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The Symbiosis of Information Visualization and Design

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Lecture for Capita Selecta - Infovis course at Computer Science - KU Leuven

Lecture for Capita Selecta - Infovis course at Computer Science - KU Leuven

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  • 1. Capita Selecta - InfovisThe Symbiosis of Visualization & Designa/prof. Andrew Vande MoereDesign Lab - Department ASRO - KU Leuven------.----------@asro.kuleuven.be - http://infosthetics.com - @infosthetics
  • 2. Hackers - United Artists - 1996
  • 3. Tron - The Electronic Gladiator - 1982
  • 4. Johnny Mnemonic - Tristar Pictures -1995
  • 5. Cyber Swap Worlds - 1997
  • 6. City of News - MIT Media Lab - 1997
  • 7. VR/Search - Andrew Vande Moere - 1998
  • 8. VR Data Visualization - ETH-Zurich
  • 9. Information Visualization for Immersive VR - Andrew Vande Moere - 2004http://www.youtube.com/watch?v=AZmcrVplqDUVR Data Visualization
  • 10. Stock Market Swarm - Andrew Vande Moere - 2004http://www.youtube.com/watch?v=LjUZ6vcTc1Q
  • 11. Stock Market Swarm - Andrew Vande Moere - 2004http://www.youtube.com/watch?v=LjUZ6vcTc1Q
  • 12. University Finances Visualization - Andrew Vande Moere - 2003
  • 13. University Finances Visualization - Andrew Vande Moere - 2003http://www.youtube.com/watch?v=duxjQKgYtNY
  • 14. Information Aesthetics - “Where Form Follows Data” - http://infosthetics.com
  • 15. ThemeRiver - pnl.gov - 1999http://vis.pnnl.gov/pdf/themeriver99.pdf
  • 16. ThemeRiver - pnl.gov - 1999http://vis.pnnl.gov/pdf/themeriver99.pdf
  • 17. Stacked Graphs – Geometry & Aesthetics - StreamGraphs - Lee Byron - 2007http://www.leebyron.com/else/streamgraph/
  • 18. The Ebb and Flow of Movies - The New York Timeshttp://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html
  • 19. From “research” in visualization to visualization “practice”
  • 20. Movie Narrative Charts - Randall Munroe - xkcdhttp://xkcd.com/657/large/
  • 21. zSoftware Evolution Storylines - Ogawa and Ma - 2010http://www.michaelogawa.com/research/storylines/
  • 22. Software Evolution Storylines - Ogawa and Ma - 2010http://www.michaelogawa.com/research/storylines/
  • 23. Design Considerations for Optimizing Storyline VisualizationsYuzuru Tanahashi and Kwan-Liu Ma - 2012http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06327274
  • 24. Design Considerations for Optimizing Storyline VisualizationsYuzuru Tanahashi and Kwan-Liu Ma - 2012http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06327274
  • 25. From visualization ‘exploration’ to visualization ‘research’
  • 26. Learning Goals1.Approaches of ‘designing’ infovis2. Appreciation of kinds of infovis3. Guidelines for ‘engaging’ infovis
  • 27. http://www.slideshare.net/mobile/jkofmsk/intro-to-information-visualization
  • 28. information visualisation “... is the use of computer- supported, interactive, visual representations of abstract data to amplify cognition”Information Visualization Definition
  • 29. “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, ...
  • 30. “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
  • 31. “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?
  • 32. “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
  • 33. 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
  • 34. “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, ...
  • 35. Web2DNAhttp://www.baekdal.com/web2dna/
  • 36. Web2DNAhttp://www.baekdal.com/web2dna/
  • 37. Web2DNA Flickr Collectionhttp://www.flickr.com/photos/tags/web2dna/
  • 38. Choice of “Metaphor”. can be potentially seemingly “useless”. yet receive a lot of interest. how to interpret “useful”?. persuasiveness of visual representations?
