Beautiful Visualization. Looking at Data through the Eyes of Experts Julie Steele, Noah Iliinsky

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• Beautiful Visualization
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• Preface
  • How This Book Is Organized
  • Conventions Used in This Book
  • Using Code Examples
  • How to Contact Us
  • Safari Books Online
  • Acknowledgments
• 1. On Beauty
  • What Is Beauty?
    • Novel
    • Informative
    • Efficient
    • Aesthetic
  • Learning from the Classics
    • The Periodic Table of the Elements
    • The London Underground Map
    • Other Subway Maps and Periodic Tables Are Weak Imitations
  • How Do We Achieve Beauty?
    • Step Outside Default Formats
    • Make It Informative
      • Intended message
        • Context of use.
    • Make It Efficient
      • Visually emphasize what matters
      • Use axes to convey meaning and give free information
      • Slice along relevant divisions
      • Use conventions thoughtfully
    • Leverage the Aesthetics
  • Putting It Into Practice
  • Conclusion
• 2. Once Upon a Stacked Time Series
  • Question + Visual Data + Context = Story
  • Steps for Creating an Effective Visualization
    • Formulate the Question
    • Gather the Data
    • Apply a Visual Representation
      • Size
      • Color
      • Location
      • Networks
      • Time
      • Using multiple visual presentation methods
  • Hands-on Visualization Creation
    • Data Tasks
      • Gathering the data
      • Sorting the data: The discovery version
      • Sorting the data: The technical version
    • Formulating the Question
      • Grouping the data
    • Applying the Visual Presentation
      • A note about area and circles
      • Presenting the data with country maps
    • Building the Visual
  • Conclusion
• 3. Wordle
  • Wordles Origins
    • Anatomy of a Tag Cloud
    • Filling a Two-Dimensional Space
  • How Wordle Works
    • Text Analysis
      • Finding words
      • Determining the script
      • Guessing the language and removing stop words
      • Assigning weights to words
    • Layout
      • Weighted words into shapes
      • The playing field
      • Placement
      • Intersection testing
  • Is Wordle Good Information Visualization?
    • Word Sizing Is Nave
    • Color Is Meaningless
    • Fonts Are Fanciful
    • Word Count Is Not Specific Enough
  • How Wordle Is Actually Used
    • Using Wordle for Traditional Infovis
  • Conclusion
  • Acknowledgments
  • References
• 4. Color: The Cinderella of Data Visualization
  • Why Use Color in Data Graphics?
    • 1. Vary Your Plotting Symbols
    • 2. Use Small Multiples on a Canvas
    • 3. Add Color to Your Data
    • So Why Bother with Color?
    • If Color Is Three-Dimensional, Can I Encode Three Dimensions with It?
  • Luminosity As a Means of Recovering Local Density
  • Looking Forward: What About Animation?
  • Methods
  • Conclusion
  • References and Further Reading
• 5. Mapping Information: Redesigning the New York City Subway Map
  • The Need for a Better Tool
  • London Calling
  • New York Blues
  • Better Tools Allow for Better Tools
  • Size Is Only One Factor
  • Looking Back to Look Forward
  • New York's Unique Complexity
  • Geography Is About Relationships
    • Include the Essentials
    • Leave Out the Clutter
    • Coloring Inside the Lines
  • Sweat the Small Stuff
    • Try It On
    • Users Are Only Human
    • A City of Neighborhoods
    • One Size Does Not Fit All
  • Conclusion
• 6. Flight Patterns: A Deep Dive
  • Techniques and Data
  • Color
  • Motion
  • Anomalies and Errors
  • Conclusion
  • Acknowledgments
• 7. Your Choices Reveal Who You Are: Mining and Visualizing Social Patterns
  • Early Social Graphs
  • Social Graphs of Amazon Book Purchasing Data
    • Determining the Network Around a Particular Book
    • Putting the Results to Work
    • Social Networks of Political Books
  • Conclusion
  • References
• 8. Visualizing the U.S. Senate Social Graph (19912009)
  • Building the Visualization
    • Gathering the Raw Data
    • Computing the Voting Affinity Matrix
    • Visualizing the Data with GraphViz
  • The Story That Emerged
  • What Makes It Beautiful?
  • And What Makes It Ugly?
    • Labels
    • Orientation
    • Party Affiliation
  • Conclusion
  • References
• 9. The Big Picture: Search and Discovery
  • The Visualization Technique
  • YELLOWPAGES.COM
    • Query Logs
    • Categorical Similarity
    • Visualization As a Substrate for Analytics
    • The Visualization
    • Advantages and Disadvantages of the Technique
  • The Netflix Prize
    • Preference Similarity
    • Labeling
    • Closer Looks
  • Creating Your Own
  • Conclusion
  • References
• 10. Finding Beautiful Insights in the Chaos of Social Network Visualizations
  • Visualizing Social Networks
  • Who Wants to Visualize Social Networks?
