Information Visualization Crash Course (AKA Information - - PowerPoint PPT Presentation

▶
information visualization crash course
SMART_READER_LITE
LIVE PREVIEW

Information Visualization Crash Course (AKA Information - - PowerPoint PPT Presentation

Jun 17, 2023 48 likes •1.53k views

Information Visualization Crash Course (AKA Information Visualization 101) Chad Stolper Assistant Professor Southwestern University (graduated from Georgia Tech CS PhD) 1 What is Infovis? Why is it Important? Human Perception Chart Basics

slide-1
SLIDE 1

Information Visualization Crash Course

Chad Stolper Assistant Professor Southwestern University

(graduated from Georgia Tech CS PhD)

1

(AKA Information Visualization 101)

slide-2
SLIDE 2

What is Infovis? Why is it Important? Human Perception Chart Basics

(If Time, Some Color Theory)

The Shneiderman Mantra Where to Learn More

2

slide-3
SLIDE 3

What is Information Visualization?

3

slide-4
SLIDE 4

Information Visualization

“The use of co compu puter er-supported, in interact eractiv ive, vi visual representations of abstract data to am amplif plify co cognit itio ion.” Card, Mackinlay, and Shneiderman 1999

4

slide-5
SLIDE 5

Co Communication Ex Expl ploratory Data Ana nalysis (EDA)

5

slide-6
SLIDE 6

Com Communi unication

  • n

6

(gone wrong)

slide-7
SLIDE 7

7

slide-8
SLIDE 8

8 X

Edward Tufte

An American statistician and professor emeritus of political science, statistics, and computer science at Yale University. He is noted for his writings

  • n information design and

as a pioneer in the field of data visualization.

  • Wikipedia
slide-9
SLIDE 9

Sp Space Sh Shuttle le C Challe llenger

January 28, 1986

9

Morning Temperature: 31°F

slide-10
SLIDE 10

10

slide-11
SLIDE 11

11

Tufte, E. R. (2012). Visual explanations: images and quantities, evidence and narrative. Cheshire, CT: Graphics Press.
slide-12
SLIDE 12

13

Video originally from: http://www.FeynmanPhysicsLectures.com

Most Watched Science Experiment

Richard Feynman, Physics Nobel laureate explained how rubber became rigid in cold temperate YouTube video: https://youtu.be/6Rwcbsn19c0

slide-13
SLIDE 13

How did this happen?

14

slide-14
SLIDE 14

15

Tufte, E. R. (2012). Visual explanations: images and quantities, evidence and narrative. Cheshire, CT: Graphics Press.

Engineers at Morton Thiokol, the rocket maker, presented on the day before and recommended not to launch.

slide-15
SLIDE 15

19

slide-16
SLIDE 16

24

slide-17
SLIDE 17

25

slide-18
SLIDE 18

26

slide-19
SLIDE 19

27

slide-20
SLIDE 20

28

slide-21
SLIDE 21

So, communication is

ex extr trem emel ely y importan

  • rtant.

Visualization can help with that – com communicat cate e ideas eas an and insigh ghts.

29

slide-22
SLIDE 22

30

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

slide-23
SLIDE 23

Visualization can also help with Ex Expl ploratory Data Ana nalysis (EDA)

But But why hy do do you u ne need d to ex explor

  • re

e dat ata a at at al all???

31

slide-24
SLIDE 24

“There are three kinds of lies: lies, damned lies, and statistics.”

33

slide-25
SLIDE 25

Mystery Data Set

34

slide-26
SLIDE 26

Mystery Data Set

Pr Proper erty Va Value mean( x ) 9 variance ( x ) 11 mean( y ) 7.5 variance ( y ) 4.122 correlation ( x,y ) 0.816 Linear Regression Line y = 3 + 0.5x

35

slide-27
SLIDE 27

36

slide-28
SLIDE 28

37

slide-29
SLIDE 29

38

slide-30
SLIDE 30

39

slide-31
SLIDE 31

Anscombe’s Quartet

40

https://en.wikipedia.org/wiki/Anscombe%27s_quartet

slide-32
SLIDE 32

Anscombe’s Quartet Sanity Checking Models Outlier Detection

41

slide-33
SLIDE 33

Data visualization leverages hum human n pe percept ption

43

slide-34
SLIDE 34

Name the five senses.