  • 39. data insight 10010110 knowledge transfer data mapping mapping inversion visualisation comprehension ! visual transferVisual Mapping Methodology
  • 40. Visualization as a “Medium”. scientific visualization. data graphics. infographics. information design. data art
  • 41. 1. (Scientific) Visualization
  • 42. LineAO - Improved Three-Dimensional Line Renderinghttp://www.informatik.uni-leipzig.de/~ebaum/Publications/eichelbaum2012a/
  • 43. DNA Coiling, Replication, Transcription and Translation - WEHIhttp://www.youtube.com/watch?v=DA2t5N72mgw
  • 44. 2. Data GraphicsEurovizion - Ben Willershttp://lifeindata.site50.net/work/eurovizion/eurovizion.html
  • 45. Statistical Atlases of the United States - 1870-1890http://www.handsomeatlas.com/
  • 46. The Jobless Rate for People Like You - The New York Timeshttp://www.nytimes.com/interactive/2009/11/06/business/economy/unemployment-lines.html
  • 47. Four Ways to Slice Obama’s 2013 Budget Proposal - The New York Timeshttp://www.nytimes.com/interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html
  • 48. 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
  • 49. 2. Information Graphics
  • 50. Starbucks Coffee Cup vs. Country Origins - Fast Food Revenue vs. Brands
  • 51. Telenet Social Media Reporthttp://blog.telenet.be/wp-content/uploads/2012/01/Telenet-Social-Media-Report-20111.jpg
  • 52. Year Report 2011 - http://feltron.com
  • 53. Debtris - David McCandless / Information is Beautifulhttp://www.informationisbeautiful.net/2010/debtris/
  • 54. 4. Data VisualizationOECD Better Life Index - Moritz Stefanerhttp://www.oecdbetterlifeindex.org/
  • 55. Flight & Expulsion - Nice Onehttp://www.niceone.org/lab/refugees/
  • 56. Take a Look at Health - Fathom Designhttp://visualization.geblogs.com/visualization/health_visualizer/
  • 57. 5. Information DesignChromosome 14 - Ben Fry
  • 58. We Feel Fine - Jonathan Harris and Sep Kamvarhttp://www.wefeelfine.org/
  • 59. WorldShapin - Compare Countries through their Shape - Carlo Zapponi & Vasundhara Parakhhttp://www.worldshap.in/#/PH/BZ/KE/
  • 60. Notabilia - Visualizing Deletion Discussions on Wikipedia - Moritz Stefanerhttp://notabilia.net/
  • 61. 6. Information ArtDNA Portrait - dna11.com
  • 62. TextArc - An Alternative Way to View a Text - Brad Paleyhttp://www.textarc.org/
  • 63. Poetry on the Road 6 - Boris Müllerhttp://www.esono.com/boris/projects/poetry06/
  • 64. DoodleBuzz - A Typographic News Explorerhttp://www.doodlebuzz.com/
  • 65. data insight 10010110 knowledge transfer data mapping mapping inversion visualisation comprehension ! visual transferVisual Mapping Methodology
  • 66. Name Trendshttp://nametrends.net/name.php?name=Andrew
  • 67. Baby Name Wizard - Martin Wattenberghttp://www.babynamewizard.com/voyager#
  • 68. Color Object - Martin WattenbergOffline Demo
  • 69. 1. The Role of Interaction
  • 70. Amazon Digital Cameras Treemap - The Hive Grouphttp://www.hivegroup.com/demos/amazon/499052.html
  • 71. Newsmap - Marumishi - 2004http://newsmap.jp/ and http://marumushi.com/projects/newsmap
  • 72. 2. The Role of Aesthetics
  • 73. A Year in Iraq and Afghanistan - The New York Times - 2009http://www.nytimes.com/2011/01/30/opinion/30casualty-chart.html
  • 74. Faces of the Fallen - Washington Post - 2009http://apps.washingtonpost.com/national/fallen/
  • 75. UK Casualties in Afghanistan and Iraq - BBC News - 2009http://www.bbc.co.uk/news/uk-10634102
  • 76. CNN Home and Away - CNN - Stamen Designhttp://edition.cnn.com/SPECIALS/war.casualties/
  • 77. Monument - Caleb Larsen - 2006http://caleblarsen.com/
  • 78. Monument - Caleb Larsen - 2006http://caleblarsen.com/
  • 79. 3. The Role of Data Focus (~ Meaning)
  • 80. Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization",IEEE International Conference on Information Visualisation (IV07), pp. 87-92.
  • 81. Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization",IEEE International Conference on Information Visualisation (IV07), pp. 87-92.
