Module 3 - cursus Big Data - Visualisation Instituut voor Permanente Vorming Introduction, Examples, principles, foundations, guidelines, design, gestalt theory, pre-attentive characteristics, visual encodings, data-ink ratio,

1. Post-­‐academic
course
Big
Data
Post-­‐academic
course
Big
Data
Joris Klerkx
Research Expert, PhD.
joris.klerkx@cs.kuleuven.be
@jkofmsk
Erik Duval
Professor
erik.duval@cs.kuleuven.be
@erikduval
Visualisatie
Big Data - module 3
IVPV - Instituut voor PermanenteVorming
28-05-2015

2. To research, design, create and evaluate useful tools
that augment the human intellect
By
‘augmen+ng
human
intellect’
we
mean
increasing
the
capability
of
a
man
to
approach
a
complex
problem
situa+on,
to
gain
comprehension
to
suit
his
particular
needs,
and
to
derive
solu+ons
to
problems
(Douglas
Engelbart,
1962).
2
Augment group - HCI research lab
Dept. Computerwetenschappen
KU Leuven
https://augmenthuman.wordpress.com
Music
Technology Enhanced
Learning
e-health
Research 2.0
Health
Media
(Consumption)
Technology Enhanced Learning
Science 2.0

3. http://eng.kuleuven.be/datavislab/
3

4. Today
Before break:
- Examples
- General guidelines while using visualisation techniques
After Break:
- Perception, Design & Design aesthetics
4

5. http://www.informationisbeautiful.net/visualizations/how-many-gigatons-of-co2/
5

6. http://www.hearts.com/ecolife/cut-paper-consumption-protect-forests/
Slides will be posted to Slideshare & Zephyr
6

7. DATA ABUNDANCE - BIG DATA
7

8. +/- 40% of world population
8

9. How to create value from of
such data?
9

10. How to generate insights from
this data?
10

11. How to facilitate human
interaction for exploration
with and understanding of
data?
11

12. 12
Source: Andrew Vande Moere

13. Why visualisation ?
13

14. algorithm
<>
human
14

15. data mining
<>
visual analytics
15

16. number
crunching
<>
human
perception
16

17. self driving car
<>
gps + dashboard
17

18. 18

19. Why visualisation ?
19

20. Anscombe`s quartet
http://en.wikipedia.org/wiki/Anscombe's_quartet
Enables discovery of visual patterns in data sets
Graphics reveal data (Tufte, 2001)
20

21. World Population Growth
A tremendous change occurred with the industrial revolution: whereas it had taken all of human history until around 1800 for world population
to reach one billion, the second billion was achieved in only 130 years (1930), the third billion in less than 30 years (1959), the fourth billion in
15 years (1974), and the fifth billion in only 13 years (1987). During the 20th century alone, the population in the world has grown from
1.65 billion to 6 billion.
Seeing is understanding
21

22. Facilitates understanding
22
http://www.bbc.co.uk/news/world-15391515

23. Facilitates human interaction for exploration and understanding
23
http://www.bbc.co.uk/news/world-15391515

24. Will there be enough food?
http://www.footprintnetwork.org/en/index.php/gfn/page/earth_overshoot_day/
Communicates data easily
24

25. http://terror.periscopic.com
Shows patterns & triggers questions
25

26. http://blog.stephenwolfram.com/2012/03/the-personal-analytics-of-my-life/
Shows trends & anomalies in the data, therefore triggers
questions 26

27. Helps to find stories, see trends
BelgiumBrazil
USA
27
India

28. Sentiment analysis in enterprise social network (slack)
28

29. Sentiment analysis in enterprise social network (slack)
Triggers questions & creates awareness
29
Should we trust SOTA NLP-algorithms?

30. Empowers users to make informed decisions
Positive Badges
Negative Badges
30

31.
Khaled Bachour, Frederic Kaplan, Pierre Dillenbourg, "An Interactive Table for Supporting Participation Balance in Face-to-Face
Collaborative Learning," IEEE Transactions on Learning Technologies, vol. 3, no. 3, pp. 203-213, July-September, 2010
Creates awareness
31

32. T. Nagel, M. Maitan, E. Duval,A.Vande Moere, J. Klerkx, K. Kloeckl, and C. Ratti.Touching transport - a case study on visualizing metropolitan public
transit on interactive tabletops. In AVI2014: 12th ACM International Working Conference on AdvancedVisual Interfaces, pages 281–288, 2014.
http://www.youtube.com/watch?v=wQpTM7ASc-w
Facilitates human interaction for exploration and understanding
32

33. http://infosthetics.com/
http://visualizing.org
http://www.visualcomplexity.com/vc/
http://visual.ly/
http://flowingdata.com
http://www.infovis-wiki.net
33

