Visualization Analysis and Design

Visualization Analysis and Design

by Tamara Munzner
Released December 2014
Publisher(s): A K Peters/CRC Press
ISBN: 9781498759717

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Book description

Learn How to Design Effective Visualization SystemsVisualization Analysis and Design provides a systematic, comprehensive framework for thinking about visualization in terms of principles and design choices. The book features a unified approach encompassing information visualization techniques for abstract data, scientific visualization techniques

Table of contents

  1. Preliminaries
  2. Preface
    1. Why a New Book?
    2. Existing Books
    3. Audience
    4. Who’s Who
    5. Structure: What’s in This Book
    6. What’s Not in This Book
    7. Acknowledgments
  3. Chapter 1 What’s Vis, and Why Do It?
    1. 1.1 The Big Picture
    2. 1.2 Why Have a Human in the Loop?
    3. 1.3 Why Have a Computer in the Loop?
    4. 1.4 Why Use an External Representation?
    5. 1.5 Why Depend on Vision?
    6. 1.6 Why Show the Data in Detail?
    7. 1.7 Why Use Interactivity?
    8. 1.8 Why Is the Vis Idiom Design Space Huge?
    9. 1.9 Why Focus on Tasks?
    10. 1.10 Why Focus on Effectiveness?
    11. 1.11 Why Are Most Designs Ineffective?
    12. 1.12 Why Is Validation Difficult?
    13. 1.13 Why Are There Resource Limitations?
    14. 1.14 Why Analyze?
    15. 1.15 Further Reading
      1. Figure 1.1
      2. Figure 1.2
      3. Figure 1.3
      4. Figure 1.4
      5. Figure 1.5
      6. Figure 1.6
      7. Figure 1.7
      8. Figure 1.8
  4. Chapter 2 What: Data Abstraction
    1. 2.1 The Big Picture
    2. 2.2 Why Do Data Semantics and Types Matter?
    3. 2.3 Data Types
    4. 2.4 Dataset Types
      1. 2.4.1 Tables
      2. 2.4.2 Networks and Trees
        1. 2.4.2.1 Trees
      3. 2.4.3 Fields
        1. 2.4.3.1 Spatial Fields
        2. 2.4.3.2 Grid Types
      4. 2.4.4 Geometry
      5. 2.4.5 Other Combinations
      6. 2.4.6 Dataset Availability
    5. 2.5 Attribute Types
      1. 2.5.1 Categorical
      2. 2.5.2 Ordered: Ordinal and Quantitative
        1. 2.5.2.1 Sequential versus Diverging
        2. 2.5.2.2 Cyclic
      3. 2.5.3 Hierarchical Attributes
    6. 2.6 Semantics
      1. 2.6.1 Key versus Value Semantics
        1. 2.6.1.1 Flat Tables
        2. 2.6.1.2 Multidimensional Tables
        3. 2.6.1.3 Fields
        4. 2.6.1.4 Scalar Fields
        5. 2.6.1.5 Vector Fields
        6. 2.6.1.6 Tensor Fields
        7. 2.6.1.7 Field Semantics
      2. 2.6.2 Temporal Semantics
        1. 2.6.2.1 Time-Varying Data
      3. 2.7 Further Reading
      1. Figure 2.1
      2. Figure 2.2
      3. Figure 2.3
      4. Figure 2.4
      5. Figure 2.5
      6. Figure 2.6
      7. Figure 2.7
      8. Figure 2.8
      9. Figure 2.9
  5. Chapter 3 Why: Task Abstraction
    1. 3.1 The Big Picture
    2. 3.2 Why Analyze Tasks Abstractly?
    3. 3.3 Who: Designer or User
    4. 3.4 Actions
      1. 3.4.1 Analyze
        1. 3.4.1.1 Discover
        2. 3.4.1.2 Present
        3. 3.4.1.3 Enjoy
      2. 3.4.2 Produce
        1. 3.4.2.1 Annotate
        2. 3.4.2.2 Record
        3. 3.4.2.3 Derive
      3. 3.4.3 Search
        1. 3.4.3.1 Lookup
        2. 3.4.3.2 Locate
        3. 3.4.3.3 Browse
        4. 3.4.3.4 Explore
      4. 3.4.4 Query
        1. 3.4.4.1 Identify
        2. 3.4.4.2 Compare
        3. 3.4.4.3 Summarize
    5. 3.5 Targets
    6. 3.6 How: A Preview
    7. 3.7 Analyzing and Deriving: Examples
      1. 3.7.1 Comparing Two Idioms
      2. 3.7.2 Deriving One Attribute
