Download the notes in PDF format. - Marije Rooze

Visualization Orientation Summaries
Uncensored Sources
Marije Rooze
01-03-2011
Literature ............................................................................................................................................ 3
The Language of New Media (2001) .................................................................................................... 3
Articles ................................................................................................................................................. 3
Knowledge exploration with concept association techniques (2009) .................................. 3
Introduction to Information Visualization ........................................................................................ 4
Narrative Visualization: Telling Stories with Data ......................................................................... 5
Excerpt from Tufte (four books) ........................................................................................................... 8
Diagrams for the masses ........................................................................................................................... 9
Web ......................................................................................................................................................11
Journalists of the future need data skills, says Berners-Lee (2010) .................................... 11
Making data dance (2010) .................................................................................................................... 11
An Interactive Exhibit for About $30 ................................................................................................ 11
Presentations ...................................................................................................................................12
A Brief History of Data Visualization (2009) ................................................................................. 12
Jonathan Harris collects stories (2007) ........................................................................................... 13
Manuel Lima | Visual Complexity ....................................................................................................... 13
Journalism in the Age of Data (2010) ............................................................................................... 14
The joy of stats (2010) ............................................................................................................................ 16
Life as Annual Report (2010) .............................................................................................................. 18
Go Figure: Graphics at The New York Times (2010) ................................................................. 18
David McCandless: The beauty of data visualization (2010).................................................. 19
Golan Levin on software (as) art (2004) ......................................................................................... 21
Natasha Tsakos' multimedia theatrical adventure ..................................................................... 21
Demo: Stunning data visualization in the AlloSphere (2009) ................................................ 21
Joshua Davis entrevista en argentina 2008 (2008) .................................................................... 22
Barry Schwartz on the paradox of choice (2005)........................................................................ 23
Tim Berners-Lee on the next Web (2009) ...................................................................................... 24
Tim Berners-Lee: The year open data went worldwide (2010) ........................................... 27
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BBC Newsnight: Information Graphics ............................................................................................ 27
Jonathan Harris – Cold: Bold (2010) ................................................................................................. 28
The Future of Art (2010) ....................................................................................................................... 30
Amanda Cox on Data Visualization .................................................................................................... 30
Carol McCall, "Can Big Data Fix Healthcare?" (2011) ................................................................ 31
Strata 2011: Hilary Mason, "What Data Tells Us" ........................................................................ 32
Making Data Engaging: A Talk with the New York Times Interactive Design Team
(2007) ............................................................................................................................................................ 34
objects to read /watch & / || summarize ...............................................................................35
have to read: ............................................................................................................................................... 35
Hal Varian on how the Web challenges managers (2009) ....................................................... 35
Information Interaction Design: A Unified Field Theory of Design (1994) by Nathan
Shedroff ......................................................................................................................................................... 35
Visualisation Insights: #6 Data Journalist & Information Designer..................................... 35
Ben Fry (2004). “Computational Information Design”. http://benfry.com/phd/ ......... 35
Shirky, Clay. Excerpt from: “Information visualization: Graphical tools for thinking
about data”. In: Esther Dysons’s monthly report, 2002 Volume 20, no. 8 ........................... 35
10 Best Data Visualization Projects of the Year – 2010 ............................................................ 35
Brewster Kahle builds a free digital library (2007) ................................................................... 35
Data Ghettos ................................................................................................................................................ 35
Lines and Bubbles and Bars, Oh My! New Ways to Sift Data .................................................. 36
Visualizing Data, Telling a Story ......................................................................................................... 36
The Data-Driven Life ............................................................................................................................... 36
The data deluge (2010) .......................................................................................................................... 36
Narrative Visualization: Telling Stories with Data (2010) ...................................................... 36
Scott McCloud - Understanding Comics .......................................................................................... 36
The value of Many Eyes
http://interactiondesign.sva.edu/classes/datavisualization/updates/ ........................... 36
Optional (nice to read): .......................................................................................................................... 36
Voronoi treemaps for the visualization of software metrics .................................................. 36
Edward Tufte – all his books :) ............................................................................................................ 36
2

Literature
The Language of New Media (2001)
Manovich (213 -
‘.. a computer database is quite different from a traditional collection of documents: It
allows one to quickly access, sort, and reorganize millions of records; it contains
different media types, and it assumes multiple indexing of data, since each record
besides the data itself contains a number of fields with user-defined values.’ (214)
Reference to Janet Murray, she defines four essential properties:
• Procedural
• Participatory
• Spatial
• Encyclopedic
‘Narratology, the branch of modern literary theory devoted to the theory of narrative,
distinguishes between narration and description. Narration is those parts of the
narrative that move the plot forward; description is those parts that do not.’ (216)
‘As its name itself implies, narratology paid most attention to narration and hardly any
to description. But in the information age, narration and description have changed roles.
If traditional cultures provided people with well-defined narratives (myths, religion)
and little ‘stand-alone’ information, today we have too much information and too few
narratives that can tie it all together.’ (217)
‘For better or worse, information access has become a key activity of the computer age...
information access is no longer just a key form of work but also a new key category of
culture.’ (217)
Articles
Knowledge exploration with concept association techniques (2009)
Different ways of accessing information. Focuses on exploratory learning, the problems
of finding information in open environments. Content based recommendation systems
based on history leads to overspecialization or ‘similarity hole’. Recommendation
systems based on collaborative filtering, such as bask analyse, used by e-commerce
companies such as amazon.
The difference between data- and goal-driven strategies is that learners use the
former to locate each piece of related information and examine the details of
each piece but they use the latter to integrate and link the related information to
form a concept that relates to the goal of an exploration. (2009:790)
Mindmaps are widely applied to represent, integrate and assess knowledge -> visualize
associations. The use of keywords as tags, tags are scalable and flexible. There is no topdown
structure but a grass-roots one.
The learners’ exploration patterns were analysed on the basis of the frequency
of all nine main categories of activities during mind mapping: self-determining
concepts, modifying concepts, deleting concepts, requesting concept
recommendation, linking concepts, requesting associations between concepts,
deleting links between concepts, browsing the web, and retrieving related
papers (2009: 796)
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Exploration is a dynamic process during which the learners iteratively seek information
in some directions that they might not have known about (798). Two types of
exploration strategies: data and goal driven. Mindmapping as a way to reduce
complexity and cognitive load.
Introduction to Information Visualization
Riccardo Mazza
Pretty general text about Information Visualizatin (IV). Brief history of IV with John
Snow and Minard’s map of Napoleon’s map, focuses especially the latter example
through the whole chapter. Is heavily based on the work of Edward Tufte, Card,
Schneiderman, Spence. Some text flow problems but quite okay (however, Figure 8 is
awful).
Tells about Tufte’s hate of abundance, of clutter (which is noise). Tufte ‘proposes
layering and seperation as one of the most powerful devices for reducing noise and
enriching the content in graphics, and is archieved by distinction of colour, shape, size,
addition of elements that direct the attention via visual signals, or ordering data to
emphasize layer differences’.
Explains the principles of Tufte (9):
1. Show the data;
2. Avoid distorting what the data have to say;
3. Present many data in a small space;
4. Make large data sets coherently;
5. Encourage inferential processes, such as comparing different pieces of data;
6. Give different perspectives on the data – from broad overview to the fine
structure (red. macro micro reading/perspective - context & focus)
The difference between scientific and information visualization. Scientific shows
phenomenal or experimental data in graphic formats (see Allosphere). Information
visualization deals with unstructured data sets as a distinct flavor.
Several issues of data (10):
1. The problem
2. Nature of the data – numerical, ordinal and categorical
3. Number of data dimensions:
a. Univeriate (one dimension)
b. Bivariate (two dimensions)
c. Trivariate (three dimensions)
d. Multivariate (four dimensions, this and higher = one of the most
challenging tasks)
4. Structure of the data
a. Linear
b. Temporal
c. Spatial or geographical
d. Hierarchical
e. Network
5. Type of interaction
a. Static
b. Transformable
c. manipulable
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The issue with data maps, ‘they can carry a huge amount of data in a small space, but
have an intrinsic problem: they may give a wrong illusion that the importance of the
information is geographical, rather than factual’.
Ways to overcome the ‘too much data, too little data area’ problem (20):
1. Zooming
2. Panning
3. Scrolling
4. Focus + context
5. Magic lenses
Narrative Visualization: Telling Stories with Data
Edward Segel and Jeffery Heer
Creating good ‘data stories’, these differ from traditional storytelling. Current tools
enable mostly data exploration and analysis. They took an empirical approach, analyzed
examples and were able to identify ‘salient dimensions of visual storytelling’. They
describe seven genres of narrative visualization:
1. Magazine style
2. Annotated Chart
3. Partitioned Poster
4. Flow Chart
5. Comic Strip
6. Slide Show
7. Video
Difference between author-driven and reader-driven experiences. They focus on
graphical and interactive elements of narrative visualization and less on cognitive and
emotion experience of the reader.
Storytelling and visual expression as integral parts of human culture, different
storytelling strategies vary among media and genre.
A wide range of storytelling techniques:
• Clear starting point (> establishing shot)
• Cultural factors (left to right reading
• Change of scenes
o Scott mcloud reference – transition types(understanding comics)
� Moment-to-moment
� Action-to-action
� Subject-to-subject
� Scene-to-scene
� Aspect-to-aspect
� Non-sequitur
Reference to Jonathan Harris ‘the human stuff is the main stuff, and the data should
enrich it’ Harris [defining stories] very loose but basic elements can probably be
summed up with Who/What/Where/Why/How.’
