Ware Information Visualization

Perception for Design
2. Auflage 2004
ISBN: 978-0-08-047849-4
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 486 Seiten, Web PDF

Information Visualization is the major revision of a classic work on information visualization. This book explores the art and science of why we see objects the way we do. Based on the science of perception and vision, the author presents the key principles at work for a wide range of applications - resulting in visualization of improved clarity, utility, and persuasiveness. This is the first work to use the science of perception to help serious designers and analysts optimize understanding and perception of their data visualizations. This unique and essential guide to human visual perception and related cognitive principles will enrich courses on information visualization and empower designers to see their way forward. Its updated review of empirical research and interface design examples will do much to accelerate innovation and adoption of information visualization. New to this edition are a new chapter on visual thinking, new sections on face perception and flow visualization, and a much-expanded chapter on color and color sequences. This book will appeal to interaction designers; graphic designers of all kinds (including web designers); financial analysts; research scientists and engineers; data miners; and managers faced with information-intensive challenges.*First work to use the science of perception to help serious designers and analysts optimize understanding and perception of their data visualizations.
* Major revision of this classic work, with a new chapter on visual thinking, new sections on face perception and flow visualization, and a much expanded chapter on color and color sequences.
*New to this edition is the full color treatment throughout, to better display over 400 illustrations.

The author takes the 'visual' in visualization very seriously. Colin Ware has advanced degrees in both computer science (MMath, Waterloo) and the psychology of perception (Ph.D., Toronto). He has published over a hundred articles in scientific and technical journals and at leading conferences, many of which relate to the use of color, texture, motion, and 3D in information visualization. In addition to his research, Professor Ware also builds useful visualization software systems. He has been involved in developing 3D interactive visualization systems for ocean mapping for over twelve years, and he directed the development of the NestedVision3D system for visualizing very large networks of information. Both of these projects led to commercial spin-offs. Professor. Ware recently moved from the University of New Brunswick in Canada to direct the Data Visualization Research Laboratory at the University of New Hampshire.

Autoren/Hrsg.

