Spis treści: „Beautiful Visualization: Looking at Data Through the Eyes of Experts”

Spis treści publikacji pt. „Beautiful Visualization: Looking at Data Through the Eyes of Experts”.

Tytuł: „Beautiful Visualization: Looking at Data Through the Eyes of Experts”
Autorzy: Julie Steele, Noah Iliinsky
Wydawca: O’Reilly Media
Data wydania: 2010-07-01
Język: Angielski
Liczba stron: 418

Spis treści

Spis treści / Table of contents

Preface / xi

1. On Beauty / 1

Noah Iliinsky

What Is Beauty? / 1

Learning from the Classics / 3

How Do We Achieve Beauty? / 6

Putting It Into Practice / 11

Conclusion / 13

2. Once Upon a Stacked Time Series / 15

Matthias Shapiro

Question + Visual Data + Context = Story / 16

Steps for Creating an Effective Visualization / 18

Hands-on Visualization Creation / 26

Conclusion / 36

3. Wordle / 37

Jonathan Feinberg

Wordle’s Origins / 38

How Wordle Works / 46

Is Wordle Good Information Visualization? / 54

How Wordle Is Actually Used / 57

Conclusion / 58

Acknowledgments / 58

References / 58

4. Color: The Cinderella of Data Visualization / 59

Michael Driscoll

Why Use Color in Data Graphics? / 59

Luminosity As a Means of Recovering Local Density / 64

Looking Forward: What About Animation? / 65

Methods / 65

Conclusion / 67

References and Further Reading / 67

5. Mapping Information: Redesigning the New York City

Subway Map / 69

Eddie Jabbour, as told to Julie Steele

The Need for a Better Tool / 69

London Calling / 71

New York Blues / 72

Better Tools Allow for Better Tools / 73

Size Is Only One Factor / 73

Looking Back to Look Forward / 75

New York’s Unique Complexity / 77

Geography Is About Relationships / 79

Sweat the Small Stuff / 85

Conclusion / 89

6. Flight Patterns: A Deep Dive / 91

Aaron Koblin with Valdean Klump

Techniques and Data / 94

Color / 95

Motion / 98

Anomalies and Errors / 99

Conclusion / 101

Acknowledgments / 102

7. Your Choices Reveal Who You Are: Mining and Visualizing Social Patterns / 103

Valdis Krebs

Early Social Graphs / 103

Social Graphs of Amazon Book Purchasing Data / 111

Conclusion / 121

References / 122

8. Visualizing the U.S. Senate Social Graph(1991–2009) / 123

Andrew Odewahn

Building the Visualization / 124

The Story That Emerged / 131

What Makes It Beautiful? / 136

And What Makes It Ugly? / 137

Conclusion / 141

References / 142

9. The Big Picture: Search and Discovery / 143

Todd Holloway

The Visualization Technique / 144

YELLOWPAGES.COM / 144

The Netflix Prize / 151

Creating Your Own / 156

Conclusion / 156

References / 156

10. Finding Beautiful Insights in the Chaos of Social Network Visualizations / 157

Adam Perer

Visualizing Social Networks / 157

Who Wants to Visualize Social Networks? / 160

The Design of SocialAction / 162

Case Studies: From Chaos to Beauty / 166

References / 173

11. Beautiful History: Visualizing Wikipedia / 175

Martin Wattenberg and Fernanda Viégas

Depicting Group Editing / 175

History Flow in Action / 184

Chromogram: Visualizing One Person at a Time / 186

Conclusion / 191

12. Turning a Table into a Tree: Growing Parallel Sets into a Purposeful Project / 193

Robert Kosara

Categorical Data / 194

Parallel Sets / 195

Visual Redesign / 197

A New Data Model / 199

The Database Model / 200

Growing the Tree / 202

Parallel Sets in the Real World / 203

Conclusion / 204

References / 204

13. The Design of “X by Y” / 205

Moritz Stefaner

Briefing and Conceptual Directions / 205

Understanding the Data Situation / 207

Exploring the Data / 208

First Visual Drafts / 211

The Final Product / 216

Conclusion / 223

Acknowledgments / 225

References / 225

14. Revealing Matrices / 227

Maximilian Schich

The More, the Better? / 228

Databases As Networks / 230

Data Model Definition Plus Emergence / 231

Network Dimensionality / 233

The Matrix Macroscope / 235

Reducing for Complexity / 239

Further Matrix Operations / 246

The Refined Matrix / 247

Scaling Up / 247

Further Applications / 249

Conclusion / 250

Acknowledgments / 250

References / 250

15. This Was 1994: Data Exploration with the NYTimes Article Search API / 255

Jer Thorp

Getting Data: The Article Search API / 255

Managing Data: Using Processing / 257

Three Easy Steps / 262

Faceted Searching / 263

Making Connections / 265

Conclusion / 270

16. A Day in the Life of the New York Times / 271

Michael Young and Nick Bilton

Collecting Some Data / 272

Let’s Clean ’Em First / 273

Python, Map/Reduce, and Hadoop / 274

The First Pass at the Visualization / 274

Scene 1, Take 1 / 277

Scene 1, Take 2 / 279

The Second Pass at the Visualization / 280

Visual Scale and Other Visualization Optimizations / 284

Getting the Time Lapse Working / 285

So, What Do We Do with This Thing? / 287

Conclusion / 287

Acknowledgments / 290

17 Immersed in Unfolding Complex Systems / 291

Lance Putnam, Graham Wakefield, Haru Ji, Basak Alper,

Dennis Adderton, and Professor JoAnn Kuchera-Morin

Our Multimodal Arena / 291

Our Roadmap to Creative Thinking / 293

Project Discussion / 296

Conclusion / 309

References / 309

18 Postmortem Visualization: The Real Gold Standard / 311

Anders Persson

Background / 312

Impact on Forensic Work / 312

The Virtual Autopsy Procedure / 315

The Future for Virtual Autopsies / 325

Conclusion / 327

References and Suggested Reading / 327

19 Animation for Visualization:

Opportunities and Drawbacks / 329

Danyel Fisher

Principles of Animation / 330

Animation in Scientific Visualization / 331

Learning from Cartooning / 331

Presentation Is Not Exploration / 338

Types of Animation / 339

Staging Animations with DynaVis / 344

Principles of Animation / 348

Conclusion: Animate or Not? / 349

Further Reading / 350

Acknowledgments / 350

References / 351

20 Visualization: Indexed / 353

Jessica Hagy

Visualization: It’s an Elephant / 353

Visualization: It’s Art / 355

Visualization: It’s Business. / 356

Visualization: It’s Timeless. / 357

Visualization: It’s Right Now. / 359

Visualization: It’s Coded. / 360

Visualization: It’s Clear. / 361

Visualization: It’s Learnable. / 363

Visualization: It’s a Buzzword. / 365

Visualization: It’s an Opportunity. / 366

Contributors / 369

Index / 375

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