Beautiful Visualization

Table of contents

1. Beautiful Visualization
2. Preface
   1. How This Book Is Organized
   2. Conventions Used in This Book
   3. Using Code Examples
   4. How to Contact Us
   5. Safari® Books Online
   6. Acknowledgments
3. 1. On Beauty
   1. What Is Beauty?
      1. Novel
      2. Informative
      3. Efficient
      4. Aesthetic
   2. Learning from the Classics
      1. The Periodic Table of the Elements
      2. The London Underground Map
      3. Other Subway Maps and Periodic Tables Are Weak Imitations
   3. How Do We Achieve Beauty?
      1. Step Outside Default Formats
      2. Make It Informative
         1. Intended message
            1. Context of use.
      3. Make It Efficient
         1. Visually emphasize what matters
         2. Use axes to convey meaning and give free information
         3. Slice along relevant divisions
         4. Use conventions thoughtfully
      4. Leverage the Aesthetics
   4. Putting It Into Practice
   5. Conclusion
4. 2. Once Upon a Stacked Time Series
   1. Question + Visual Data + Context = Story
   2. Steps for Creating an Effective Visualization
      1. Formulate the Question
      2. Gather the Data
      3. Apply a Visual Representation
         1. Size
         2. Color
         3. Location
         4. Networks
         5. Time
         6. Using multiple visual presentation methods
   3. Hands-on Visualization Creation
      1. Data Tasks
         1. Gathering the data
         2. Sorting the data: The discovery version
         3. Sorting the data: The technical version
      2. Formulating the Question
         1. Grouping the data
      3. Applying the Visual Presentation
         1. A note about area and circles
         2. Presenting the data with country maps
      4. Building the Visual
   4. Conclusion
5. 3. Wordle
   1. Wordle's Origins
      1. Anatomy of a Tag Cloud
      2. Filling a Two-Dimensional Space
   2. How Wordle Works
      1. Text Analysis
         1. Finding words
         2. Determining the script
         3. Guessing the language and removing stop words
         4. Assigning weights to words
      2. Layout
         1. Weighted words into shapes
         2. The playing field
         3. Placement
         4. Intersection testing
   3. Is Wordle Good Information Visualization?
      1. Word Sizing Is Naïve
      2. Color Is Meaningless
      3. Fonts Are Fanciful
      4. Word Count Is Not Specific Enough
   4. How Wordle Is Actually Used
      1. Using Wordle for Traditional Infovis
   5. Conclusion
   6. Acknowledgments
   7. References
6. 4. Color: The Cinderella of Data Visualization
   1. Why Use Color in Data Graphics?
      1. 1. Vary Your Plotting Symbols
      2. 2. Use Small Multiples on a Canvas
      3. 3. Add Color to Your Data
      4. So Why Bother with Color?
      5. If Color Is Three-Dimensional, Can I Encode Three Dimensions with It?
   2. Luminosity As a Means of Recovering Local Density
   3. Looking Forward: What About Animation?
   4. Methods
   5. Conclusion
   6. References and Further Reading
7. 5. Mapping Information: Redesigning the New York City Subway Map
   1. The Need for a Better Tool
   2. London Calling
   3. New York Blues
   4. Better Tools Allow for Better Tools
   5. Size Is Only One Factor
   6. Looking Back to Look Forward
   7. New York's Unique Complexity
   8. Geography Is About Relationships
      1. Include the Essentials
      2. Leave Out the Clutter
      3. Coloring Inside the Lines
   9. Sweat the Small Stuff
      1. Try It On
      2. Users Are Only Human
      3. A City of Neighborhoods
      4. One Size Does Not Fit All
   10. Conclusion
8. 6. Flight Patterns: A Deep Dive
   1. Techniques and Data
   2. Color
   3. Motion
   4. Anomalies and Errors
   5. Conclusion
   6. Acknowledgments
9. 7. Your Choices Reveal Who You Are: Mining and Visualizing Social Patterns
   1. Early Social Graphs
   2. Social Graphs of Amazon Book Purchasing Data
      1. Determining the Network Around a Particular Book
      2. Putting the Results to Work
      3. Social Networks of Political Books
   3. Conclusion
   4. References
10. 8. Visualizing the U.S. Senate Social Graph (1991–2009)
