Visualization Design and Analysis: Abstractions, Principles, and Methods

306 Pages·2012·63.26 MB·English

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Visualization Design and Analysis:
Abstractions, Principles, and Methods
Tamara Munzner
Department of Computer Science
University of British Columbia
(DRAFT – Saturday 1st September, 2012 – 23:35 – DRAFT
– DO NOT CITE)
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Contents
Preface xi
0.1 Why A New Book . . . . . . . . . . . . . . . . . . . . . . xi
0.2 Existing Books . . . . . . . . . . . . . . . . . . . . . . . . xii
0.3 Audience . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
0.4 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . xiv
I Introduction 1
1 Visualization Definition and Implications 3
1.1 A Human In The Loop . . . . . . . . . . . . . . . . . . . . 4
1.2 A Computer In The Loop . . . . . . . . . . . . . . . . . . 5
1.3 Visual Perception . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 An External Representation . . . . . . . . . . . . . . . . . 6
1.5 Show The Data . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6 Driving Task . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.7 The Meaning of Better . . . . . . . . . . . . . . . . . . . . 9
1.8 Interactivity . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.9 Resource Limitations . . . . . . . . . . . . . . . . . . . . . 10
1.10 Visualization Design Space . . . . . . . . . . . . . . . . . 12
1.11 Most Designs Ineffective . . . . . . . . . . . . . . . . . . . 13
1.12 Further Reading . . . . . . . . . . . . . . . . . . . . . . . 14
2 A Visualization Design Framework 17
2.1 Book Structure . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2 Four Levels of Design . . . . . . . . . . . . . . . . . . . . 22
2.2.1 Domain Problem and Data Characterization . . . 24
2.2.2 Task and Data Type Abstraction . . . . . . . . . . 24
2.2.3 Visual Encoding and Interaction Design . . . . . . 26
2.2.4 Algorithm Design. . . . . . . . . . . . . . . . . . . 26
2.3 Threats and Validation. . . . . . . . . . . . . . . . . . . . 27
2.3.1 Domain Threats . . . . . . . . . . . . . . . . . . . 29
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vi CONTENTS
2.3.2 Abstraction Threats . . . . . . . . . . . . . . . . . 29
2.3.3 Encoding and Interaction Threats . . . . . . . . . 30
2.3.4 Algorithm Threats . . . . . . . . . . . . . . . . . . 31
2.3.5 Mismatches . . . . . . . . . . . . . . . . . . . . . . 31
2.4 Example Gallery . . . . . . . . . . . . . . . . . . . . . . . 32
2.4.1 Genealogical Graphs . . . . . . . . . . . . . . . . . 32
2.4.2 MatrixExplorer . . . . . . . . . . . . . . . . . . . . 34
2.4.3 Flow Maps . . . . . . . . . . . . . . . . . . . . . . 36
2.4.4 LiveRAC . . . . . . . . . . . . . . . . . . . . . . . 36
2.4.5 LinLog. . . . . . . . . . . . . . . . . . . . . . . . . 38
2.4.6 Sizing the Horizon . . . . . . . . . . . . . . . . . . 39
2.5 Problem-Driven vs. Technique-Driven Work . . . . . . . . 41
2.6 Further Reading . . . . . . . . . . . . . . . . . . . . . . . 41
II Abstractions 43
3 Data Abstractions 47
3.1 Semantics vs. Types . . . . . . . . . . . . . . . . . . . . . 47
3.2 Attribute Types . . . . . . . . . . . . . . . . . . . . . . . 49
3.2.1 Categorical . . . . . . . . . . . . . . . . . . . . . . 50
3.2.2 Ordered: Ordinal and Quantitative . . . . . . . . . 50
3.2.3 Attribute Count . . . . . . . . . . . . . . . . . . . 51
3.2.4 Hierarchical Attributes. . . . . . . . . . . . . . . . 51
