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Capita Selecta - Infovis
The Symbiosis of Visualization & Design
a/prof. Andrew Vande Moere
Design Lab - Department ASRO - KU Leuven
------.----------@asro.kuleuven.be - http://infosthetics.com - @infosthetics
Hackers - United Artists - 1996
Tron - The Electronic Gladiator - 1982
Johnny Mnemonic - Tristar Pictures -1995
Cyber Swap Worlds - 1997
City of News - MIT Media Lab - 1997
VR/Search - Andrew Vande Moere - 1998
VR Data Visualization - ETH-Zurich
Information Visualization for Immersive VR - Andrew Vande Moere - 2004
http://www.youtube.com/watch?v=AZmcrVplqDU
VR Data Visualization
Stock Market Swarm - Andrew Vande Moere - 2004
http://www.youtube.com/watch?v=LjUZ6vcTc1Q
Stock Market Swarm - Andrew Vande Moere - 2004
http://www.youtube.com/watch?v=LjUZ6vcTc1Q
University Finances Visualization - Andrew Vande Moere - 2003
University Finances Visualization - Andrew Vande Moere - 2003
http://www.youtube.com/watch?v=duxjQKgYtNY
Information Aesthetics - “Where Form Follows Data” - http://infosthetics.com
ThemeRiver - pnl.gov - 1999
http://vis.pnnl.gov/pdf/themeriver99.pdf
ThemeRiver - pnl.gov - 1999
http://vis.pnnl.gov/pdf/themeriver99.pdf
Stacked Graphs – Geometry & Aesthetics - StreamGraphs - Lee Byron - 2007
http://www.leebyron.com/else/streamgraph/
The Ebb and Flow of Movies - The New York Times
http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html
From “research” in visualization to visualization “practice”
Movie Narrative Charts - Randall Munroe - xkcd
http://xkcd.com/657/large/
z




Software Evolution Storylines - Ogawa and Ma - 2010
http://www.michaelogawa.com/research/storylines/
Software Evolution Storylines - Ogawa and Ma - 2010
http://www.michaelogawa.com/research/storylines/
Design Considerations for Optimizing Storyline Visualizations
Yuzuru Tanahashi and Kwan-Liu Ma - 2012
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06327274
Design Considerations for Optimizing Storyline Visualizations
Yuzuru Tanahashi and Kwan-Liu Ma - 2012
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06327274
From visualization ‘exploration’ to visualization ‘research’
Learning Goals
1.Approaches of ‘designing’ infovis
2. Appreciation of kinds of infovis
3. Guidelines for ‘engaging’ infovis
http://www.slideshare.net/mobile/jkofmsk/intro-to-information-visualization
information visualisation
                          “... is the use of computer-
                      supported, interactive, visual
                   representations of abstract data
                           to amplify cognition”


Information Visualization Definition
“information visualisation is the use of
      computer-supported, interactive,
      visual representations of abstract
      data to amplify cognition”

. automatic/automated algorithm

. versus custom or hand-made (sketching!)

. facilitates high complexity, analytics, ...
“information visualisation is the use of
     computer-supported, interactive,
     visual representations of abstract
     data to amplify cognition”

. to make assumptions, test hypotheses

. to allow individualized exploration scenarios

. while and during the exploration itself
“information visualisation is the use of
     computer-supported, interactive,
     visual representations of abstract
     data to amplify cognition”

. just ‘representing’ values or conveying meaning?

. guiding users, show example insights, highlighting

. engagement? involvement? immersion?
“information visualisation is the use of
     computer-supported, interactive,
     visual representations of abstract
     data to amplify cognition”

. data without natural representation

. requires metaphor to be perceived

. complexity: size, dimensionality, time-variance
because the data is abstract...
                    “the challenge is to invent
                           new metaphors for
                      presenting information &
                            developing ways to
                             manipulate these
                    metaphors to make sense
                      out of the information...”
Information Visualization ‘Design’ Challenge
“information visualisation is the use of
      computer-supported, interactive,
      visual representations of abstract
      data to amplify cognition”

. analytics versus communication

. creating insights: new, valuable, deep,...

. requires different kinds of visuals, interactivity, ...
Web2DNA
http://www.baekdal.com/web2dna/
Web2DNA
http://www.baekdal.com/web2dna/
Web2DNA Flickr Collection
http://www.flickr.com/photos/tags/web2dna/
Choice of “Metaphor”
. can be potentially seemingly “useless”

. yet receive a lot of interest

. how to interpret “useful”?

