book

Visualization Analysis and Design

by Tamara Munzner

December 2014

Intermediate to advanced

428 pages

11h 8m

English

A K Peters/CRC Press

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Why a New Book?Existing BooksAudienceWho’s WhoStructure: What’s in This BookWhat’s Not in This BookAcknowledgments
1.1 The Big Picture1.2 Why Have a Human in the Loop?1.3 Why Have a Computer in the Loop?1.4 Why Use an External Representation?1.5 Why Depend on Vision?1.6 Why Show the Data in Detail?1.7 Why Use Interactivity?1.8 Why Is the Vis Idiom Design Space Huge?1.9 Why Focus on Tasks?1.10 Why Focus on Effectiveness?1.11 Why Are Most Designs Ineffective?1.12 Why Is Validation Difficult?1.13 Why Are There Resource Limitations?1.14 Why Analyze?1.15 Further ReadingFigure 1.1Figure 1.2Figure 1.3Figure 1.4Figure 1.5Figure 1.6Figure 1.7Figure 1.8
2.1 The Big Picture2.2 Why Do Data Semantics and Types Matter?2.3 Data Types2.4 Dataset Types2.4.1 Tables2.4.2 Networks and Trees2.4.2.1 Trees2.4.3 Fields2.4.3.1 Spatial Fields2.4.3.2 Grid Types2.4.4 Geometry2.4.5 Other Combinations2.4.6 Dataset Availability2.5 Attribute Types2.5.1 Categorical2.5.2 Ordered: Ordinal and Quantitative2.5.2.1 Sequential versus Diverging2.5.2.2 Cyclic2.5.3 Hierarchical Attributes2.6 Semantics2.6.1 Key versus Value Semantics2.6.1.1 Flat Tables2.6.1.2 Multidimensional Tables2.6.1.3 Fields2.6.1.4 Scalar Fields2.6.1.5 Vector Fields2.6.1.6 Tensor Fields2.6.1.7 Field Semantics2.6.2 Temporal Semantics2.6.2.1 Time-Varying Data2.7 Further ReadingFigure 2.1Figure 2.2Figure 2.3Figure 2.4Figure 2.5Figure 2.6Figure 2.7Figure 2.8Figure 2.9
3.1 The Big Picture3.2 Why Analyze Tasks Abstractly?3.3 Who: Designer or User3.4 Actions3.4.1 Analyze3.4.1.1 Discover3.4.1.2 Present3.4.1.3 Enjoy3.4.2 Produce3.4.2.1 Annotate3.4.2.2 Record3.4.2.3 Derive3.4.3 Search3.4.3.1 Lookup3.4.3.2 Locate3.4.3.3 Browse3.4.3.4 Explore3.4.4 Query3.4.4.1 Identify3.4.4.2 Compare3.4.4.3 Summarize3.5 Targets3.6 How: A Preview3.7 Analyzing and Deriving: Examples3.7.1 Comparing Two Idioms3.7.2 Deriving One Attribute3.7.3 Deriving Many New Attributes3.8 Further ReadingFigure 3.1Figure 3.2Figure 3.3Figure 3.4Figure 3.5Figure 3.6Figure 3.7Figure 3.8Figure 3.9Figure 3.10Figure 3.11Figure 3.12Figure 3.13
4.1 The Big Picture4.2 Why Validate?4.3 Four Levels of Design4.3.1 Domain Situation4.3.2 Task and Data Abstraction4.3.3 Visual Encoding and Interaction Idiom4.3.4 Algorithm4.4 Angles of Attack4.5 Threats to Validity4.6 Validation Approaches4.6.1 Domain Validation4.6.2 Abstraction Validation4.6.3 Idiom Validation4.6.4 Algorithm Validation4.6.5 Mismatches4.7 Validation Examples4.7.1 Genealogical Graphs4.7.2 MatrixExplorer4.7.3 Flow Maps4.7.4 LiveRAC4.7.5 LinLog4.7.6 Sizing the Horizon4.8 Further ReadingFigure 4.1Figure 4.2Figure 4.3Figure 4.4Figure 4.5Figure 4.6Figure 4.7Figure 4.8Figure 4.9Figure 4.10Figure 4.11Figure 4.12Figure 4.13Figure 4.14Figure 4.15Figure 4.16Figure 4.17