  • 82. Narrative Visualization: Telling Stories with DataEdward Segel and Jeffrey Heerhttp://vis.stanford.edu/files/2010-Narrative-InfoVis.pdfGenres of Narrative Visualization, Balancing Author-Driven versus Reader-Driven Stories
  • 83. Narrative Visualization: Telling Stories with DataEdward Segel and Jeffrey Heerhttp://vis.stanford.edu/files/2010-Narrative-InfoVis.pdf
  • 84. Our Irresistible Fascination with All Things Circularhttp://www.perceptualedge.com/articles/visual_business_intelligence/our_fascination_with_all_things_circular.pdf
  • 85. Our Irresistible Fascination with All Things Circularhttp://www.perceptualedge.com/articles/visual_business_intelligence/our_fascination_with_all_things_circular.pdf
  • 86. Aesthetic Effect in Data Visualization - Nick Cawthon and Andrew Vande Moere - 2007
  • 87. Aesthetic Effect in Data Visualization - Nick Cawthon and Andrew Vande Moere - 2007
  • 88. Aesthetic Effect in Data Visualization - Most Beautiful
  • 89. Aesthetic Effect in Data Visualization - Least Beautiful
  • 90. Aesthetic Effect in Data Visualization - Correct Responses
  • 91. Aesthetic Effect in Data Visualization - Least Correct Responses
  • 92. Aesthetic Effect in Data Visualization - Low Abandonment Rate
  • 93. Aesthetic Effect in Data Visualization - High Abandonment Rate
  • 94. 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 otherEvaluating the Effect of Style in Information VisualizationAndrew Vande Moere, Martin Tomitsch, Christoph Wimmer, Christoph Boesch, and ThomasGrechenig, IEEE Infovis 2012
  • 95. “Reversible”“Factual”Gapminder (2007)Many Eyes (2007)OECD eXplorer (2009)
  • 96. “Irreversible”“Meaningful” Bitalizer (2008) Poetry on the Road (2004) Texone (2005)
  • 97. Partly “Reversible”Partly “Factual” Digg Swarm (2007) ReMap (2009) We Feel Fine (2006)
  • 98. “Analytical” Style (ANA)
  • 99. “Magazine” Style (MAG)
  • 100. “Artistic” Style (ART)
  • 101. Interaction 18m09s (average) 12m49s (average) 11m55s (average) 181.0 interactions 87.7 interactions 88.9 interactions (average) (average) (average) Analytical Magazine Artistic
  • 102. No Reported Insights 1 participant 11 participants 9 participants Analytical Magazine Artistic
  • 103. Interface “Insights’ 6% 6 insights 12% 13 insights 29% 27 insights Analytical Magazine Artistic
  • 104. 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)
  • 105. 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)
  • 106. 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)
  • 107. 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)
  • 108. 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)
  • 109. 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, ...
  • 110. Design Study Methodology: Reflections from the Trenches and the StacksMichael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
  • 111. Design Study Methodology: Reflections from the Trenches and the StacksMichael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
  • 112. your techniqueDesign Study Methodology: Reflections from the Trenches and the StacksMichael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
  • 113. Design Study Methodology: Reflections from the Trenches and the StacksMichael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
  • 114. Edward Tufte
  • 115. French Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  • 116. temperaturetimetemp[day]
  • 117. longitudelatitudearmy[size, day]army[position, day]
  • 118. 1. Show comparisons, contrasts, differences2. Show causality, mechanism, explanation,systematic structure3. Show multivariate data; that is, show morethan 1 or 2 variables4. Completely integrate words, numbers, images,diagrams5.Thoroughly describe the evidence: title, authorsand sponsors, data sources, add measurementscales, highlight relevant issues6.Analytical presentations ultimately stand or falldepending on the quality, relevance andintegrity of their contentPrinciples for the Analysis and Presentation of Data - Tufte
  • 119. 1. Show comparisons, contrasts,differences
  • 120. 2. Show causality, mechanism, explanation, systematic structureFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  • 121. 3. Show multivariate data; that is, show more than 1 or 2 variablesFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  • 122. 4. Completely integrate words, numbers, images, diagramsFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  • 123. 5.Thoroughly describe the evidence: title, authors and sponsors, data sources, add measurement scales, highlight relevant issuesFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  • 124. 6.Analytical presentations ultimately stand or fall depending on the quality, relevance and integrity of their contentFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  • 125. The Friendly Graphic - Tufte (p. 183, 1983)
  • 126. http://www.informationisbeautifulawards.com/2012/02/hollywood-dataviz-challenge-
  • 127. • Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization", IEEE International Conference on Information Visualisation (IV07), 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 (IV07), 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...
  • 128. • 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...
  • 129. Thank you! Questions?------.----------@asro.kuleuven.be /// http://infosthetics.com /// @infosthetics