Information Visualization Crash Course
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- Lucinda George
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1 Information Visualization Crash Course (AKA Information Visualization 101) Chad Stolper Assistant Professor Southwestern University (graduated from Georgia Tech CS PhD) 1
2 What is Infovis? Why is it Important? Human Perception Chart Basics (If Time, Some Color Theory) The Shneiderman Mantra Where to Learn More 9
3 Questions Encouraged! 10
4 What is Information Visualization? 12
5 Information Visualization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition. Card, Mackinlay, and Shneiderman
6 Communication Exploratory Data Analysis 14
7 Communication 15
8 Communication Gone Wrong 16
9 17
10 Space Shuttle Challenger January 28,
11 Morning Temperature: 31 F 19
12 What happened? 20
13 21
14 22
15 23
16 24
17 How did this happen? 25
18 Morton Thiokol s Presentation 26
19 27
20 28
21 29
22 30
23 31
24 36
25 37
26 38
27 39
28 40
29 So, communication is extremely important. Visualization can help with that. 41
30 42
31 Visualization can also help with Exploratory Data Analysis (EDA) 43
32 There are three kinds of lies: lies, damned lies, and statistics. 44
33 Mystery Data Set 45
34 Mystery Data Set Property Value mean( x ) 9 variance ( x ) 11 mean( y ) 7.5 variance ( y ) correlation ( x,y ) Linear Regression Line y = x 46
35 47
36 48
37 49
38 50
39 51
40 Anscombe s Quartet 52
41 Anscombe s Quartet Sanity Checking Models Outlier Detection 53
42 Anscombe s Quartet Sanity Checking Models Outlier Detection 54
43 Anscombe s Quartet Sanity Checking Models Outlier Detection 55
44 Anscombe s Quartet Sanity Checking Models Outlier Detection 56
45 Human Perception 58
46 Name the five senses. 60
47 Sense Bandwidth (bits/sec) Sight 10,000,000 Touch 1,000,000 Hearing 100,000 Smell 100,000 Taste 1,
48 A (Simple) Model of Human Visual Perception 62
49 A (Simple) Model of Human Perception Stage 1 Stage 2 Parallel detection of basic features into an iconic store Serial processing of object identification and spatial layout 63
50 Stage 1: Pre-Attentive Processing Rapid Parallel Automatic (Fleeting) 64
51 Stage 2: Serial Processing Relatively Slow (Incorporates Memory) Manual 65
52 Stage 1: Pre-Attentive Processing The eye moves every 200ms 66
53 Stage 1: Pre-Attentive Processing The eye moves every 200ms (so this processing occurs every 200ms-250ms) 67
54 Example
55 Example
56 A few more examples from Prof. Chris Healy at NC State 70
57 Left Side Right Side 71
58 Raise your hand if a RED DOT is present 72
59 73
60 74
61 Color (hue) is pre-attentively processed. 75
62 Raise your hand if a RED DOT is present 76
63 77
64 78
65 Shape is pre-attentively processed. 79
66 Determine if a RED DOT is present 80
67 81
68 82
69 Hue and shape together are NOT pre-attentively processed. 83
70 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 84
71 Stephen Few Now You See It pg
72 Pre-Attentive à Cognitive 86
73 Gestalt Psychology Berlin, Early 1900s 87
74 Gestalt Psychology Goal was to understand pattern perception Gestalt (German) = seeing the whole picture all at once Identified 8 Laws of Grouping 88
75 Gestalt Psychology 1. Proximity 2. Similarity 7. Good Gestalt 8. Past Experience 3. Closure 4. Symmetry 5. Common Fate 6. Continuity 89
76 How many groups are there? 90
77 91
78 Proximity 92
79 How many groups are there? 93
80 94
81 Similarity 95
82 How many shapes are there? 96
83 97
84 Closure 98
85 How many items are there? 99
86 [ ] { } [ ] 100
87 Symmetry [ ] { } [ ] 101
88 How many sets are there? 102
89 103
90 Common Fate 104
91 How many objects are there? 105
92 106
93 Continuity 107
94 How many objects are there? 108
95 109
96 Good Gestalt 110
97 What is this word? (Please Shout) 111
98 FLICK 112
99 Past Experience FLICK 113
100 Past Experience FLICK 114
101 Pre-Attentive Processing Gestalt Laws 115
102 Detect Quickly 116
103 Detect Quickly Detect Accurately 117