5.1 The Big Picture5.2 Why Marks and Channels?5.3 Defining Marks and Channels5.3.1 Channel Types5.3.2 Mark Types5.4 Using Marks and Channels5.4.1 Expressiveness and Effectiveness5.4.2 Channel Rankings5.5 Channel Effectiveness5.5.1 Accuracy5.5.2 Discriminability5.5.3 Separability5.5.4 Popout5.5.5 Grouping5.6 Relative versus Absolute Judgements5.7 Further ReadingFigure 5.1Figure 5.2Figure 5.3Figure 5.4Figure 5.5Figure 5.6Figure 5.7Figure 5.8Figure 5.9Figure 5.10Figure 5.11Figure 5.12Figure 5.13Figure 5.14Figure 5.15
6.1 The Big Picture6.2 Why and When to Follow Rules of Thumb?6.3 No Unjustified 3D6.3.1 The Power of the Plane6.3.2 The Disparity of Depth6.3.3 Occlusion Hides Information6.3.4 Perspective Distortion Dangers6.3.5 Other Depth Cues6.3.6 Tilted Text Isn’t Legibile6.3.7 Benefits of 3D: Shape Perception6.3.8 Justification and Alternatives6.3.9 Empirical Evidence6.4 No Unjustified 2D6.5 Eyes Beat Memory6.5.1 Memory and Attention6.5.2 Animation versus Side-by-Side Views6.5.3 Change Blindness6.6 Resolution over Immersion6.7 Overview First, Zoom and Filter, Details on Demand6.8 Responsiveness Is Required6.8.1 Visual Feedback6.8.2 Latency and Interaction Design6.8.3 Interactivity Costs6.9 Get It Right in Black and White6.10 Function First, Form Next6.11 Further ReadingFigure 6.1Figure 6.2Figure 6.3Figure 6.4Figure 6.5Figure 6.6Figure 6.7Figure 6.8Figure 6.9
7.1 The Big Picture7.2 Why Arrange?7.3 Arrange by Keys and Values7.4 Express: Quantitative Values7.5 Separate, Order, and Align: Categorical Regions7.5.1 List Alignment: One Key7.5.2 Matrix Alignment: Two Keys7.5.3 Volumetric Grid: Three Keys7.5.4 Recursive Subdivision: Multiple Keys7.6 Spatial Axis Orientation7.6.1 Rectilinear Layouts7.6.2 Parallel Layouts7.6.3 Radial Layouts7.7 Spatial Layout Density7.7.1 Dense7.7.2 Space-Filling7.8 Further ReadingFigure 7.1Figure 7.2Figure 7.3Figure 7.4Figure 7.5Figure 7.6Figure 7.7Figure 7.8Figure 7.9Figure 7.10Figure 7.11Figure 7.12Figure 7.13Figure 7.14Figure 7.15Figure 7.16Figure 7.17Figure 7.18Figure 7.19Figure 7.20
8.1 The Big Picture8.2 Why Use Given?8.3 Geometry8.3.1 Geographic Data8.3.2 Other Derived Geometry8.4 Scalar Fields: One Value8.4.1 Isocontours8.4.2 Direct Volume Rendering8.5 Vector Fields: Multiple Values8.5.1 Flow Glyphs8.5.2 Geometric Flow8.5.3 Texture Flow8.5.4 Feature Flow8.6 Tensor Fields: Many Values8.7 Further ReadingFigure 8.1Figure 8.2Figure 8.3Figure 8.4Figure 8.5Figure 8.6Figure 8.7Figure 8.8Figure 8.9Figure 8.10Figure 8.11Figure 8.12