44

slide-35
SLIDE 35

45

Sense Sense Ba Bandwidth (bi bits/ s/sec) sec) Sight 10,000,000 Touch 1,000,000 Hearing 100,000 Smell 100,000 Taste 1,000

http://www.britannica.com/EBchecked/topic/287907/information-theory/214958/Physiology

slide-36
SLIDE 36

A (Simple) Model

  • f Human Visual Perception

46

slide-37
SLIDE 37

A (Simple) Model of Human Perception

47

Parallel detection of basic features into an iconic store Serial processing of

  • bject identification and

spatial layout

Stage 1 Stage 2

slide-38
SLIDE 38

Stage 1: Pre-Attentive Processing

Rapid Parallel Automatic

(Fleeting = lasting for a short time)

48

slide-39
SLIDE 39

Stage 2: Serial Processing Relatively Slow (Incorporates Memory) Manual

49

slide-40
SLIDE 40

Stage 1: Pre-Attentive Processing

The eye moves every 200ms (so this processing occurs every 200ms-250ms)

50

slide-41
SLIDE 41

Example

1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

51

slide-42
SLIDE 42

Example

1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

52

slide-43
SLIDE 43

A few more examples from

  • Prof. Chris Healy at NC State

53

slide-44
SLIDE 44

54

Left Side Right Side

slide-45
SLIDE 45

Raise your hand if a RE RED DOT is present…

(On the left or on the right?)

55

slide-46
SLIDE 46

56

slide-47
SLIDE 47

57

slide-48
SLIDE 48

Color (hue) is pre-attentively processed.

58

slide-49
SLIDE 49

Raise your hand if a RED DOT is present…

59

slide-50
SLIDE 50

60

slide-51
SLIDE 51

61

slide-52
SLIDE 52

Shape is pre-attentively processed.

62

slide-53
SLIDE 53

Determine if a RED DOT is present…

63

slide-54
SLIDE 54

64

slide-55
SLIDE 55

65

slide-56
SLIDE 56

Hue and shape together are NOT pre-attentively processed.

66

slide-57
SLIDE 57

Pre-Attentive Processing

  • length
  • width
  • size
  • curvature
  • number
  • terminators
  • intersection
  • closure
  • hue
  • lightness
  • flicker
  • direction of motion
  • binocular lustre
  • stereoscopic depth
  • 3-D depth cues
  • lighting direction

67

slide-58
SLIDE 58

Stephen Few “Now You See It”

  • pg. 39

68

slide-59
SLIDE 59

Pre-Attentive à Cognitive

69

slide-60
SLIDE 60

Gestalt Psychology

Berlin, Early 1900s

70

slide-61
SLIDE 61

Gestalt Psychology

Goal was to understand pattern perception

Gestalt (German) = “seeing the whole picture all at once” instead of a collection of parts

Identified 8 “Laws of Grouping”

71

http://study.com/academy/lesson/gestalt-psychology-definition-principles-quiz.html

slide-62
SLIDE 62

Gestalt Psychology

  • 1. Proximity
  • 2. Similarity
  • 3. Closure
  • 4. Symmetry
  • 5. Common Fate
  • 6. Continuity
  • 7. Good Gestalt
  • 8. Past Experience

72

slide-63
SLIDE 63

How many groups are there?

73

slide-64
SLIDE 64

74

slide-65
SLIDE 65

Proximity

75

slide-66
SLIDE 66

How many groups are there?

76

slide-67
SLIDE 67

77

slide-68
SLIDE 68

Similarity

78

slide-69
SLIDE 69

How many shapes are there?

79

slide-70
SLIDE 70

80

slide-71
SLIDE 71

Closure

81

slide-72
SLIDE 72

How many items are there?

82

slide-73
SLIDE 73

( ) { } [ ]

83

slide-74
SLIDE 74

( ) { } [ ]

Symmetry

84

slide-75
SLIDE 75

How many sets are there?

85

slide-76
SLIDE 76

86

slide-77
SLIDE 77

87

Common Fate

slide-78
SLIDE 78

How many objects are there?

88

slide-79
SLIDE 79

89

slide-80
SLIDE 80

Continuity

90

slide-81
SLIDE 81

How many objects are there?

91

slide-82
SLIDE 82

92

slide-83
SLIDE 83

Good Gestalt

93

slide-84
SLIDE 84

What is this word?

94

slide-85
SLIDE 85

FLIGHT

95

slide-86
SLIDE 86

Past Experience

96

FLIGHT

slide-87
SLIDE 87

Pre-Attentive Processing Gestalt Laws

101

slide-88
SLIDE 88

Detect Quickly

102

slide-89
SLIDE 89

Detect quickly does NOT mean

detect accurately

Id Ideally you want both.

103

slide-90
SLIDE 90

104

Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.Heer and Bostock. Proc ACM Conf. Human Factors in Computing Systems (CHI) 2010, p. 203–212.

Positions Rectangular areas

(aligned or in a treemap)

Angles Circular areas

slide-91
SLIDE 91

105

Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.Heer and Bostock. Proc ACM Conf. Human Factors in Computing Systems (CHI) 2010, p. 203–212.

