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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.

Julie Steele、Noah Iliinsky等編。

Table of Contents
Chapter 1 On Beauty What Is Beauty?
Learning from the Classics
How Do We Achieve Beauty?
Putting It Into Practice
Conclusion
Chapter 2 Once Upon a Stacked Time Series Question + Visual Data + Context = Story
Steps for Creating an Effective Visualization
Hands-on Visualization Creation
Conclusion
Chapter 3 Wordle Wordle's Origins
How Wordle Works
Is Wordle Good Information Visualization?
How Wordle Is Actually Used
Conclusion
Acknowledgments
References
Chapter 4 Color: The Cinderella of Data Visualization Why Use Color in Data Graphics?
Luminosity As a Means of Recovering Local Density
Looking Forward: What About Animation?
Methods
Conclusion
References and Further Reading
Chapter 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
Sweat the Small Stuff
Conclusion
Chapter 6 Flight Patterns: A Deep Dive Techniques and Data
Color
Motion
Anomalies and Errors
Conclusion
Acknowledgments
Chapter 7 Your Choices Reveal Who You Are: Mining and Visualizing Social Patterns Early Social Graphs
Social Graphs of Amazon Book Purchasing Data
Conclusion
References
Chapter 8 Visualizing the U.S. Senate Social Graph (1991–2009) Building the Visualization
The Story That Emerged
What Makes It Beautiful?
And What Makes It Ugly?
Conclusion
References
Chapter 9 The Big Picture: Search and Discovery The Visualization Technique
YELLOWPAGES.COM
The Netflix Prize
Creating Your Own
Conclusion
References
Chapter 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
References
Chapter 11 Beautiful History: Visualizing Wikipedia Depicting Group Editing
History Flow in Action
Chromogram: Visualizing One Person at a Time
Conclusion
Chapter 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
Chapter 13 The Design of "X by Y" Briefing and Conceptual Directions
Understanding the Data Situation
Exploring the Data
First Visual Drafts
The Final Product
Conclusion
Acknowledgments
References
Chapter 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
Chapter 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
Chapter 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
Scene 1, Take 1
Scene 1, Take 2
The Second Pass at the Visualization
Visual Scale and Other Visualization Optimizations
Getting the Time Lapse Working
So, What Do We Do with This Thing?
Conclusion
Acknowledgments
Chapter 17 Immersed in Unfolding Complex Systems Our Multimodal Arena
Our Roadmap to Creative Thinking
Project Discussion
Conclusion
References
Chapter 18 Postmortem Visualization: The Real Gold Standard Background
Impact on Forensic Work
The Virtual Autopsy Procedure
The Future for Virtual Autopsies
Conclusion
References and Suggested Reading
Chapter 19 Animation for Visualization: Opportunities and Drawbacks Principles of Animation
Animation in Scientific Visualization
Learning from Cartooning
Presentation Is Not Exploration
Types of Animation
Staging Animations with DynaVis
Principles of Animation
Conclusion: Animate or Not?
Further Reading
Acknowledgments
References
Chapter 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.
Appendix Contributors
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