3.3 Dataset Types . . . . . . . . . . . . . . . . . . . . . . . . 51
3.3.1 Tables . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.3.2 Networks and Trees . . . . . . . . . . . . . . . . . 53
3.3.3 Text and Logs . . . . . . . . . . . . . . . . . . . . 54
3.3.4 Static Files vs. Dynamic Streams . . . . . . . . . . 54
3.4 Attribute Semantics . . . . . . . . . . . . . . . . . . . . . 55
3.4.1 Spatial vs. Nonspatial . . . . . . . . . . . . . . . . 56
3.4.2 Temporal vs. Nontemporal . . . . . . . . . . . . . 56
3.4.3 Key vs. Value. . . . . . . . . . . . . . . . . . . . . 57
3.4.4 Continuous vs. Discrete . . . . . . . . . . . . . . . 59
3.5 Dataset Semantics . . . . . . . . . . . . . . . . . . . . . . 59
3.5.1 Spatial vs. Abstract . . . . . . . . . . . . . . . . . 59
3.5.2 Timevarying vs. Static . . . . . . . . . . . . . . . . 60
3.6 Derived and Transformed Data . . . . . . . . . . . . . . . 61
3.6.1 Derived Attributes . . . . . . . . . . . . . . . . . . 61
3.6.2 Derived Spaces . . . . . . . . . . . . . . . . . . . . 63
3.6.2.1 Strahler Numbers for Trees.. . . . . . . . 63
3.6.2.2 Feature Detection in Fluid Dynamics. . . 64
3.6.2.3 Graph-Theoretic Scagnostics. . . . . . . . 65
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CONTENTS vii
3.7 Data Abstraction . . . . . . . . . . . . . . . . . . . . . . . 70
3.8 Further Reading . . . . . . . . . . . . . . . . . . . . . . . 70
4 Task Abstractions 73
4.1 Further Reading . . . . . . . . . . . . . . . . . . . . . . . 74
III Principles 75
5 Visual Encoding Principles 79
5.1 Relative vs. Absolute Judgements . . . . . . . . . . . . . 79
5.2 Marks and Channels . . . . . . . . . . . . . . . . . . . . . 82
5.2.1 Marks . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.2.2 Channels . . . . . . . . . . . . . . . . . . . . . . . 82
5.2.3 Using Marks and Channels . . . . . . . . . . . . . 82
5.2.4 Channel Types . . . . . . . . . . . . . . . . . . . . 84
5.2.5 Mark Types . . . . . . . . . . . . . . . . . . . . . . 84
5.2.6 Expressiveness and Effectiveness . . . . . . . . . . 84
5.2.7 Channel Rankings . . . . . . . . . . . . . . . . . . 85
5.3 Channel Effectiveness . . . . . . . . . . . . . . . . . . . . 87
5.3.1 Accuracy . . . . . . . . . . . . . . . . . . . . . . . 87
5.3.2 Discriminability. . . . . . . . . . . . . . . . . . . . 89
5.3.3 Separability . . . . . . . . . . . . . . . . . . . . . . 89
5.3.4 Popout . . . . . . . . . . . . . . . . . . . . . . . . 92
5.4 Channel Characteristics . . . . . . . . . . . . . . . . . . . 94
5.4.1 Planar Position . . . . . . . . . . . . . . . . . . . . 94
5.4.2 Color . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.4.2.1 Color Vision Processes . . . . . . . . . . 95
5.4.2.2 Color Spaces . . . . . . . . . . . . . . . . 95
5.4.2.3 Luminance . . . . . . . . . . . . . . . . . 98
5.4.2.4 Saturation . . . . . . . . . . . . . . . . . 98
5.4.2.5 Hue . . . . . . . . . . . . . . . . . . . . . 99
5.4.3 Size . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.4.4 Tilt/Angle . . . . . . . . . . . . . . . . . . . . . . 101
5.4.5 Shape . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.4.6 Stipple . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.4.7 Curvature . . . . . . . . . . . . . . . . . . . . . . . 102
5.4.8 Motion . . . . . . . . . . . . . . . . . . . . . . . . 102
5.5 Further Reading . . . . . . . . . . . . . . . . . . . . . . . 103
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6 Dynamic Design Principles 105
