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

1. Data/Visualization
Jeffrey Lancaster
Emerging Technologies Coordinator
Science & 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. Activity
What kind of data do you use/create?
What is important about that data?
Who are the actors involved in
making that data?
What is the meaning of the data?
What would you like to emphasize
about 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

27. A bunch of good datavis

28. A bunch of good datavis

29. A bunch of good datavis

30. A bunch of good datavis

31. A bunch of good datavis

33. Datavis tools
http://selection.datavisualization.ch/
http://visual.ly
http://flowingdata.com/

34. A bunch of bad datavis
http://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 datavis
The y-axis has been truncated to ‘magnify’ differences in values
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 datavis
http://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 datavis
http://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 data(vis)
http://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
http://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data

40. A bunch of bad datavis
http://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data

41. A bunch of bad datavis
http://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data

42. A bunch of bad datavis

43. A few words on design
Color, line, shape, space, layout, graphics, motion, time, etc.

44. Color
Considerations:
• Color relationships: e.g. complementary, primary, secondary, tertiary

45. Color
Considerations:
• Color relationships: e.g. complementary, primary, secondary, tertiary
• Color properties: e.g. saturation, tint, hue, shade

46. Color
Considerations:
• Color relationships: e.g. complementary, primary, secondary, tertiary
• Color properties: e.g. saturation, tint, hue, shade
• Color meaning: e.g. hot, cold

47. Color
Considerations:
• 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

48. Line
Line thickness can:
• Improve the ‘designerness’ of a graphic
• Emphasize differences
• Emphasize distances
• Obscure variance in data points

49. Motion & Time
Time 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?

50. Hacking d3.js
http://d3js.org/
http://bost.ocks.org/mike/uberdata/

51. Data/Visualization
Next time: Markup, APIs
Then: GIS
Jeffrey Lancaster
Emerging Technologies Coordinator
Science & Engineering Library, Columbia University
jeffrey.lancaster@columbia.edu
@j_lancaster