Foundations of Data Visualization

Foreword......Page 5
Preface......Page 7
Acknowledgements......Page 11
Contents......Page 13
Contributors......Page 15
Part I Theoretical Underpinnings of Data Visualization......Page 18
1.1 Visualization: Definition and Essential Aspects......Page 22
1.1.1 Terminology......Page 24
1.2.1 A Trajectory: User- and Activity-Centered Design......Page 25
1.3 Evaluation of a Theory Component......Page 28
References......Page 30
2.1 Definitions......Page 32
2.2.1 Abstraction in the Arts......Page 34
2.2.3 Abstraction in Shape Analysis......Page 35
2.2.5 Abstract Thinking......Page 36
2.2.7 Abstraction Ontology......Page 37
2.3 Abstraction for Visualization......Page 38
2.3.1 Task Abstraction......Page 39
2.4 Visual Abstraction......Page 40
2.4.1 Meaningful Abstraction......Page 42
2.4.2 Abstraction Axes and Abstraction Spaces......Page 43
2.5 An Information-Theoretic Analysis of Abstraction  as a Process......Page 45
2.6 Summary......Page 52
References......Page 53
3.1 Introduction......Page 55
3.2 Measurement in Science......Page 58
3.3 Types of Visualization Measures......Page 59
3.3.1 Measures of Perceptual Characteristics......Page 60
3.3.2 Task-Oriented Quality Measures......Page 62
3.3.3 Structure-Oriented Measures......Page 64
3.3.4 Meta-Perceptual Process Measures......Page 67
3.4 Toward a ``Bigger Picture\'\'......Page 69
References......Page 72
4.1 Introduction......Page 76
4.2.1 Definitions......Page 78
4.2.2 Models of Knowledge-Assisted Visualization......Page 79
4.2.3 Examples......Page 81
4.3 The Importance of Knowledge in Data Analysis: An Information-Theoretic Perspective......Page 86
4.4 Ontologies as External Knowledge Bases......Page 88
4.5.1 Guidance......Page 91
4.5.2 Guidance-Enriched Visual Analytics......Page 92
4.5.3 A Model for Guidance-Enriched Visual Analytics......Page 93
4.5.4 Example of Guidance-Enriched VA: Cyclical Patterns in Univariate, Evenly Spaced Time-Series......Page 95
4.6 Conclusions and Critical Reflection......Page 96
References......Page 97
5 Mathematical Foundations in Visualization......Page 101
5.1 Data and Basic Terminology......Page 102
5.1.1 The Structure mathcalS......Page 103
5.1.2 The Attribute Space mathcalA......Page 104
5.2 Differential Structures......Page 105
5.2.1 Differential Operators......Page 106
5.2.2 Differential Equations......Page 108
5.2.3 Differential Geometry......Page 110
5.2.4 Manifolds......Page 112
5.3.1 Data Representation......Page 113
5.3.2 Simplicial Complexes......Page 114
5.3.4 Reconstruction and Interpolation......Page 115
5.4 Symmetries, Invariances, and Features......Page 116
5.4.2 Transformations, Symmetries, and Invariances......Page 117
5.5 Cluster Analysis......Page 118
5.6 Statistics for Visualization......Page 120
5.7 Topological Data Analysis......Page 121
5.7.1 Topology, Homology, and Betti Numbers......Page 122
5.7.2 From Homology to Persistent Homology......Page 123
5.7.3 Topological Structures......Page 124
5.8 Color Spaces......Page 126
References......Page 129
6 Transformations, Mappings, and Data Summaries......Page 134
6.1 Types of Data......Page 135
6.2 Functions......Page 137
6.3 Extracting Features from Functions......Page 142
6.3.1 Integral Curves of Functional Data......Page 147
6.4 Visualizing Instances by Dimensionality Reduction......Page 152
6.5 Data Summaries......Page 157
6.5.1 Statistical Summaries......Page 159
6.6 Transformations on Unstructured and Discrete Data......Page 162
6.6.1 Organizing Data in Bags......Page 163
6.6.2 Edit Distance......Page 164
6.6.3 Kernel Methods......Page 165
6.6.4 Neural Networks......Page 166
References......Page 167
Part II Empirical Studies in Visualization......Page 171
7.1 An Overview of Empirical Studies in Visualization......Page 173
7.2 Independent, Dependent, and Other Variables......Page 175
7.3.1 Independent Variables......Page 178
7.3.2 Dependent Variables and Derived Variables......Page 181
7.4.1 A Study on Using Visual Embellishments in Visualization......Page 183
7.4.2 A Study on Visual Semiotics and Uncertainty Visualization......Page 185
7.4.3 An EEG Study on Visualization Effectiveness......Page 187
7.5 Conclusions......Page 188
References......Page 189
8.1 Introduction......Page 192
8.2 A Conversation About Empirical Evaluation of Visualization Approaches......Page 194
8.3 Concluding Remarks......Page 202
References......Page 203
9.1 Introduction......Page 206
9.1.1 Putting Long-Term Case Studies in the Context of Empirical Evaluation......Page 208
9.2.1 Goals......Page 209
9.2.2 Multi-dimensional In-depth Long-Term Case Studies......Page 210
9.3.1 Evaluating the Rank-by-Feature Framework......Page 211
9.3.3 Evaluating the Jigsaw Analysis Tool......Page 212
9.3.4 Evaluating the Impact of a Medical Visualization Tool......Page 213
9.3.5 Generalized Experiences......Page 214
9.4 Planning, Conducting, and Reporting......Page 215
9.5 Challenges and Limitations......Page 216
9.6 Conclusions......Page 217
References......Page 218
10 Vis4Vis: Visualization for (Empirical) Visualization Research......Page 220
10.1 Introduction......Page 221
10.2 Background of Empirical Studies......Page 222