. persuasiveness of visual representations?
data                                insight

      10010110                   knowledge
                                   transfer


               data mapping
                                               mapping
                                               inversion


           visualisation                       comprehension
                                               !
                             visual transfer


Visual Mapping Methodology
Visualization as a “Medium”
. scientific visualization

. data graphics

. infographics

. information design

. data art
1. (Scientific) Visualization
LineAO - Improved Three-Dimensional Line Rendering
http://www.informatik.uni-leipzig.de/~ebaum/Publications/eichelbaum2012a/
DNA Coiling, Replication, Transcription and Translation - WEHI
http://www.youtube.com/watch?v=DA2t5N72mgw
2. Data Graphics
Eurovizion - Ben Willers
http://lifeindata.site50.net/work/eurovizion/eurovizion.html
Statistical Atlases of the United States - 1870-1890
http://www.handsomeatlas.com/
The Jobless Rate for People Like You - The New York Times
http://www.nytimes.com/interactive/2009/11/06/business/economy/unemployment-lines.html
Four Ways to Slice Obama’s 2013 Budget Proposal - The New York Times
http://www.nytimes.com/interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html
Spotlight on Profitability - Information is Beautiful Competition Entry (not winning...)
http://www.informationisbeautifulawards.com/2012/02/hollywood-visualisation-challenge-
design-shortlist/
http://szucskrisztina.hu/images/holly.png
2. Information Graphics
Starbucks Coffee Cup vs. Country Origins - Fast Food Revenue vs. Brands
Telenet Social Media Report
http://blog.telenet.be/wp-content/uploads/2012/01/Telenet-Social-Media-Report-20111.jpg
Year Report 2011 - http://feltron.com
Debtris - David McCandless / Information is Beautiful
http://www.informationisbeautiful.net/2010/debtris/
4. Data Visualization
OECD Better Life Index - Moritz Stefaner
http://www.oecdbetterlifeindex.org/
Flight & Expulsion - Nice One
http://www.niceone.org/lab/refugees/
Take a Look at Health - Fathom Design
http://visualization.geblogs.com/visualization/health_visualizer/
5. Information Design
Chromosome 14 - Ben Fry
We Feel Fine - Jonathan Harris and Sep Kamvar
http://www.wefeelfine.org/
WorldShapin - Compare Countries through their Shape - Carlo Zapponi & Vasundhara Parakh
http://www.worldshap.in/#/PH/BZ/KE/
Notabilia - Visualizing Deletion Discussions on Wikipedia - Moritz Stefaner
http://notabilia.net/
6. Information Art
DNA Portrait - dna11.com
TextArc - An Alternative Way to View a Text - Brad Paley
http://www.textarc.org/
Poetry on the Road 6 - Boris Müller
http://www.esono.com/boris/projects/poetry06/
DoodleBuzz - A Typographic News Explorer
http://www.doodlebuzz.com/
data                                insight

      10010110                   knowledge
                                   transfer


               data mapping
                                               mapping
                                               inversion


           visualisation                       comprehension
                                               !
                             visual transfer


Visual Mapping Methodology
Name Trends
http://nametrends.net/name.php?name=Andrew
Baby Name Wizard - Martin Wattenberg
http://www.babynamewizard.com/voyager#
Color Object - Martin Wattenberg
Offline Demo
1. The Role of Interaction
Amazon Digital Cameras Treemap - The Hive Group
http://www.hivegroup.com/demos/amazon/499052.html
Newsmap - Marumishi - 2004
http://newsmap.jp/ and http://marumushi.com/projects/newsmap
2. The Role of Aesthetics
A Year in Iraq and Afghanistan - The New York Times - 2009
http://www.nytimes.com/2011/01/30/opinion/30casualty-chart.html
Faces of the Fallen - Washington Post - 2009
http://apps.washingtonpost.com/national/fallen/
UK Casualties in Afghanistan and Iraq - BBC News - 2009
http://www.bbc.co.uk/news/uk-10634102
CNN Home and Away - CNN - Stamen Design
http://edition.cnn.com/SPECIALS/war.casualties/
Monument - Caleb Larsen - 2006
http://caleblarsen.com/
Monument - Caleb Larsen - 2006
http://caleblarsen.com/
3. The Role of Data Focus (~ Meaning)
Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization",
IEEE International Conference on Information Visualisation (IV'07), pp. 87-92.
Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization",
IEEE International Conference on Information Visualisation (IV'07), pp. 87-92.
Narrative Visualization: Telling Stories with Data
Edward Segel and Jeffrey Heer
http://vis.stanford.edu/files/2010-Narrative-InfoVis.pdf
Genres of Narrative Visualization, Balancing Author-Driven versus Reader-Driven Stories
Narrative Visualization: Telling Stories with Data
Edward Segel and Jeffrey Heer
http://vis.stanford.edu/files/2010-Narrative-InfoVis.pdf
Our Irresistible Fascination with All Things Circular
http://www.perceptualedge.com/articles/visual_business_intelligence/
our_fascination_with_all_things_circular.pdf
Our Irresistible Fascination with All Things Circular
http://www.perceptualedge.com/articles/visual_business_intelligence/
our_fascination_with_all_things_circular.pdf
Aesthetic Effect in Data Visualization - Nick Cawthon and Andrew Vande Moere - 2007
Aesthetic Effect in Data Visualization - Nick Cawthon and Andrew Vande Moere - 2007
Aesthetic Effect in Data Visualization - Most Beautiful
Aesthetic Effect in Data Visualization - Least Beautiful
Aesthetic Effect in Data Visualization - Correct Responses
Aesthetic Effect in Data Visualization - Least Correct Responses
Aesthetic Effect in Data Visualization - Low Abandonment Rate
Aesthetic Effect in Data Visualization - High Abandonment Rate
Goal
        •visualizationimpact of style in information
         to measure