Gershon and Page:
• Highlighting
• Continuity editing (making the medium invisible)
• Redundant messaging across media
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Case studies
• Steriods or not – NY Times
o Seamless transitions, ordering by visual consistency (prevents the
viewer to reorient).
• Budget Forecasts – NY Times
o Consistent visual platform, details on demand, multimessaging
o Interactive slideshow that uses single frame interactivity: interaction
manipulates items within a single frame without taking the user to new
visual scenes
o Presentation martine glass structure: following tight narrative path early
on (the stem of the glass) and then opening up later for free exploration
(the body of the glass).
• Afganistan: Behind the Front line – Financial times
o Details-on-demand
o Can be improved, more annotations, too much terminology-heavy
paragraphs, which scares the general audience
o ‘This graph may suffer by putting exploratory power into the hands of
the viewer without sufficient guidance. A synthesis of summary could be
very useful toward this end.’ (1142)
• Gapminder Human Development Trends
o Animated transition morphing
o Exposed interactivity is part of the narrativity
• The Minnisota Employer Explorer
o Social interactions features, commentary and visualization is linked
together
o Intention was to engage people, this failed (most comments were from
the producers and from visualization-enthusiasts)
o Usability issue: the visualization was placed below-the-fold on the
website
o Suggestion of the writers: may be more fruitful to annotate stories with
data, then annotate data with stories. (1143)
Analyzed 58 visualizations, were from 71% online journalism, 20% business and 9%
visualization research. They made three divisions of features:
• Genre: taxonomy of visual narrative types
o Magazine Style
o Annotated chart
o Partitioned Poster
o Flow Chart
o Comic Strip
o Slide Show
o Film/Video/Animation
• Visual Narrative: visual devices that assist and facilitate the narrative
o Visual structure: mechanisms that communicate the overall structure of
the narrative to the viewer and allow them to identify his position within
the larger organization of the visualization.
o Highlighting: visual mechanisms that help direct the viewer’s attention
to particular elements in the display.
o transition guidance: techniques for moving within or between visual
scenes without disorienting the viewer (like continuity editing in
movies)
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• Narrative Structure: mechanisms that communicate the overall structure of the
narrative of the viewer and allow him to identify within the larger organization
of the visualization.
o Ordering: the ways of arranging the path viewers take through the
visualization.
o Interactivity: different ways a user can manipulate the visualization
� Might engage the user but detract from the author’s intended
message.
o Messaging: refers to the way a visualization communicates observations
and commentaries to the viewer.
� Might clarify visual elements but produce clutter.
See figure (blablaa) for a table with their very nice and extensive research.
Figure 7 here
‘Three important patterns stand out from the date: (1) the clustering of different
ordering structures, (2) the consistency of interaction design, and (3) the underutilization
of narrative messaging.’ (1145).
Under-utilization of ‘tacit tutorials’ and ‘stimulating default views’:
• Tacit tutorials introduce a visualization’s interactive functionality by animating
the interactive components along with the presentation to make it clear how to
manipulate the display.
• Stimulating the default views provide initial presentations of data and analysis
intended to excite the user, a device analogous to journalistic leads.
Two different approaches:
• Author-driven
o Strict linear path;
o relies heavy on messaging
o Includes no interactivity
• Reader-driven
o No prescribed ordering of messaging
o No messaging
o High degree of interactivity
Most visualization can be placed into the author-driven or reader-driven approach.
However, some fall in between, ‘an important attribute of narrative visualization is its
flexibility in balancing both elements.’ A few hybride models have become most
common, they discuss three common schema’s
• Martini Glass Structure
o Starts author-driven and gradually becomes reader-driven (small
tutorial in the beginning)
• Interactive Slideshow
o Work well with both complex datasets and narratives.
• Drill-Down Story
o Relies more on Reader driven approach
Then comes the discussion: ‘Generalizing across our examples, data stories appear to be
most effective when they have constrained interaction at various checkpoints within a
narrative, allowing the user to explore the data without veering too far from the
intended narrative’. What will happen when people become accustomed to narrative
visualization? How do you ‘stimulate default views’? Ends with a quote from Blundell:
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‘We’re supposed to be tellers of tales as well as purveyors of facts. When we don’t live
up to that responsibility we don’t get read.’
Excerpt from Tufte (four books)
Edward Tufte
“This fine texture of exquisite detail leads to personal micro-readings, individual stories
about the data: shops visited, hotels stayed at, walks taken, office windows at a floor
worked on – all in the extended context of an entire building, street and neighbourhood.’
(53)
‘A most unconventional design strategy is revealed: to clarify, add detail.’ (53)
‘panorama, vista, and prospect deliver to viewers the freedom of choice that derives
from an overview, a capacity to compare and sort through detail. And that microinformation,
like smaller texture in landscape perception, provides a credible refuge
where the pace of visualization is condensed, slowed and personalized.’ (55)
different views of the same object = small multiple design
‘Small multiples resemble the frames of a movie: a series of graphics, showing the same
combination of variables, indexed by changes in another variable.’ (50)
These designs are good for comparison, as long as you are consistent:
‘Consistency of design puts the emphasis on change in data, not changes in data frames.’
(51)
‘If the visual task is contrast, comparison, and choice – as so often is – then the more
relevant information within eyespan, the better. Vacant, low-density displays, the
dreaded posterization of data spread over pages and pages, require viewers to rely on
visual memory – a weak skill – to make a contrast, a comparison, a choice.’ (59 – met
footnote)
‘High-density designs also allow viewers to select, to narrate, to recast and personalize
data for their own uses. Thus control of information is given over to viewers, not to
editors, designers, or decorators.’ (59)
in user interfaces for computers, a problem undermining information exchange between
human and software is constant context switching > changing the visual environment
whereby users have to rely on short-term memory > which means they cannot use it for
significant issues of analysis.
‘Clutter and confusion are faiures of design, not attributes of information.’ (60)
‘Simpleness is another aesthetic preference, not an information display strategy, not a
guide to clarity.’ (60)
‘small multiples are economical: once viewers understand the design of one slice, they
have immediate access to the data in all the other slices.’ (49)
Tuftes mathematical approach of data density. The data density of a graphic = number
of entries in data matrix / area of data graphic.
Tuftes lie factor = size of effect shown in graphic / size of effect in data. A lie factor lower
then 0.95 and higher 1.05 indicate substantial distortion.
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About a visual hierarchy, when everything (background, structure, content) is
emphasized, nothing is emphasized; the design will often be noisy, cluttered, and
informationaly flat.
‘Bases on universal ideas of figure-ground, large-motion-covers-small-motion,
hierarchical layering, and contentdriven design, the strategy of the smallest effective
difference applies to all display technologies; these examples come from paper, video,
and computer screen.’
Discusses Minard (1781 – 1870) graph which shows Napoleon;s army in Russia.
‘Probably best statistical graphic.’
Sparklines are wordlike graphics with an intensity of visual distinctions comparable to
words and letters. They are data words: data-intense, design simple, word-sized
graphics.
‘Words visually present both an overall shape and letter-by-letter detail; since most
readers have seen the word previously, the visual task is usually one of quick
recognition.’
Graphic from E. J. Marey about the train schedule.
Data ink is the non-erasable core of a graphic, the non-redundant ink arranged in
response to variation in the numbers represented.
Data-ink ratio= data ink/ total ink used to print the graphic
Diagrams for the masses
Raul Nino Zambrano and Yuri Engelhardt
Starts with the use of diagrams in the movie An Inconvenient Truth. According to
Womack (2006) the documentary showed that ‘information visualization is able to
communicate the intricacies of global warming in a way no other discipline can. Its
messages can be immediate and powerful, without sacrificing the level of detail
necessary to represent the complex subject accurately’.
There is a trend in the topic ‘diagrams for the masses’. The paper addresses the
corresondences between Neurath’s idealism and these recent trends in information
visualization with regards to ideas about data accessibility, the empowerment that this
access may produce and the assumed benefits of using visuals.
Otto Neurath, member of the Vienna Circle, aimed for a new way to convey information
that could be comprehensible for a wide range of people, and put it in practice in the
different museums that he directed. He, and others, developed International System of
Typographic Picture Education (Isotype). Isotype is an iconic language that uses
simplified pictures and specific compositions rules to ‘present something worthwhile
information, show up some relationship or development in a striking manner, to arouse
interest, direct the attention and present a visual argument which stimulates the
onlooker to active participation.’
An interesting quote from Twyman: “The output of the Isotype movement as a whole
draws attention to two things which are of special interest to many designers today.
First, it demonstrates that successful designing depends on a large degree on clarity of
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thinking; secondly, it provides support for the view that the graphic designer’s primary
role is to serve the needs of society’ (Twyman 1975/1981: 17).
Linked to Hans Rosling and Gapminder (acquired by Google). Both share the idea that
statistical information must be accessible to everyone, both belief that this could lead to
empowerment. And finally both have the assumption that to make scientific facts
understandable it can be best visually displayed.
‘Both Neurath and Rosling have the conviction that dissiminating data and
making it comprehensible for a big public could play a role in social
transformation.’ (286)
Reference to famous quote of Neurath: ‘words devide, pictures unite’. He wanted to
educate people through the eye. Neurath predicted that ‘communication of knowledge
through pictures will play an increasingly large part of the future’.
