1. Data Visualization - An introduction
Prof Jan Aerts
Biodata Visualization and Analysis
ESAT/SCD
University of Leuven
Belgium
twitter: @jandot
Google+: +Jan Aerts
jan.aerts@esat.kuleuven.be
http://biovizanlab.wordpress.com
http://saaientist.blogspot.com

2. 1. What is data visualization?

3. “A good sketch is better than a long speech” (Napoleon)

4. “A good sketch is better than a long speech” (Napoleon)
shows: size of the army, geographical coordinates, direction that the army
was traveling, location of the army with respect to certain dates, temperature
along the path of the retreat

5. John Snow - cholera map

6. Shape of Songs: “Like a Prayer” (Madonna)
Martin Wattenberg

7. http://multimedia.mcb.harvard.edu/anim_innerlife.html

9. What I use as a definition:
“computer-based visualization systems providing visual representations of
datasets intended to help people carry out some task more effectively.” (T
Munzner)

11. cognition <=> perception
cognitive task => perceptive task
“eyes beat memory”

12. Why do we visualize data?
• record information
• blueprints, photographs,
seismographs, ...
• analyze data to support reasoning
• develop & assess hypotheses
• discover errors in data
• expand memory
• find patterns (see Snow’s cholera map)
• communicate information
• share & persuade
• collaborate & revise

13. exploration explanation
pictorial superiority effect
“information”
72hr
“informa” “i”
65% 1%

14. 2. Exploration <-> explanation

15. exploration explanation

16. exploration explanation
visual
infographics
analytics

17. exploration explanation
visual
infographics
analytics

18. exploration explanation
visual
infographics
analytics
hypothesis
generation

19. exploration explanation
“visual analytics”
=> identify unexpected patterns

20. exploration explanation
J van Wijk

21. Anscombe’s quartet
• uX = 9.0
• uY = 7.5
• sigma X = 3.317
• sigma Y = 2.03
• Y = 3 + 0.5X
• R2 = 0.67

24. A concrete example: hive plots

25. same network
Martin Krzewinsky

26. different networks!
Martin Krzewinsky

27. 3D, anyone?

28. 3D, anyone?
occlusion
interaction complexity
perspective distortion
text legibility

29. Functions in linux operation system:
“function A calls function B”
Gene interaction data:
“gene A regulates gene B”

30. regulator
workhorse
manager

31. 3. Why specifically learn about dataviz?

32. Isn’t it all just about using common sense?

33. • huge space of design alternatives => many tradeoffs
• many possibilities known to be ineffective
• avoid random walk through parameter space
• avoid some of our past mistakes
• extensive experimentation has already been done
• guidelines continue to evolve
• we reflect on lessons learned in design studies
• iterative refinement usually wise

34. 4. Stages of data visualization

35. How do we get from data to visualization? We need to understand:
• properties of the data
• properties of the image
• the rules mapping data to image

36. 4.1. Properties of the data

37. S Stevens “On the theory of scales and measurements” (1946)

38. 4.2. Properties of the image - perception

39. Semiology of graphics
• Jacques Bertin, Gauthier-Villars 1967, EHESS 1998
• semiology = study of signs and sign processes, likeness, analogy, metaphor,
symbolism, signification, and communication (Wikipedia)
• visual encoding:
• what - points, lines, areas (, patterns, trees/networks, grids)
• where - positional: XY (1D, 2D, 3D)
• how - retinal: Z (size, lightness, texture, colour, orientation, shape)
• when - temporal: animation

40. “marks” - geometric primitives
H
V
S
“channels” - control appearance of marks

41. Gestalt laws - interplay between parts and the
whole (Kurt Koffka)
series of principles
Election results Florida:
• black = Bush
• white = Gore

43. Gestalt - Principle of Simplicity
Every pattern we see is seen such that we see a structure that is as simple as
possible.

44. Gestalt - Principle of Proximity
Things that are close to each other are seen as belonging together (=>
clusters)

45. Gestalt - Principle of Similarity
Things that are similar in some way are perceived as belonging together.

46. Gestalt - Principle of Closure
You will try to complete a pattern.

47. Gestalt - Principle of Connectedness
Things that are connected are perceived as belonging together. This encoding
is stronger than similarity, shape, colour, and size.

