data/visualization - digital center cohort - 13_0222
DESCRIPTION
This presentation about the visualization aspect of data visualization was for the Digital Center Cohort meeting on February 22, 2013.TRANSCRIPT
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Data/Visualization
Jeffrey LancasterEmerging Technologies Coordinator
Science & Engineering Library, Columbia University
[email protected]@j_lancaster
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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)
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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)
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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)
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Why Visualize?
“Uncertainty in visualization can obfuscate meaning to the reader.”
- Jer Thorp -
https://www.youtube.com/watch?v=ix3grNuYxpA (27:50)
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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?
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Datavis? No. Information graphic? Yes.
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Datavis? No. Information graphic? Yes.
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Datavis? No. Information graphic? Yes.
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Datavis? No. Information graphic? Yes.
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Datavis? No. Information graphic? Yes.
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Datavis? No. Information graphic? Yes.
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A bunch of good datavis
See Tufte.
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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A bunch of good datavis
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Datavis toolshttp://selection.datavisualization.ch/http://visual.lyhttp://flowingdata.com/
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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
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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
The y-axis has been truncated to ‘magnify’ differences in values
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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
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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
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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
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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
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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
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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
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A bunch of bad datavis
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A few words on design
Color, line, shape, space, layout, graphics, motion, time, etc.
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ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary
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ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary• Color properties: e.g. saturation, tint, hue, shade
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ColorConsiderations:• Color relationships: e.g. complementary, primary, secondary, tertiary• Color properties: e.g. saturation, tint, hue, shade• Color meaning: e.g. hot, cold
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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
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LineLine thickness can:• Improve the ‘designerness’ of a graphic• Emphasize differences• Emphasize distances• Obscure variance in data points
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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?
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Hacking d3.jshttp://d3js.org/http://bost.ocks.org/mike/uberdata/
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Data/Visualization
Next time: Markup, APIsThen: GIS
Jeffrey LancasterEmerging Technologies Coordinator
Science & Engineering Library, Columbia University
[email protected]@j_lancaster