Zambrano and Engelhardt propose to distinguish three categories of ‘diagrams for the
masses’. The categories are incremental:
• View
o Passive
o Examples: Isotype charts, diagrams from An Inconvenient Truth
• Interact and Explore
o More active role, interactive exploration
o Examples: Gapminder, Crisis in Darfur
• Create and Share
o Very active: upload own dataset, choose visualization format, add
coments, publish and share
o ‘Diagrams for and by the masses’ > peer production
o Examples: ManyEyes, Swivel, Ushadidi
ManyEyes website claims that their goal ‘is to democratize visualization and to enable a
new social kind of data analysis’.
All these examples show the same spirit: ‘Data accessibility for ‘ordinary people’, a
conscious choice of visuals above words, and a conviction that a better comprehension
of statistical data through visuals could lead to desirable social change is clearly found
both then and now.’
Trellise and Crosstabs
Stephen Few (98 – 104)
Trellis displays, coined by William Cleveland and Richard Becker in the early 1990s.
these should exhibit the following characteristics:
• Individual graphs only differ in terms of the data that they display. Each graph
displays a subset of a single larger set of data, divided according to some
categorical variable, such by region or by department.
• Each graph is the same type, shape, and size, and shares the same categorical
and quantitative scales. Quantitative scales in each graph begin and with the
same values (otherwise values in different graphs cannot be accurately
compared).
• Graphs can be arranged horizontally (side by side), vertically (one above
another), or both (as a matrix of columns and rows)
• Graphs are sequented in a meaningful order, usually based on the values that ae
featured in the graphs.
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Trellis displays lose much of their value when the indidivual graphs are arranged in an
arbitrary order – that is, an order that ignores the magnitudes of the values.
Visual crosstab: are powerful means to examine and compare data across several
dimensions at once.
Quantitative scales must only be consistent among a series of graphs when it’s
appropriate and useful to compare the magnitudes of values between them. In fact,
variables could be expressed as different units of measure entirely, such as dollars
(reveneus, expenses, and profit) versus number of items (order count), which would
make it extremely impractical to use the same scales for all variables.
Web
Journalists of the future need data skills, says Berners-Lee (2010)
http://www.guardian.co.uk/technology/organgrinder/2010/nov/19/berners-lee-journalismdata
Journalists need to be data-savy, data driven journalism is the future according to Tim
Berners Lee. The geeky journalist.
Making data dance (2010)
http://www.economist.com/node/17663585
Interview with Hans Rosling, creator and frontman of the software tool Gapminder, who
appears in the new BBC documentary. “I produce a road map for the modern world,”.
Making statistics sexy. Trying to change the mindset of students by presenting
information in a visual way. Reference to Florence Nightingale, using a visualization as a
convincing method. Infographics to convince it’s users. Making data available and usable
(open data): Robin Hood of the free data. Relation to open-source code. Rosling makes
grand narratives with his tool, everything is connected to each other. Visualizations as
predictor of the future. The enthusiasm and passion of Rosling is more important and
persuasive than rock solid data.
An Interactive Exhibit for About $30
http://www.nytimes.com/2011/03/17/arts/design/arduinos-provide-interactive-exhibits-forabout-30.html?_r=1&ref=nickbilton
article about the use of Arduino in current museums. The Arduino is a tiny,
programmable computer where you can use for sensors (like light and touch). It’s used
for behind the scenes. There is said that:
But this nondescript gadget has become the driving force behind most of the
interactive exhibits seen in museums and galleries today.
It makes interactive installations way more affordable. ‘Once an artist has a chip,
inexpensive sensors can be added to make the device sentient.’ The Do-It-Yourself
movement has also inspired artists, it democratized interactivity. You don’t have to be a
hacker or a geek to do this. Increases participation in the museums.
‘The big online community has helped to put the Arduino into designers’ hands and has
made it a major part of museums around the world.’
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“The two most important introductions for art in the past 20 years have been the
Arduino and Processing,” explained Paola Antonelli, senior curator in the
Department of Architecture and Design at the Museum of Modern Art.”
Could be used to investigate how people interact with art (what distance, how long etc).
‘Artists at N.Y.U. have used the Arduino to create works that blur the line between art
and robotics.’
Massimo Banzi, an Italian engineer and one of the driving forces behind creation
of the Arduino, said the original goal was to make a low-cost and simple-to-use
product that artists and designers could employ to create responsive artwork
without having to understand complex programming or electrical engineering.
Mr. Banzi sees the uses for the device as just the beginning of interactive
museums.
The summary of the tools to make interactive works.
Presentations
A Brief History of Data Visualization (2009)
http://cobb.stanford.edu/courses/cs547/090306/090306-cs547-300.wmv
Talk by Jeffrey Heer for Stanford computer science, he studied at Berkeley and worked
with Danah Boyd. Starts with old examples of visualization starting at 6500 bc. Special
reference to Nightingale with quote:
‘to affect tro’ the Eyes what we fail to
convey to the public through their
word-proof ears’. Engaging the public
rather than scan through, a tool, visual
rhetoric. In 1900s data became
creativity visualized. Around 1900
came the ‘dark ages’ of visualization,
no innovation. Main cause was the rise
of formal methods in statistics and
social science. At 1950s a new era
starts. Begins with a book ‘How to lie
with statistics, Huff & Geis, 1954.
Three elementary perceptual tasks in
semiology: resemblance, order, proportion (Bertin). Sees statistican John Tukey as one
of the hero’s, classic paper future of data analysis. Advocates for exploratory data
analysis. Advent of computers created novel visualization and interaction. The web is a
special platform for visualizations, e.g. treemap, google.maps (panning and zooming and
mash-ups), graphics New York Times. Data is ubitiquous and becoming massively
available on the web, extracting value from data. Messiness of data, data integrity,
managing data, categorizing,
1. The reconcilation of Graphical and Formal Methods
2. The rise of graphical literacy and expression (Hans Rosling & inconvenient truth
example + flight patterns + many eyes (IBM)) the template approach (lego
analogy) – small multiple displays
3. The Social life of Visualization: how can user interfaces support and encourage
collaborative analysis (e.g. baby name voyager, sense.us) enabling collaboration.
Authorship in software – are, for example, Ben Fry and Casey Reas authors? [Reference
to film theory and cahiers du cinema?]
12

Underlying assumption that the data is already out there but sometimes you have or
should generate the data itself. Trusting the data. Infovis, science visualization (lay-out
given), geographical visualization (geoviz).
abstract (http://hci.stanford.edu/courses/cs547/abstracts/08-09/090213-heer.html)
Jonathan Harris collects stories (2007)
http://www.ted.com/talks/lang/eng/jonathan_harris_collects_stories.html
Jonathan Harris is an anthropogist, storyteller, artist and geek, Likes partial glimpes in
life, let your own mind fill in the rest. Digital footprints, blogposts, photos, updates. Tells
about We Feel Fine (feelings from the English speaking world). Montage compasition
(photos + sentences). Internet as a presentation mode. Telling about the whale hunt,
photographic hartbeat. What does make up a story? Fundamental components that
make up a story. Stories have characters, concepts, contexts, time, excitement level. In
traditional media we have narrators, in real life this is not the case. You can make
substories with the whale hunt. Experimenting with the interface, interactivity and
elements. Bhutan, bruto happiness. Express happiness by baloons, write your wish on
one balloon, show your hands, make a funny face.
Figure 1 Jonathan Harris wishing balloons
Manuel Lima | Visual Complexity
http://digup.tv/index.php?rub=videos&item=70
Figure 2 diagram "Understanding Spectrum" by Nathan Shedroff
Interview with Manuel Lima, researcher and interaction designer. Created visual
complexity. Making sense of the petabytes of data. A couple years ago (four, five years)
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information visualization was very scientific. Reference to Nathan Shedroff and his
diagram “understanding Spectrum” (see figure). ‘Meaningful data is not a fact now these
days’. Convert data into meaningful knowledge. In excel sheets it is hard to find
patterns. ‘Infoviz … science and art come together in a sort harmonious way’. Artists
becoming more interesting in programming. ‘aesthetics in(of?) many ways come as an
outcome. Is not the initial goal of a dataviz project. The goal of dataviz is to make a
system more understandable. If you not really reach that goal, then it become more as
generative art.’ Visualization create a new language, with visual complexity finding the
common threat. Start understanding of this new syntax, beter visualizations of the
future.
Journalism in the Age of Data (2010)
http://datajournalism.stanford.edu/
This video report has been released a couple months ago and it is a must-see video for
everyone who is thinking about data visualization. You meet the important figures in
data visualization, the hot topics and important issues within the field. It is hard not to
get excited about the video, and also about the topic. The next era will be, according to
some people in the video, the era of analysts, the visualizers. There is a huge amount of
data and there is a need to give meaning to it. The value of making a visualization is
putting it in a context, showing patterns that are likely invisible in an excel sheet. The
ultimate goal for Amanda Cox, statistican by New York Times, is to change the
worldview of people. A good visualization can make people see the world in another
way. There are, of course, also bad visualizations. In the video we see some people
complaining about the heavy datadriven designs. They are seen as beautiful but shallow,
there is no real depth. So we could say that there are highbrow and lowbrow
visualizations. There is special attention to the storytelling aspect of visualizations. One
could use the interactive slideshow (or as the NY timesx calles it, the stepper) to provide
a clear path one has to follow. I had to think about the political metaphors which George
Lakoff discusses in his book. Lakoff sees contemporary conservatism as the Strict Father
and contemporary libarlism as the Nurturant Parent. The stepper is in this way the
nurturing parent, the handholder. I will expand this topic another time. Another topic
they breach is the template, the template is easy, a high ROI (this word is actually
mentioned in the video!) and usable for the laymen. It is also uninteresting, 13 in a
dozen etc. There is another approach, tools to make tools. Like Processing, or a project
from Jeffrey Heer. It is, by the way, striking that Processing is not mentioned that much.