           • by comparing 3 different ‘design alternatives’
               • in terms of visual and interactive style
           • style demonstrators based on real-world
              examples
           • then contrasted resulting insights against each
              other


Evaluating the Effect of Style in Information Visualization
Andrew Vande Moere, Martin Tomitsch, Christoph Wimmer, Christoph Boesch, and Thomas
Grechenig, IEEE Infovis 2012
“Reversible”
“Factual”
Gapminder (2007)




Many Eyes (2007)




OECD eXplorer (2009)
“Irreversible”
“Meaningful”




    Bitalizer (2008)   Poetry on the Road (2004)   Texone (2005)
Partly “Reversible”
Partly “Factual”

          Digg Swarm (2007)

              ReMap (2009)




                              We Feel Fine (2006)
“Analytical” Style (ANA)
“Magazine” Style (MAG)
“Artistic” Style (ART)
Interaction

 18m09s
  (average)
                        12m49s
                         (average)
                                                   11m55s
                                                    (average)




   181.0
  interactions
                           87.7
                         interactions
                                                      88.9
                                                    interactions
   (average)              (average)                  (average)




           Analytical                   Magazine                   Artistic
No Reported Insights


     1
  participant
                         11
                        participants
                                                 9
                                              participants




           Analytical              Magazine                  Artistic
Interface “Insights’


  6%
  6 insights
                        12%
                        13 insights
                                                 29%
                                                 27 insights




           Analytical                 Magazine                 Artistic
Insight Analysis
                 ANA       MAG         ART
 Difference    24% (24)   26% (26)   17% (11)
     Cluster   22% (22)   15% (15)     9% (6)
Distribution   11% (11)   12% (12)   17% (11)
Compound         9% (9)   14% (14)    11% (7)
       Trend     8% (8)    4% (4)     8% (5)
    Outliers     6% (6)   10% (10)   15% (10)
       Value     6% (6)     1% (1)    0% (0)
Association      5% (5)    3% (3)     6% (4)
Meaning (*)      3% (3)    4% (4)     14% (9)
   Extreme       4% (4)    6% (6)     0% (0)
Categories       2% (2)    1% (1)     0% (0)
       Rank      1% (1)    2% (2)     3% (2)
Insight Analysis
    Rating (1 - 5)           ANA          MAG            ART
 uncertain - confident    4.10 (1.11)   4.21 (0.87)   4.17 (0.95)

       difficult - easy   3.78 (1.17)   3.63 (1.29)   4.00 (1.24)

      shallow - deep     3.18 (1.10)   2.93 (1.08)   2.54 (1.17)
Insight Analysis
            Rating (1 - 5)           ANA          MAG            ART
        uncertain - confident     4.10 (1.11)   4.21 (0.87)   4.17 (0.95)

               difficult - easy   3.78 (1.17)   3.63 (1.29)   4.00 (1.24)

               shallow - deep    3.18 (1.10)   2.93 (1.08)   2.54 (1.17)