6.1 Classes of Change . . . . . . . . . . . . . . . . . . . . . . 105
6.1.1 Changing Selection . . . . . . . . . . . . . . . . . . 105
6.1.2 Changing Highlighting . . . . . . . . . . . . . . . . 106
6.1.3 Changing Viewpoint: Navigating . . . . . . . . . . 106
6.1.4 Changing Spatial Ordering: Sorting . . . . . . . . 106
6.1.5 Changing Visual Encoding . . . . . . . . . . . . . 107
6.2 Latency and Feedback . . . . . . . . . . . . . . . . . . . . 107
6.2.1 Visual Feedback . . . . . . . . . . . . . . . . . . . 108
6.2.2 Latency and Interaction Design . . . . . . . . . . . 108
6.3 Interactivity Costs . . . . . . . . . . . . . . . . . . . . . . 109
6.4 Memory and Attention . . . . . . . . . . . . . . . . . . . . 110
6.5 Spatial Cognition . . . . . . . . . . . . . . . . . . . . . . . 110
6.6 Further Reading . . . . . . . . . . . . . . . . . . . . . . . 112
IV Methods 115
7 Making Views 119
7.1 Using Space . . . . . . . . . . . . . . . . . . . . . . . . . . 121
7.1.1 Quantitative Values: Express . . . . . . . . . . . . 121
7.1.2 Categorical Regions: Separate, Order, and Align . 123
7.1.3 Keys . . . . . . . . . . . . . . . . . . . . . . . . . . 124
7.1.3.1 1 Key: List . . . . . . . . . . . . . . . . . 124
7.1.3.2 2 Keys: Matrix . . . . . . . . . . . . . . . 125
7.1.3.3 Multiple Keys: Partition and Subdivide . 129
7.1.4 Value Attributes . . . . . . . . . . . . . . . . . . . 134
7.1.5 Given: Use . . . . . . . . . . . . . . . . . . . . . . 136
7.1.5.1 1 Value: Scalar Fields . . . . . . . . . . . 136
7.1.5.2 2 Values: Vector Fields . . . . . . . . . . 137
7.1.5.3 3+ Values: Tensor Fields . . . . . . . . . 137
7.1.5.4 Geographic Data . . . . . . . . . . . . . . 137
7.1.6 Spatial Arrangement . . . . . . . . . . . . . . . . . 137
7.1.6.1 Rectilinear Layouts . . . . . . . . . . . . 138
7.1.6.2 Parallel Layouts . . . . . . . . . . . . . . 138
7.1.6.3 Radial Layouts: Position and Angle . . . 142
7.1.7 Spacefilling . . . . . . . . . . . . . . . . . . . . . . 144
7.1.8 Dense . . . . . . . . . . . . . . . . . . . . . . . . . 145
7.2 Link Marks . . . . . . . . . . . . . . . . . . . . . . . . . . 147
7.2.1 Connection . . . . . . . . . . . . . . . . . . . . . . 147
7.2.2 Containment . . . . . . . . . . . . . . . . . . . . . 156
7.3 Using Color . . . . . . . . . . . . . . . . . . . . . . . . . . 161
7.3.0.1 Categorical Colormaps . . . . . . . . . . 162
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7.3.0.2 Ordered Colormaps: Sequential and Di-
verging . . . . . . . . . . . . . . . . . . . 162
7.3.0.3 Bivariate Colormaps . . . . . . . . . . . . 166
7.3.0.4 Colorblind-safe Colormap Design . . . . . 166
7.4 Combining Views . . . . . . . . . . . . . . . . . . . . . . . 167
7.5 Coordinating Multiple Side-by-Side Views . . . . . . . . . 168
7.5.1 Encoding Channels Shared . . . . . . . . . . . . . 168
7.5.2 Data Shared . . . . . . . . . . . . . . . . . . . . . 170
7.5.3 Navigation Synchronized. . . . . . . . . . . . . . . 176
7.5.4 Views Linked With Marks . . . . . . . . . . . . . . 176
7.5.5 Combinations . . . . . . . . . . . . . . . . . . . . . 177
7.6 Superimposing Layers . . . . . . . . . . . . . . . . . . . . 179
7.6.1 Static Layers . . . . . . . . . . . . . . . . . . . . . 180
7.6.2 Dynamic Layers . . . . . . . . . . . . . . . . . . . 184
7.7 Further Reading . . . . . . . . . . . . . . . . . . . . . . . 186
8 Reducing Items and Attributes 189
8.1 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
8.1.1 Filtering Items . . . . . . . . . . . . . . . . . . . . 191