Eventhough we see Ben Fry (creator of Processing) often enough. There is also a person
missing, I am talking about Hans Rosling. His son is there, presenting the Google Date
Explorer. But where is the father? He is a real star, partly thanks to the Ted
presentations. There are definitely more stars arising, this field is ‘up and coming’. I am
excited. Hope to work there when I am older.
The video contains seven chapters, the following paragraphs contains a more elaborate
summarization.
I. Introduction. IBM is occupied with democratizing visualization, making it available to
everyone. Now busy with ManyEyes. Vision is the biggest band width we have in terms
of sensory information to the outside world. Visualization takes advantage of the fact
that we are so programmed to understand the world around us in terms of what we see.
Best way to learn is to do it yourself [literacy]. Ben Fry, trying to get people more
curious about a certain topic. Has to be a lot more about art and writing then about
programming. It is obvious that there is a data problem and that is not going away.
II. Data vis in Journalism. IBM sees New York Times as a model, creating sophisticated
visualizations. Amanda Cox, one has to relate to data. Jeffrey Heer, putting data in
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context. Example of the unemployment rate, how the unemployment rate varies
between different groups. Amanda Cox, the highest goal is to change of what you think
about the world. See something different. BBC hires now people who are data-savy, into
the datadriven journalism. How do you get the story from the data, how to investigate
the data in the best way? Give a reason, an incentive, to look at the data. [You can say,
explore it yourself. But if you need some incentive to go out exploring]. Amanda Cox,
looks at the data, some you have to throw away. You need a narrative, any kind of
narrative. Nigel Holmes says you can do beautiful things with computers but are
completely incomprehensible.
III. Telling data stories. Interaction is key in visualization, just like in games. Interactive
slideshow, NY calles this the stepper (‘like handholding’). Presenting information in
small chunks which captures the user and hopefully will lead to more clicks. Nigel
Holmes states that with the web, ‘information can breathe’. Also makes data less
overwhelming. Jeffrey Heer, Drill down story. From macro to micro, allows ‘navel
gazing’.
IV. A new era in infographics. Malofie is the most important news infographics gathering
in the world according to Alberto Cairo. Infographics is usually dominated by fashion,
the current fashion is strange visualizations, strange looking graphic forms. Example is
the winner of 2009 Movie Box office illustration of NY times, Cairo found it really
shallow. It frustrates you. Kris Viesselman: ‘there is a big move from a narrative,
illustrative graphics to statistic rich data-driven pieces’. [is this a highbrow – lowbrow
debate?] According to Alvaro Valino, we have ‘to evolve in this statistical era’. [statistical
era vs. information era].
V. Life as a Data Stream. The Feltron report, displays the year of Nicholas Felton.
Beautiful presented according to Jim Grimwald. Feltron is probably interviewed in his
own apartment, his books are ordered by color. Eric Rodenbeck of Stamen Design shows
how one can present real time information (Dynamic real-time gathering). Looking at
Twitter traffic during the MTV awards. Ny times does also do some realtime updating.
Matthew Ericson warns for some challenges associated with realtime: just that
something is dumped in the datafeed does not necessarily mean that you want to
visualize it straight as it is. Second challenge is that it is scalable: it is not allowed to
break when new data is an outlier (go off the chart). Breaking newsstories in a dataset.
VI. Exploring Data. Ola Rosling, son of the famous Hans Rosling, works at Google on the
public data explorer. ‘Numbers in isolation are often not that useful, you need the
context. If you are not an expert, you are not aware of the context… by showing the
trend, people can see if a number is low, high, average. Dana Priest states that the
problem has been trying to convert that data into pleasing, simple visuals. [but life is
messy, maybe to sterile?] Reference to chat with Jonathan and Aaron]. Journalists are
under a great time pressure to publish a story. Eric Rodenbeck, enabling discovery by
looking at the data and discover things in it you wouldn’t have necessarily thought of to
look for is a pretty important part of what we are thinking about the studio. Making
interfaces to allow you explore and discover and ask questions that you wouldn’t
normally think to ask. Shawn Alles, freeing the data. Completeness in the data is
important to have. Sarah Slobin: ‘that’s the new thing, right? Transparency is the new
black’ (about data.gov). Martin Wattenberg (IBM research), ‘visualization can promote
openness. Just by the fact of it’s existence. The moment you try to make a visualization
you realize you need data, and if you have holes in your data they become apparent and
so. Visualization conserve two roles: one is the explicit role of letting us see stuff coming
[?] the other is the sort of funny, passive aggressive role that of if you need to do a
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visualization you find yourself pushed into the mode of wanting to make data open.’
[very interesting]
VII. Technologies and Tools
Good design environment, Flash is a popular program. Ny times, when they started
there was a one-off bases, but quickly code was written in a way that it could be re-used.
Object oriented programming, abstracting functions. Flare library, protovis by Stanford,
getting results with less effort more pleasure. Jeffrey Heer says examples are important,
they show what kind of visualizations are possible enriches people thinking in what are
the available options and what are the right ways to convey data. And from the technical
site provides information how to create this visualizations. From flash to java. Flash is
not available on other platforms (like telephones) so some of these browser native
techniques maybe be the better bet in reaching a wide diversity of interactive devices.
Ny times, the more you automate, the less interesting it becomes but I think tool
building is important. CaliforniaWatch, building tools which are reusable so that you get
a better return of investment (ROI). Creating templates, timeline, barchart, maps with a
comprehensible CMS (content management system). Making it more accessible. [this is
the template vs the manual debate].
VIII. First steps
Swiffle, easy way to make visualizations. Lay man can make visualizations through, for
example, the google products. Get to know your strengths, mapping, data, animation.
Knowledge of statistics is very important. Different conferences like IEEE, SIGGRAPH,
society for news design annual meeting and the earlier mentioned Malofiej. It is very
upcoming.
The joy of stats (2010)
http://www.bbc.co.uk/programmes/b00wgq0l
Nice documentary which is presented and narrated by Hans Rosling. A lot of concepts
and history is covered in this documentary. We get to know Rosling better, see some
interviews. Lot of great examples and visualization that make the concepts graspable.
Funny anecdotes. Distribution, correlation. Exciting times
Hans Rosling is the creator of Gapminder. Although not officially a statistician (global
health), Rosling is very much into statistics. He is eager to show the beauty of statistics
and tries to shake of the boring image of statistics. You can make data sing with it. With
statistics the datadeluge, as it is been called, is leading us to a ever greater
understanding of life on earth and the universe beyond. Thanks to computational power,
it can fundamentally transform the process of scientific discovery. I kid you not,
statistics is the sexiest subject around.
Rosling became obsessed with stats when he realized that many people in Sweden don’t
know about the rest of the world. Making sense of the changing world. Uses freely
available information from the UN and the World bank. Rosling’s mission is to share his
insights to anyone who will listen. And show that statistics is nothing to be frightened of.
Interview with two people from Stamen and shows the crimemap from San Franchico.
The map gives insights on where the crimes are and also the type: Narcotics, Robbery,
Simple Assault, Vandalism, Theft, Vehicle Theft. According Stamen users have, while
using the tool, no idea that there are doing statistics. Main point is according to Rosling,
that public statistics is making citizens more powerful and the authorities more
accountable. Data inspires new methods, they have a real life impact. “I think our dream
government data analysis project would really be focused on life information, stuff that
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was being reported and pushed out in the world over the Internet as it was happening.
You know: trash pickups, traffic accidents, busses. And I think through the kind of stats
gathering power of the Internet it is possible to really to begin to see the working of city
displayed as a unified interface.’ (Stamen, 11:45)
Open data empowers citizens and letting us hold our rulers account. Statistics are
necessarily to monitor the government. But it started the other way around. It started
around two centuries ago. Sweden invented the table, which gave an accurate picture of
its people. The stats gave insight in the fact that a lot of woman were dying during
childbirth and a high amount of young children were also dying. The government took
action. This was the beginning of modern Sweden according to Rosling.
Taking the average of all the numbers. This is sometimes very useful. Eventhough traffic
accidents are all individual events (most times then) there is still a contant to be found.
It is the same number every year. ‘these individual particular events sum up into a social
phenomenon. Gapminder also uses averages. But, eventhough useful, they don’t tell the
whole story. Rosling explains this through a great example. On average, Swedish people
have 1.999 legs. But almost everyone has more than the average amount of legs.
Variation in data is just as important as the average. You can transform number into
shapes. The shape is called the distribution. There is one shape that occurred all the
time, it is called the Normal distribution. The plausan shape describes how likely it is
that out of the ordinary things will happen. Staticans use shapes to reveal patterns in the
data. ‘if the story in the numbers is told by a beautiful and clever image than everyone
understands.’
A special focus of Florence Nightingale’s work. She was a statistican, it was for her a
religious duty. She was in the war and began counting the death of soldiers (in 1858-
1859). She used graphics to present her case. The most known is the polar area graph.
David McCandless, data designer, made the billion pound‘ogram. When you see it
visualized like his graph, you get a different relationship with them. You see the scale
and the patterns. You understand them, and then you can really put it into perspective.
Rosling shows data in a way that people will enjoy and understand it. Statistics give a
clear description of the world. But we can not only explore what is happening but also
why by using the powerful method correlation. Crime correlates to poverty. Smoking is
linked to lungcancer, this took a huge effort a Richard Doll. But it can be very tricky. Data
is the oxion of science. The invention of the printing press kicked of the first information
eplosion. In the 19 th century there came a second revolution with the telegraph,
gramaphone and camera and later radio and television. To the computer and the
internet we went digital, now a great vastness of data (one zetabyte). The datadeluge is
staggering but with computers and statistics I am confident we can handle it.
