1. Visual Variables for Information Visualization
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Scientific Visualization
Visual Variables for Information Visualization
Lecture 11
Ying Zhu
Georgia State University

2. Background
The discussion of visual variables is larged based on Jacques Bertin’s work
Jacques Bertin, “Semiology of graphics: diagrams, networks, maps.” University of Wisconsin Press, 1983 (first published in French in Translated in 1983)

3. Background Jacques Bertin’s semiology of graphics
“ Jacques Bertin is one of the fundamental gurus of Information Visualization since he was the first in articulating a coherent and reasoned theory for the analysis of quantitative graphic representation.”
- infovis.com

4. Background
The information that is being visualized may not have any obvious visual manifestation
The process of creating mapping from the information to the visual representation is non-trivial
What are the best visual variables for a particular set of information?

5. Visualization pipeline

6. Basic visual units (marks)
Points
Location, size, shape, color
Lines
Length, location,
Change in thickness, texture, or color does not change the meaning of the line
Changing location will change its meaning

7. Basic visual units Areas Length, width
Changing length and width will change the meaning
Changing position, color, value, or texture does not change its meaning

8. Basic visual units Surfaces Similar to area but exist in 3D
Changing color, texture does not change its meaning
Changes in position, size, shape, or orientation will change its meaning

9. Basic visual units Volumes Length, width, and height
Their size is their meaning
Changing position, color, or texture doesn’t change its meaning
Changing size, shape, or orientation will change its meaning

10. Visual units
Each visual unit may have multiple visual variables

11. Visual variables

12. Which visual variables do Tableau use?
Position
Size
Shape
Color
Value
Text

13. Visual variables
Each visual variable may have multiple characters

14. Characters of visual variables
Selective
Is change in this visual variable alone enough to allow us to select it from a group?
How easy is it to spot an outlier?
Associative
Is a change in this visual variable enough to allow us to perceive them as a group?
How easy is it to see a cluster?
Quantitative
Is there a numerical reading obtainable from changes in this visual variable?

15. Characters of visual variables
Order
Are changes in this visual variable perceived as ordered?
How easy is it to spot a trend?
How easy is it to rank things numerically?
Length
How many changes in value can still be recognized with confidence as separate?
How big a range of data can this visual variable encode?

16. Characters of visual variables
Interpretation of symbolic meanings
Bertin didn’t discuss this
How easy is it to interpret the symbolic (not numeric) meaning of a visual variable?
Greatly influence the experience of reading a visualization

17. Characteristics of each visual variable
We will discuss the five characteristics of each visual variable
Selective
Associative
Quantitative
Order
Length

18. Position

19. Example

20. Example

21. Example

22. Size

23. Size
Numerical readings interpreted from changes in size alone are usually approximate and often less accurate
Using size to represent numerical variable should be done with caution

24. Example: Human Poverty Index

25. Example: pie chart

26. Example

27. Example: size or position?

28. Example

29. Example: line thickness
Monsieur Minard’s visualization of Napoleon’s invasion of Russia

30. Shape

31. Shape
While changes in shapes are distinguishable, this distinction can often require considerable interpretation effort
Quick visual interpretation of all shapes is often difficult
Shape is not a quantitative visual variable
Shape is not an ordered visual variable

32. Shape

33. Shape
The representation power of shape comes from its infinite length and from symbolic interpretation
The link between the shape and the intended meaning must be explicit
to reduce the mental workload of symbolic interpretation
But it’s often difficult to memorize the meanings when many shapes are used

34. Example: Chernoff Face

35. Words and text We can see a word as a special case of shape
Selective (?)
Associative (?)
Quantitative (No)
Order (No)
Texts often require serial processing
On the other hand, visual marks can often be processed in parallel

36. Numbers Again, we can see a number as a special case of shape
Selective (?)
Associative (?)
Quantitative (yes!)
Order (it depends)
This is why spreadsheets are generally not good for spotting outliers, clusters, or trends

37. Value

38. Value
Changing a mark’s value is achieved by changes in darkness of lightness of the mark.
Color is divided into hue, saturation, and value.
The color in the later slides actually refer to hue
Changes in saturation are not discussed

39. Value

40. Value Changes in value do not provide numerical readings
One grey may be seen as darker or lighter than other grey, it will not be seen as 4 times as dark as the other grey.
Value (grey scale) is not quantitative.

41. Example

42. Example

43. Color

44. Color
Color is not quantitative since the relationship between two marks differing on color will not be read numerically
Color is not ordered since changes in color do not easily lend themselves to readings of greater or lesser
The link between the color and the intended meaning are often not explicit
making it difficult to interpret, especially when many colors are used

45. Color

46. Example

47. Example

48. Orientation

49. Orientation
Numerical values, quantities or ratios are not associated with changes in orientation.
There seems to be some notion of order if the changes in orientation are progressive
If they are organized randomly then this sense of order does not exist

50. Orientation
While variations in orientation is theoretically infinite, practically it may be wise to limit its use to four variables: vertical, horizontal and two opposing diagonals

51. Example: flow visualization

52. Grain, pattern, and texture

53. Grain

54. Pattern
The characters of pattern is basically the same as shape

55. Texture

56. Example: flags

57. Example: logos

58. Example: Game companies in Atlanta

59. Flags and logos
Well known flags and logos usually requires very low mental workload for interpreting its symbolic meaning
Famous flags and logos are selective and associative (e.g., when you go grocery shopping)
Obscure logos are generally not selective

60. Motion Selective: probably Associative: yes Quantitative: no
Order: probably
Length: considerable variations

61. Other visual variables
Bertin’s book did not include depth, occlusion, and transparency, which should be addressed.

62. What visual marks and visual variables are used?

63. Rankings of visual variables
Based on the works of Drs. Jock Mackinlay, William Cleveland, et al.
Rank visual variables for different data types
Quantitative (numerical) data
Ordinal data
Nominal data

64. Rankings of visual variables
Ranking by accuracy for quantitative data
Position
Length
Angle
Slop
Area
Volume
Density
Color saturation

65. Rankings of visual variables
Ranking by accuracy for ordinal data
Position
Density
Color Saturation
Color Hue
Texture
Connection
Containment
Length
Angle
Slope
Area
Volume

66. Rankings of visual variables
Ranking by accuracy for nominal data
Position
Color Hue
Texture
Connection
Containment
Density
Color Saturation
Shape
Length
Angle
Slope
Area
Volume

67. Summary Selective Associative Quantitative
All the visual variables are selective
Associative
Everyone is associative except for shape
Quantitative
Position: yes
Size: maybe

68. Summary Order Length Position, size, and value
All have theoretically infinite length but limited by the resolution of computer display

69. Interpretation of symbolic meanings
All visual variables require some mental effort to interpret their symbolic meanings
This is also part of visual mapping
Developers often pay less attention to this aspect of visual mapping
However, the complexity of reading a visualization is largely influenced by the symbolic interpretation

70. Readings
M.S.T. Carpendale, “Considering Visual Variables as a Basis for Information Visualization”, Technical Report, Dept. of Computer Science, University of Calgary 2001

71. Reference
Jacques Bertin, “Semiology of graphics: diagrams, networks, maps.” University of Wisconsin Press, 1983 (first published in French in Translated in 1983)

72. Readings Interview with Jacques Bertin