Beautiful visualization: [looking at data through the eyes of experts]
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing [u.a.]
O'Reilly
2010
|
| Ausgabe: | 1. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XVI, 397 S. Ill., graph. Darst. |
| ISBN: | 9781449379865 |
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Datensatz im Suchindex
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| adam_text |
Titel: Beautiful visualization
Autor: Steele, Julie
Jahr: 2010
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
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
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
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
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
vi CONTENTS
9 The Big Picture: Search and Discovery.143
Todd Holloway
The Visualization Technique 144
YELL0WPAGES.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
CONTENTS vii
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
viii CONTENTS
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 Aiper,
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
CONTENTS ix
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
X CONTENTS |
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