bowdoin: data driven socities 2014 - what is dataviz 01/29/14

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Data Driven Societies: From Data/Tech to DataViz Professors Gaze & Gieseking

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Data Driven Societies Digital & Computational Studies Bowdoin College January 29, 2014 Professors Gieseking & Gaze Lecture Slides "How We Make Sense of Data in Visualization"

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Page 1: Bowdoin: Data Driven Socities 2014 - What is DataViz 01/29/14

Data Driven Societies: From Data/Tech to DataVizProfessors Gaze & Gieseking

Page 2: Bowdoin: Data Driven Socities 2014 - What is DataViz 01/29/14

Data & Technology & Statistics, Oh My

History of data and technology

Statistical thinking

Data visualization

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Page 3: Bowdoin: Data Driven Socities 2014 - What is DataViz 01/29/14

Visualize This

“...there are right ways and wrong ways to show data; there are displays that reveal the truth and displays that do not.”— Edward Tufte, Visualizing Explanations (45)

Page 4: Bowdoin: Data Driven Socities 2014 - What is DataViz 01/29/14

The Beginnings of Data Visualization: William Playfair

Page 5: Bowdoin: Data Driven Socities 2014 - What is DataViz 01/29/14

Tufte: Snow’s Cholera Map 1854

Page 6: Bowdoin: Data Driven Socities 2014 - What is DataViz 01/29/14

Tufte: Challenger O’Rings 1987

Page 7: Bowdoin: Data Driven Socities 2014 - What is DataViz 01/29/14

Tufte: What is Bad DataViz?

✦ Disappearing legend✦ Chartjunk - visual elements in charts and graphs not

necessary for their comprehension ✦ “Chartjunk indicates statistical stupidity, just as weak

writing reflects weak thought. ... Good design brings absolute attention to data.” (48)

✦ Lack of clarity in depicting cause and effect✦ Wrong order

Page 8: Bowdoin: Data Driven Socities 2014 - What is DataViz 01/29/14

Yau: Visualization as Medium

flowingdata.com/2010/10/18/true-size-of-africa/

Page 9: Bowdoin: Data Driven Socities 2014 - What is DataViz 01/29/14

Yau: Visualization as Medium

Patrick Smith, 2010

Page 10: Bowdoin: Data Driven Socities 2014 - What is DataViz 01/29/14

Tufte & Yau: What is Good DataViz?

TufteCertain key rules must be applied:✦ Place data in appropriate

context to assess cause/effect

✦ Make quantitative comparisons

✦ Consider alternative explanations and contrary cases

✦ Assess possible errors in data

YauYou may need to throw out the rules but these ideas generally apply:✦ Let the type of visual and tool fit

question✦ Visualization is storytelling✦ Entertainment and humor can

be helpful✦ Let data guide design✦ Recognize how interface

interactions with data