1. The visual display of quantitative data Joyce Chapman, Consultant for Communications & Data Analysis State Library of North Carolina, 6-11-2014 1

2. Agenda  Visual perception and quantitative communication  Fundamental concepts of graphs  General design for communication This webinar will be recorded and made available here: http://statelibrary.ncdcr.gov/ld/webinars.html 2

3. 3 What is the message?

4. Visual perception and quantitative communication 4

5. Stimulus  Stimulation  Perception 5

6. Pre-attentive processing 6 Extremely fast, pre-conscious visual processing

7. Pre-attentive processing 7 9128732198432789543287 6784905043267812837698 7843928364382398731092 3478957438298374209123 0980934591283754845645 8934678238328009748349

8. Pre-attentive processing 8 9128732198432789543287 6784905043267812837698 7843928364382398731092 3478957438298374209123 0980934591283754845645 8934678238328009748349

9. Pre-attentive attributes Attributes of form 9

10. Pre-attentive attributes Attributes of color 10

11. Pre-attentive attributes Attributes of spatial position and motion 11

12. But which of these visual attributes can be used to encode quantitative information? 12

13. Pre-attentive attributes Very precise quantitative perception 13

14. Pre-attentive attributes Less precise quantitative perception 14

15. Pre-attentive attributes Scatterplots take advantage of 2D spatial positioning 15

16. Pre-attentive attributes Line charts also take advantage of 2D spatial positioning 16

17. Pre-attentive attributes Bar charts take advantage of 2D spatial positioning (the end of each bar) and line length 17

18. Pre-attentive attributes The humble pie chart 18

19. Pre-attentive attributes The humble pie chart Which is larger, B or D? 19

20. Pre-attentive attributes Some limitations of our brains  Up to 8 different hues  Up to 4 different orientations or sizes  Less than 10 of other attributes  We can only process one attribute at a time 20

21. 21

22. 22

23. Fundamental concepts of graphs 23

24. Table A structure for organizing and displaying information. Quantitative values are encoded as text. Graph A visual display of quantitative information. Quantitative values are encoded as visual objects. 24

25.  When to use tables  When you will need to look up individual values  When you will need to compare individual values  When precise values are required  When the quantitative information to be communicated involves more than one unit of measure  When to use graphs  When the message is contained in the shape of values  To reveal relationships among multiple values  When there is a large amount of data to distill 25

26. How to choose a graph type Different types of quantitative relationships require different forms of graphs  Points  Lines  Bars  Shapes with 2D area 26

27. How to choose a graph type  Points 27

28. How to choose a graph type  Lines 28

29. How to choose a graph type  Lines 29

30. How to choose a graph type  Bars 30

31. How to choose a graph type  2D area 31

32. Relationships in graphs 1. Nominal comparison 2. Time series 3. Correlation 4. Part-to-whole 5. Deviation 6. Distribution 32

33. Relationships in graphs Nominal comparison Points lines bars 2D area ? 33

34. Relationships in graphs Nominal comparison Points lines bars 2D area  Categorical subdivisions have no connection  Values are discrete  Aims to highlight relative size 34

35. Relationships in graphs Nominal comparison 35

36. Relationships in graphs Time series Points lines bars 2D area ? 36

37. Relationships in graphs Time series Points lines bars 2D area  Our culture visualizes time as linear and left to right  The visual weight of bars detracts from message in the shape of the data  Points don’t work because dots floating in space cannot denote the sequential nature of time 37

38. Relationships in graphs Time series 38

39. Relationships in graphs Correlation Points lines bars 2D area ? 39

40. Relationships in graphs Correlation Points lines bars 2D area  Must show two sets of quantitative values in relation to each other instead of one  Both X and Y axis provide quantitative scales 40

41. Relationships in graphs Parts-to-whole Points lines bars 2D area ? 41

42. Relationships in graphs Parts-to-whole Points lines bars 2D area  Discrete value comparison  Individual bars are better than stacked bars 42

43. Relationships in graphs Parts-to-whole 43

44. Relationships in graphs Parts-to-whole 44

45. Relationships in graphs Deviation Points lines bars 2D area ? 45

46. Relationships in graphs Deviation Points lines bars 2D area  Usually teamed with another relationship  When combined with time-series, lines are best  When combined with anything else or standing alone, bars are usually used. 46

47. Relationships in graphs Deviation 47

48. Relationships in graphs Distribution Points lines bars 2D area ? 48

49. Relationships in graphs Distribution Points lines bars 2D area boxplots  The shape of the distribution is most important  Consider whether you have one or many distributions (lines for multiple, histogram for single) 49

50. Relationships in graphs Histograms: distribution 50

51. Relationships in graphs Box plots: distribution 51

52. General design for communication 52

53. "Above all else show the data." – Edward Tufte 53

54. Data-ink ratio 54

55. Data-ink ratio 55

56. Data-ink ratio 56

57. Who, what, where, when? 57 Create by the News & Observer, 4-12-2014 Contact jane.doe@no.org Figure 1.

58. Avoid “Chart junk”: 3D effects for non-3D data 58

59. Maintain visual correspondence to quantity 59

60. 60  eee Use zero-based scales  How much more satisfied were patrons at the Lilly library than the Iris library? With the baseline at zero  How much more satisfied were patrons at the Lilly library than the Iris library?

61. Concepts and charts for this presentation were borrowed from this book  Few, Stephen. (2004). Show me the numbers: designing tables and graphs to enlighten. Further reading, if you’re interested  Few, Stephen. (2009). Now you see it: simple visualization techniques for quantitative analysis.  Tufte, Edward. (1983). The Visual Display of Quantitative Information. 61

62. Questions? Contact: joyce.chapman@ncdcr.govjoyce.chapman@ncdcr.gov 919-807-7421 Find this Powerpoint and recorded webinar here: http://statelibrary.ncdcr.gov/ld/webinars.html http://statelibrary.ncdcr.gov/ld/webinars.html 62

63. To find out about continuing education opportunities offered by the State Library:  Join the CE listserv: https://lists.ncmail.net/mailman/listinfo/ceinfo https://lists.ncmail.net/mailman/listinfo/ceinfo  Sign up for email updates from the State Library blog: http://statelibrarync.org/ldblog/http://statelibrarync.org/ldblog/ 63

64. Example: How could this chart be improved? Find more examples here: http://www.perceptualedge.com/examples.php 64

65. Fix this chart Executives want to understand both the range of selling prices and the mean selling prices over 12 months. 65

66. Fix this chart Executives want to understand both the range of selling prices and the mean selling prices over 12 months. 66