8.1.2 Attribute Filtering . . . . . . . . . . . . . . . . . . 194
8.2 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . 196
8.2.1 Item Aggregation . . . . . . . . . . . . . . . . . . . 196
8.2.2 Attribute Aggregation . . . . . . . . . . . . . . . . 205
8.3 Navigation. . . . . . . . . . . . . . . . . . . . . . . . . . . 209
8.3.1 Zooming . . . . . . . . . . . . . . . . . . . . . . . . 209
8.3.1.1 Geometric Zooming. . . . . . . . . . . . . 209
8.3.1.2 Semantic Zooming. . . . . . . . . . . . . 209
8.3.2 Panning . . . . . . . . . . . . . . . . . . . . . . . . 211
8.3.3 Combined Navigation . . . . . . . . . . . . . . . . 211
8.3.4 Constrained Navigation. . . . . . . . . . . . . . . . 213
8.3.5 Animated Transitions . . . . . . . . . . . . . . . . 214
8.4 Camera-Oriented Attribute Reduction . . . . . . . . . . . 215
8.4.1 Slicing and Cutting . . . . . . . . . . . . . . . . . 215
8.4.2 Projection . . . . . . . . . . . . . . . . . . . . . . . 217
8.5 Focus+Context . . . . . . . . . . . . . . . . . . . . . . . . 218
8.5.1 Selective Filtering . . . . . . . . . . . . . . . . . . 219
8.5.1.1 Elision and Aggregation . . . . . . . . . . 220
8.5.1.2 Layering . . . . . . . . . . . . . . . . . . 220
8.5.2 Geometric Distortion. . . . . . . . . . . . . . . . . 220
8.5.2.1 3D Perspective. . . . . . . . . . . . . . . 221
8.5.2.2 Fisheye Lenses.. . . . . . . . . . . . . . . 221
8.5.2.3 Hyperbolic Geometry. . . . . . . . . . . . 222
8.5.2.4 Stretch and Squish Navigation. . . . . . . 224
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8.5.2.5 Magnification Fields. . . . . . . . . . . . 225
8.6 Further Reading . . . . . . . . . . . . . . . . . . . . . . . 226
9 Case Studies 229
9.1 VisDB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
9.2 Hierarchical Clustering Explorer . . . . . . . . . . . . . . 233
9.3 PivotGraph . . . . . . . . . . . . . . . . . . . . . . . . . . 238
9.4 InterRing . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
9.5 Rivet PView . . . . . . . . . . . . . . . . . . . . . . . . . 243
9.6 Constellation . . . . . . . . . . . . . . . . . . . . . . . . . 244
9.7 TODO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
9.8 Further Reading . . . . . . . . . . . . . . . . . . . . . . . 246
10 Guidelines and Slogans 247
10.1 Function First, Form Next . . . . . . . . . . . . . . . . . . 248
10.2 No Unjustified 3D . . . . . . . . . . . . . . . . . . . . . . 249
10.2.1 The Power of the Plane, The Danger of Depth . . 249
10.2.2 Occlusion Hides Information . . . . . . . . . . . . 251
10.2.3 PerspectiveDistortionUndercutsPowerofthePlane 252
10.2.4 Other Depth Cues . . . . . . . . . . . . . . . . . . 253
10.2.5 Tilted Text Isn’t Legibile . . . . . . . . . . . . . . 254
10.2.6 3D Benefits . . . . . . . . . . . . . . . . . . . . . . 255
10.2.7 Justification and Alternatives . . . . . . . . . . . . 255
10.2.8 Empirical Evidence . . . . . . . . . . . . . . . . . . 259
10.3 Eyes Beat Memory: Side-by-Side vs. Temporal Change . 261
10.3.1 Animation vs. Side-By-Side Views . . . . . . . . . 261
10.3.2 Change Blindness. . . . . . . . . . . . . . . . . . . 262
10.4 Pixels are Precious: Resolution Over Immersion . . . . . . 263
10.5 Overview First, Zoom and Filter, Detail on Demand . . . 264
10.6 Focus+Context Distortion Is Costly . . . . . . . . . . . . 264
10.7 Networks: Connection vs. Containment vs. Matrix . . . . 267
10.8 Further Reading . . . . . . . . . . . . . . . . . . . . . . . 271
Bibliography 273