104 Angles Positions Circular areas Rectangular areas (aligned or in a treemap) Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.Heer and Bostock. Proc ACM Conf. Human Factors in Computing Systems (CHI) 2010, p
105 Crowdsourced Results Positions Angles Circular areas Rectangular areas (aligned or in a treemap) Log Error Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.Heer and Bostock. Proc ACM Conf. Human Factors in Computing Systems (CHI) 2010, p
106 More accurate I Position I IMll 1 I Length F-l Less accurate Iha I0.I I rl l kj cl Color mot Shown) Volume I I Mackinlay,
107 Stephen Few Now You See It pg
108 What does this tell us? 122
109 Barcharts, scatterplots, and line charts are really effective for quantitative data
110
111
112
113 (and for statistical distributions) Tukey Box Plots 127
114 Outliers Largest < Q IQR Largest < Q3 Median Smallest > Q1 Smallest > Q1-1.5 IQR 128
115 129
116 Tufte s Chart Principles 130
117 Edward Tufte 131
118 Edward Tufte 132
119 Tufte s Chart Principles DO NOT LIE! Maximize Data-Ink Ratio Minimize Chart Junk 133
120 Tufte s Chart Principles DO NOT LIE! Maximize Data-Ink Ratio Minimize Chart Junk 134
121 135
122 136
123 137
124 138
125
126 Tufte s Chart Principles DO NOT LIE! Maximize Data-Ink Ratio Minimize Chart Junk 140
127 141
128
129 Please 143
130 No pie charts. No 2.5D charts. 144
131 145
132
133
134 148
135 PLEASE DON T EVER DO THIS! 149
136
137 Two times to use a pie chart 151
138
139
140 But otherwise 154
141 Barcharts, scatterplots, and line charts are really effective for quantitative data
142 Anyone else bored by my color choices? 156
143 In fact, grayscale can be risky 157
144 In fact, grayscale can be risky 158
145 Color is Powerful 159
146 Color Call attention to information Increase appeal Increase memorability Another dimension to work with 160
147 How many of you have heard of RGB? 161
148 162
149 We see in RGB, but we don t interpret in RGB 163
150 How many have heard of HSV? 164
151 HSV Color Model Hue/ Color Saturation/Chroma Value/Lightness 165
152 166
153 Hue Post & Greene,
154 Hue 168
155 Hue and Colorblindness 10% of males and 1% of females are Red-Green Colorblind 170
156 171
157 176
158 Color and Quantitative Data Gray scale Full spectral scale Single sequence part spectral scale Single sequence single hue scale Double-ended multiple hue scale 177
159 Color and Quantitative Data Can you order these (lowàhi)? 178
160 Binary Categorical Categorical Categorical Diverging Sequential via Munzner 179
161 Color Scales Color Brewer 180
162 Overview Zoom+Filter Details on Demand Shneiderman Mantra (Information-Seeking Mantra) 181
163 182
164 183
165 184
166 185
167 186
168 187
169 188
170 Where to learn more? 193
171 CS 7450 Information Visualization Every Fall 194
172 vis.gatech.edu 195
173 How to Make Good Charts Edward Tufte s One-Day Workshop Edward Tufte, Visual Display of Quantitative Information Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten Designing- Enlighten/dp/ /ref=la_B001H6IQ5M_1_ 2?s=books&ie=UTF8&qid= &sr=
174 Visualization Theory Books Tamara Munzner VIS Tutorial and Book Colin Ware, Information Visualization: Perception for Design Technologies/dp/ Stephen Few, Now You See It Quantitative/dp/ /ref=pd_bxgy_b_img_z Edward Tufte, Envisioning Information Edward Tufte, Visual Explanations Edward Tufte, Beautiful Evidence Tamara Munzner, Visualization Analysis & Design Peters/dp/
175 Perception and Color Websites Chris Healy, NC State tml Color Brewer Maureen C. Stone (Color Links, Blog, Workshops) Subtleties of Color by Robert Simmon of NASA 198
176 Visualization Blogs Flowing Data by Nathan Yau Information Aesthetics by Andrew Vande Moere Information is Beautiful by David McCandless Visual.ly Blog Indexed Comic by Jessica Hagy 199
177 Infographics Visual.ly/view (wtfviz.net) 200
178 Thanks! Chad Stolper 201
179 Jessica Hagy thisisindexed.com Questions? Chad Stolper 202
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