34. Defining visualisation
34

35. Information Visualisation is the use
of interactive visual
representations to amplify
cognition [Card. et. al]
Definition
35

36. Visualization
Slide
source:
John
Stasko
Scientific
visualization
Information
visualization
36

37. InformationVisualisation
Concerned with data that does not have a well-defined
representation in 2D or 3D space (i.e.,“abstract data”)
Slide
source:
Robert
Putman 37

38. Scientific visualisation
Specifically concerned with data that has a well-defined representation in 2D or 3D space (e.g., from
simulation mesh or scanner).
Slide
source:
Robert
Putman 38

39. http://www.visual-analytics.eu/faq
Also: Visual Analytics
39

40. Guidelines & Facts
40

41. How many circles?
41

42. Humans have advanced perceptual abilities
Our brains makes us extremely good at recognizing visual patterns
42

43. 43
Humans have little short term memory
Our brain remembers relatively little of what we perceive.
Most of us can only hold three to seven chunks of data at the same time.

44. Visual Information Seeking Mantra
https://www.youtube.com/watch?v=og7bzN0DhpI (9:51 - 11:22 )44

45. http://www.bbc.com/future/bespoke/20140724-flight-risk/
Overview first, zoom & filter, details-on-demand
45

46. http://infovis-lvm.github.io
Overview first, zoom & filter, details-on-demand
46

47. Real data is ugly and needs to be cleaned
http://hcil2.cs.umd.edu/trs/2011-34/2011-34.pdf
http://www.netmagazine.com/features/seven-dirty-secrets-data-visualisation
https://code.google.com/p/google-refine/
http://vis.stanford.edu/wrangler/Pre-process your data
47

48. http://nieuws.vtm.be/verkiezingen/gemeente?province=P1&city=G73
Always check & pre-process your data
48
Verkiezingen
14/10/12

49. Forget about 3D graphs (on a 2D screen..)
Occlusion
Complex to interact with
Doesn’t add anything to the data
49

50. Source: Stephen Few
What if we need to add a 3rd variable?
50

51. Use small coordinated graphs to add variables
51
Forget about 3D graphs
Source: Stephen Few

52. Which student has more blogposts?
• Size & angle are difficult to compare
• Without labels & legends, impossible to show exact quantitative
differences
• Limited Short term (visual) memory
52

53. Source: Stephen Few
Save the pies for dessert (S. Few)
Try using either of the pies to put the slices in order by size
53

54. deredactie.be
demorgen.be
vtm.be
Verkiezingen
14/10/12
54

55. Obviously there are exceptions to the rule
55
http://themetapicture.com/the-sunny-side-of-the-pyramid/

56. 0"
5"
10"
15"
20"
25"
30"
blogposts" tweets" comments"on"
blogs"
reports"
submi6ed"
Student'1'
Student"1"
0" 5" 10" 15" 20" 25" 30"
blogposts"
comments"on"blogs"
tweets"
reports"submi6ed"
Student'1'
Student"1"
Use Common Sense
0"
5"
10"
15"
20"
25"
30"
blogposts" comments"on"
blogs"
tweets" reports"
submi6ed"
Student'1'
Student"1"
56

57. 0" 10" 20" 30" 40" 50" 60"
Student"1"
Student"2"
Student"3"
Student"4"
blogposts"
tweets"
comments"on"blogs"
reports"submi:ed"
0%# 20%# 40%# 60%# 80%# 100%#
Student#1#
Student#2#
Student#3#
Student#4#
blogposts#
tweets#
comments#on#blogs#
reports#submi;ed#
Use Common Sense
What are you comparing?
What story do you get from it?
57

58. Which graph makes it easier to focus on the pattern of change
through time, instead of the individual values?
Choose graph that answers your questions about your data
58Source: Stephen Few

59. vtm.be
deredactie.be
nieuwsblad.be
Verkiezingen
14/10/12
Communicate the correct story
59

60. Don’t use visualisations to mislead
60

61. Don’t use visualisations to mislead
61

62. Source: Stephen Few
62

63. Source: Stephen Few 63

64. 64

65. http://fellinlovewithdata.com/research/deceptive-visualizations
65

66. http://fellinlovewithdata.com/research/deceptive-visualizations
66

67. How much better are the drinking water conditions in Willowtown as
compared to Silvatown?
67
http://fellinlovewithdata.com/research/deceptive-visualizations

