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Beautiful Visualization. Looking at Data through the Eyes of Experts - Helion

Beautiful Visualization. Looking at Data through the Eyes of Experts
ebook
Autor: Julie Steele, Noah Iliinsky
ISBN: 978-1-4493-9068-6
stron: 418, Format: ebook
Data wydania: 2010-04-23
Księgarnia: Helion

Cena książki: 160,65 zł (poprzednio: 186,80 zł)
Oszczędzasz: 14% (-26,15 zł)

Dodaj do koszyka Beautiful Visualization. Looking at Data through the Eyes of Experts

Visualization is the graphic presentation of data -- portrayals meant to reveal complex information at a glance. Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding.

This book examines the methods of two dozen visualization experts who approach their projects from a variety of perspectives -- as artists, designers, commentators, scientists, analysts, statisticians, and more. Together they demonstrate how visualization can help us make sense of the world.

  • Explore the importance of storytelling with a simple visualization exercise
  • Learn how color conveys information that our brains recognize before we're fully aware of it
  • Discover how the books we buy and the people we associate with reveal clues to our deeper selves
  • Recognize a method to the madness of air travel with a visualization of civilian air traffic
  • Find out how researchers investigate unknown phenomena, from initial sketches to published papers

Contributors include:

Nick Bilton,Michael E. Driscoll,Jonathan Feinberg,Danyel Fisher,Jessica Hagy,Gregor Hochmuth,Todd Holloway,Noah Iliinsky,Eddie Jabbour,Valdean Klump,Aaron Koblin,Robert Kosara,Valdis Krebs,JoAnn Kuchera-Morin et al.,Andrew Odewahn,Adam Perer,Anders Persson,Maximilian Schich,Matthias Shapiro,Julie Steele,Moritz Stefaner,Jer Thorp,Fernanda Viegas,Martin Wattenberg,and Michael Young.

Dodaj do koszyka Beautiful Visualization. Looking at Data through the Eyes of Experts

 

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Dodaj do koszyka Beautiful Visualization. Looking at Data through the Eyes of Experts

Spis treści

Beautiful Visualization. Looking at Data through the Eyes of Experts eBook -- spis treści

  • Beautiful Visualization
  • SPECIAL OFFER: Upgrade this ebook with OReilly
  • 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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