shallow – deep (expert rating)   2.44 (0.78)   2.36 (0.70)   2.28 (0.64)
Insight Analysis
 Rating (1 - 5)           ANA          MAG            ART
   ugly - beautiful   3.48 (0.85)   3.08 (1.03)   3.11 (1.02)
  obtrusive - fluid    3.27 (0.95)   3.08 (1.01)   2.80 (1.00)
Insight Analysis
          Rating (1 - 5)             ANA          MAG            ART
             ugly - beautiful    3.48 (0.85)   3.08 (1.03)   3.11 (1.02)
             obtrusive - fluid    3.27 (0.95)   3.08 (1.01)   2.80 (1.00)
          ambiguous - clear      3.39 (1.17)   1.98 (0.89)   2.00 (0.86)
difficult - easy to understand    3.55 (1.04)   2.08 (1.07)   2.14 (1.07)
  intended inform – express      2.80 (1.15)   3.54 (1.18)   3.66 (1.06)
             useless - useful    3.61 (0.95)   2.70 (1.09)   2.45 (0.90)
      frustrating - enjoyable    3.43 (1.00)   2.54 (1.16)   2.34 (1.06)
          unusable - usable      3.77 (0.91)   2.78 (1.13)   2.64 (1.12)
           boring - engaging     3.43 (0.93)   3.10 (0.95)   2.80 (1.00)
  non-functional - functional    3.93 (0.82)   2.80 (1.18)   2.50 (1.13)
                    tool - art   2.30 (1.07)   3.32 (1.19)   3.68 (0.93)
Conclusions
•style impacts perception of usability
 • analytical style was perceived as more
   understandable, clear, enjoyable, engaging,
   useful, functional, ...
•style does not impact insight depth
 • participants were able to overcome huge
   incomprehensibility issues of ART, and in a
   minimum amount of time

• style has impact on ‘kind’ of insights
 • analytical focus of facts versus meaning of
   content, explanation of reasoning, ...
Design Study Methodology: Reflections from the Trenches and the Stacks
Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012
http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
Design Study Methodology: Reflections from the Trenches and the Stacks
Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012
http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
your technique




Design Study Methodology: Reflections from the Trenches and the Stacks
Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012
http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
Design Study Methodology: Reflections from the Trenches and the Stacks
Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012
http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
Edward Tufte
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
temperature
time
temp[day]
longitude
latitude
army[size, day]
army[position, day]
1. Show comparisons, contrasts, differences
2. Show causality, mechanism, explanation,
systematic structure
3. Show multivariate data; that is, show more
than 1 or 2 variables
4. Completely integrate words, numbers, images,
diagrams
5.Thoroughly describe the evidence: title, authors
and sponsors, data sources, add measurement
scales, highlight relevant issues
6.Analytical presentations ultimately stand or fall
depending on the quality, relevance and
integrity of their content
Principles for the Analysis and Presentation of Data - Tufte
1. Show comparisons, contrasts,
differences
2. Show causality, mechanism,
 explanation, systematic structure




French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
3. Show multivariate data; that is,
 show more than 1 or 2 variables
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
4. Completely integrate words,
 numbers, images, diagrams
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
5.Thoroughly describe the
 evidence: title, authors and
 sponsors, data sources, add
 measurement scales, highlight
 relevant issues
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
6.Analytical presentations
 ultimately stand or fall depending
 on the quality, relevance and
 integrity of their content
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
The Friendly Graphic - Tufte (p. 183, 1983)
http://www.informationisbeautifulawards.com/2012/02/hollywood-dataviz-challenge-
• Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization",
 IEEE International Conference on Information Visualisation (IV'07), IEEE, Zurich, Switzerland, pp.
 87-92.
• Cawthon N. and Vande Moere A. (2007), "The Effect of Aesthetic on the Usability of Data
 Visualization", IEEE International Conference on Information Visualisation (IV'07), IEEE, Zurich,
 Switzerland, pp. 637-648.
• Vande Moere A., Tomitsch M., Wimmer C., Boesch C. and Grechenig T. (2012), "Evaluating the
 Effect of Style in Information Visualization", IEEE Transactions on Visualization and Computer
 Graphics, 18(12), December 2012, pp.2739-2748.
• Sedlmair, Michael; Meyer, Miriah; Munzner, Tamara; , "Design Study Methodology: Reflections
 from the Trenches and the Stacks”, IEEE Transactions on Visualization and Computer Graphics,
 18 (12), pp.2431-2440.
• Edward Segel, Jeffrey Heer, Narrative Visualization: Telling Stories with Data, IEEE Trans.
 Visualization & Comp. Graphics (Proc. InfoVis), 2010.
• Jeffrey Heer, Michael Bostock,Vadim Ogievetsky, A Tour through the Visualization Zoo
  http://queue.acm.org/detail.cfm?id=1805128
...
• http://www.tableausoftware.com/public/
    first explorations of dataset for insights
•
    http://visualizing.org
    http://www.informationisbeautifulawards.com/
    check winning entries!

• http://infosthetics.com
    http://flowingdata.com/
    blog with wide selection

• http://selection.datavisualization.ch/
    collection of good tools!