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Preface
0.1 Why A New Book
This book was written to scratch my own itch: the book I wanted to
teach out of for my graduate infovis course did not exist. The itch grew
through the years of teaching my own course at UBC seven times, co-
teaching a course at Stanford in 2001, and helping with the design of an
early visualization course at Stanford in 1996 as a teaching assistant.
Iwasdissatisfiedwithteachingprimarilyfromoriginalresearchpapers.
While it is very useful for graduate students to learn to read papers, what
was missing was a synthesis view and a framework to guide thinking. The
principlesandmethodsthatIintendedaparticularpapertoillustratewere
often only indirectly alluded to in the paper itself. Even after assigning
many papers or book chapters as preparatory reading before each lecture,
Iwasfrustratedbythemanymajorgapsintheideasdiscussed. Moreover,
the reading load was so heavy that it was impossible to fit in any design
exercises along the way, so the students only gained direct experience as
designers in a single monolithic final project.
I was also dissatisfied with the lecture structure of my own course be-
cause of a problem shared by nearly every other course in the field: an
incoherentapproachtocrosscuttingthesubjectmatter. Coursesthatlurch
from one set of crosscuts to another are intellectually unsatisfying in that
theymakevisualizationseemlikeagrab-bagofassortedtopicsratherthan
a field with a unifying theoretical framework. There are at least four ma-
jor ways to crosscut visualization material. One is by the field from which
we draw techniques: cognitive science for perception and color, human-
computer interaction for user studies and user-centered design, computer
graphics for rendering, and so on. Another is by the problem domain
addressed: biology vs. software engineering vs. computer networking vs.
medicinevs. social/casualuse. Yetanotherisbythefamiliesoftechniques:
focus+context vs. overview/detail vs. volume rendering vs. statistical
graphics. Finally, evaluation is an important and central topic that should
beinterwoventhroughout,butitdidnotfitintothestandardpipelinesand
models. It was typically relegated to a single lecture, usually near the end,
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xii Preface
so that it felt like an afterthought.
Thestructureofthisbookisinspiredbyanestedmodelofvisualization
thatunifiesdesignandevaluationissuesinasingleframework. Itsplitsthe
design issues into four levels of concerns: problem characterization, data
and task abstraction, visual encoding and interaction techniques suitable
forthechosenabstractions,andalgorithmstoinstantiatethosetechniques.
ThebookbeginswithabstractionoftasksanddatainPartII,followedbya
set of principles of visual encoding and interaction in Part III. These prin-
ciples do draw on other fields including perception and human-computer
interaction, but the focus is on the underpinnings needed to understand
visualization methods rather than these fields for their own sake. At the
book’s core in Part IV is a taxonomy of methods that encompasses the
design space of encoding and interaction techniques. These methods are a
general framework through which any specific technique can be analyzed,
starting from the highest-level categories of the construction of views and
the reduction of data. Many techniques and systems illustrate the taxon-
omy,andseveralmorecasestudiesareanalyzedafterthefullsetofmethods
is covered.