Ware, Colin

Inhaltsverzeichnis

1;Cover;1
2;Contents;6
3;Figure Credits;16
4;Foreword;18
5;Preface;20
6;Chapter 1 Foundation for a Science of Data Visualisation;28
6.1;Visualization Stages;31
6.2;Experimental Semiotics Based on Perception;32
6.2.1;Semiotics of Graphics;33
6.2.2;Pictures as Sensory Languages;35
6.2.3;Sensory versus Arbitrary Symbols;37
6.2.4;Properties of Sensory and Arbitrary Representation;40
6.2.5;Testing Claims about Sensory Representations;42
6.2.6;Arbitrary Conventional Representations;42
6.2.7;The Study of Arbitrary Conventional Symbols;44
6.3;A Model of Perceptual Processing;47
6.3.1;Stage 1: Parallel Processing to Extract Low-Level Properties of the Visual Scene;47
6.3.2;Stage 2: Pattern Perception;48
6.3.3;Stage 3: Sequential Goal-Directed Processing;49
6.4;Types of Data;50
6.4.1;Entities;50
6.4.2;Relationships;50
6.4.3;Attributes of Entities or Relationships;51
6.4.4;Operations Considered as Data;52
6.5;Metadata;53
6.6;Conclusion ;54
7;Chapter 2 The Environment, Optics, Resolution and the Display;56
7.1;The Environment;57
7.1.1;Visible Light;57
7.1.2;Ecological Optics;57
7.1.3;Optical Flow;59
7.1.4;Textured Surfaces and Texture Gradients;60
7.1.5;The Paint Model of Surfaces;62
7.2;The Eye;65
7.2.1;The Visual Angle Defined;67
7.2.2;The Lens;68
7.2.3;Optics and Augmented-Reality Systems;69
7.2.4;Optics in Virtual-Reality Displays;72
7.2.5;Chromatic Aberration;72
7.2.6;Receptors;73
7.2.7;Simple Acuities;74
7.2.8;Acuity Distribution and the Visual Field;77
7.2.9;Brain Pixels and the Optimal Screen;80
7.2.10;Spatial Contrast Sensitivity Function;84
7.2.11;Visual Stress;89
7.3;The Optimal Display;89
7.3.1;Aliasing;90
7.3.2;Number of Dots;92
7.3.3;Superacuities and Displays;92
7.3.4;Temporal Requirements of the Perfect Display;93
7.4;Conclusion ;94
8;Chapter 3 Lightness, Brightness, Contrast and Constancy;96
8.1;Neurons, Receptive Fields, and Brightness Illusions;97
8.1.1;Simultaneous Brightness Contrast;99
8.1.2;Mach Bands;101
8.1.3;The Chevreul Illusion ;101
8.1.4;Simultaneous Contrast and Errors in Reading Maps;102
8.1.5;Contrast Effects and Artifacts in Computer Graphics;102
8.1.6;Edge Enhancement;104
8.2;Luminance, Brightness, Lightness, and Gamma;107
8.2.1;Luminance;108
8.2.2;Brightness;110
8.2.3;Adaptation, Contrast, and Lightness Constancy;111
8.2.4;Contrast and Constancy;113
8.2.5;Perception of Surface Lightness;114
8.2.6;Lightness Differences and the Gray Scale;115
8.2.7;Monitor Illumination and Monitor Surrounds;117
8.3;Conclusion ;120
9;Chapter 4 Color;124
9.1;Trichromacy Theory;125
9.2;Color Blindness;126
9.3;Color Measurement;127
9.3.1;Change of Primaries;129
9.4;CIE System of Color Standards;130
9.4.1;Chromaticity Coordinates;131
9.4.2;Color Differences and Uniform Color Spaces;135
9.5;Opponent Process Theory;137
9.5.1;Naming;137
9.5.2;Cross-Cultural Naming;139
9.5.3;Unique Hues;139
9.5.4;Neurophysiology;140
9.5.5;Categorical Colors;140
9.5.6;Properties of Color Channels;140
9.6;Color Appearance;143
9.6.1;Color Contrast;144
9.6.2;Saturation;144
9.6.3;Brown ;145
9.7;Applications of Color in Visualization;146
9.7.1;Application 1: Color Specification Interfaces and Color Spaces;146
9.7.2;Application 2: Color for Labeling;150
9.7.3;Application 3: Color Sequences for Data Maps;154
9.7.4;Application 4: Color Reproduction;165
9.7.5;Application 5: Color for Exploring Multidimensional Discrete Data;167
9.8;Conclusion ;170
10;Chapter 5 Visual Attention and Information that Pops Out;172
10.1;Searching the Visual Field;173
10.1.1;Useful Field of View;173
10.1.2;Tunnel Vision and Stress;174
10.1.3;The Role of Motion in Attracting Attention;174
10.2;Reading from the Iconic Buffer;174
10.2.1;Preattentive Processing;176
10.2.2;Rapid Area Judgments;181
10.2.3;Coding with Combinations of Features;181
10.2.4;Conjunctions with Spatial Dimensions;182
10.2.5;Highlighting;183
10.2.6;Designing a Symbol Set;184
10.3;Neural Processing, Graphemes, and Tuned Receptors;186
10.3.1;The Grapheme;187
10.4;The Gabor Model and Texture in Visualization;188
10.4.1;Texture Segmentation;190
10.4.2;Tradeoffs in Information Density: An Uncertainty Principle;190
10.5;Texture Coding Information;191
10.5.1;Primary Perceptual Dimensions of Texture;191
10.5.2;Generation of Distinct Textures;193
10.5.3;Spatial-Frequency Channels, Orthogonality, and Maps;194
10.5.4;Texture Resolution;196
10.5.5;Texture Contrast Effects;197
10.5.6;Other Dimensions of Visual Texture;197
10.5.7;Texture Field Displays ;199
10.6;Glyphs and Multivariate Discrete Data;203
10.6.1;Restricted Classification Tasks;204
10.6.2;Speeded Classification Tasks;205
10.6.3;Integral–Separable Dimension Pairs;207
10.6.4;Monotonicity of Visual Attributes;208
10.6.5;Multidimensional Discrete Data;209
10.6.6;Stars, Whiskers, and Other Glyphs;211
10.7;Conclusion ;212
11;Chapter 6 Static and Moving Patterns;214
11.1;Gestalt Laws;216
11.1.1;Proximity;216
11.1.2;Similarity;217
11.1.3;Connectedness;218
11.1.4;Continuity;218
11.1.5;Symmetry;219
11.1.6;Closure;221
11.1.7;Relative Size;223
11.1.8;Figure and Ground;223
11.2;More on Contours;225