• http://thewhyaxis.info
    http://www.perceptualedge.com/blog/
    what is good, what is bad, and why?

• http://moritz.stefaner.eu/
    http://www.periscopic.com/
    http://stamen.com/
    http://tulpinteractive.com/
    high quality infovis examples...
Thank you! Questions?
------.----------@asro.kuleuven.be /// http://infosthetics.com /// @infosthetics

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The Symbiosis of Information Visualization and Design

  • 1. Capita Selecta - Infovis The Symbiosis of Visualization & Design a/prof. Andrew Vande Moere Design Lab - Department ASRO - KU Leuven ------.----------@asro.kuleuven.be - http://infosthetics.com - @infosthetics
  • 2.
  • 3. Hackers - United Artists - 1996
  • 4. Tron - The Electronic Gladiator - 1982
  • 5. Johnny Mnemonic - Tristar Pictures -1995
  • 7. City of News - MIT Media Lab - 1997
  • 8. VR/Search - Andrew Vande Moere - 1998
  • 9. VR Data Visualization - ETH-Zurich
  • 10. Information Visualization for Immersive VR - Andrew Vande Moere - 2004 http://www.youtube.com/watch?v=AZmcrVplqDU VR Data Visualization
  • 11. Stock Market Swarm - Andrew Vande Moere - 2004 http://www.youtube.com/watch?v=LjUZ6vcTc1Q
  • 12. Stock Market Swarm - Andrew Vande Moere - 2004 http://www.youtube.com/watch?v=LjUZ6vcTc1Q
  • 13. University Finances Visualization - Andrew Vande Moere - 2003
  • 14. University Finances Visualization - Andrew Vande Moere - 2003 http://www.youtube.com/watch?v=duxjQKgYtNY
  • 15. Information Aesthetics - “Where Form Follows Data” - http://infosthetics.com
  • 16.
  • 17. ThemeRiver - pnl.gov - 1999 http://vis.pnnl.gov/pdf/themeriver99.pdf
  • 18. ThemeRiver - pnl.gov - 1999 http://vis.pnnl.gov/pdf/themeriver99.pdf
  • 19. Stacked Graphs – Geometry & Aesthetics - StreamGraphs - Lee Byron - 2007 http://www.leebyron.com/else/streamgraph/
  • 20. The Ebb and Flow of Movies - The New York Times http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html
  • 21. From “research” in visualization to visualization “practice”
  • 22. Movie Narrative Charts - Randall Munroe - xkcd http://xkcd.com/657/large/
  • 23. z Software Evolution Storylines - Ogawa and Ma - 2010 http://www.michaelogawa.com/research/storylines/
  • 24. Software Evolution Storylines - Ogawa and Ma - 2010 http://www.michaelogawa.com/research/storylines/
  • 25. Design Considerations for Optimizing Storyline Visualizations Yuzuru Tanahashi and Kwan-Liu Ma - 2012 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06327274
  • 26. Design Considerations for Optimizing Storyline Visualizations Yuzuru Tanahashi and Kwan-Liu Ma - 2012 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06327274
  • 27. From visualization ‘exploration’ to visualization ‘research’
  • 28. Learning Goals 1.Approaches of ‘designing’ infovis 2. Appreciation of kinds of infovis 3. Guidelines for ‘engaging’ infovis
  • 30. information visualisation “... is the use of computer- supported, interactive, visual representations of abstract data to amplify cognition” Information Visualization Definition
  • 31. “information visualisation is the use of computer-supported, interactive, visual representations of abstract data to amplify cognition” . automatic/automated algorithm . versus custom or hand-made (sketching!) . facilitates high complexity, analytics, ...
  • 32. “information visualisation is the use of computer-supported, interactive, visual representations of abstract data to amplify cognition” . to make assumptions, test hypotheses . to allow individualized exploration scenarios . while and during the exploration itself
  • 33. “information visualisation is the use of computer-supported, interactive, visual representations of abstract data to amplify cognition” . just ‘representing’ values or conveying meaning? . guiding users, show example insights, highlighting . engagement? involvement? immersion?
  • 34. “information visualisation is the use of computer-supported, interactive, visual representations of abstract data to amplify cognition” . data without natural representation . requires metaphor to be perceived . complexity: size, dimensionality, time-variance
  • 35. because the data is abstract... “the challenge is to invent new metaphors for presenting information & developing ways to manipulate these metaphors to make sense out of the information...” Information Visualization ‘Design’ Challenge
