WordPress Websites for Engineers: Elevate Your Brand
Data/Visualization - Digital Center Cohort - 13_0222
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?
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
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?
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
Editor's Notes
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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.