10.3 Example: Eye Tracking Studies and Evaluation......Page 223
10.4.1 Data and Visualization Types......Page 227
10.5 Future Research Perspectives and Call for Action......Page 228
References......Page 231
11.1 Introduction......Page 236
11.2.1 What is Visualization for?......Page 238
11.2.2 Faithfulness and Integrity Versus Embellishment and Distortion......Page 241
11.2.3 Human-Centric Processes Versus Machine-Centric Processes......Page 242
11.3 Isms in Computer Science......Page 243
11.4 Isms in Psychology......Page 246
11.5 Conclusions......Page 249
References......Page 250
12 Open Challenges in Empirical Visualization Research......Page 253
12.1 Introduction......Page 254
12.2.2 Possible Approaches......Page 255
12.3.1 Current Challenges......Page 256
12.4.1 Current Challenges......Page 257
12.5.1 Current Challenges......Page 258
12.5.2 Possible Approaches......Page 259
12.6.2 Possible Approaches......Page 260
References......Page 261
Part III Collaboration with Domain Experts......Page 263
13 Case Studies for Working with Domain Experts......Page 265
13.1.1 Lessons Learned......Page 266
13.2.2 Process and Output......Page 269
13.2.3 Lessons Learned......Page 271
13.3.1 Domain Problem......Page 272
13.3.2 Process and Output......Page 273
13.3.3 Lessons Learned......Page 274
13.4.2 Process and Output......Page 275
13.4.3 Lessons Learned......Page 277
13.5 Case Study: In Situ Simulation Visualization of Parameter Spaces......Page 278
13.5.2 Process and Output......Page 279
13.6 Case Study: Protein Analysis and Visualization (CAVER)......Page 280
13.6.1 Domain Problem......Page 281
13.6.2 Process and Output......Page 282
13.6.3 Lessons Learned......Page 283
13.7.1 Domain Problem......Page 284
13.7.2 Process and Output......Page 285
13.7.3 Lessons Learned......Page 286
References......Page 287
14.1 Why Do We Collaborate with Universities?......Page 289
14.2 How Do We Organize Collaboration with Academic Partners? (Collaboration Management)......Page 290
14.4 Lessons Learned......Page 291
Reference......Page 293
15 Collaborating Successfully with Domain Experts......Page 294
15.1.1 The Knowledge of Domain Experts Makes the Difference......Page 295
15.1.2 Methodology Helps......Page 297
15.2 Creating Impact......Page 299
15.2.1 Impact in Visualization......Page 300
References......Page 301
Part IV Developing Visualizations for Broad Audiences......Page 303
16 Reflections on Visualization for Broad Audiences......Page 304
16.2 The Complexity of Data and the Imperative for Simplicity......Page 305
16.3 Scope, Intent, and Goals......Page 308
16.4 Manifesto—Why Do We Need to Engage......Page 309
16.6 Manifesto—A Common Base for Exploration and Explanation......Page 310
16.7 Visualizations in Different Settings......Page 311
References......Page 312
17.1 Context, Goals, and Approach......Page 313
17.2 Climate Research—Climate Change......Page 314
17.3 Climate Projections—The CMIP Multi-model Ensemble......Page 318
17.4 Displays Utilized for Climate Communication......Page 321
References......Page 323
18 Reaching Broad Audiences from a Large Agency Setting......Page 325
18.1 NASA\'s Black Marble Product Suite......Page 326
18.2 Visualization of Earth Observations with NASA\'s Worldview and the Global Imagery Browse Services (GIBS)......Page 329
18.3 Visualization Production Untangling Scientific Complexity......Page 332
References......Page 344
19.1 The Need for Visualization in Science Centers and Museums......Page 347
19.2 Exploranation—Explanation Through Exploration......Page 349
19.2.1 The Norrköping Visualization Center C......Page 350
19.3 Reflections and Challenges......Page 351
19.3.2 Provenance and Managing Heterogeneous Data to Bridge the Gap Between Experts and Laymen......Page 352
19.3.3 Tailored Visualization and Interaction Techniques......Page 353
19.3.4 Support for Collaborative and Performative Interaction......Page 354
19.3.5 Natural User Interfaces and Semantic Level Interaction......Page 356
19.4 Facilitated Experiences in Large-Scale Immersive Environments......Page 357
19.4.1 Interactive Astrovisualization in Digital  Dome Theaters......Page 358
19.4.2 Experiences and Design Recommendations......Page 362
19.5 Interactive Multi-touch Tables at Science Centers......Page 363
19.5.1 The Virtual Autopsy Table......Page 364
19.5.2 The Gebelein Man at the British Museum......Page 365
19.5.3 Discovering and Learning About the Microcosmos......Page 366
19.5.4 Design Recommendations: Artifacts at the Tip of Your Fingers......Page 367
References......Page 369
20 Reaching Broad Audiences in an Educational Setting......Page 371
20.1 NASA Visualization Explorer: Adopting the Newspaper Model in the Classroom......Page 372
20.2 Scaling Visualizations to Fit in Curriculum and on Student Devices......Page 381
20.3 Crafting Visualization Development Experiences in Course Settings......Page 383
20.4 Common Themes and Final Thoughts......Page 385
References......Page 386
21.1 Simplicity in spite of Growing Data Size and Complexity......Page 387
21.3 A Few Thoughts on the Future of Our Practice......Page 389
References......Page 394