      3. 3.7.3 Deriving Many New Attributes
    8. 3.8 Further Reading
      1. Figure 3.1
      2. Figure 3.2
      3. Figure 3.3
      4. Figure 3.4
      5. Figure 3.5
      6. Figure 3.6
      7. Figure 3.7
      8. Figure 3.8
      9. Figure 3.9
      10. Figure 3.10
      11. Figure 3.11
      12. Figure 3.12
      13. Figure 3.13
  6. Chapter 4 Analysis: Four Levels for Validation
    1. 4.1 The Big Picture
    2. 4.2 Why Validate?
    3. 4.3 Four Levels of Design
      1. 4.3.1 Domain Situation
      2. 4.3.2 Task and Data Abstraction
      3. 4.3.3 Visual Encoding and Interaction Idiom
      4. 4.3.4 Algorithm
    4. 4.4 Angles of Attack
    5. 4.5 Threats to Validity
    6. 4.6 Validation Approaches
      1. 4.6.1 Domain Validation
      2. 4.6.2 Abstraction Validation
      3. 4.6.3 Idiom Validation
      4. 4.6.4 Algorithm Validation
      5. 4.6.5 Mismatches
    7. 4.7 Validation Examples
      1. 4.7.1 Genealogical Graphs
      2. 4.7.2 MatrixExplorer
      3. 4.7.3 Flow Maps
      4. 4.7.4 LiveRAC
      5. 4.7.5 LinLog
      6. 4.7.6 Sizing the Horizon
    8. 4.8 Further Reading
      1. Figure 4.1
      2. Figure 4.2
      3. Figure 4.3
      4. Figure 4.4
      5. Figure 4.5
      6. Figure 4.6
      7. Figure 4.7
      8. Figure 4.8
      9. Figure 4.9
      10. Figure 4.10
      11. Figure 4.11
      12. Figure 4.12
      13. Figure 4.13
      14. Figure 4.14
      15. Figure 4.15
      16. Figure 4.16
      17. Figure 4.17
  7. Chapter 5 Marks and Channels
    1. 5.1 The Big Picture
    2. 5.2 Why Marks and Channels?
    3. 5.3 Defining Marks and Channels
      1. 5.3.1 Channel Types
      2. 5.3.2 Mark Types
    4. 5.4 Using Marks and Channels
      1. 5.4.1 Expressiveness and Effectiveness
      2. 5.4.2 Channel Rankings
    5. 5.5 Channel Effectiveness
      1. 5.5.1 Accuracy
      2. 5.5.2 Discriminability
      3. 5.5.3 Separability
      4. 5.5.4 Popout
      5. 5.5.5 Grouping
    6. 5.6 Relative versus Absolute Judgements
    7. 5.7 Further Reading
      1. Figure 5.1
      2. Figure 5.2
      3. Figure 5.3
      4. Figure 5.4
      5. Figure 5.5
      6. Figure 5.6
      7. Figure 5.7
      8. Figure 5.8
      9. Figure 5.9
      10. Figure 5.10
      11. Figure 5.11
      12. Figure 5.12
      13. Figure 5.13
      14. Figure 5.14
      15. Figure 5.15
  8. Chapter 6 Rules of Thumb
    1. 6.1 The Big Picture
    2. 6.2 Why and When to Follow Rules of Thumb?
    3. 6.3 No Unjustified 3D
      1. 6.3.1 The Power of the Plane
      2. 6.3.2 The Disparity of Depth
      3. 6.3.3 Occlusion Hides Information
      4. 6.3.4 Perspective Distortion Dangers
      5. 6.3.5 Other Depth Cues
      6. 6.3.6 Tilted Text Isn’t Legibile
      7. 6.3.7 Benefits of 3D: Shape Perception
      8. 6.3.8 Justification and Alternatives
      9. 6.3.9 Empirical Evidence
    4. 6.4 No Unjustified 2D
    5. 6.5 Eyes Beat Memory
      1. 6.5.1 Memory and Attention
      2. 6.5.2 Animation versus Side-by-Side Views
      3. 6.5.3 Change Blindness
    6. 6.6 Resolution over Immersion
    7. 6.7 Overview First, Zoom and Filter, Details on Demand
    8. 6.8 Responsiveness Is Required
      1. 6.8.1 Visual Feedback
      2. 6.8.2 Latency and Interaction Design
      3. 6.8.3 Interactivity Costs
    9. 6.9 Get It Right in Black and White
    10. 6.10 Function First, Form Next
    11. 6.11 Further Reading
      1. Figure 6.1
      2. Figure 6.2
      3. Figure 6.3
      4. Figure 6.4
      5. Figure 6.5
      6. Figure 6.6
      7. Figure 6.7
      8. Figure 6.8
      9. Figure 6.9
  9. Chapter 7 Arrange Tables
