Spis treści: „Visualization Analysis and Design”

Spis treści publikacji pt. „Visualization Analysis and Design”.

Tytuł: „Visualization Analysis and Design”
Autor: Tamara Munzner
Wydawca: A K Peters/CRC Press
Data wydania: 2014-11-26
Język: Angielski
Liczba stron: 428

Spis treści

Spis treści / Table of contents

Preface / xv

Why a New Book? / xv

Existing Books / xvi

Audience / xvii

Who’s Who / xviii

Structure: What’s in This Book / xviii

What’s Not in This Book / xx

Acknowledgments / xx

1 What’s Vis, and Why Do It? / 1

1.1 The Big Picture / 1

1.2 Why Have a Human in the Loop? / 2

1.3 Why Have a Computer in the Loop? / 4

1.4 Why Use an External Representation? / 6

1.5 Why Depend on Vision? / 6

1.6 Why Show the Data in Detail? / 7

1.7 Why Use Interactivity? / 9

1.8 Why Is the Vis Idiom Design Space Huge? / 10

1.9 Why Focus on Tasks? / 11

1.10 Why Focus on Effectiveness? / 11

1.11 Why Are Most Designs Ineffective? / 12

1.12 Why Is Validation Difficult? / 14

1.13 Why Are There Resource Limitations? / 14

1.14 Why Analyze? / 16

1.15 Further Reading / 18

2 What: Data Abstraction 20

2.1 The Big Picture / 21

2.2 Why Do Data Semantics and Types Matter? / 21

2.3 Data Types / 23

2.4 Dataset Types / 24

2.4.1 Tables / 25

2.4.2 Networks and Trees / 26

2.4.2.1 Trees / 27

2.4.3 Fields / 27

2.4.3.1 Spatial Fields / 28

2.4.3.2 Grid Types / 29

2.4.4 Geometry / 29

2.4.5 Other Combinations / 30

2.4.6 Dataset Availability / 31

2.5 Attribute Types / 31

2.5.1 Categorical / 32

2.5.2 Ordered: Ordinal and Quantitative / 32

2.5.2.1 Sequential versus Diverging / 33

2.5.2.2 Cyclic / 33

2.5.3 Hierarchical Attributes / 33

2.6 Semantics / 34

2.6.1 Key versus Value Semantics / 34

2.6.1.1 Flat Tables / 34

2.6.1.2 Multidimensional Tables / 36

2.6.1.3 Fields / 37

2.6.1.4 Scalar Fields / 37

2.6.1.5 Vector Fields / 37

2.6.1.6 Tensor Fields / 38

2.6.1.7 Field Semantics / 38

2.6.2 Temporal Semantics / 38

2.6.2.1 Time-Varying Data / 39

2.7 Further Reading / 40

3 Why: Task Abstraction / 42

3.1 The Big Picture / 43

3.2 Why Analyze Tasks Abstractly? / 43

3.3 Who: Designer or User / 44

3.4 Actions / 45

3.4.1 Analyze / 45

3.4.1.1 Discover / 47

3.4.1.2 Present / 47

3.4.1.3 Enjoy / 48

3.4.2 Produce / 49

3.4.2.1 Annotate / 49

3.4.2.2 Record / 49

3.4.2.3 Derive / 50

3.4.3 Search / 53

3.4.3.1 Lookup / 53

3.4.3.2 Locate / 53

3.4.3.3 Browse / 53

3.4.3.4 Explore / 54

3.4.4 Query / 54

3.4.4.1 Identify / 54

3.4.4.2 Compare / 55

3.4.4.3 Summarize / 55

3.5 Targets / 55

3.6 How: A Preview / 57

3.7 Analyzing and Deriving: Examples / 59

3.7.1 Comparing Two Idioms / 59

3.7.2 Deriving One Attribute / 60

3.7.3 Deriving Many New Attributes / 62

3.8 Further Reading / 64

4 Analysis: Four Levels for Validation / 66

4.1 The Big Picture / 67

4.2 Why Validate? / 67

4.3 Four Levels of Design / 67

4.3.1 Domain Situation / 69

4.3.2 Task and Data Abstraction / 70

4.3.3 Visual Encoding and Interaction Idiom / 71

4.3.4 Algorithm / 72

4.4 Angles of Attack / 73

4.5 Threats to Validity / 74

4.6 Validation Approaches / 75

4.6.1 Domain Validation / 77

4.6.2 Abstraction Validation / 78

4.6.3 Idiom Validation / 78

4.6.4 Algorithm Validation / 80

4.6.5 Mismatches / 81

4.7 Validation Examples / 81

4.7.1 Genealogical Graphs / 81

4.7.2 MatrixExplorer / 83

4.7.3 Flow Maps / 85

4.7.4 LiveRAC / 87

4.7.5 LinLog / 89

4.7.6 Sizing the Horizon / 90

4.8 Further Reading / 91

5 Marks and Channels 94

5.1 The Big Picture / 95

5.2 Why Marks and Channels? / 95

5.3 Defining Marks and Channels / 95

5.3.1 Channel Types / 99

5.3.2 Mark Types / 99

5.4 Using Marks and Channels / 99

5.4.1 Expressiveness and Effectiveness / 100