Interview with Google, especially about google translation. Scientists tried to teach
computer languages but this didn’t work. To much subtlety and ambiguity. Google treats
everything as an exception. It learns correlation between words and phrases. You don’t
have to know Chinese, but you do have to know statistics, computer sciences,
infrastructure to build this large computational systems. Google is also busy with speech
recognition, like a babblefish. I can talk with an Arabic over the phone with the use of
this phone.
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Figure 3 Hans Rosling presenting his data in 3D
Instrumenting the earth. Sensors all over the world, tracking temperature, wind. this
data is transforming the sciences. Example the Sloan data, it also is all on the Internet.
The theory now is that the galaxy is expanding. How are galaxies made. The more data,
the more things can be discovered. It is about scaling.
“the vast amount of data today allows researchers in all sorts of fields to test their
theories on a previously unimaginable scale. But, more than this, it may even change the
fundamental ways science is done. With the power of today’s computers applied to all
this data, the machines might even be able to guide the researchers”. [punt van
Andersen]
Sciences becomes datadriven instead of hypothesis driven. As last there is the example
of my favorite project We Feel Fine explained by Sev Kamvar. Amazing project but I
don’t think it was presented the best way. Statistics rule out prejudice and shows how it
actual is. Authors of our own destiny.
Race gapminder. jokes, enthousiasm, breaking the frame, telling stories. Tells the great
inventions, scale, patterms, statistics as a way to deal with information.
Life as Annual Report (2010)
http://www.slate.com/id/2277605/
An interview with graphic designer Nicolas Felton. He tracks the whole year all his
moves (email, taxi riders, coffee, music) and visualizes this into beautiful annual reports.
Started as an internet project for fun, in 2006 he began seriously tracking all his moves.
Through different devices like mobile, fitpit, computer. He spends typically around ten
minutes a day. There is a wide interest in his report, Felton suspects that the public is
not interested in his life but more in the stories he has examined and how that be
reflected to their own lifes. The hook in this case is the compartive aspects. You can
personally relate to his work. Felton also hopes that people see his work as a mirror
which they can look into and find insight or curiosity about their own activities and
behaviours.
Go Figure: Graphics at The New York Times (2010)
http://newmediadays.dk/amanda-cox
Presentation of Amada Cox, a statistician working at the graphics department of the
New York Times. They have a team of 25 people, which is amazingly huge. They reveal
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patterns, provide context and describe relationships and creating a sense of wonder
(pompous). Maximize clarity multiple it by ‘Auwh’. By a good graphic something
surprising pops out. That things that are interesting in the data shows in the structure it
is presented. NY merging real time information with what you have to watch (with
elections), makes is more interesting. Addresses how readers interact with the data and
sometimes bring more context and annotation. Building on top of each other. The
annotation layer create something greater.
Provide context, provide a sense of scale. Example with the destruction in Port-Au-
Prince. With a slider shows before the destruction and after.
Designers adore sliders, cheap way to get a story in there. There is a beginning, middle
and end. Creates a narrative structure.
The background singer idea, all good songs have background singers. in terms of
engagement if you can interact, have the background, to play with it. Shows a
information dense page, she calls this a ‘phonebook mentality’. The form of a graphic
one has to depend of the content itself. You create a form or a structure and might see
things that you never have seen otherwise.
Cox is against tagclouds, make you see things you already know. Comparing things with
something else makes it often interesting.
They also reveal patterns. Ask questions: is it helpful to make this information
interactive. Use the medium where you are working in. Providing context, what does a
three seconds difference is in an Olympic ski finish. They created a symphony which is a
interesting way to show it. Revealing patterns that are interesting. And also make an
annotation layer. Sentence about the ‘Why’ is still super important. Sometimes it helps a
level of detail, imagining where a person sat in an airplane for example.
NY is not afraid of the linear approach, story based. There is a balance between real
interactivity and storytelling that the world is still figuring out. To get efficient traffic,
make photo slideshows, straight from the Wire.
Templates are important with breaking news, in general we templated less than
expected. You should have the basic form, like the stepper. You want to tell it in a way
that is best for the story right now. They spend a lot of time creating working systems
for other media devices/platforms (like the Iphone and Ipad).
If you would hire two people, would you bother? Yes, one who learned javascript and be
a good thinker and who is strong reporter and catches the good stories. You start small,
and build on that. She sketches with code!
Amanda.cox@gmail.com
David McCandless: The beauty of data visualization (2010)
http://www.ted.com/talks/lang/eng/david_mccandless_the_beauty_of_data_visualization.html
Information overload, data glut, there is to solve that, by using our eyes more. Making
sense of data or seeing patterns and connections. Example the billion dollar graph (also
in Rosling’s joy of stats). When you are lost in information, a map can be kind of useful.
After that a media panic graph (see Figure 2), I imagine that Tufte would not like this. He
loves finding hiding data, being a data detective. Data is the new soil. For me it feels like
a fertile, creative medium. Over the years, online, we have layed down a huge amount of
data. Visualizations, data visualization and infographics, feel like a flower. David shows
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then the famous, Peak Break0up Times, the statistics for this data were scraped from
Facebook. Interesting things can emerge with data. He was first a programmer and then
started to writing, recent years started to visualize. We are blasted by information
design and we all become designers. Visualization is effortless, it all pours in. There is a
physicist, Tor Norretranders who converted the bandwidth of the senses into computer
terms (see Figure 3). Your sense of sight is the fastest. A lot of our vision is pouring into
us without we being aware of it. The eye is extremely sensitive to patterns in variation
in color, shape and pattern. The language of the eye. If you combine this with the
language of the mind, words numbers and concepts. You start to speak two languages,
each enhancing the other. We can use this new language to alter our perspective or
change our views. You can look at things in different ways. Absolute figures don’t tell the
whole story, or give the complete picture. We need relative figures that are connected to
other data so we can see a fuller picture. And that can lead to us changing our
perspective. He refers to Hans Rosling, let the dataset change your mindset.
Visualization is a form of knowledge compression. It’s a way of squeezing an enormous
amount of data in one visualization. There is something unthreatening about seeing a
political perspective, versus being told or forced to listen to one. It’s actually – you’re
capable of holding conflicting viewpoints joyously when you can see them. It’s fun to
engage with them. Design is about solving problems and providing elegant solutions.
And information design is about solving information problems, and it feels like we have
a lot of information problems in our society at the moment. From the overload and the
saturation to the breakdown of trust and reliability and runaway skepticism and lack of
transparency. Or even just interestingness. Visualizing information can give us a very
quick solution to those kind of problems. And even when the information is terrible, the
visual can be quite beautiful.
Figure 4 Media panic graph David McCandless
Figure 5 Tor Norretranders senses translated
into computer terms.
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Golan Levin on software (as) art (2004)
http://www.ted.com/talks/golan_levin_on_software_as_art.html
software engineer, was by MIT, making art with computers. Using the computer
to make personal art. Makes a live performance. Making music by drawing lines.
Look very abstract art, simple primitive objects. Which tranform, could be an
abstract animation or a Dada film from the 1920s(Opus 2, Rayogram). But then
more slick, sometimes quite beautiful (see Figure 4).
Figure 6 Golan Levin Music Visuals
Natasha Tsakos' multimedia theatrical adventure
http://www.ted.com/talks/natasha_tsakos_multimedia_theatrical_adventure.html
Theater awakens our senses and opens the door to imagination. Our ability to
imagine makes us explorers. Our ability to imagine makes us inventors and
creators and unique. Tsakos uses media in her theatre show, she sees it as an
equal partner, she says:’ I want to have humor, beauty, simplicity and complexity
and use metaphors to suggest ideas.’ Sounds like a quite hefty task. She tells that
people are looking for connection and that when you are a little extraordinary,
not quite human, then people will feel inclined to participate and feel out loud
(reminds me of the pamphlet robot). Because through your mask they let
themselves go. Her character, Zero, has become a person on itself with friends
and goals. He also has comic books and videogames. “The natural progression of
science and art finding each other to better touch and define the human
experience... This is a time of communication, connection, and creative
collaboration... In the science of today, we become artists. In the art of today, we
become scientists.”
Demo: Stunning data visualization in the AlloSphere (2009)
http://www.ted.com/talks/joann_kuchera_morin_tours_the_allosphere.html
This video is about visualizing scientific data. The allosphere that is created by
engineers, artists and scientists. They ‘map complex mathematical algorithms
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that unfold in time and space, vissually and sonically’. The allosphere allows to
explore data in a totally new, immersive way. For example, you can fly through a
brain that is mapped visually and sonically.
She callls it scientific and artistic installation. ‘For artists, we can make new
worlds that we can discover and explore.’ It makes it possible to make the data
more comprehenisible and, for example, start to understand more about
quantum mathematics (see Figure 5). Bringing together of arts, science and
engineering. Into a new age of math, science and art.
How did they become seperated? Can we see this goes back to the time of Aristoteles?
Information is singing to you, literally!
Figure 7 visualization of quantum information flow
Joshua Davis entrevista en argentina 2008 (2008)
http://www.youtube.com/watch?v=64kslKUk8uQ
If you would meet Joshua Davis and ask him what he does, he would tell you that he is a
designer. But Joshua Davis is a lot more than that. He does physics, math and is an artist.
Davis writes programs that generate graphic designs which is called computational or
algorithmic design. He writes programs that generates, beautiful designs. Around 99%
of the time he doesn’t have an idea. He just sits down and start making things.