    1. 7.1 The Big Picture
    2. 7.2 Why Arrange?
    3. 7.3 Arrange by Keys and Values
    4. 7.4 Express: Quantitative Values
    5. 7.5 Separate, Order, and Align: Categorical Regions
      1. 7.5.1 List Alignment: One Key
      2. 7.5.2 Matrix Alignment: Two Keys
      3. 7.5.3 Volumetric Grid: Three Keys
      4. 7.5.4 Recursive Subdivision: Multiple Keys
    6. 7.6 Spatial Axis Orientation
      1. 7.6.1 Rectilinear Layouts
      2. 7.6.2 Parallel Layouts
      3. 7.6.3 Radial Layouts
    7. 7.7 Spatial Layout Density
      1. 7.7.1 Dense
      2. 7.7.2 Space-Filling
    8. 7.8 Further Reading
      1. Figure 7.1
      2. Figure 7.2
      3. Figure 7.3
      4. Figure 7.4
      5. Figure 7.5
      6. Figure 7.6
      7. Figure 7.7
      8. Figure 7.8
      9. Figure 7.9
      10. Figure 7.10
      11. Figure 7.11
      12. Figure 7.12
      13. Figure 7.13
      14. Figure 7.14
      15. Figure 7.15
      16. Figure 7.16
      17. Figure 7.17
      18. Figure 7.18
      19. Figure 7.19
      20. Figure 7.20
  10. Chapter 8 Arrange Spatial Data
    1. 8.1 The Big Picture
    2. 8.2 Why Use Given?
    3. 8.3 Geometry
      1. 8.3.1 Geographic Data
      2. 8.3.2 Other Derived Geometry
    4. 8.4 Scalar Fields: One Value
      1. 8.4.1 Isocontours
      2. 8.4.2 Direct Volume Rendering
    5. 8.5 Vector Fields: Multiple Values
      1. 8.5.1 Flow Glyphs
      2. 8.5.2 Geometric Flow
      3. 8.5.3 Texture Flow
      4. 8.5.4 Feature Flow
    6. 8.6 Tensor Fields: Many Values
    7. 8.7 Further Reading
      1. Figure 8.1
      2. Figure 8.2
      3. Figure 8.3
      4. Figure 8.4
      5. Figure 8.5
      6. Figure 8.6
      7. Figure 8.7
      8. Figure 8.8
      9. Figure 8.9
      10. Figure 8.10
      11. Figure 8.11
      12. Figure 8.12
  11. Chapter 9 Arrange Networks and Trees
    1. 9.1 The Big Picture
    2. 9.2 Connection: Link Marks
    3. 9.3 Matrix Views
    4. 9.4 Costs and Benefits: Connection versus Matrix
    5. 9.5 Containment: Hierarchy Marks
    6. 9.6 Further Reading
      1. Figure 9.1
      2. Figure 9.2
      3. Figure 9.3
      4. Figure 9.4
      5. Figure 9.5
      6. Figure 9.6
      7. Figure 9.7
      8. Figure 9.8
      9. Figure 9.9
      10. Figure 9.10
  12. Chapter 10 Map Color and Other Channels
    1. 10.1 The Big Picture
    2. 10.2 Color Theory
      1. 10.2.1 Color Vision
      2. 10.2.2 Color Spaces
      3. 10.2.3 Luminance, Saturation, and Hue
      4. 10.2.4 Transparency
    3. 10.3 Colormaps
      1. 10.3.1 Categorical Colormaps
      2. 10.3.2 Ordered Colormaps
      3. 10.3.3 Bivariate Colormaps
      4. 10.3.4 Colorblind-Safe Colormap Design
    4. 10.4 Other Channels
      1. 10.4.1 Size Channels
      2. 10.4.2 Angle Channel
      3. 10.4.3 Curvature Channel
      4. 10.4.4 Shape Channel
      5. 10.4.5 Motion Channels
      6. 10.4.6 Texture and Stippling
    5. 10.5 Further Reading
      1. Figure 10.1
      2. Figure 10.2
      3. Figure 10.3
      4. Figure 10.4
      5. Figure 10.5
      6. Figure 10.6
      7. Figure 10.7
      8. Figure 10.8
      9. Figure 10.9
      10. Figure 10.10
      11. Figure 10.11
      12. Figure 10.12
      13. Figure 10.13
      14. Figure 10.14
  13. Chapter 11 Manipulate View
    1. 11.1 The Big Picture
    2. 11.2 Why Change?
    3. 11.3 Change View over Time
    4. 11.4 Select Elements
      1. 11.4.1 Selection Design Choices
      2. 11.4.2 Highlighting
      3. 11.4.3 Selection Outcomes
    5. 11.5 Navigate: Changing Viewpoint
      1. 11.5.1 Geometric Zooming
      2. 11.5.2 Semantic Zooming