5.4.2 Channel Rankings / 101

5.5 Channel Effectiveness / 103

5.5.1 Accuracy /103

5.5.2 Discriminability / 106

5.5.3 Separability / 106

5.5.4 Popout / 109

5.5.5 Grouping / 111

5.6 Relative versus Absolute Judgements / 112

5.7 Further Reading / 114

6 Rules of Thumb / 116

6.1 The Big Picture / 117

6.2 Why and When to Follow Rules of Thumb? / 117

6.3 No Unjustified 3D / 117

6.3.1 The Power of the Plane / 118

6.3.2 The Disparity of Depth / 118

6.3.3 Occlusion Hides Information / 120

6.3.4 Perspective Distortion Dangers / 121

6.3.5 Other Depth Cues / 123

6.3.6 Tilted Text Isn’t Legibile / 124

6.3.7 Benefits of 3D: Shape Perception / 124

6.3.8 Justification and Alternatives / 125

Example: Cluster–Calendar Time-Series Vis / 125

Example: Layer-Oriented Time-Series Vis / 128

6.3.9 Empirical Evidence / 129

6.4 No Unjustified 2D / 131

6.5 Eyes Beat Memory / 131

6.5.1 Memory and Attention / 132

6.5.2 Animation versus Side-by-Side Views / 132

6.5.3 Change Blindness / 133

6.6 Resolution over Immersion / 134

6.7 Overview First, Zoom and Filter, Details on Demand / 135

6.8 Responsiveness Is Required / 137

6.8.1 Visual Feedback / 138

6.8.2 Latency and Interaction Design / 138

6.8.3 Interactivity Costs / 140

6.9 Get It Right in Black and White / 140

6.10 Function First, Form Next / 140

6.11 Further Reading / 141

7 Arrange Tables 144

7.1 The Big Picture /145

7.2 Why Arrange? / 145

7.3 Arrange by Keys and Values /145

7.4 Express: Quantitative Values / 146

Example: Scatterplots / 146

7.5 Separate, Order, and Align: Categorical Regions / 149

7.5.1 List Alignment: One Key / 149

Example: Bar Charts / 50

Example: Stacked Bar Charts / 51

Example: Streamgraphs / 153

Example: Dot and Line Charts / 155

7.5.2 Matrix Alignment: Two Keys / 157

Example: Cluster Heatmaps / 158

Example: Scatterplot Matrix / 160

7.5.3 Volumetric Grid: Three Keys / 161

7.5.4 Recursive Subdivision: Multiple Keys / 161

7.6 Spatial Axis Orientation / 162

7.6.1 Rectilinear Layouts / 162

7.6.2 Parallel Layouts / 162

Example: Parallel Coordinates / 162

7.6.3 Radial Layouts / 166

Example: Radial Bar Charts / 167

Example: Pie Charts / 168

7.7 Spatial Layout Density / 171

7.7.1 Dense / 172

Example: Dense Software Overviews //172

7.7.2 Space-Filling / 174

7.8 Further Reading / 175

8 Arrange Spatial Data /178

8.1 The Big Picture / 179

8.2 Why Use Given? / 179

8.3 Geometry / 180

8.3.1 Geographic Data / 180

Example: Choropleth Maps / 181

8.3.2 Other Derived Geometry / 182

8.4 Scalar Fields: One Value / 182

8.4.1 Isocontours / 183

Example: Topographic Terrain Maps / 183

Example: Flexible Isosurfaces / 185

8.4.2 Direct Volume Rendering / 186

Example: Multidimensional Transfer Functions / 187

8.5 Vector Fields: Multiple Values /189

8.5.1 Flow Glyphs / 191

8.5.2 Geometric Flow / 191

Example: Similarity-Clustered Streamlines / 192

8.5.3 Texture Flow / 193

8.5.4 Feature Flow / 193

8.6 Tensor Fields: Many Values / 194

Example: Ellipsoid Tensor Glyphs / 194

8.7 Further Reading / 197

9 Arrange Networks and Trees / 200

9.1 The Big Picture / 201

9.2 Connection: Link Marks / 201

Example: Force-Directed Placement / 204

Example: sfdp / 207

9.3 Matrix Views / 208

Example: Adjacency Matrix View / 208

9.4 Costs and Benefits: Connection versus Matrix / 209

9.5 Containment: Hierarchy Marks / 213

Example: Treemaps / 213

Example: GrouseFlocks / 215

9.6 Further Reading / 216

10 Map Color and Other Channels / 218

10.1 The Big Picture / 219

10.2 Color Theory / 219

10.2.1 Color Vision / 219

10.2.2 Color Spaces / 220

10.2.3 Luminance, Saturation, and Hue / 223

10.2.4 Transparency / 225

10.3 Colormaps / 225

