Data/Visualization - Digital Center Cohort - 13_0222
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Data/Visualization - Digital Center Cohort - 13_0222

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This presentation about the visualization aspect of data visualization was for the Digital Center Cohort meeting on February 22, 2013.

This presentation about the visualization aspect of data visualization was for the Digital Center Cohort meeting on February 22, 2013.

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  • Example of an Ishihara color test plate.[Note 1] The numeral "74" should be clearly visible to viewers with normal color vision. Viewers with dichromacy or anomalous trichromacy may read it as "21", and viewers with achromatopsia may not see numbers.
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Data/Visualization - Digital Center Cohort - 13_0222 Presentation Transcript

  • 1. Data/Visualization Jeffrey Lancaster Emerging Technologies CoordinatorScience & Engineering Library, Columbia University jeffrey.lancaster@columbia.edu @j_lancaster
  • 2. Why Visualize? “You can lie and cheat with data visualization.“There is an inherent trust in the form. “Graphs are scientific!” - Jer Thorp - https://www.youtube.com/watch?v=ix3grNuYxpA (27:50)
  • 3. Why Visualize? Datavis is easy; the mechanics of it are known. Making an account is easy.But that doesn’t tell you what happened. Narrative is harder. https://www.youtube.com/watch?v=ix3grNuYxpA (27:50)
  • 4. Why Visualize? “The Ohh-Ahh Principle: Ohh! = Visual Ahh! = Learning“Good datavis requires a balance of Ohh! and Ahh!” - Jer Thorp - https://www.youtube.com/watch?v=ix3grNuYxpA (27:50)
  • 5. Why Visualize?“Uncertainty in visualization can obfuscate meaning to the reader.” - Jer Thorp - https://www.youtube.com/watch?v=ix3grNuYxpA (27:50)
  • 6. ActivityWhat kind of data do you use/create?What is important about that data?Who are the actors involved inmaking that data?What is the meaning of the data?What would you like to emphasizeabout that data?
  • 7. Datavis? No. Information graphic? Yes.
  • 8. Datavis? No. Information graphic? Yes.
  • 9. Datavis? No. Information graphic? Yes.
  • 10. Datavis? No. Information graphic? Yes.
  • 11. Datavis? No. Information graphic? Yes.
  • 12. Datavis? No. Information graphic? Yes.
  • 13. A bunch of good datavis See Tufte.
  • 14. A bunch of good datavis
  • 15. A bunch of good datavis
  • 16. A bunch of good datavis
  • 17. A bunch of good datavis
  • 18. A bunch of good datavis
  • 19. A bunch of good datavis
  • 20. A bunch of good datavis
  • 21. A bunch of good datavis
  • 22. A bunch of good datavis
  • 23. A bunch of good datavis
  • 24. A bunch of good datavis
  • 25. A bunch of good datavis
  • 26. A bunch of good datavis
  • 27. A bunch of good datavis
  • 28. A bunch of good datavis
  • 29. A bunch of good datavis
  • 30. Datavis toolshttp://selection.datavisualization.ch/http://visual.lyhttp://flowingdata.com/
  • 31. A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data
  • 32. A bunch of bad datavis The y-axis has been truncated to ‘magnify’ differences in valueshttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data
  • 33. A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data
  • 34. A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data
  • 35. A bunch of bad data(vis)http://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data
  • 36. A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data
  • 37. A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data
  • 38. A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data
  • 39. A bunch of bad datavis
  • 40. A few words on designColor, line, shape, space, layout, graphics, motion, time, etc.
  • 41. ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary
  • 42. ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary• Color properties: e.g. saturation, tint, hue, shade
  • 43. ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary• Color properties: e.g. saturation, tint, hue, shade• Color meaning: e.g. hot, cold
  • 44. ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary• Color properties: e.g. saturation, tint, hue, shade• Color meaning: e.g. hot, cold• Color blindness: e.g. red-green
  • 45. LineLine thickness can:• Improve the ‘designerness’ of a graphic• Emphasize differences• Emphasize distances• Obscure variance in data points
  • 46. Motion & TimeTime can be a 4th dimension used to visualize data• Can time mean anything other than time (a.k.a. chronology)?• How to embed in a static document?• What are the difficulties of presenting an visualization that changes over time?• When are motion and time inappropriate?
  • 47. Hacking d3.jshttp://d3js.org/http://bost.ocks.org/mike/uberdata/
  • 48. Data/Visualization Next time: Markup, APIs Then: GIS Jeffrey Lancaster Emerging Technologies CoordinatorScience & Engineering Library, Columbia University jeffrey.lancaster@columbia.edu @j_lancaster