Serendipity plays a big role in his work. He experiments a lot, and gets a lot bad results
but once in a while something worthwhile returns. Great examples of creative coding.
The question arrives how creative is his work it generated by computers without a big
master plan? Personally I think it is still very creative, eventhough it is flexible and
dynamic. Davis is a cool, skateboarding guy fully covered by tattoos who has this urge to
make things. But he is also a designer who works for big companies like IBM and BMW,
can you be a true artist when you are so commercial? Of course, think about Vermeer
and Mondriaan, they got paid. They made portraits that were commissioned. Maybe he
is more free because he is allowed to experiment more than the master painters.
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Figure 8 example Joshua Davis work1
Figure 9 example Joshua Davis work 2
Barry Schwartz on the paradox of choice (2005)
http://www.ted.com/talks/barry_schwartz_on_the_paradox_of_choice.html
interesting presentation, very convincing with nice illustrations and funny anecdotes
that bring his message over. The only thing that irritated me was his outfit, Schwartz
looks like he just arrived from a camping trip. You almost expect that he carries a toilet
roll under his right arm. And I would like to redesign his powerpoint slides, they are
pretty awful.
The official dogma of western industrial societies is the following: if we are interested in
maximizing the welfare of our citizens, they way to do that is to maximize individual
freedom (see Figure 13) The way to maximize freedom is to maximize choice. The more
choice people have, the more freedom people have and the more freedom the have, the
more welfare the have. There is an explosion of choice. This is true in shops, but also
healthcare. When you go to a doctor you get choices: treatment a and treatment b. Both
with different benefits and risks. The patient has to make a choice, he is a consumer.
This is called patient autonomy, Schwarz calls this the shifting of the burden and the
responsibility for decision making. Identity has become flexible, you can reinvent
yourself every day. Choices about marriage, career and social life are pressuring
students. Technology allows us to work every minute of every day from any place on the
planet. This results to an overload of making decisions.
All these choice have two negative effects on people. One effect is that is produces
paralysis, rather than liberation. The second effect, even if we overcome the paralysis
and make a choice, we end up less satisfied with the result of choice than we would be if
we had fewer options to choose from. There are different reasons for this, the imagined
alternative induces you to regret the decision you made, and this regret subtracts from
the satisfaction you get out of the decision you have made, even if it was a good decision.
Second, what economics call the opportunity costs, which means the value you give to a
certain object derives from the things you compare it to. Whenever you are choosing
one thing you are not choosing all the other things (see Figure 8). As third, escalations of
expectations. With all the options available, expectations of the product go up. You
compare the things what you got to what you expected and the result is that you are
disappointed and less satisfied. When perfection is the expectation you will never be
pleasantly surprised. The secret to happiness is low expectations. When you feel
disappointed about the choice you have made, you blame yourself. You made the wrong
choice, no one forced to. Schwartz make a correlation with the explosion of clinical
depressions and the explosion of choice. He believes that the explosion of choices have a
significant contributor to the rise of depressions and suicides.
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Some choice is better than none, but too much choice is bad. What enables all of this
choice in industrial societies is material affluence. The problem he is talking about is the
peculiar problem of modern, affluent Western societies. Income redistribution will make
everyone better of = Pareto-improving move. Because of how all this excess plagues us.
Everyone need a fish bowl, without a bowl we are paralysed and miserable. However we
should be able to grow in the bowl so it has to be big.
- PR expectation management ->
Figure 10 Making your own choices
Figure 12 Effects of choice
Figure 11 example oppurtinity's costs
Figure 13 Official dogma
Tim Berners-Lee on the next Web (2009)
http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
Interesting personality, maybe a little nervous. He is slissing a little. Very English guy:
‘it’s been a blast, isn’t it?’. He focuses on the unlocked potential in data and his new idea:
linked data. The tendency of officials to hug their database.
Tim Berners Lee invented the World Wide Web, a system that reframed the way we use
information, the way we work together. Now, twenty years later, he asks help for a new
reframing. He invented html and all other elements basically because he was frustrated.
He worked as a software engineer at CERN in a huge, exciting lab with a lot of diversity
in the people who were working there. They had all sort of different computers and all
sorts of different data formats, all kinds of documentation systems. In all that diversity,
if he wanted to figure out how to build something out of a bit of this and a bit of that,
everything he looked into, he had to connect some new machine, he had to learn to some
24

new program, he would find the information I wanted in some new data format. And
everything was very incompatible and very frustrating.
The frustration was all this unlocked potential. And he wanted to create a virtual place
where all these documents could be found. He send a proposal to his boss, and never got
feedback. A copy was found after he died, he had written “Vague, but exciting” in pencil,
in the corner. At that time it was difficult to explain what the web was like, now it is
reverse. What was difficult to get them to imagine that that link could have gone to
virtually any document you could imagine. Some people did make that leap of
imagination and a grassroots movement began.
That was so exciting, not the technology, not the things that people have done with it,
but actual the community, the spirit of all these people getting together, sending emails.
I asked everyone to put your documents on this web thing, and, you did. The things that
happened with the web was far bigger that we originally imagined.
Now, I want you to put your data on the web. Turns out that there is still a huge
unlocked potential. There is still a huge frustration that people have because we haven’t
got data on the web as data. What is the difference between documents and data?
Documents you read, follow links and that’s it. With data you can do all kinds of stuff
with a computer.
Reference to presentation of Hans Rosling. His presentation shattered a lot of beliefs
that people had about the economics in the developing world. He put a slide like Figure
14 in his presentation. The data is underground, the data is brown and boxy and boring
and that’s how we think of it, isn’t it? Because data you can’t naturally use by itself. But
in fact, data drives a huge amount of what happens in our lives. And it happens because
somebody takes that data and does something with it. Rosling says: ‘it is really
important to have a lot of data’.
Tim Berners Lee want to think about a world where everybody had put data on the web
and so virtually everything you can imagine is on the web. He calls that Linked Data. The
technology is linked data, and it’s extremely simple. If you want to put something on the
web there are three rules:
1. http shouldn’t be only used for documents but things that the documents are
about. We’re using them for people, places, products, events. All kind of
conceptual things, they have names now that start with HTTP.
2. I get important information back.
3. When I get information back, is also shows relationships.
Data is relationships. When it has relationships, whenever it expresses a relationship,
then the other thing that it is related to is given one of those names that starts http. So I
can look up a person, and the city is was born, than the region, and the town it’s in, and
the population of it. That is linked data.
The idea of linked data is that we get lots and lots boxes and lot and lots of plants. The
interesting thing about data is the more things you have to connect together, the more
powerful it is. A grassroots example of dbPedia, takes data from wikipedia and put it
into a blob (?) of linked data. Linked data is scaleable, it grows. Just as dbpedia.
Data comes in fact in lots and lots of different forms, think of the diversity of the web, it’s
a really important thing that the web allows you to put all kinds of data up there. So it is
with data, there a lots of different data. Governmental data, enterprise data, scientific
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data ,personal data, weather data, there’s about events, data about talks and there’s
news and all kinds of stuff.
Figure 14 underground data
American governmental said that they will put data online, Berners Lee hopes that it will
be linked data. And not just for transparency, even though transparency in government
is important, but this data shows how American lives. You can use it for your company
or your school assignment. But governments want to keep there data, Hans Rosling calls
it database hugging. They first want to make a beautiful website, that’s okay according
to Berners Lee, but first give us the unadultered data. Raw data now!
Locked data slow our progress of understanding and solving current problems and
dilemma’s of the world. By opening up data, the probability that things get solved is
more likely. You can now get answers for questions that bridge across different
disciplines is very important.
Not all data comes from huge instutions. Data is about our lives, you log on to your social
networking site, you accept someone as a friend and BING! a relationship is made, this is
data. Everything you do is data, they repurpose it (for commercial use). The silos have
to be broken, there has to be inter-operability between social networking sites.
Figure 15 The closed garden social networks
Openstreetmap example, crowdsourcing. If everyone is doing a little bit it will lead to a
incredible resource because everybody else does theirs. That is what linked data is all
about, it’s about people doing their bit, to produce a little bit, and it all connecting.
Incremental and a low amount of participation.
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When you connect data together, you get power that in a way doesn’t happen just with
the web, with documents. You get this really huge power out of it. There maybe not be a
immediate return of investment (ROI) but eventually it will only really pay off when
everybody else has done it. Demand linked data!
Tim Berners-Lee: The year open data went worldwide (2010)
http://www.ted.com/talks/tim_berners_lee_the_year_open_data_went_worldwide.html
A year after his last presentation. He asked to put data on the web and gives now a small
update of the project. He says: ‘there is a open data movement afoot, now, around the
world.’ Example of how the U.K. government made some data open. For example, a
mashable map that shows data of bicycle accidents. Example of putting information
about water supply and etnicity. A big part of the non-white neighborhoud had no
watter supply (see Figure 16).
Figure 16 corelation between etnicity and water supply
That is the power of putting one piece of information with another piece of information
and showing the result. Example, where does my money go? There is now a great
compitition between data.gov and data.gov.uk. Furthermore, he talked about community
generated data. Example of amount of edits on openstreetmap made by ITO world. After
the disaster in haiti, lots of people started editing the maps. The map shows refugee
camps, road blocks and more things that are needed for rescue and relief work. We have
only just started.