    1. Building the Visualization
       1. Gathering the Raw Data
       2. Computing the Voting Affinity Matrix
       3. Visualizing the Data with GraphViz
    2. The Story That Emerged
    3. What Makes It Beautiful?
    4. And What Makes It Ugly?
       1. Labels
       2. Orientation
       3. Party Affiliation
    5. Conclusion
    6. References
11. 9. The Big Picture: Search and Discovery
    1. The Visualization Technique
    2. YELLOWPAGES.COM
       1. Query Logs
       2. Categorical Similarity
       3. Visualization As a Substrate for Analytics
       4. The Visualization
       5. Advantages and Disadvantages of the Technique
    3. The Netflix Prize
       1. Preference Similarity
       2. Labeling
       3. Closer Looks
    4. Creating Your Own
    5. Conclusion
    6. References
12. 10. Finding Beautiful Insights in the Chaos of Social Network Visualizations
    1. Visualizing Social Networks
    2. Who Wants to Visualize Social Networks?
    3. The Design of SocialAction
    4. Case Studies: From Chaos to Beauty
       1. The Social Network of Senatorial Voting
       2. The Social Network of Terrorists
    5. References
13. 11. Beautiful History: Visualizing Wikipedia
    1. Depicting Group Editing
       1. The Data
       2. History Flow: Visualizing Edit Histories
       3. Age of Edit
       4. Authorship
       5. Individual Authors
    2. History Flow in Action
       1. Communicating the Results
    3. Chromogram: Visualizing One Person at a Time
       1. Showing All the Data
       2. What We Saw
       3. Analyzing the Data
    4. Conclusion
14. 12. Turning a Table into a Tree: Growing Parallel Sets into a Purposeful Project
    1. Categorical Data
    2. Parallel Sets
    3. Visual Redesign
    4. A New Data Model
    5. The Database Model
    6. Growing the Tree
    7. Parallel Sets in the Real World
    8. Conclusion
    9. References
15. 13. The Design of "X by Y"
    1. Briefing and Conceptual Directions
    2. Understanding the Data Situation
    3. Exploring the Data
    4. First Visual Drafts
       1. The Visual Principle
    5. The Final Product
       1. All Submissions
       2. By Prize
       3. By Category
       4. By Country
       5. By Year
       6. By Year and Category
       7. Exhibition
    6. Conclusion
    7. Acknowledgments
    8. References
16. 14. Revealing Matrices
    1. The More, the Better?
    2. Databases As Networks
    3. Data Model Definition Plus Emergence
    4. Network Dimensionality
    5. The Matrix Macroscope
    6. Reducing for Complexity
    7. Further Matrix Operations
    8. The Refined Matrix
    9. Scaling Up
    10. Further Applications
    11. Conclusion
    12. Acknowledgments
    13. References
17. 15. This Was 1994: Data Exploration with the NYTimes Article Search API
    1. Getting Data: The Article Search API
    2. Managing Data: Using Processing
    3. Three Easy Steps
    4. Faceted Searching
    5. Making Connections
    6. Conclusion
18. 16. A Day in the Life of the New York Times
    1. Collecting Some Data
    2. Let's Clean 'Em First
    3. Python, Map/Reduce, and Hadoop
    4. The First Pass at the Visualization
       1. Processing
       2. The Underlay Map
       3. Now, Where's That Data We Just Processed?
    5. Scene 1, Take 1
       1. No Scale
       2. No Sense of Time
       3. Time-Lapse
    6. Scene 1, Take 2
       1. Let's Run This Thing and See What Happens!
    7. The Second Pass at the Visualization
       1. Back to That Scale Problem
       2. Massaging the Data Some More
       3. The New Data Format
    8. Visual Scale and Other Visualization Optimizations
    9. Getting the Time Lapse Working
       1. Semiautomating
       2. Math for Rendering Time-Lapse Video
    10. So, What Do We Do with This Thing?
    11. Conclusion
    12. Acknowledgments
19. 17. Immersed in Unfolding Complex Systems
    1. Our Multimodal Arena
    2. Our Roadmap to Creative Thinking
       1. Beauty and Symmetry
       2. The Computational Medium
       3. Interpretation As a Filter
    3. Project Discussion
       1. Allobrain
       2. Artificial Nature
       3. Hydrogen Bond
       4. Hydrogen Atom
       5. Hydrogen Atom with Spin
       6. Coherent Precession of Electron Spin
    4. Conclusion
    5. References
20. 18. Postmortem Visualization: The Real Gold Standard
    1. Background
    2. Impact on Forensic Work
    3. The Virtual Autopsy Procedure
       1. Data Acquisition
          1. Computed tomography: Use of dual energy CT
          2. MRI: Use of synthetic magnetic resonance imaging
       2. Visualization: Image Analysis
       3. Objective Documentation
       4. Advantages and Disadvantages of Virtual Autopsy
    4. The Future for Virtual Autopsies
    5. Conclusion
    6. References and Suggested Reading
21. 19. Animation for Visualization: Opportunities and Drawbacks
    1. Principles of Animation
    2. Animation in Scientific Visualization
    3. Learning from Cartooning
       1. The Downsides of Animation
       2. GapMinder and Animated Scatterplots
          1. Too many dots?
       3. Testing Animated Scatterplots
          1. Exploration with animation is slower
          2. Animation is less accurate
    4. Presentation Is Not Exploration
    5. Types of Animation
       1. Dynamic Data, Animated Recentering
       2. A Taxonomy of Animations
    6. Staging Animations with DynaVis
    7. Principles of Animation
    8. Conclusion: Animate or Not?
    9. Further Reading
    10. Acknowledgments
    11. References
22. 20. Visualization: Indexed.
    1. Visualization: It's an Elephant.
    2. Visualization: It's Art.
    3. Visualization: It's Business.
    4. Visualization: It's Timeless.
    5. Visualization: It's Right Now.
    6. Visualization: It's Coded.
    7. Visualization: It's Clear.
    8. Visualization: It's Learnable.
    9. Visualization: It's a Buzzword.
    10. Visualization: It's an Opportunity.
23. A. Contributors
24. B. Colophon
25. Index
26. About the Authors
27. Copyright