  • The Design of SocialAction
  • Case Studies: From Chaos to Beauty
    • The Social Network of Senatorial Voting
    • The Social Network of Terrorists
  • References
• 11. Beautiful History: Visualizing Wikipedia
  • Depicting Group Editing
    • The Data
    • History Flow: Visualizing Edit Histories
    • Age of Edit
    • Authorship
    • Individual Authors
  • History Flow in Action
    • Communicating the Results
  • Chromogram: Visualizing One Person at a Time
    • Showing All the Data
    • What We Saw
    • Analyzing the Data
  • Conclusion
• 12. Turning a Table into a Tree: Growing Parallel Sets into a Purposeful Project
  • Categorical Data
  • Parallel Sets
  • Visual Redesign
  • A New Data Model
  • The Database Model
  • Growing the Tree
  • Parallel Sets in the Real World
  • Conclusion
  • References
• 13. The Design of "X by Y"
  • Briefing and Conceptual Directions
  • Understanding the Data Situation
  • Exploring the Data
  • First Visual Drafts
    • The Visual Principle
  • The Final Product
    • All Submissions
    • By Prize
    • By Category
    • By Country
    • By Year
    • By Year and Category
    • Exhibition
  • Conclusion
  • Acknowledgments
  • References
• 14. Revealing Matrices
  • The More, the Better?
  • Databases As Networks
  • Data Model Definition Plus Emergence
  • Network Dimensionality
  • The Matrix Macroscope
  • Reducing for Complexity
  • Further Matrix Operations
  • The Refined Matrix
  • Scaling Up
  • Further Applications
  • Conclusion
  • Acknowledgments
  • References
• 15. This Was 1994: Data Exploration with the NYTimes Article Search API
  • Getting Data: The Article Search API
  • Managing Data: Using Processing
  • Three Easy Steps
  • Faceted Searching
  • Making Connections
  • Conclusion
• 16. A Day in the Life of the New York Times
  • Collecting Some Data
  • Let's Clean 'Em First
  • Python, Map/Reduce, and Hadoop
  • The First Pass at the Visualization
    • Processing
    • The Underlay Map
    • Now, Where's That Data We Just Processed?
  • Scene 1, Take 1
    • No Scale
    • No Sense of Time
    • Time-Lapse
  • Scene 1, Take 2
    • Let's Run This Thing and See What Happens!
  • The Second Pass at the Visualization
    • Back to That Scale Problem
    • Massaging the Data Some More
    • The New Data Format
  • Visual Scale and Other Visualization Optimizations
  • Getting the Time Lapse Working
    • Semiautomating
    • Math for Rendering Time-Lapse Video
  • So, What Do We Do with This Thing?
  • Conclusion
  • Acknowledgments
• 17. Immersed in Unfolding Complex Systems
  • Our Multimodal Arena
  • Our Roadmap to Creative Thinking
    • Beauty and Symmetry
    • The Computational Medium
    • Interpretation As a Filter
  • Project Discussion
    • Allobrain
    • Artificial Nature
    • Hydrogen Bond
    • Hydrogen Atom
    • Hydrogen Atom with Spin
    • Coherent Precession of Electron Spin
  • Conclusion
  • References
• 18. Postmortem Visualization: The Real Gold Standard
  • Background
  • Impact on Forensic Work
  • The Virtual Autopsy Procedure
    • Data Acquisition
      • Computed tomography: Use of dual energy CT
      • MRI: Use of synthetic magnetic resonance imaging
    • Visualization: Image Analysis
    • Objective Documentation
    • Advantages and Disadvantages of Virtual Autopsy
  • The Future for Virtual Autopsies
  • Conclusion
  • References and Suggested Reading
• 19. Animation for Visualization: Opportunities and Drawbacks
  • Principles of Animation
  • Animation in Scientific Visualization
  • Learning from Cartooning
    • The Downsides of Animation
    • GapMinder and Animated Scatterplots
      • Too many dots?
    • Testing Animated Scatterplots
      • Exploration with animation is slower
      • Animation is less accurate
  • Presentation Is Not Exploration
  • Types of Animation
    • Dynamic Data, Animated Recentering
    • A Taxonomy of Animations
  • Staging Animations with DynaVis
  • Principles of Animation
  • Conclusion: Animate or Not?
  • Further Reading
  • Acknowledgments
  • References
• 20. Visualization: Indexed.
  • Visualization: It's an Elephant.
  • Visualization: It's Art.
  • Visualization: It's Business.
  • Visualization: It's Timeless.
  • Visualization: It's Right Now.
  • Visualization: It's Coded.
  • Visualization: It's Clear.
  • Visualization: It's Learnable.
  • Visualization: It's a Buzzword.
  • Visualization: It's an Opportunity.
• A. Contributors
• B. Colophon
• Index
• About the Authors
• SPECIAL OFFER: Upgrade this ebook with OReilly
• Copyright

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