Crowdsourced Results

1.0 1.5 2.0 2.5 3.0 T1 T2 T3 T4 T5 T6 T7 T8 T9

Log Error

Positions Rectangular areas (aligned or in a treemap) Angles Circular areas
slide-92
SLIDE 92

Automating the Design of Graphical Presentations

l

125

More accurate Less accurate

I I

Position

IMll

1 I

Length

F-l

Iha I

I0.I

I I

Volume

rl

l¶kJ

Color

cl

mot

Shown)

  • Fig. 14. Accuracy ranking of quantitative

perceptual tasks. Higher tasks are accom- plished more accurately than lower tasks. Cleveland and McGill empirically verified the basic properties of this ranking. Quantitative Ordinal Nominal Position Position Color Saturation Position Color Hue Texture Connection Containment Density Color Saturation Color Saturation Shape Length Angle Slope Area Volume

  • Fig. 15. Ranking of perceptual tasks. The tasks shown in the gray boxes are not relevant to these

types of data.

An example analysis for area perception is shown in Figure 16. The top line shows that a series of decreasing areas can be used to encode a tenfold quantitative

  • range. Of course, in a real diagram such as Figure 13, the areas would be laid out

randomly, making it more difficult to judge the relative sizes of different areas accurately (hence, area is ranked fifth in Figure 14). Nevertheless, small mis- judgments about the size of an area only leads to small misperceptions about the corresponding quantitative value that is encoded. The middle line shows that area can encode three ordinal values. However, one must be careful to make sure

ACM Transactions

  • n Graphics, Vol. 5, No. 2, April

1986.

Mackinlay, 1986

106

slide-93
SLIDE 93

Stephen Few “Now You See It”

  • pg. 41

107

slide-94
SLIDE 94

What does this tell us?

108

slide-95
SLIDE 95

Barcharts, scatterplots, and line charts are really effective for quantitative data

20 40 20 40 20 40 20 40 20 40

109

slide-96
SLIDE 96

(and for statistical distributions) Tukey Box Plots

113

slide-97
SLIDE 97

Median Outliers Largest < Q3 + 1.5 IQR Smallest > Q1 - 1.5 IQR Largest < Q3 Smallest > Q1

114

slide-98
SLIDE 98

115

slide-99
SLIDE 99

Tufte’s Chart Principles

116

Edward Tufte

slide-100
SLIDE 100

Tufte’s Chart Principles

DO NOT LIE!

Maximize Data-Ink Ratio Minimize Chart Junk

119

slide-101
SLIDE 101

Tufte’s Chart Principles

DO NOT LIE!

Maximize Data-Ink Ratio Minimize Chart Junk

120

slide-102
SLIDE 102

121

slide-103
SLIDE 103

122

“Cumulative”

slide-104
SLIDE 104

http://www.perceptualedge.com/blog/?p=790

123

slide-105
SLIDE 105

124

http://xkcd.com/1138/

slide-106
SLIDE 106

Tufte’s Chart Principles

DO NOT LIE!

Maximize Data-Ink Ratio Minimize Chart Junk

126

slide-107
SLIDE 107

http://skilfulminds.com/2011/04/05/exploring-the-usefulness-of-chartjunk-at-stl-ux-2011/ 127

slide-108
SLIDE 108

128

  • Chartjunk. (2017, October 05). Retrieved December 01, 2017, from https://en.wikipedia.org/wiki/Chartjunk
slide-109
SLIDE 109

Please…

130

slide-110
SLIDE 110

No pie charts. No 2.5D charts.

131

slide-111
SLIDE 111

132

slide-112
SLIDE 112

37 37 36 36 24 24

2 1

133

slide-113
SLIDE 113

5 10 15 20 25 30 35 40

134

slide-114
SLIDE 114

135

slide-115
SLIDE 115

PL PLEASE ASE DON’ DON’T EVER EVER DO DO THI HIS!

136

slide-116
SLIDE 116

10 20 30 40

137

slide-117
SLIDE 117

But otherwise…

141

slide-118
SLIDE 118

Barcharts, scatterplots, and line charts are re really eff ffective for quantitative data

20 40 20 40 20 40 20 40 20 40

142

slide-119
SLIDE 119

Anyone else bored by my color choices?

143

slide-120
SLIDE 120

In fact, grayscale can be risky…

144

slide-121
SLIDE 121

In fact, grayscale can be risky…

145

slide-122
SLIDE 122

Color is Powerful

146

slide-123
SLIDE 123

Call attention to information Increase appeal Increase memorability Another dimension to work with

Color

147

slide-124
SLIDE 124

148

Have you heard of RGB?

