Data/Visualization                Jeffrey Lancaster       Emerging Technologies CoordinatorScience & Engineering Library, ...
Why Visualize?   “You can lie and cheat with data            visualization.“There is an inherent trust in the form.       ...
Why Visualize? Datavis is easy; the mechanics of it are  known. Making an account is easy.But that doesn’t tell you what h...
Why Visualize?     “The Ohh-Ahh Principle:          Ohh! = Visual         Ahh! = Learning“Good datavis requires a balance ...
Why Visualize?“Uncertainty in visualization can obfuscate         meaning to the reader.”                     - Jer Thorp ...
ActivityWhat kind of data do you use/create?What is important about that data?Who are the actors involved inmaking that da...
Datavis? No. Information graphic?               Yes.
Datavis? No. Information graphic?               Yes.
Datavis? No. Information graphic?               Yes.
Datavis? No. Information graphic?               Yes.
Datavis? No. Information graphic?               Yes.
Datavis? No. Information graphic?               Yes.
A bunch of good datavis          See Tufte.
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
A bunch of good datavis
Datavis toolshttp://selection.datavisualization.ch/http://visual.lyhttp://flowingdata.com/
A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical...
A bunch of bad datavis                    The y-axis has been truncated to ‘magnify’ differences in valueshttp://simplysta...
A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical...
A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical...
A bunch of bad data(vis)http://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphic...
A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical...
A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical...
A bunch of bad datavishttp://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical...
A bunch of bad datavis
A few words on designColor, line, shape, space, layout, graphics, motion, time, etc.
ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary
ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary• Color properties: e.g. satura...
ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary• Color properties: e.g. satura...
ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary• Color properties: e.g. satura...
LineLine thickness can:• Improve the ‘designerness’ of a graphic• Emphasize differences• Emphasize distances• Obscure vari...
Motion & TimeTime can be a 4th dimension used to visualize data• Can time mean anything other than time (a.k.a. chronology...
Hacking d3.jshttp://d3js.org/http://bost.ocks.org/mike/uberdata/
Data/Visualization        Next time: Markup, APIs               Then: GIS                Jeffrey Lancaster       Emerging ...
Data/Visualization - Digital Center Cohort - 13_0222
Data/Visualization - Digital Center Cohort - 13_0222
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.

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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.
  • Which is behind this
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  • Data/Visualization - Digital Center Cohort - 13_0222

    1. 1. Data/Visualization Jeffrey Lancaster Emerging Technologies CoordinatorScience & Engineering Library, Columbia University jeffrey.lancaster@columbia.edu @j_lancaster
    2. 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. 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. 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. 5. Why Visualize?“Uncertainty in visualization can obfuscate meaning to the reader.” - Jer Thorp - https://www.youtube.com/watch?v=ix3grNuYxpA (27:50)
    6. 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. 7. Datavis? No. Information graphic? Yes.
    8. 8. Datavis? No. Information graphic? Yes.
    9. 9. Datavis? No. Information graphic? Yes.
    10. 10. Datavis? No. Information graphic? Yes.
    11. 11. Datavis? No. Information graphic? Yes.
    12. 12. Datavis? No. Information graphic? Yes.
    13. 13. A bunch of good datavis See Tufte.
    14. 14. A bunch of good datavis
    15. 15. A bunch of good datavis
    16. 16. A bunch of good datavis
    17. 17. A bunch of good datavis
    18. 18. A bunch of good datavis
    19. 19. A bunch of good datavis
    20. 20. A bunch of good datavis
    21. 21. A bunch of good datavis
    22. 22. A bunch of good datavis
    23. 23. A bunch of good datavis
    24. 24. A bunch of good datavis
    25. 25. A bunch of good datavis
    26. 26. A bunch of good datavis
    27. 27. A bunch of good datavis
    28. 28. A bunch of good datavis
    29. 29. A bunch of good datavis
    30. 30. Datavis toolshttp://selection.datavisualization.ch/http://visual.lyhttp://flowingdata.com/
    31. 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. 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. 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. 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. 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. 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. 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. 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. 39. A bunch of bad datavis
    40. 40. A few words on designColor, line, shape, space, layout, graphics, motion, time, etc.
    41. 41. ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary
    42. 42. ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary• Color properties: e.g. saturation, tint, hue, shade
    43. 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. 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. 45. LineLine thickness can:• Improve the ‘designerness’ of a graphic• Emphasize differences• Emphasize distances• Obscure variance in data points
    46. 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. 47. Hacking d3.jshttp://d3js.org/http://bost.ocks.org/mike/uberdata/
    48. 48. Data/Visualization Next time: Markup, APIs Then: GIS Jeffrey Lancaster Emerging Technologies CoordinatorScience & Engineering Library, Columbia University jeffrey.lancaster@columbia.edu @j_lancaster
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