9.1 The Big Picture9.2 Connection: Link Marks9.3 Matrix Views9.4 Costs and Benefits: Connection versus Matrix9.5 Containment: Hierarchy Marks9.6 Further ReadingFigure 9.1Figure 9.2Figure 9.3Figure 9.4Figure 9.5Figure 9.6Figure 9.7Figure 9.8Figure 9.9Figure 9.10
10.1 The Big Picture10.2 Color Theory10.2.1 Color Vision10.2.2 Color Spaces10.2.3 Luminance, Saturation, and Hue10.2.4 Transparency10.3 Colormaps10.3.1 Categorical Colormaps10.3.2 Ordered Colormaps10.3.3 Bivariate Colormaps10.3.4 Colorblind-Safe Colormap Design10.4 Other Channels10.4.1 Size Channels10.4.2 Angle Channel10.4.3 Curvature Channel10.4.4 Shape Channel10.4.5 Motion Channels10.4.6 Texture and Stippling10.5 Further ReadingFigure 10.1Figure 10.2Figure 10.3Figure 10.4Figure 10.5Figure 10.6Figure 10.7Figure 10.8Figure 10.9Figure 10.10Figure 10.11Figure 10.12Figure 10.13Figure 10.14
11.1 The Big Picture11.2 Why Change?11.3 Change View over Time11.4 Select Elements11.4.1 Selection Design Choices11.4.2 Highlighting11.4.3 Selection Outcomes11.5 Navigate: Changing Viewpoint11.5.1 Geometric Zooming11.5.2 Semantic Zooming11.5.3 Constrained Navigation11.6 Navigate: Reducing Attributes11.6.1 Slice11.6.2 Cut11.6.3 Project11.7 Further ReadingFigure 11.1Figure 11.2Figure 11.3Figure 11.4Figure 11.5Figure 11.6Figure 11.7Figure 11.8Figure 11.9
12.1 The Big Picture12.2 Why Facet?12.3 Juxtapose and Coordinate Views12.3.1 Share Encoding: Same/Different12.3.2 Share Data: All, Subset, None12.3.3 Share Navigation: Synchronize12.3.4 Combinations12.3.5 Juxtapose Views12.4 Partition into Views12.4.1 Regions, Glyphs, and Views12.4.2 List Alignments12.4.3 Matrix Alignments12.4.4 Recursive Subdivision12.5 Superimpose Layers12.5.1 Visually Distinguishable Layers12.5.2 Static Layers12.5.3 Dynamic Layers12.6 Further ReadingFigure 12.1Figure 12.2Figure 12.3Figure 12.4Figure 12.5Figure 12.6Figure 12.7Figure 12.8Figure 12.9Figure 12.10Figure 12.11Figure 12.12Figure 12.13Figure 12.14Figure 12.15Figure 12.16Figure 12.17
13.1 The Big Picture13.2 Why Reduce?13.3 Filter13.3.1 Item Filtering13.3.2 Attribute Filtering13.4 Aggregate13.4.1 Item Aggregation13.4.2 Spatial Aggregation13.4.3 Attribute Aggregation: Dimensionality Reduction13.4.3.1 Why and When to Use DR?13.4.3.2 How to Show DR Data?13.5 Further ReadingFigure 13.1Figure 13.2Figure 13.3Figure 13.4Figure 13.5Figure 13.6Figure 13.7Figure 13.8Figure 13.9Figure 13.10Figure 13.11Figure 13.12Figure 13.13
14.1 The Big Picture14.2 Why Embed?14.3 Elide14.4 Superimpose14.5 Distort14.6 Costs and Benefits: Distortion14.7 Further ReadingFigure 14.1Figure 14.2Figure 14.3Figure 14.4Figure 14.5Figure 14.6Figure 14.7Figure 14.8Figure 14.9Figure 14.10
15.1 The Big Picture15.2 Why Analyze Case Studies?15.3 Graph-Theoretic Scagnostics15.4 VisDB15.5 Hierarchical Clustering Explorer15.6 PivotGraph15.7 InterRing15.8 Constellation15.9 Further ReadingFigure 15.1Figure 15.2Figure 15.3Figure 15.4Figure 15.5Figure 15.6Figure 15.7Figure 15.8Figure 15.9Figure 15.10Figure 15.11Figure 15.12Figure 15.13Figure 15.14Figure 15.15Figure 15.16Figure 15.17Figure 15.18Figure 15.19Figure 15.20

Content preview from Visualization Analysis and Design

Chapter 15

Analysis Case Studies

15.1 The Big Picture

Figure 15.1 shows the six vis systems analyzed in this chapter as full case studies. The Scagnostics system provides a scalable summary of large scatterplot matrices through a derived SPLOM based on classifying the geometric shapes formed by the point distributions in the original scatterplots [Wilkinson et al. 05, Wilkinson et al. 06]. The VisDB system for database vis treats an entire database as a very large table of data, visually encoding it with dense and space-filling layouts with items colored according to their relevance for a specific query [Keim and Kriegel 94]. The Hierarchical Clustering Explorer (HCE) system supports systematic exploration of a multidimensional tables, such as ...