  • 36. “information visualisation is the use of computer-supported, interactive, visual representations of abstract data to amplify cognition” . analytics versus communication . creating insights: new, valuable, deep,... . requires different kinds of visuals, interactivity, ...
  • 40. Choice of “Metaphor” . can be potentially seemingly “useless” . yet receive a lot of interest . how to interpret “useful”? . persuasiveness of visual representations?
  • 41. data insight 10010110 knowledge transfer data mapping mapping inversion visualisation comprehension ! visual transfer Visual Mapping Methodology
  • 42. Visualization as a “Medium” . scientific visualization . data graphics . infographics . information design . data art
  • 44. LineAO - Improved Three-Dimensional Line Rendering http://www.informatik.uni-leipzig.de/~ebaum/Publications/eichelbaum2012a/
  • 45. DNA Coiling, Replication, Transcription and Translation - WEHI http://www.youtube.com/watch?v=DA2t5N72mgw
  • 46. 2. Data Graphics Eurovizion - Ben Willers http://lifeindata.site50.net/work/eurovizion/eurovizion.html
  • 47. Statistical Atlases of the United States - 1870-1890 http://www.handsomeatlas.com/
  • 48. The Jobless Rate for People Like You - The New York Times http://www.nytimes.com/interactive/2009/11/06/business/economy/unemployment-lines.html
  • 49. Four Ways to Slice Obama’s 2013 Budget Proposal - The New York Times http://www.nytimes.com/interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html
  • 50. Spotlight on Profitability - Information is Beautiful Competition Entry (not winning...) http://www.informationisbeautifulawards.com/2012/02/hollywood-visualisation-challenge- design-shortlist/ http://szucskrisztina.hu/images/holly.png
  • 52. Starbucks Coffee Cup vs. Country Origins - Fast Food Revenue vs. Brands
  • 53. Telenet Social Media Report http://blog.telenet.be/wp-content/uploads/2012/01/Telenet-Social-Media-Report-20111.jpg
  • 54. Year Report 2011 - http://feltron.com
  • 55. Debtris - David McCandless / Information is Beautiful http://www.informationisbeautiful.net/2010/debtris/
  • 56. 4. Data Visualization OECD Better Life Index - Moritz Stefaner http://www.oecdbetterlifeindex.org/
  • 57. Flight & Expulsion - Nice One http://www.niceone.org/lab/refugees/
  • 58. Take a Look at Health - Fathom Design http://visualization.geblogs.com/visualization/health_visualizer/
  • 60. We Feel Fine - Jonathan Harris and Sep Kamvar http://www.wefeelfine.org/
  • 61. WorldShapin - Compare Countries through their Shape - Carlo Zapponi & Vasundhara Parakh http://www.worldshap.in/#/PH/BZ/KE/
  • 62. Notabilia - Visualizing Deletion Discussions on Wikipedia - Moritz Stefaner http://notabilia.net/
  • 63. 6. Information Art DNA Portrait - dna11.com
  • 64. TextArc - An Alternative Way to View a Text - Brad Paley http://www.textarc.org/
  • 65. Poetry on the Road 6 - Boris Müller http://www.esono.com/boris/projects/poetry06/
  • 66. DoodleBuzz - A Typographic News Explorer http://www.doodlebuzz.com/
  • 67. data insight 10010110 knowledge transfer data mapping mapping inversion visualisation comprehension ! visual transfer Visual Mapping Methodology
  • 68.
  • 70. Baby Name Wizard - Martin Wattenberg http://www.babynamewizard.com/voyager#
  • 71. Color Object - Martin Wattenberg Offline Demo
  • 72. 1. The Role of Interaction
  • 73. Amazon Digital Cameras Treemap - The Hive Group http://www.hivegroup.com/demos/amazon/499052.html
  • 74. Newsmap - Marumishi - 2004 http://newsmap.jp/ and http://marumushi.com/projects/newsmap
  • 75. 2. The Role of Aesthetics
  • 76. A Year in Iraq and Afghanistan - The New York Times - 2009 http://www.nytimes.com/2011/01/30/opinion/30casualty-chart.html
  • 77. Faces of the Fallen - Washington Post - 2009 http://apps.washingtonpost.com/national/fallen/
  • 78. UK Casualties in Afghanistan and Iraq - BBC News - 2009 http://www.bbc.co.uk/news/uk-10634102
  • 79. CNN Home and Away - CNN - Stamen Design http://edition.cnn.com/SPECIALS/war.casualties/
  • 80. Monument - Caleb Larsen - 2006 http://caleblarsen.com/
  • 81. Monument - Caleb Larsen - 2006 http://caleblarsen.com/
  • 82. 3. The Role of Data Focus (~ Meaning)
  • 83. Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization", IEEE International Conference on Information Visualisation (IV'07), pp. 87-92.
