Beautiful Visualization · Looking at Data through the Eyes of Experts by Steele, Julie -- Read -- Imperial Library of Trantor

Index

Beautiful Visualization 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

Wordle’s 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 Naïve 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 (1991–2009)

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 Copyright

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