BBC Newsnight: Information Graphics
http://www.youtube.com/watch?v=q2Wnu1SOhKs&feature=player_embedded
A BBC interview about visualization. Quite interesting, nice to watch. But same old
boring examples, as if nothing else exists. I liked the discussion at the end, i disagreed
with Neville Brody but he brought an interesting perspective. I wouldn’t be suprised if
he had some books of Edward Tufte on his bookshelf.
first a presentation of David Sillito. Numbers have turned into pictures, more beautiful
than before. Introduces Hans Rosling, as an information superstar, and David Mccandles.
Interview with the latter about his billion dolar map. Further examples of the allosphere,
we feel fine (‘turning emotions into pictures’ (?)). Interview with Peter Gramsie, author
of the story of graphic design. Gramsie tells about Otto Neurath as the ‘grandfather of
symbols’ or statistical data shown in dataform. Otto’s phrase: Words devide, pictures
unite. Then an interview with Stefanie Posavec: “something mundane or everyday, when
it is converted into a really beautiful visualization and you just think ‘I have never
looked at that this way before” and I think that shift in perception and that new way of
looking at it makes it art and makes it beautiful.”
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The downside of visualizations, how they can ‘deceive’ on a whole new scale. Mccandles
thinks that data visualizations have political implications because the images are very
attractive and quite powerful and they are seen as complete face value. The battle of
ideas can be the battle of who can create the most compelling visuals.
In the studio a discussion with Mccandles and Neville Brody, art director. Brody dislikes
images that are too pretty but don’t bring over the message. The stories don’t come out
of it, they miss the core message. Information is political, but only is politial in the way
you use it. And if you use it ultimateley as decoration, i think it is missing some of the
core meaning of our lives... the idea of making beautiful information kind of misses the
point a little bit.
Some work of david is shown. Brody congratulates David, but misses something. These
images feel like a puzzle, they make you feel clever. But you don’t feel moved or
empowered to make decissions based on this.
Mccandles: ‘color, shape and space are the language of the eye, words and numbers are
the language of the mind. If you combine the two you speak in a new language and it
becomes a bit more powerful. And get’s in there a bit more. Brody: ‘honestly, we live in
the most generic period I think, in history. Then he verbally attacks the work of
Mccandles. The presenter calls it coffee table graphics. Brody talks about the anti-design
festival. Are Mccandles graphics comforting, shouldn’t it revolutionize it. Brody thinks
it’s very trivial, very banal.Brody: ‘the government is in the process of decoupling itself
from culture and we certainly known that politics are looking for ways to make
themselves more beautiful, maybe this is a way for them to do it. He likes to see a more
determined grassroots movement
Figure 17 BBC Interview with David MCcandles and Neville Brody
Jonathan Harris – Cold: Bold (2010)
http://www.aiga.org/content.cfm/video-gain-2010-harris
Introduced as an artist, computer scientist and a radical person with a huge focus. Not
afraid to take a stance, not afraid to dig deeper, not good in small talk.
He talks about being an artist but also how to write code, and how hard this sometimes
may be. Can lead to schzenofrenia. Always busy with physical and digital. Making a
mural to get girls to your room. Went to the digital for better preservation. Programmer
analytic and controlled. Shows some earlier work.
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Focus on one, on online dating, an exposition in MoMa. Neon lights, communication of
advertising. First as an appartement, but when he saw HBO appartements, his idea was
shot. Eventually thought on balloons, 28.000 lines of code. It was a dehumanizing
experience, but necessary. Different movements in the project:
• Who I am
• What I want
• Matchmaker
• Snippets
o Opening statements
o Closing statements
• Dna – symbol of identity
• Breakdowns
o Top first dates (coffee, movie)
On online dating sites, people talk about themselves in 200 words, describe the most
essential things. So very fertile ground for human emotions, expectations. (but people
will only talk favorable about themselves, right?)
My mind is in the machine and my body feels like an interface. The web is a asexual
place, our hormones are dissolving. Merges man and woman in androgous users. Harris’
got the feeling that his feeling are representative for the feelings what will come. It’s
about empathy.
Programs are small and beautiful. Big programs become a real mess, very complex and
when you have to think about all these things, you don’t have enough empathy for
people. There is a trait off: being a good, feeling person and a bad programmer. There is
this battle.
Rational beings, programming is very logical and rational. You can become to detached
when you write code. How can you express yourselves in code? When you write or paint
there is this direct expression of your art, with code there are all these layers of
abstraction about how you feel and what exactly comes out at the end. Those layers of
abstraction takes your humanity.
You can outsource/delegate your code, but you will get mediocre work. Your initial idea
will always not work. The program will resist you, so you try another way and another
and gradually you will find a path that works. But you will only see this path when you
are actually there, doing the thing. You have to be sensitive to the medium.
Web was first very diverse, and weird. But now there are these companies who make a
‘house’ for you, everyone now lives in the same house.
Ideas, everyone can have ideas. But it is about the outcome, how does the outcome make
you feel, improve you as a person? You should ask: is this what I am building making
people better? Designers should make things that make things better. The digital world
is shaped by a couple dozen/thousands akward guys.
Can technology enhance individuality? As opposed as it becoming a shopping mall, what
it looks now. At the moment I still believe it. Technology is in an unhealthy place.
Integration between technology and body. Most of the work I have created is on a
2dimensional screen. Interface devices can grow. Hopefully more expressive devices. No
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notion of time in computers. Is computation the opposite of feeling? Computation is a
very cerebral activity, we’re very into the brain and less into in the body.
How is code beautiful? I think code is beautiful because you can maintain for yourself
the illusion that you understand and that you can control. But this is an illusion. I think
programmers is afraid of their bodies. A sociopathic beauty. Hunger to control, feeling
that is not overwhelming you. When you have that mental model in your mind it is a
very comfortable place.
He is a persona non-grata. Depression form of anger that is not expressed. ‘I actually was
kinda happy when I was depressed. I’m freaked if everything goes great, because I know
I will go bad very soon.’
The Future of Art (2010)
http://www.emergence.cc/2011/02/the-future-of-art/
An immediated autodocumentary. Starts with a headshot of Aaron Koblin. Koblin says
that due to all this free time and the Internet, people can tap in any form of creation. ‘To
some extend that trumps the Renaissance, that order of magnitude’. This is a matter of
scale. Koblin is mostly interested in ways how people can work together through the
Internet, to create things that would otherwise be impossible (the digital natives).
‘What we see is richer and more full content, that’s more contextually, you know,
it’s more human.
Caleb Larsen talks about the democratization and availability of connected computers.
He thinks that we will see more democratic acts of curation. Heather Kelley tells that it is
about making connections between things that might not otherwise be obvious.
Vincent Moon, the amount of information available makes more possible, but how can
we deal with this? ‘Basically we deal with it on a daily basis one by one… it’s a very
interesting subjective process’ He constantly remixes history, and thereby ‘telling our
whole history through that.’
‘The idea of originality and propertairy myth.
Ken Wahl
Amanda Cox on Data Visualization
http://www.cbc.ca/spark/2011/01/full-interview-amanda-cox-on-data-visualization/
Amanda Cox is a graphics designer at The New York Times. Through a summer school
program she came to the NY Times. She is interested in structure and form, how can you
tell stories with this data and structure that reveals patterns. What makes for a good
data visualization? A good visualization one tells you what you don’t already know. Does
not confirm stereotypes. Tell you something immediate, punching you in the face,
tapping you onto the shoulder.
Online you have the interactive aspect; you have space, can do things that are impossible
on paper. Incorporate sound and movement. She thinks that the latter is a cheap way to
do storytelling. To tell a story through time, is an opportunity on the web.
Gives the example of The Jobless Rate For People Like You, something like 300
demographic groups. You can see yourself in the context of other people. The range is
from 3% to 50%. This is something we do all the time, but now we did it all at the same
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time. All three hundred lines at once, and you can drill down to your own situation.
Seeing yourself in context with other people, it puts data in a deeper context. Placing
yourself in context, you first start by yourself but then you’re trying to go beyond that.
Trying to understand the world more, pushing a little further. Fun exploratory
something, right?
Ask questions about inequality, recession. Seeing them all together was attractive.
Averages are hard to work with, no one is average. The average person does not exist.
The world is clearly generating more data then it has in the past and we have the
technology to do things with it more than we have in the past. Sometimes the best way
to do something with the data is data visualization, often times it is not. Showing the
structure behind data. How can you make visualization that reveals something
important, that popout the hidden patterns.
Dangers of visualization, according to Amanda Cox the same as any journalist. Does this
visualization represent the truth, is it not biased? Does this accurately reflect some
version of the truth?
The fight with ‘it’s just art’ or the opposite ‘it’s just scientific’. The best analogy Amanda
Cox had heard is about music, some music you never would line up together. There is no
right or wrong.
When you have the data, how do you figure out how to tell that story? Amanda tells that
she has to try out a lot of heads, trying different forms and see which one looks close
enough telling a story. By the NY Times, there is an incredible range of types of graphics.
People have different styles, you could see from who what it is. If you want to treat data
like it’s special, it’s a good thing to have an extensive library of forms.
Storytelling and interactivity is still a difficult combination. According to Amanda, no
one is really good in doing them both together. Part of this problem is technological, part
of it is content. We’re still figuring out where the story goes and balance that with real
interactivity. There hasn’t been a compelling change.
The New York Times has a lab in collaboration with IBM’s Many Eyes that allows people
to upload there own datasets. The Guardian provides their data from their
visualizations, it’s not something the most interesting thing to do: clean up your
spreadsheet for public use. The Times hasn’t done that much.
‘A lot of the questions about data vis can be answered as you think of them as questions
about journalism.’ You can make the analogy, should I write all my quotes down and put
them online. It also is a question about property. It is a open question, what the real
answer is.