10.3.1 Categorical Colormaps / 226

10.3.2 Ordered Colormaps / 229

10.3.3 Bivariate Colormaps / 234

10.3.4 Colorblind-Safe Colormap Design / 235

10.4 Other Channels / 236

10.4.1 Size Channels / 236

10.4.2 Angle Channel / 237

10.4.3 Curvature Channel / 238

10.4.4 Shape Channel / 238

10.4.5 Motion Channels / 238

10.4.6 Texture and Stippling / 239

10.5 Further Reading / 240

11 Manipulate View / 242

11.1 The Big Picture / 243

11.2 Why Change? / 244

11.3 Change View over Time / 244

Example: LineUp / 246

Example: Animated Transitions / 248

11.4 Select Elements / 249

11.4.1 Selection Design Choices / 250

11.4.2 Highlighting / 251

Example: Context-Preserving Visual Links / 253

11.4.3 Selection Outcomes / 254

11.5 Navigate: Changing Viewpoint / 254

11.5.1 Geometric Zooming / 255

11.5.2 Semantic Zooming / 255

11.5.3 Constrained Navigation / 256

11.6 Navigate: Reducing Attributes / 258

11.6.1 Slice / 258

Example: HyperSlice / 259

11.6.2 Cut / 260

11.6.3 Project / 261

11.7 Further Reading / 261

12 Facet into Multiple Views / 264

12.1 The Big Picture / 265

12.2 Why Facet? / 265

12.3 Juxtapose and Coordinate Views / 267

12.3.1 Share Encoding: Same/Different / 267

Example: Exploratory Data Visualizer (EDV) / 268

12.3.2 Share Data: All, Subset, None / 269

Example: Bird’s-Eye Maps / 270

Example: Multiform Overview–Detail Microarrays / 271

Example: Cerebral / 274

12.3.3 Share Navigation: Synchronize / 276

12.3.4 Combinations / 276

Example: Improvise /277

12.3.5 Juxtapose Views / 278

12.4 Partition into Views / 279

12.4.1 Regions, Glyphs, and Views / 279

12.4.2 List Alignments / 281

12.4.3 Matrix Alignments / 282

Example: Trellis /282

12.4.4 Recursive Subdivision / 285

12.5 Superimpose Layers / 288

12.5.1 Visually Distinguishable Layers / 289

12.5.2 Static Layers / 289

Example: Cartographic Layering / 289

Example: Superimposed Line Charts / 290

Example: Hierarchical Edge Bundles / 292

12.5.3 Dynamic Layers / 294

12.6 Further Reading / 295

13 Reduce Items and Attributes / 298

13.1 The Big Picture / 299

13.2 Why Reduce? / 299

13.3 Filter / 300

13.3.1 Item Filtering / 301

Example: FilmFinder / 301

13.3.2 Attribute Filtering / 303

Example: DOSFA / 304

13.4 Aggregate / 305

13.4.1 Item Aggregation / 305

Example: Histograms / 306

Example: Continuous Scatterplots / 307

Example: Boxplot Charts / 308

Example: SolarPlot / 310

Example: Hierarchical Parallel Coordinates / 311

13.4.2 Spatial Aggregation ./313

Example: Geographically Weighted Boxplots / 313

13.4.3 Attribute Aggregation: Dimensionality Reduction / 315

13.4.3.1 Why and When to Use DR? / 316

Example: Dimensionality Reduction for Document Collections / 316

13.4.3.2 How to Show DR Data? / 319

13.5 Further Reading / 320

14 Embed: Focus+Context / 322

14.1 The Big Picture / 323

14.2 Why Embed? / 323

14.3 Elide / 324

Example: DOITrees Revisited / 325

14.4 Superimpose / 326

Example: Toolglass and Magic Lenses / 326

14.5 Distort / 327

Example: 3D Perspective / 327

Example: Fisheye Lens / 328

Example: Hyperbolic Geometry / 329

Example: Stretch and Squish Navigation / 331

Example: Nonlinear Magnification Fields /333

14.6 Costs and Benefits: Distortion / 334

14.7 Further Reading / 337

15 Analysis Case Studies 340

15.1 The Big Picture / 341

15.2 Why Analyze Case Studies? / 341

15.3 Graph-Theoretic Scagnostics / 342

15.4 VisDB / 347

15.5 Hierarchical Clustering Explorer / 351

15.6 PivotGraph / 355

15.7 InterRing / 358

15.8 Constellation / 360

15.9 Further Reading / 366

Figure Credits / 369

Bibliography / 375

Idiom and System Examples Index / 397

Concept Index / 399

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