Thisbookfocusesonanalysisanddesignattheabstractionandencod-
ing/interactiontechniquelevelsofthemodel. Ihaveleftoutalgorithmsfor
reasonsofspaceandtime,notofinterest–todiscussthematausefullevel
of detail would lead to a book that is an order of magnitude longer. Also,
manygoodresourcesexisttolearnaboutalgorithms,includingoriginalpa-
persandsomeofthepreviousbooksdiscussedbelow. Incontrast,previous
sources didn’t provide enough of a framework for analysis, particularly at
the abstraction level.
0.2 Existing Books
Visualization is a young field and there are not many books that provide a
synthesisviewofthefield. Thefewbooksthatexistallfellshortindifferent
ways. Tufte is a curator of glorious examples [Tufte 83,Tufte 91,Tufte 97],
buthefocusesonwhatcanbedoneonthestaticprintedpageforpurposes
of exposition. The hallmark of the last twenty years of computer-based
visualizationisinteractivityratherthansimplystaticpresentation,andthe
useofvisualizationforexplorationoftheunknowninadditiontoexposition
of the known. Tufte does not address these topics, so while I use them as
supplementary material, I find they cannot serve as the backbone for my
own visualization course. However, any or all of them would work well as
supplementary reading for a course structured around this book; my own
favorite for this role is Envisioning Information [Tufte 91].
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Preface xiii
Some instructors use the reader by Card, Mackinlay and Shneider-
man [Card et al. 99]. Most of the book is a collection of seminal papers,
and thus shares the same problem as directly reading original papers: a
lack of synthesis. The first chapter provides a useful synthesis view of the
field, butitisonlyonechapter. Thisbookisanattempttowriteanentire
book along those lines, and one that is informed by the wealth of progress
in our field in the past dozen years.
Ware has written a thorough book on visualization design as seen
through the lens of perception [Ware 04], and I have used it as the back-
bone for my own course for many years. While it discusses many issues
on how one could design a visualization, it does not cover what has been
doneinthisfieldforthepastfifteenyearsfromasynthesispointofview. I
wantedabookthatallowsabeginningstudenttolearnfromthiscollective
experience rather than starting from scratch. This book does not attempt
toteachthewonderfultopicofperceptionperse,coveringonlytheaspects
directlyneededtogetstartedwithvisualizationandleavingtherestasfur-
therreading. Ware’sshorterbookonVisualThinkingForDesign[Ware08]
wouldbeexcellentsupplementalreadingforacoursestructuredaroundthis
book.
The recent book of Ward, Grinstein, and Keim [Ward et al. 10] works
fromthebottomupwithalgorithmsasthebase,whereasIwantedtowork
fromthetopdownfromthelevelsabovethealgorithmic. Thisbookoffersa
considerablymoreextensivemodelandframeworkthanSpence[Spence07].
Wilkinson’s Grammar of Graphics [Wilkinson 05] is a deep and thoughtful
work, but is dense enoughthat it is moresuitable for visualization insiders
thanforbeginners. Conversely,Few’sbook[Few04]isextremelyapproach-
able and has been used at the undergraduate level, but the scope is much
more limited than the coverage of this book.
0.3 Audience
Theprimaryaudienceofthisbookisstudentsinafirstvisualizationcourse,
particularly at the graduate level. While admittedly written from a com-
puter scientist’s point of view, the book aims to be accessible not only to
those with a computer science background but also those without, such as
studentsinageographydepartmentorinaschoolofinformationorlibrary
science. Other audiences are people from other fields with an interest in
visualization who would like to understand the principles and methods of
this field, and practitioners in the field who might use it as a reference
for a more formal analysis and improvements of production visualization
applications.
This book is intended for people with an interest in the design and
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