68. Another example
68

69. http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html
69

70. Human Perception
70
Our brains makes us extremely good at recognizing visual patterns

71. Source: Katrien Verbert 71

72. Source: Katrien Verbert 72

73. A limited set of visual properties that are detected
- very rapidly (< 200 to 250 ms),
- accurately,
- with little effort,
- before focused attention
by the low-lever visual system on them.
Healey,
C.,
&
Enns,
J.
(2012).
AFenGon
and
Visual
Memory
in
VisualizaGon
and
Computer
Graphics.
IEEE
Transac+ons
on
Visualiza+on
and
Computer
Graphics
,
18
(7),
1170-­‐1188.
Pre-attentive characteristics
Note that eye movements take at least 200 ms to initiate.
73

74. Pre-attentive characteristics
Find the red dot
<> Hue
Find the dot
<> shape
Find the red dot
conjunction
not pre-attentive
http://www.csc.ncsu.edu/faculty/healey/PP/
helps to spot differences in multi-element display
74

75. Pre-attentive characteristics
Line orientation Length, width Closure Size
Curvature Density, contrast Intersection 3D depth
Not all of them allow showing exact quantitative differences
Helps to spot differences in multi-element display
75
http://www.csc.ncsu.edu/faculty/healey/PP/

76. http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
http://artspilesenglish.blogspot.be/2011/11/gestalt-theory-exercise-for-3rdlevel.html
76
Gestalt Laws (“Pattern” laws)
Basic rules or design principles that describe perceptual phenomena.
Explain the way users or humans see patterns in visualisations.

77. Figure & Ground
77

78. Smallness
78Source: Katrien Verbert

79. Common Fate
Objects with a common movement, that move in the same
direction, at the same pace, at the same time are organised as a
group (Ehrenstein, 2004).
79
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation

80. Law of Isomorphism
Is similarity that can be behavioural or perceptual, and can
be a response based on the viewers previous experiences
(Luchins & Luchins, 1999; Chang, 2002).This law is the basis
for symbolism (Schamber, 1986).
80
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation

81. London Tube Map
Which Gestalt laws do you see?
81

82. Design
How to create your visualization? a workflow
82

83. B. McDonnel and N. Elmqvist. Towards utilizing gpus in information visualization:A model and implementation of
image-space operations.Visualization and Computer Graphics, IEEE Transactions on, 15(6):1105–1112, 2009.
http://www.infovis-wiki.net/index.php/Visualization_Pipeline
83

84. Data
- structure
time, hierarchy, network, 1D, 2D, nD, …
- questions
where, when, how often, …
- audience
domain & visualisation expertise, …
84

85. S. Stevens. On the theory of scales of measurement. Science, 103(2684), 1946.
Structure
Time? hierarchical? 1D? 2D? nD? network? …
85

86. Questions (to get things going)
What is the average amount of students that bought the course book ?
What? When? How much? How often?
When did students start looking at the course material?
How much hours did Peter work on this assignment?
(Why did Peter have to redo his assignment?)
How often did Peter retake the course before he passed?
(why?)
86

87. 87
Visual mapping
Encode data characteristics into visual form
Each mark (point, line, area,…) represents a data element
Think about relationships between elements (position)
“Simplicity is the ultimate sophistication.”
Leonardo daVinci

88. Size
http://www.informationisbeautiful.net/2009/visualising-the-guardian-datablog/
88

89. X
4
How much bigger is the lower bar?
Slide
adapted
from
Michael
Porath
&
Katrien
Verbert
Length
89

90. X
5
How much bigger is the right circle?
Slide
adapted
from
Michael
Porath
&
Katrien
Verbert
Area
90

91. X
9
How much bigger is the right circle?
91Slide
adapted
from
Michael
Porath
&
Katrien
Verbert

92. Apparent magnitude curves
http://makingmaps.net/2007/08/28/perceptual-­‐scaling-­‐of-­‐map-­‐symbols
Slide
adapted
from
Michael
Porath
92

93. Which one is more accurate?
Slide
adapted
from
Michael
Porath
93
Compensating magnitude to match perception

94. Color
Color Principles - Hue, Saturation, andValue
https://www.youtube.com/watch?v=l8_fZPHasdo94
Use maximum +/- 5 colors (for categories,.. ) (short term memory)
http://en.wikipedia.org/wiki/HSL_and_HSV

95. • hue: categorical
• saturation: ordinal and quantitative
• luminance: ordinal and quantitative
How to choose colors
source from: Katrien Verbert 95

96. http://gizmodo.com/why-a-white-cup-makes-your-coffee-taste-more-intense-1663691154
intensity, sweetness, aroma, bitterness, and quality
96
How to choose colors

97. http://colorbrewer2.org

98. Position
98

99. Position & color
http://time.com/12933/what-you-think-you-know-about-the-web-is-wrong/
99

100. J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics, 5(2):110–141, 1986.
100

101. 101
J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics, 5(2):110–141,
1986.