48. Gestalt - Principle of Good Continuation
Objects that are arranged in a straight or smooth line tend to be seen as a
unit.

49. Gestalt - Principle of Common Fate
Objects that move in the same direction tend to be seen as a unit.

50. Gestalt - Principle of Familiarity

54. Gestalt - Principle of Symmetry
Symmetrical areas tend to be seen as figures against asymmetrical
backgrounds.

55. Context affects perceptual tasks

56. Pre-attentive vision
= ability of low-level human visual system to rapidly identify certain basic visual
properties
• some features “pop out”
• used for:
• target detection
• boundary detection
• counting/estimation
• ...
• visual system takes over => all cognitive power available for interpreting the
figure, rather than needing part of it for processing the figure

57. Really fast; see http://www.csc.ncsu.edu/faculty/healey/PP/

58. Limitations of preattentive vision
1. Combining pre-attentive features does not always work => would need to
resort to “serial search” (most channel pairs; all channel triplets)
e.g. is there a red square in this picture
2. Speed depends on which channel (use one that is good for
categorical; see further (“accuracy”))

59. 4.3. Mapping data to image: visual encoding

60. Language of graphics
• graphics = sign system:
• each mark (point, line, area) represents a data element
• choose visual variables to encode relationships between data elements
• difference, similarity, order, proportion
• only position supports all relationships (see later)
• huge range of alternatives for data with many attributes
• find images that express & effectively convey the information

61. Which encoding should I use?
• From huge list of possibilities, you have to choose the best one.
• Principle of Consistency
• properties of the representation should match properties of the data (e.g.
pie chart: area vs radius)
• Principle of Importance Ordering
• encode the most important piece of information in the most “effective”
way (i.e. spatial position)

63. Steven’s psychophysical law
= proposed relationship between the magnitude of a physical stimulus and its
perceived intensity or strength

64. Accuracy of quantitative perceptual tasks
how much (quantitative) what/where (qualitative)
McKinlay

65. Accuracy of quantitative perceptual tasks
how much (quantitative) what/where (qualitative)
McKinlay

66. Accuracy of quantitative perceptual tasks
how much (quantitative) what/where (qualitative)
McKinlay
“power of the plane”

67. Accuracy of quantitative perceptual tasks
how much (quantitative) what/where (qualitative)
grouping: see Gestalt laws
McKinlay

68. COLOUR

69. COLOUR ... is tricky, and often used wrong

70. Colour space
• = mathematical model to talk about colour
• RGB (red-green-blue)
• most common, but less useful
• HSV (hue-saturation-value)
• more useful

71. colorbrewer2.org
in R: please use RColorBrewer!

72. Context affects colour perception

73. Context affects colour perception

74. Dangers of Depth (3D)
• We do NOT see in 3D; we see in 2.05D.
• occlusion
• interaction complexity
• perspective distortion

75. 3D example

76. Lie factor
size of effect shown in graphic
“lie factor” =
size of effect in data

77. 3D scatter plots are better as series of 2D projections

78. Dynamic data
• animation is good sometimes, but often not:
• we can only follow 3-4 visual cues simultaneously
• change in “mental map”
• change blindness (e.g. http://nivea.psycho.univ-paris5.fr/CBMovies/
BarnTrackFlickerMovie.gif)

79. http://vimeo.com/2035117

81. 5. Interaction

82. Overview, zoom and filter, details on demand
(Schneiderman’s Information Seeking Mantra)

83. Operations on the data
• sorting
• filtering
• browsing/exploring
• comparison
• characterizing trends & distributions
• finding anomalies & outliers
• ...

84. Techniques to support these operations
• re-orderable matrices
• brushing
• linked views
• overview & detail
• focus & context
• ...

85. 6. Validation

86. Evaluate the right thing
Munzner, 2009

87. Slide/picture acknowledgments
• Jeffrey Heer
• Tamara Munzner
• Jessie Kennedy
• Nils Gehlenborg
• Miriah Meyer

88. “I think this presentation went quite well...”