Julie Steele

Beautiful Visualization (eBook, PDF)

Looking at Data through the Eyes of Experts

• Format: PDF

Produktbeschreibung

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 exerciseLearn how color conveys information that our brains recognize before we're fully aware of itDiscover how the books we buy and the people we associate with reveal clues to our deeper selvesRecognize a method to the madness of air travel with a visualization of civilian air trafficFind 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.

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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 418
• Erscheinungstermin: 23. April 2010
• Englisch
• ISBN-13: 9781449391331
• Artikelnr.: 52935958

Autorenporträt

Julie Steele is an Editor at O'Reilly currently working on titles related to Python, SQL, PHP, web frameworks and CMS, databases (relational and non-relational), big data and cloud computing, and data visualization. She's also interested in data transparency and open government, and recently completed a master's degree in political science at Rutgers University. Noah Illinsky has spent the last several years thinking about effective approaches to creating diagrams and other types of information visualization. He also works in interface and interaction design, all from a functional and user-centered perspective. Before becoming a designer he was a programmer for several years. He has a master's in Technical Communication from the University of Washington, and a bachelor's in Physics from Reed College.

Inhaltsangabe

Preface
How This Book Is Organized
Conventions Used in This Book
Using Code Examples
How to Contact Us
Safari® Books Online
Acknowledgments
Chapter 1: On Beauty
1.1 What Is Beauty?
1.2 Learning from the Classics
1.3 How Do We Achieve Beauty?
1.4 Putting It Into Practice
1.5 Conclusion
Chapter 2: Once Upon a Stacked Time Series
2.1 Question + Visual Data + Context = Story
2.2 Steps for Creating an Effective Visualization
2.3 Hands-on Visualization Creation
2.4 Conclusion
Chapter 3: Wordle
3.1 Wordle's Origins
3.2 How Wordle Works
3.3 Is Wordle Good Information Visualization?
3.4 How Wordle Is Actually Used
3.5 Conclusion
3.6 Acknowledgments
3.7 References
Chapter 4: Color: The Cinderella of Data Visualization
4.1 Why Use Color in Data Graphics?
4.2 Luminosity As a Means of Recovering Local Density
4.3 Looking Forward: What About Animation?
4.4 Methods
4.5 Conclusion
4.6 References and Further Reading
Chapter 5: Mapping Information: Redesigning the New York City Subway Map
5.1 The Need for a Better Tool
5.2 London Calling
5.3 New York Blues
5.4 Better Tools Allow for Better Tools
5.5 Size Is Only One Factor
5.6 Looking Back to Look Forward
5.7 New York's Unique Complexity
5.8 Geography Is About Relationships
5.9 Sweat the Small Stuff
5.10 Conclusion
Chapter 6: Flight Patterns: A Deep Dive
6.1 Techniques and Data
6.2 Color
6.3 Motion
6.4 Anomalies and Errors
6.5 Conclusion
6.6 Acknowledgments
Chapter 7: Your Choices Reveal Who You Are: Mining and Visualizing Social Patterns
7.1 Early Social Graphs
7.2 Social Graphs of Amazon Book Purchasing Data
7.3 Conclusion
7.4 References
Chapter 8: Visualizing the U.S. Senate Social Graph (1991-2009)
8.1 Building the Visualization
8.2 The Story That Emerged
8.3 What Makes It Beautiful?
8.4 And What Makes It Ugly?
8.5 Conclusion
8.6 References
Chapter 9: The Big Picture: Search and Discovery
9.1 The Visualization Technique
9.2 YELLOWPAGES.COM
9.3 The Netflix Prize
9.4 Creating Your Own
9.5 Conclusion
9.6 References
Chapter 10: Finding Beautiful Insights in the Chaos of Social Network Visualizations
10.1 Visualizing Social Networks
10.2 Who Wants to Visualize Social Networks?
10.3 The Design of SocialAction
10.4 Case Studies: From Chaos to Beauty
10.5 References
Chapter 11: Beautiful History: Visualizing Wikipedia
11.1 Depicting Group Editing
11.2 History Flow in Action
11.3 Chromogram: Visualizing One Person at a Time
11.4 Conclusion
Chapter 12: Turning a Table into a Tree: Growing Parallel Sets into a Purposeful Project
12.1 Categorical Data
12.2 Parallel Sets
12.3 Visual Redesign
12.4 A New Data Model
12.5 The Database Model
12.6 Growing the Tree
12.7 Parallel Sets in the Real World
12.8 Conclusion
12.9 References
Chapter 13: The Design of "X by Y"
13.1 Briefing and Conceptual Directions
13.2 Understanding the Data Situation
13.3 Exploring the Data
13.4 First Visual Drafts
13.5 The Final Product
13.6 Conclusion
13.7 Acknowledgments
13.8 References
Chapter 14: Revealing Matrices
14.1 The More, the Better?
14.2 Databases As Networks
14.3 Data Model Definition Plus Emergence
14.4 Network Dimensionality
14.5 The Matrix Macroscope
14.6 Reducing for Complexity
14.7 Further Matrix Operations
14.8 The Refined Matrix
14.9 Scaling Up
14.10 Further Applications
14.11 Conclusion
14.12 Acknowledgments
14.13 References
Chapter 15: This Was 1994: Data Exploration with the NYTimes Article Search API
15.1 Getting Data: The Article Search API
15.2 Managing Data: Using Processing
15.3 Three Easy Steps
15.4 Faceted Searching
15.5 Making Connections
15.6 Conclusion
Chapter 16: A Day in the Life of the New York Times
16.1 Collecting Some Data
16.2 Let's Clean 'Em First
16.3 Python, Map/Reduce, and Hadoop
16.4 The First Pass at the Visualization
16.5 Scene 1, Take 1
16.6 Scene 1, Take 2
16.7 The Second Pass at the Visualization
16.8 Visual Scale and Other Visualization Optimizations
16.9 Getting the Time Lapse Working
16.10 So, What Do We Do with This Thing?
16.11 Conclusion
16.12 Acknowledgments
Chapter 17: Immersed in Unfolding Complex Systems
17.1 Our Multimodal Arena
17.2 Our Roadmap to Creative Thinking
17.3 Project Discussion
17.4 Conclusion
17.5 References
Chapter 18: Postmortem Visualization: The Real Gold Standard
18.1 Background
18.2 Impact on Forensic Work
18.3 The Virtual Autopsy Procedure
18.4 The Future for Virtual Autopsies
18.5 Conclusion
18.6 References and Suggested Reading
Chapter 19: Animation for Visualization: Opportunities and Drawbacks
19.1 Principles of Animation
19.2 Animation in Scientific Visualization
19.3 Learning from Cartooning
19.4 Presentation Is Not Exploration
19.5 Types of Animation
19.6 Staging Animations with DynaVis
19.7 Principles of Animation
19.8 Conclusion: Animate or Not?
19.9 Further Reading
19.10 Acknowledgments
19.11 References
Chapter 20: Visualization: Indexed.
20.1 Visualization: It's an Elephant.
20.2 Visualization: It's Art.
20.3 Visualization: It's Business.
20.4 Visualization: It's Timeless.
20.5 Visualization: It's Right Now.
20.6 Visualization: It's Coded.
20.7 Visualization: It's Clear.
20.8 Visualization: It's Learnable.
20.9 Visualization: It's a Buzzword.
20.10 Visualization: It's an Opportunity.
Contributors
Colophon

Rezensionen

"...'Beautiful Visualization' sollte sich jeder Illustrator [...] neben die Tastatur legen." -- c't, September 2010