  • 84. Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization", IEEE International Conference on Information Visualisation (IV'07), pp. 87-92.
  • 85. Narrative Visualization: Telling Stories with Data Edward Segel and Jeffrey Heer http://vis.stanford.edu/files/2010-Narrative-InfoVis.pdf Genres of Narrative Visualization, Balancing Author-Driven versus Reader-Driven Stories
  • 86. Narrative Visualization: Telling Stories with Data Edward Segel and Jeffrey Heer http://vis.stanford.edu/files/2010-Narrative-InfoVis.pdf
  • 87. Our Irresistible Fascination with All Things Circular http://www.perceptualedge.com/articles/visual_business_intelligence/ our_fascination_with_all_things_circular.pdf
  • 88. Our Irresistible Fascination with All Things Circular http://www.perceptualedge.com/articles/visual_business_intelligence/ our_fascination_with_all_things_circular.pdf
  • 89. Aesthetic Effect in Data Visualization - Nick Cawthon and Andrew Vande Moere - 2007
  • 90. Aesthetic Effect in Data Visualization - Nick Cawthon and Andrew Vande Moere - 2007
  • 91. Aesthetic Effect in Data Visualization - Most Beautiful
  • 92. Aesthetic Effect in Data Visualization - Least Beautiful
  • 93. Aesthetic Effect in Data Visualization - Correct Responses
  • 94. Aesthetic Effect in Data Visualization - Least Correct Responses
  • 95. Aesthetic Effect in Data Visualization - Low Abandonment Rate
  • 96. Aesthetic Effect in Data Visualization - High Abandonment Rate
  • 97. Goal •visualizationimpact of style in information to measure • by comparing 3 different ‘design alternatives’ • in terms of visual and interactive style • style demonstrators based on real-world examples • then contrasted resulting insights against each other Evaluating the Effect of Style in Information Visualization Andrew Vande Moere, Martin Tomitsch, Christoph Wimmer, Christoph Boesch, and Thomas Grechenig, IEEE Infovis 2012
  • 99. “Irreversible” “Meaningful” Bitalizer (2008) Poetry on the Road (2004) Texone (2005)
  • 100. Partly “Reversible” Partly “Factual” Digg Swarm (2007) ReMap (2009) We Feel Fine (2006)
  • 104. Interaction 18m09s (average) 12m49s (average) 11m55s (average) 181.0 interactions 87.7 interactions 88.9 interactions (average) (average) (average) Analytical Magazine Artistic
  • 105. No Reported Insights 1 participant 11 participants 9 participants Analytical Magazine Artistic
  • 106. Interface “Insights’ 6% 6 insights 12% 13 insights 29% 27 insights Analytical Magazine Artistic
  • 107. Insight Analysis ANA MAG ART Difference 24% (24) 26% (26) 17% (11) Cluster 22% (22) 15% (15) 9% (6) Distribution 11% (11) 12% (12) 17% (11) Compound 9% (9) 14% (14) 11% (7) Trend 8% (8) 4% (4) 8% (5) Outliers 6% (6) 10% (10) 15% (10) Value 6% (6) 1% (1) 0% (0) Association 5% (5) 3% (3) 6% (4) Meaning (*) 3% (3) 4% (4) 14% (9) Extreme 4% (4) 6% (6) 0% (0) Categories 2% (2) 1% (1) 0% (0) Rank 1% (1) 2% (2) 3% (2)
  • 108. Insight Analysis Rating (1 - 5) ANA MAG ART uncertain - confident 4.10 (1.11) 4.21 (0.87) 4.17 (0.95) difficult - easy 3.78 (1.17) 3.63 (1.29) 4.00 (1.24) shallow - deep 3.18 (1.10) 2.93 (1.08) 2.54 (1.17)
  • 109. Insight Analysis Rating (1 - 5) ANA MAG ART uncertain - confident 4.10 (1.11) 4.21 (0.87) 4.17 (0.95) difficult - easy 3.78 (1.17) 3.63 (1.29) 4.00 (1.24) shallow - deep 3.18 (1.10) 2.93 (1.08) 2.54 (1.17) shallow – deep (expert rating) 2.44 (0.78) 2.36 (0.70) 2.28 (0.64)
  • 110. Insight Analysis Rating (1 - 5) ANA MAG ART ugly - beautiful 3.48 (0.85) 3.08 (1.03) 3.11 (1.02) obtrusive - fluid 3.27 (0.95) 3.08 (1.01) 2.80 (1.00)
  • 111. Insight Analysis Rating (1 - 5) ANA MAG ART ugly - beautiful 3.48 (0.85) 3.08 (1.03) 3.11 (1.02) obtrusive - fluid 3.27 (0.95) 3.08 (1.01) 2.80 (1.00) ambiguous - clear 3.39 (1.17) 1.98 (0.89) 2.00 (0.86) difficult - easy to understand 3.55 (1.04) 2.08 (1.07) 2.14 (1.07) intended inform – express 2.80 (1.15) 3.54 (1.18) 3.66 (1.06) useless - useful 3.61 (0.95) 2.70 (1.09) 2.45 (0.90) frustrating - enjoyable 3.43 (1.00) 2.54 (1.16) 2.34 (1.06) unusable - usable 3.77 (0.91) 2.78 (1.13) 2.64 (1.12) boring - engaging 3.43 (0.93) 3.10 (0.95) 2.80 (1.00) non-functional - functional 3.93 (0.82) 2.80 (1.18) 2.50 (1.13) tool - art 2.30 (1.07) 3.32 (1.19) 3.68 (0.93)
