Data Visualization

1. Data Visualization

2. Lies, Damn Lies, and Bad Graphs

3. Lies, Damn Lies, and Bad Graphs

4. Lies, Damn Lies, and Bad Graphs

5. Visual Medium • Reports • Paper (static, with time) • Web (dynamic and interactive) • Presentations (static and dynamic)

6. “It’s not just about producing graphics for publication,” Aldhous explains. “It’s about playing around and making a bunch of graphics that help you explore your data. This kind of graphical analysis is a really useful way to help you understand what you’re dealing with, because if you can’t see it, you can’t really understand it. But when you start graphing it out, you can really see what you’ve got.”

10. Four sets of data with the same correlation of 0.816

12. Percent Blue relative to Red?

13. Percent Blue relative to Red?

18. Bad

19. Better

20. Even Better*

26. #Introduction #History of Plots #The Explanatory Power of Graphics #Basic Philosophy of Approach #Graphical Integrity #Data Densities #Data Compression #Multifunctioning Graphical Elements #Maximize data-ink; minimize non-data ink #Small Multiples #Chartjunk #Colors #General Philosophy for Increasing Data Comprehension #Techniques for Increasing Data Comprehension #When NOT to Use Graphics #Aesthetics

28. Chartjunk and Graphics Integrity

29. Types of chartjunk • Chartjunk are non-data-ink or redundant data-ink decoration • Unintended Optical Art (Moiré vibration) • The Grid • The Duck: Self-promoting Graphics

30. Unintended Optical Art • Mainly rely on moiré effects • Distracting appearance of vibration and movement • The most common form of graphical clutter

31. Moiré Vibrations

32. The Grid • Dark grid lines are chartjunk • The grid should usually be muted or completely supressed

33. The Grid (cont’d) • Marey’s train schedule

34. The Duck • Self-promoting graphics: when the data measures become design elements

35. Duck Examples

36. "In our excitement to produce what we could only make before with great effort, many of us have lost sight of the real purpose of quantitative displays — to provide the reader with important, meaningful, and useful insight." — Stephen Few

37. Graphical Integrity • Graphical excellence begins with telling the truth about the data Some examples of Lie

38. Two Principles • The representation of numbers, as physically measured on the surface of the graphics, should be directly proportional to the numerical quantities represented • Clear, detailed and thorough labeling should be used to defeat distoration

39. Violating rule 1 18 miles/gallon: 0.6 inches; 27.5miles/gallon: 5.3 inches

40. Lie Factor • Rule 1 can be measured by Lie factor size of effect shown in graphics size of effect in data • Lie Factor equal to one is ideal • The previous slide has a lie factor of 14.8 Lie Factor =

41. Design and Data Variation • Show data variation, not design variation • 1973-1978: one vertical inch • equals to $8.00. In 1979, • One vertical inch equals $3-4 • 1973-1978: one horzontal inch • equals 3.7 years, while 1979 • equals 0.57 year

42. Example • Lie factor: 9.5 • The price of oil is inflated so need to be repaired.

43. Government Spending • Tricks to exaggerate the growth of spending

44. Real Government Spending • Tricks to exaggerate the growth of spending • Tricks to exaggerate the growth of spending

45. Visual Area and Numerical Measure • Tricking the reviewer with design variation is to use areas to show 1D data Lie factor: 2.8

46. Content is Essential • Graphics must not quote data out of context

47. Content is Essential • Graphics must not quote data out of context

48. On Using Color…

50. Rule #3: Use color only when needed to serve a particular communication goal.