102. 102

103. Offer precise controls for sharing on the Internet...
Users should navigate through 50 settings with more than 170 options
Example
Facebook privacy statement
Questions?
How did its complexity change over time?
How does its length compare to privacy statements
of other tools?
103

104. How did its complexity change over time?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html
104

105. How does its length compare to privacy statements
of other tools?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html
105

106. Example:
Encoding weather forecast on a smartphone
106 http://partlycloudy-app.com

107. EXERCISE
Find all possible ways to visualize a
small data set of two numbers { 75, 37 }
107

108. http://blog.visual.ly/45-ways-to-communicate-two-quantities/
108

109. Design aesthetics
Data ink design principles
109

110. Five principles
1. Above all else show the data.
2. Maximize the data-ink ratio, within reason.
3. Erase non-data ink, within reason.
4. Erase redundant data-ink.
5. Revise and edit.
Source: Katrien Verbert
"The success of a visualization is based on deep
knowledge and care about the substance, and the
quality, relevance and integrity of the content."
(Tufte, 1983)
110

111. Data-ink
“A large share of ink on a graphic should present data
information, the ink changing as the data change. Data-ink is
the non-erasable core of a graphic...” (Tufte,1983)
111

112. Data-ink ratio =
data-ink
Total ink used to print graphic
= Proportion of a graphic’s ink devoted to the
non-redundant display of data-information.
= 1.0 – proportion of graphic that can be
erased without the loss of information
Data-ink ratio
112

113. Data-ink ratio
113

114. What is the data-ink ratio?
< 0.05
114

115. What is the data-ink ratio of this graphic?
< 0.001
Source: Katrien Verbert 115

116. Five Principles
1. Above all else show the data.
2. Maximize the data-ink ratio.
• Within reason
• Every bit of ink on a graphic requires a reason
3. Erase non-data ink, within reason.
4. Erase redundant data-ink.
5. Revise and edit.
116

117. Maximize the data-ink ratio, within reason
“A pixel
is a
terrible
thing to
waste.”
(Shneiderman)
Slide
source:
Chris
North,
Virginia
Tech 117

118. Five Principles
1. Above all else show the data.
2. Maximize the data-ink ratio, within reason.
3. Erase non-data ink, within reason.
4. Erase redundant data-ink.
5. Revise and edit.
118

119. 119
source:
Joey
Cherdarchuk

120. 120
source:
Joey
Cherdarchuk

121. 121
source:
Joey
Cherdarchuk

122. 122
source:
Joey
Cherdarchuk

123. 123
source:
Joey
Cherdarchuk

124. 124
source:
Joey
Cherdarchuk

125. 125
source:
Joey
Cherdarchuk

126. 126
source:
Joey
Cherdarchuk

127. 127
source:
Joey
Cherdarchuk

128. 128
source:
Joey
Cherdarchuk

129. 129
source:
Joey
Cherdarchuk

130. 130
source:
Joey
Cherdarchuk

131. 131
source:
Joey
Cherdarchuk

132. 132
source:
Joey
Cherdarchuk

133. 133
source:
Joey
Cherdarchuk

134. 134
source:
Joey
Cherdarchuk

135. 135
source:
Joey
Cherdarchuk

136. 136
source:
Joey
Cherdarchuk

137. 137
source:
Joey
Cherdarchuk

138. 138
source:
Joey
Cherdarchuk

139. 139
source:
Joey
Cherdarchuk

140. 140
source:
Joey
Cherdarchuk

141. 141
source:
Joey
Cherdarchuk

142. “Perfection is achieved not
when there is nothing more
to add, but when there is
nothing left to take away”
– Antoine de Saint-Exupery
142

143. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. William S. Cleveland;
Robert McGill (PDF)
7 foundational papers
The Structure of the Information Visualization Design Space. Stuart K. Card and Jock Mackinlay (PDF)
Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays. Christopher Ahlberg and Ben Shneiderman
(PDF)
High-Speed Visual Estimation Using Preattentive Processing. C. G. Healey, K. S. Booth and J. T. Enns (PDF)
Automating the Design of Graphical Presentations of Relational Information. Jock Mackinlay (PDF)
How NOT to Lie with Visualization. Bernice E. Rogowitz, Lloyd A. Treinish (PDF).
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Ben Shneiderman (PDF).
http://fellinlovewithdata.com/guides/7-classic-foundational-vis-papers
143

144. ?
Joris Klerkx
Research Expert, PhD.
joris.klerkx@cs.kuleuven.be
@jkofmsk
https://augmenthuman.wordpress.com
144