      3. 11.5.3 Constrained Navigation
    6. 11.6 Navigate: Reducing Attributes
      1. 11.6.1 Slice
      2. 11.6.2 Cut
      3. 11.6.3 Project
    7. 11.7 Further Reading
      1. Figure 11.1
      2. Figure 11.2
      3. Figure 11.3
      4. Figure 11.4
      5. Figure 11.5
      6. Figure 11.6
      7. Figure 11.7
      8. Figure 11.8
      9. Figure 11.9
  14. Chapter 12 Facet into Multiple Views
    1. 12.1 The Big Picture
    2. 12.2 Why Facet?
    3. 12.3 Juxtapose and Coordinate Views
      1. 12.3.1 Share Encoding: Same/Different
      2. 12.3.2 Share Data: All, Subset, None
      3. 12.3.3 Share Navigation: Synchronize
      4. 12.3.4 Combinations
      5. 12.3.5 Juxtapose Views
    4. 12.4 Partition into Views
      1. 12.4.1 Regions, Glyphs, and Views
      2. 12.4.2 List Alignments
      3. 12.4.3 Matrix Alignments
      4. 12.4.4 Recursive Subdivision
    5. 12.5 Superimpose Layers
      1. 12.5.1 Visually Distinguishable Layers
      2. 12.5.2 Static Layers
      3. 12.5.3 Dynamic Layers
    6. 12.6 Further Reading
      1. Figure 12.1
      2. Figure 12.2
      3. Figure 12.3
      4. Figure 12.4
      5. Figure 12.5
      6. Figure 12.6
      7. Figure 12.7
      8. Figure 12.8
      9. Figure 12.9
      10. Figure 12.10
      11. Figure 12.11
      12. Figure 12.12
      13. Figure 12.13
      14. Figure 12.14
      15. Figure 12.15
      16. Figure 12.16
      17. Figure 12.17
  15. Chapter 13 Reduce Items and Attributes
    1. 13.1 The Big Picture
    2. 13.2 Why Reduce?
    3. 13.3 Filter
      1. 13.3.1 Item Filtering
      2. 13.3.2 Attribute Filtering
    4. 13.4 Aggregate
      1. 13.4.1 Item Aggregation
      2. 13.4.2 Spatial Aggregation
      3. 13.4.3 Attribute Aggregation: Dimensionality Reduction
        1. 13.4.3.1 Why and When to Use DR?
        2. 13.4.3.2 How to Show DR Data?
    5. 13.5 Further Reading
      1. Figure 13.1
      2. Figure 13.2
      3. Figure 13.3
      4. Figure 13.4
      5. Figure 13.5
      6. Figure 13.6
      7. Figure 13.7
      8. Figure 13.8
      9. Figure 13.9
      10. Figure 13.10
      11. Figure 13.11
      12. Figure 13.12
      13. Figure 13.13
  16. Chapter 14 Embed: Focus+Context
    1. 14.1 The Big Picture
    2. 14.2 Why Embed?
    3. 14.3 Elide
    4. 14.4 Superimpose
    5. 14.5 Distort
    6. 14.6 Costs and Benefits: Distortion
    7. 14.7 Further Reading
      1. Figure 14.1
      2. Figure 14.2
      3. Figure 14.3
      4. Figure 14.4
      5. Figure 14.5
      6. Figure 14.6
      7. Figure 14.7
      8. Figure 14.8
      9. Figure 14.9
      10. Figure 14.10
  17. Chapter 15 Analysis Case Studies
    1. 15.1 The Big Picture
    2. 15.2 Why Analyze Case Studies?
    3. 15.3 Graph-Theoretic Scagnostics
    4. 15.4 VisDB
    5. 15.5 Hierarchical Clustering Explorer
    6. 15.6 PivotGraph
    7. 15.7 InterRing
    8. 15.8 Constellation
    9. 15.9 Further Reading
      1. Figure 15.1
      2. Figure 15.2
      3. Figure 15.3
      4. Figure 15.4
      5. Figure 15.5
      6. Figure 15.6
      7. Figure 15.7
      8. Figure 15.8
      9. Figure 15.9
      10. Figure 15.10
      11. Figure 15.11
      12. Figure 15.12
      13. Figure 15.13
      14. Figure 15.14
      15. Figure 15.15
      16. Figure 15.16
      17. Figure 15.17
      18. Figure 15.18
      19. Figure 15.19
      20. Figure 15.20
  18. Figure Credits
  19. Bibliography

Product information

  • Title: Visualization Analysis and Design
  • Author(s): Tamara Munzner
  • Release date: December 2014
  • Publisher(s): A K Peters/CRC Press
  • ISBN: 9781498759717