gathered or shared.

An Environmental Scan of Built Environment Data Related to ...

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