Carol McCall, "Can Big Data Fix Healthcare?" (2011)
http://www.youtube.com/watch?v=1-C0Vtc-sHw
Presentation by Carol McCall from Strata 2011.
There is a big problem in the Healthcare. The usual suspect are: an aging population;
greater incidence of diseas; poor lifestyles; culture of abundance and new technology
(treatments, procedures, devices, drugs, tests). The rate per person spending is growing
leaps and bounds. The underlying problem is a bad business model. ‘Riding a twohorses-ass-gauge
track on a train with no brakes’.
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Is healthcare broken, or is poorly designed? Let’s get to work but keep in mind:
• Never underestimate resistance to change
o One person’s cost in another person’s income
o Look to (re)design
o Fill in what’s missing
• Pick good problem(s)
o Focus on ‘Hot Spots’
o Achieve the Triple Aim (the small machine that get things done)
o Build the needed knowledge architectures
• Your data will suck. Get over it.
o Focus on identity, activity and interaction
o Build [meta]data and semantic interoperability
Taking drugs is rarely documented, so when an elderly is admitted to the hospital they
say that is something with her/his heart. There is very few knowledge bases of all of the
known conflicts.
They made a study with data from 1.2 million studies to predict adverse Drug Events.
You can’t prevent, what you can’t predict. They used the results to develop a predictive
model. And they didn’t find the answer, but created a way to begin (‘cutting the Gordian
knot”). Use available data to create better models, harness open science, to upgrade
alghoritmes.
Strata 2011: Hilary Mason, "What Data Tells Us"
http://www.youtube.com/watch?v=KWszSUm-x2Y&feature=relmfu
Data is becoming a big thing, we have data people. People from social sciences and
computer sciences and the hacking community. We established methods to do
something with data. And we have brains, to think about data and building things on
data. But the biggest thing now, is that we have momentum, we get attention.
Some challenges. Is timeliness in data. People that work in real-time don’t have the time
to go back. We need to store it in a way we can access it, query it and operate on it in
real-time. There are many opportunities, but it is chaotic. We need imagination. We have
all these great things but use them to solve the same problems we have always solved.
Mason talks about bitly, it’s very easy technology but we accumulate a lot of data. She
divides it into three types:
• Narcistic data
• Segments of data:
o by location, by time, by topic
• Global vision of data
o Temperature of the ocean of the world
o Clicks from Egypt, clicks dropped in the day from jan 27 to jan 28
She made a tagcloud based on her bitly clicks. This tells something about her, but also
about her interests and gives people a handle what to recommend to her. They are
creating now News.Me. this forms news in a way what is my optimal view of the news
but I also can see other people optimal views.
The trending data streams shows what’s happening in the world. The data is as a
window to the world. We can make it a better world. What would you do with all of this
data, all of this power?
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Strata 2011: Scott Yara, "Your Data Rules the World"
http://www.youtube.com/watch?v=IL52KRcqz-Y
Scott Yara starts with a quote from Steve Jobs (1995): ‘To make step-function changes,
revolutionary changes, seems to take a very unique combination of timing, technology,
talent… and luck to make significant change in our industry. It hasn’t happened that
often.’
Yara beliefs that data is one of those step-function change in the industry. From the
personal computer to the Internet to data. Your data rules the world. Your data is
contributing to this global dataset, and that dataset is changing the world as we know it.
There is a whole new economy build around data itself, that is a really big deal.
The future is here! It’s just not evenly distributed… (coined by Tim o’Reilly?)
According to Joe Hellerstein, there is the ‘Industrial Revolution of Data’. Machines are
generating more information than we as humans are (?). This explosion of information.
What also happens is this undeniable convergence of technology. ‘The cloud’ is a big
technology shift. Computing and the processing of this is now 100 – 1000x times faster
and cheaper then it ever was before (??).
He gives some examples of data-heroics. First Google Flu Trends, data reuse to create a
simple but insightful visualization. They try to be predictive.
“You take a normal dataset and apply it to solve really really interesting problems that
have impact on the world.”
Then the We Feel Fine example. Amazing and powerful stuff. Data is also changing the
government. Government 2.0, example of usaspending.gov. transparency and access to
data hopefully can lead overtime to new forms of government efficiency and
transparency. We still have a long time to go. There needs to be a big call to action. It
starts with education. We need investment in data scientist and computer scientist. We
need people for analyzing, visualizing and sharing information.
Data can be an inspirational movement, as been the web for the last 20 years. End with
quote from Tim o’Reilly: ’Data is the next intel inside’.
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Making Data Engaging: A Talk with the New York Times Interactive Design
Team (2007)
http://www.uie.com/brainsparks/2008/01/10/spoolcast-making-data-engaging-a-talk-withthe-new-york-times-interactive-design-team/
Radio show with New York Times graphic journalists, Andrew DeVigal and Steve
Duenes. Talk about the home run analyzer
(http://www.nytimes.com/ref/sports/20070731_BONDS_GRAPHIC.html?th&emc=th).
Was first mend for one time but wrote a script for it, is now a data driven application. It
updated itself, that function was later added in. Most interactive graphics are ‘one-offs’
they are there for a few days but after that they are not promoted on the site. Like a
newspaper story, comes up and goes away. They are tide to they daily news.
They delive high impact, multimedia stories.
They also develop templates that allow a wider range of users to make multimedia
stories.
They talk about the NYTimes Debate Analyzer, it’s a way to act with a transcript when a
debate is taking place. You can scroll through, rewind, look at the videos as well as the
transscripts. There is a search box that lets you search for specific words in the
transcripts. Part of the inspiration comes from the print graphics.
Part by analyzing the speeches you can see how things change, how the emphasis of the
speech changes. That analyzes is done at the time of the deadline. It is a toolkit, when the
platform is build out there is no customization. We want to have the same formats, stay
consistent over time. We tell how the story unfolds.
The structure is broad and flexible enough still holds up. Still accommodates to new
needs. But still a lot of legwork: recording the videos, timestamping the speeches. There
is a lot of legwork to fit it in the template.
They further talking about the Exonerated, Freed, and What Happened Then project. A
project that tells about people who were sentenced to prison but are freed based on dna,
or ‘a multimedia exploration of 200 prisoners exonerated by DNA evidence.’ Tell the
stories of all people, show small bites of people, and as you drill down you can explore
the personal stories. They combine text, images and audio to tell the story. They had a
lot of collaboration to get to a way to represent the information. With multiple data
points and audio files.
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How are you inspired by other folks that make things. Do you explore other applications
that are not related to representing the news? Both explore different outlets, Ipod is
ubitiquous, hard not to be inspired by it. Also inspired by various things, things that are
done at older graphics desks, Apple products and Google products, but also advertising.
They made a line drawing out what existed and on what floor. Through different layers.
But also through their own expertise, try to like to think to break some new grounds.
There is plenty of internal inspiration.
They talk about the Trailer Living, Then and Now, which explores how vacation trailers
have changed over the years. Drag around the magnifier. There is something compelling
about dragging the magnifier around. In print, there is no magnifier application.
objects to read /watch & / || summarize
have to read:
Hal Varian on how the Web challenges managers (2009)
http://mkqpreview1.qdweb.net/Governance/Leadership/Hal_Varian_on_how_t
he_Web_challenges_managers_2286
Information Interaction Design: A Unified Field Theory of Design (1994) by
Nathan Shedroff
Visualisation Insights: #6 Data Journalist & Information Designer
http://www.visualisingdata.com/index.php/2011/01/visualisation-insights-6-datajournalist-information-designer/
http://thevcl.com/storytelling/
http://eagereyes.org/blog/2010/stories-dont-tell-themselves
http://www.visualisingdata.com/
Ben Fry (2004). “Computational Information Design”.
http://benfry.com/phd/
Shirky, Clay. Excerpt from: “Information visualization: Graphical tools for
thinking about data”. In: Esther Dysons’s monthly report, 2002 Volume 20,
no. 8
10 Best Data Visualization Projects of the Year – 2010
http://flowingdata.com/2010/12/14/10-best-data-visualization-projects-of-the-year-
%E2%80%93-2010/
Brewster Kahle builds a free digital library (2007)
http://www.ted.com/talks/brewster_kahle_builds_a_free_digital_library.html
Data Ghettos
http://www.mattwaite.com/posts/2008/jan/03/data-ghettos/
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Lines and Bubbles and Bars, Oh My! New Ways to Sift Data
http://www.nytimes.com/2008/08/31/technology/31novel.html
Visualizing Data, Telling a Story
Behind the scenes of The Guardian’s interactive Wikileaks coverage.
http://www.cjr.org/the_news_frontier/visualizing_data_telling_a_sto.php
The Data-Driven Life
http://www.nytimes.com/2010/05/02/magazine/02self-measurementt.html?pagewanted=all
The data deluge (2010)
http://www.economist.com/node/15579717
http://www.ted.com/talks/carmen_agra_deedy_spins_stories.html
Maeda, J - Laws of simplicity
Manovich, storytelling vs databasing
Narrative Visualization: Telling Stories with Data (2010)
Edward Segel, Jeffrey Heer
Abstract (http://vis.stanford.edu/papers/narrative)
Scott McCloud - Understanding Comics
The value of Many Eyes
http://interactiondesign.sva.edu/classes/datavisualization/updates/
article Manovich
beatiful visualization (2010)
Optional (nice to read):
Statistics:
http://www.robertniles.com/stats/
lying with statistics
http://www.stats.org/faq.htm
Voronoi treemaps for the visualization of software metrics
Edward Tufte – all his books :)
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