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

▶
information visualization
SMART_READER_LITE
LIVE PREVIEW

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

Feb 27, 2023 168 likes •1.67k views

Class Website CX4242 Information Visualization Crash Course (AKA Information Visualization 101) Chad Stolper Google (graduated from Georgia Tech CS PhD) 1 What is Infovis? Why is it Important? Human Perception Chart Basics (If Time,

slide-1
SLIDE 1

Information Visualization Crash Course

Chad Stolper Google

(graduated from Georgia Tech CS PhD)

1

(AKA Information Visualization 101)

Class Website

CX4242

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 computer-supported, interactive, visual representations of abstract data to amplify cognition.” Card, Mackinlay, and Shneiderman 1999

4
slide-5
SLIDE 5

Communication Exploratory Data Analysis (EDA)

5
slide-6
SLIDE 6

Communication

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

Space Shuttle Challenger

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 extremely important.

Visualization can help with that – communicate ideas and insights.

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 Exploratory Data Analysis (EDA)

But why do you need to explore data at all???

31
slide-24
SLIDE 24

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

33

https://en.wikipedia.org/wiki/Lies,_damned_lies,_and_statistics

slide-25
SLIDE 25

Mystery Data Set

34
slide-26
SLIDE 26

Mystery Data Set

Property 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 human perception

43
slide-34
SLIDE 34

Name the five senses.

44
slide-35
SLIDE 35 45

Sense Bandwidth (bits/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 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  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

  • nce” 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 95

CLIP

slide-86
SLIDE 86

Past Experience

96

CLIP

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

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.
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.

significantly find McGill’ confident “Squar ified” McGill’ “quick ” ⇥ first

Crowdsourced Results

1.0 1.5 2.0 2.5 3.0

Log Error

McGill’ confidence confidence

⇥ squarified ’ modified ified qualification April 10–15, 2010, Atlanta, GA, USA

slide-92
SLIDE 92

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

110
slide-97
SLIDE 97 111
slide-98
SLIDE 98

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

112
slide-99
SLIDE 99

Tufte’s Chart Principles

113

Edward Tufte

slide-100
SLIDE 100

Tufte’s Chart Principles

DO NOT LIE!

Maximize Data-Ink Ratio Minimize Chart Junk

116
slide-101
SLIDE 101

Tufte’s Chart Principles

DO NOT LIE!

Maximize Data-Ink Ratio Minimize Chart Junk

117
slide-102
SLIDE 102 118
slide-103
SLIDE 103 119

“Cumulative”

slide-104
SLIDE 104

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

120
slide-105
SLIDE 105 121 http://xkcd.com/1138/
slide-106
SLIDE 106

Tufte’s Chart Principles

DO NOT LIE!

Maximize Data-Ink Ratio Minimize Chart Junk

123
slide-107
SLIDE 107 http://skilfulminds.com/2011/04/05/exploring-the-usefulness-of-chartjunk-at-stl-ux-2011/ 124
slide-108
SLIDE 108 125
  • Chartjunk. (2017, October 05). Retrieved December 01, 2017, from https://en.wikipedia.org/wiki/Chartjunk
slide-109
SLIDE 109

Please…

127
slide-110
SLIDE 110

No pie charts. No 2.5D charts.

128
slide-111
SLIDE 111 129
slide-112
SLIDE 112

37 36 24

2 1

130
slide-113
SLIDE 113

5 10 15 20 25 30 35 40

131
slide-114
SLIDE 114 132
slide-115
SLIDE 115

PLEASE DON’T EVER DO THIS!

133
slide-116
SLIDE 116

10 20 30 40

134
slide-117
SLIDE 117

But otherwise…

138
slide-118
SLIDE 118

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

20 40 20 40 20 40 20 40 20 40

139
slide-119
SLIDE 119

Anyone else bored by my color choices?

140
slide-120
SLIDE 120

In fact, grayscale can be risky…

141
slide-121
SLIDE 121

In fact, grayscale can be risky…

142
slide-122
SLIDE 122

Color is Powerful

143
slide-123
SLIDE 123

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

Color

144
slide-124
SLIDE 124 145

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…

146
slide-126
SLIDE 126 147

Hue Lightness Saturation

Source: color picker in Affinity Designer

HSV Color Model

slide-127
SLIDE 127

Hue

Post & Greene, 1986

148
slide-128
SLIDE 128

Hue

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

149
slide-129
SLIDE 129

Hue and Colorblindness

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

150
slide-130
SLIDE 130 151
slide-131
SLIDE 131 152

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

slide-132
SLIDE 132

Color and Quantitative Data

Can you order these (lowhi)?

154
slide-133
SLIDE 133 155

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

slide-134
SLIDE 134 156

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)

157

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

slide-136
SLIDE 136 158
slide-137
SLIDE 137

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

159
slide-138
SLIDE 138 160

http://www.babynamewizard.com/voyager

slide-139
SLIDE 139

Where to learn more?

169
slide-140
SLIDE 140

CS 7450 Information Visualization Every Fall

170
slide-141
SLIDE 141

Visualization @GeorgiaTech

vis.gatech.edu

171
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

172
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

173
slide-144
SLIDE 144

Perception and Color Websites

  • Chris Healy, NC State

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

  • 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/

174
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/

175
slide-146
SLIDE 146

Infographics

Visual.ly/view

(wtfviz.net)

176
slide-147
SLIDE 147

Thanks!

Chad Stolper

chadstolper@gatech.edu

177
slide-148
SLIDE 148

Questions?

Chad Stolper

chadstolper@gatech.edu

178

thisisindexed.com Jessica Hagy