  • 112. Conclusions •style impacts perception of usability • analytical style was perceived as more understandable, clear, enjoyable, engaging, useful, functional, ... •style does not impact insight depth • participants were able to overcome huge incomprehensibility issues of ART, and in a minimum amount of time • style has impact on ‘kind’ of insights • analytical focus of facts versus meaning of content, explanation of reasoning, ...
  • 113. Design Study Methodology: Reflections from the Trenches and the Stacks Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012 http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
  • 114. Design Study Methodology: Reflections from the Trenches and the Stacks Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012 http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
  • 115. your technique Design Study Methodology: Reflections from the Trenches and the Stacks Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012 http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
  • 116. Design Study Methodology: Reflections from the Trenches and the Stacks Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012 http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
  • 118. French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 121. 1. Show comparisons, contrasts, differences 2. Show causality, mechanism, explanation, systematic structure 3. Show multivariate data; that is, show more than 1 or 2 variables 4. Completely integrate words, numbers, images, diagrams 5.Thoroughly describe the evidence: title, authors and sponsors, data sources, add measurement scales, highlight relevant issues 6.Analytical presentations ultimately stand or fall depending on the quality, relevance and integrity of their content Principles for the Analysis and Presentation of Data - Tufte
  • 122. 1. Show comparisons, contrasts, differences
  • 123. 2. Show causality, mechanism, explanation, systematic structure French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 124. 3. Show multivariate data; that is, show more than 1 or 2 variables French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 125. 4. Completely integrate words, numbers, images, diagrams French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 126. 5.Thoroughly describe the evidence: title, authors and sponsors, data sources, add measurement scales, highlight relevant issues French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 127. 6.Analytical presentations ultimately stand or fall depending on the quality, relevance and integrity of their content French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 128. The Friendly Graphic - Tufte (p. 183, 1983)
  • 130. • Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization", IEEE International Conference on Information Visualisation (IV'07), IEEE, Zurich, Switzerland, pp. 87-92. • Cawthon N. and Vande Moere A. (2007), "The Effect of Aesthetic on the Usability of Data Visualization", IEEE International Conference on Information Visualisation (IV'07), IEEE, Zurich, Switzerland, pp. 637-648. • Vande Moere A., Tomitsch M., Wimmer C., Boesch C. and Grechenig T. (2012), "Evaluating the Effect of Style in Information Visualization", IEEE Transactions on Visualization and Computer Graphics, 18(12), December 2012, pp.2739-2748. • Sedlmair, Michael; Meyer, Miriah; Munzner, Tamara; , "Design Study Methodology: Reflections from the Trenches and the Stacks”, IEEE Transactions on Visualization and Computer Graphics, 18 (12), pp.2431-2440. • Edward Segel, Jeffrey Heer, Narrative Visualization: Telling Stories with Data, IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2010. • Jeffrey Heer, Michael Bostock,Vadim Ogievetsky, A Tour through the Visualization Zoo http://queue.acm.org/detail.cfm?id=1805128 ...
  • 131. • http://www.tableausoftware.com/public/ first explorations of dataset for insights • http://visualizing.org http://www.informationisbeautifulawards.com/ check winning entries! • http://infosthetics.com http://flowingdata.com/ blog with wide selection • http://selection.datavisualization.ch/ collection of good tools! • http://thewhyaxis.info http://www.perceptualedge.com/blog/ what is good, what is bad, and why? • http://moritz.stefaner.eu/ http://www.periscopic.com/ http://stamen.com/ http://tulpinteractive.com/ high quality infovis examples...
  • 132. Thank you! Questions? ------.----------@asro.kuleuven.be /// http://infosthetics.com /// @infosthetics