11.2.1;Perceiving Direction: Representing Vector Fields;227
11.2.2;Comparing 2D Flow Visualization Techniques;228
11.3;Perception of Transparency: Overlapping Data;232
11.3.1;Pattern Learning;233
11.4;The Perceptual Syntax of Diagrams;237
11.4.1;The Grammar of Node–Link Diagrams;237
11.4.2;The Grammar of Maps;242
11.5;Patterns in Motion;244
11.5.1;Form and Contour in Motion;246
11.5.2;Moving Frames;247
11.5.3;Expressive Motion;248
11.5.4;Perception of Causality ;249
11.5.5;Perception of Animate Motion;250
11.5.6;Enriching Diagrams with Simple Animation;251
11.6;Conclusion ;252
12;Chapter 7 Visual Objects and Data Objects;254
12.1;Image-Based Object Recognition;255
12.1.1;Applications of Images in User Interfaces;257
12.2;Structure-Based Object Recognition;260
12.2.1;Geon Theory;260
12.2.2;Silhouettes;260
12.3;Faces;264
12.4;The Object Display and Object-Based Diagrams;266
12.4.1;The Geon Diagram;268
12.5;Perceiving the Surface Shapes of Objects;270
12.5.1;Spatial Cues for Representing Scalar Fields;271
12.5.2;Integration of Cues for Surface Shape;274
12.5.3;Interaction of Shading and Contour;275
12.5.4;Guidelines for Displaying Surfaces;279
12.5.5;Bivariate Maps: Lighting and Surface Color;281
12.6;Cushion Maps;282
12.7;Integration;282
12.8;Conclusion ;284
13;Chapter 8 Space Perception and the Display of Data in Space;286
13.1;Depth Cue Theory;286
13.1.1;Perspective Cues;287
13.1.2;Pictures Seen from the Wrong Viewpoint;290
13.1.3;Occlusion;292
13.1.4;Depth of Focus ;293
13.1.5;Cast Shadows;293
13.1.6;Shape-from-Shading;295
13.1.7;Eye Accommodation;296
13.1.8;Structure-from-Motion;296
13.1.9;Eye Convergence;297
13.1.10;Stereoscopic Depth;298
13.1.11;Problems with Stereoscopic Displays;300
13.1.12;Making Effective Stereoscopic Displays;301
13.1.13;Artificial Spatial Cues;306
13.1.14;Depth Cues in Combination;307
13.2;Task-Based Space Perception;310
13.2.1;Tracing Data Paths in 3D Graphs;311
13.2.2;Judging the Morphology of Surfaces and Surface Target Detection;314
13.2.3;Patterns of Points in 3D Space;315
13.2.4;Judging Relative Positions of Objects in Space;316
13.2.5;Judging the Relative Movement of Self within the Environment;317
13.2.6;Reaching for Objects;318
13.2.7;Judging the “Up” Direction;319
13.2.8;The Aesthetic Impression of 3D Space (Presence);320
13.3;Conclusion ;321
14;Chapter 9 Images, Worlds and Gestures;324
14.1;Coding Words and Images;324
14.2;The Nature of Language;326
14.3;Visual and Spoken Language;328
14.3.1;Images vs. Words;330
14.3.2;Links between Images and Words;333
14.3.3;Static Links;334
14.3.4;Gestures as Linking Devices;336
14.3.5;Deixis;336
14.3.6;Symbolic Gestures;337
14.3.7;Expressive Gestures;338
14.3.8;Visual Momentum in Animated Sequences;338
14.4;Animated Visual Languages;339
14.5;Conclusion ;342
15;Chapter 10 Interacting with Visualizations;344
15.1;Data Selection and Manipulation Loop;345
15.1.1;Choice Reaction Time;345
15.1.2;2D Positioning and Selection;346
15.1.3;Hover Queries;347
15.1.4;Path Tracing;348
15.1.5;Two-Handed Interaction;348
15.1.6;Learning;349
15.1.7;Control Compatibility;349
15.1.8;Vigilance;351
15.2;Exploration and Navigation Loop;352
15.2.1;Locomotion and Viewpoint Control;352
15.2.2;Frames of Reference;360
15.2.3;Map Orientation;364
15.2.4;Focus, Context, and Scale;365
15.2.5;Rapid Interaction with Data;372
15.3;Conclusion ;376
16;Chapter 11 Thinking with Visualizations;378
16.1;Memory Systems;379
16.1.1;Visual Working Memory;379
16.1.2;Visual Working Memory Capacity;382
16.1.3;Rensink’s Model;389
16.2;Eye Movements;390
16.2.1;Accommodation;391
16.2.2;Eye Movements, Search, and Monitoring;391
16.2.3;Long-Term Memory;393
16.3;Problem Solving with Visualizations;397
16.3.1;Visual Problem Solving Processes;398
16.3.2;The Problem Solving Strategy;399
16.3.3;Visual Query Construction ;399
16.3.4;The Pattern-Finding Loop;400
16.3.5;The Eye Movement Control Loop;401
16.3.6;The Intrasaccadic Scanning Loop;401
16.3.7;Implications for Interactive Visualization Design;401
16.3.8;Interfaces to Knowledge Structures;406
16.4;Creative Problem Solving;410
16.5;Conclusion ;412
17;Appendix A Changing Primaries;414
18;Appendix B CIE Color Measurement System;416
19;Appendix C The Perceptual Evaluation of Visualization Techniques and Systems;420
19.1;Research Goals;420
19.2;Psychophysics;421
19.2.1;Detection Methods;422
19.2.2;Method of Adjustment;424
19.3;Cognitive Psychology;424
19.4;Structural Analysis;425
19.4.1;Testbench Application for Discovery;425
19.4.2;Structured Interviews;426
19.4.3;Rating Scales;426
19.5;Statistical Exploration;427
19.5.1;Principal Components Analysis;427
19.5.2;Multidimensional Scaling ;427
19.5.3;Clustering;428
19.5.4;Multiple Regression;428
19.6;Cross-Cultural Studies;428
19.7;Child Studies;428
19.8;Practical Problems in Conducting User Studies;429
19.8.1;Experimenter Bias;429
19.8.2;How Many Subjects to Use?;430
19.8.3;Combinatorial Explosion;430
19.8.4;Task Identification;431
19.8.5;Controls;431
19.8.6;Getting Help ;431
20;Bibliography;432
21;Subject Index;478
22;Author Index;506
23;About the Author;512