RGB color model. (2017, November 20). Retrieved December 01, 2017, from https://en.wikipedia.org/wiki/RGB_color_model

Additive color model: colors create by mixing red, green, blue light

slide-125
SLIDE 125

We see in RGB, but we don’t interpret in RGB…

149

slide-126
SLIDE 126

150

Hue Lightness Saturation

Source: color picker in Affinity Designer

HSV Color Model

slide-127
SLIDE 127

Hue

Post & Greene, 1986

151

slide-128
SLIDE 128

Hue

http://blog.xkcd.com/2010/05/03/color-survey-results/

152

slide-129
SLIDE 129

Hue and Colorblindness

10% of males and 1% of females are Red-Green Colorblind

153

slide-130
SLIDE 130

154

slide-131
SLIDE 131

155

http://viz.wtf/post/98981561686/ht-matthewbgilmore-noaas-new-weather-modelling

slide-132
SLIDE 132

Color and Quantitative Data

Can you order these (lowàhi)?

157

slide-133
SLIDE 133

158

us

Binary Diverging Categorical Sequential Categorical Categorical

http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html via Munzner

slide-134
SLIDE 134

159

Color Brewer for Picking Color Scales

COLORBREWER 2.0. (n.d.). Retrieved December 01, 2017, from http://colorbrewer2.org/
slide-135
SLIDE 135

Overview Zoom+Filter Details on Demand

Shneiderman Mantra (Information-Seeking Mantra)

160

https://www.mat.ucsb.edu/g.legrady/academic/courses/11w259/schneiderman.pdf

slide-136
SLIDE 136

161

slide-137
SLIDE 137

http://visual.ly/every-single-death-game-thrones-series

162

slide-138
SLIDE 138

163

http://www.babynamewizard.com/voyager

slide-139
SLIDE 139

Where to learn more?

172

slide-140
SLIDE 140

CS 7450 Information Visualization Every Fall

173

slide-141
SLIDE 141

Visualization @GeorgiaTech

vis.gatech.edu

174

slide-142
SLIDE 142

How to Make Good Charts

  • Edward Tufte’s One-Day Workshop

– http://www.edwardtufte.com/tufte/courses

  • Edward Tufte, Visual Display of Quantitative

Information

– http://www.edwardtufte.com/tufte/books_vdqi

  • Stephen Few, Show Me the Numbers:

Designing Tables and Graphs to Enlighten

– http://www.amazon.com/Show-Me-Numbers- Designing- Enlighten/dp/0970601972/ref=la_B001H6IQ5M_1_ 2?s=books&ie=UTF8&qid=1385050724&sr=1-2

175

slide-143
SLIDE 143

Visualization Theory “Books”

  • Tamara Munzner VIS Tutorial and Book

– http://www.cs.ubc.ca/~tmm/talks.html – http://www.cs.ubc.ca/~tmm/vadbook/

  • Colin Ware, Information Visualization: Perception for Design

– http://www.amazon.com/Information-Visualization-Perception-Interactive- Technologies/dp/1558605118

  • Stephen Few, Now You See It

– http://www.amazon.com/Now-You-See-Visualization- Quantitative/dp/0970601980/ref=pd_bxgy_b_img_z

  • Edward Tufte, Envisioning Information

– http://www.edwardtufte.com/tufte/books_ei

  • Edward Tufte, Visual Explanations

– http://www.edwardtufte.com/tufte/books_visex

  • Edward Tufte, Beautiful Evidence

– http://www.edwardtufte.com/tufte/books_be

  • Tamara Munzner, Visualization Analysis & Design

– http://www.amazon.com/Visualization-Analysis-Design-AK- Peters/dp/1466508914

176

slide-144
SLIDE 144

Perception and Color Websites

  • Chris Healy, NC State

– http://www.csc.ncsu.edu/faculty/healey/PP/index.h tml

  • Color Brewer

– http://colorbrewer2.org/

  • Maureen C. Stone (Color Links, Blog,

Workshops)

– http://www.stonesc.com/color/index.htm

  • Subtleties of Color by Robert Simmon of

NASA

– http://blog.visual.ly/subtleties-of-color/

177

slide-145
SLIDE 145

Visualization Blogs

  • Flowing Data by Nathan Yau

– http://flowingdata.com/

  • Information Aesthetics by Andrew Vande Moere

– http://infosthetics.com/

  • Information is Beautiful by David McCandless

– http://www.informationisbeautiful.net/

  • Visual.ly Blog

– http://blog.visual.ly/

  • Indexed Comic by Jessica Hagy

– http://thisisindexed.com/

178

slide-146
SLIDE 146

Infographics

Visual.ly/view

(wtfviz.net)

179

slide-147
SLIDE 147

Thanks!

Chad Stolper

chadstolper@gatech.edu

180

slide-148
SLIDE 148

Questions?

Chad Stolper

chadstolper@gatech.edu

181

thisisindexed.com Jessica Hagy