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1. Flight Patterns Color HD kk kk Pdi ESI yi Tube Ale Figure 6 10 The East Coast wakes up this still image from 7 31 am EST on March 20 2005 shows high activity on the East Coast and virtual stillness on the West Coast except for a few redeye flights flying northeast from Hawaii BEAUTIFUL VISUALIZATION Fiight Pattems Color HD kkk 19255 planea ii 14 10 EST vo Tube fale Figure 6 11 At 4 10 p m EST we see a very different story at this moment air traffic peaks with 19 255 planes in the air On my website I ve also included a video of a 3D visualization that plots altitude along the z axis in a 3D projection In order for this axis to be discernible versus the lateral scale of the continent I ve exaggerated the altitude considerably and it makes for a dense but interesting visualization It doesn t print well however I recommend you take a look online if you re interested Anomalies and Errors Like many datasets the data I used in Flight Patterns contained a number of errors and anomalies some of which I removed For example while trying to find the fast est flight in the dataset I identified one flight that crossed the entire country in 6 minutes clearly an error Another flight zigzagged dramatically and impossibly north and south while crossing the country another clear error I removed both of these flights There were other anomalies however that I kept For example the f2. seas aw Looking at Data Through the Eyes of Experts ar Edited by i A Julie Steele a ey _ amp Noah liinsky O Reilly Ebooks Your bookshelf on your devices ilii Mobi APK When you buy an ebook through oreilly com you get lifetime access to the book and whenever possible we provide it to you in four DRM free file formats PDF epub Kindle compatible mobi and Android apk ebook that you can use on the devices of your choice Our ebook files are fully searchable and you can cut and paste and print them We also alert you when we ve updated the files with corrections and additions Learn more at http oreilly com ebooks You can also purchase O Reilly ebooks through iTunes the Android Marketplace and Amazon com Beautiful Visualization Edited by Julie Steele and Noah Iliinsky Copyright 2010 O Reilly Media Inc All rights reserved Published by O Reilly Media Inc 1005 Gravenstein Highway North Sebastopol CA 95472 O Reilly books may be purchased for educational business or sales promotional use Online editions are also available for most titles Attp my safaribooksonline com For more information contact our corporate institutional sales department 800 998 9938 or corporate oreilly com Editor Julie Steele Indexer Julie Hawks Production Editor Rachel Monaghan Cover Designer Karen Montgomery Copyeditor Rachel Head Interior Designer Ron Bilodeau Proofreader Rac
3. of them guiding thousands of people from origin to destination and with a remarkable safety record In this sense Flight Patterns is more than a data visualization it is a showcase for the miracle of modern air travel Acknowledgments I owe the idea and inspiration for Flight Patterns to two colleagues at UCLA Gabriel Dunne and Scott Hessels In 2005 we started an art project called Celestial Mechanics ittp cmlab com that depicts air and space systems in motion A small part of the proj ect was devoted to aircraft flight data and they provided me with the data so I could build what became Flight Patterns Thanks also to Mark Hansen of UCLA and Wired magazine especially Carl DeTorres for further assistance in procuring the data for these images Want to read more You can find this book at oreilly com in print or ebook format It s also available at your favorite book retailer including iTunes the Android Market Amazon and Barnes amp Noble O REILLY Spreading the knowledge of innovators i ii 2 oreilly com m E
4. 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 17 18 A Day in the Life of the New York Times Michael Young and Nick Bilton 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 Immersed in Unfolding Complex Systems Lance Putnam Graham Wakefield Haru Ji Basak Alper Dennis Adderton and Professor JoAnn Kuchera Morin Our Multimodal Arena Our Roadmap to Creative Thinking Project Discussion Conclusion References Postmortem Visualization The Real Gold Standard Anders Persson Background Impact on Forensic Work The Virtual Autopsy Procedure The Future for Virtual Autopsies Conclusion References and Suggested Reading 272 273 274 274 277 279 280 284 285 287 287 290 291 293 296 309 309 311 312 312 315 325 327 327 CONTENTS ix 19 Animation for Visualization Opportuniti
5. 6 2 and 6 3 When you think about it this is quite interesting The sky is wide open without any natural restrictions whatsoever so planes can travel by any route they choose And yet when looking at Flight Patterns it almost appears as if there s a map to the sky a kind of aerial highway system with designated routes between various destinations You can even make out the roads Why is this happening To be honest I don t know for sure The routes may simply be the most efficient flight paths or more likely I think they may be determined by a combination of many factors the airplanes autopilot systems government mandated flight paths directions from the carriers air traffic control systems rules meant to limit traffic over areas with large populations and meteorological factors such as wind direction and air pressure Regardless I think this tendency is striking because it shows the logical organization of a completely open space It s for this rea son that I chose the word patterns for the name of the project BEAUTIFUL VISUALIZATION Figure 6 2 Closeup of a section of Figure 6 1 that reflects what expected to find throughout the data flight paths going in every direction Figure 6 3 Another closeup that reflects what found to be common instead clear bright lines that indicate flight paths followed closely by high volumes of planes CHAPTER 6 FLIGHT PATTERNS A DEEP DIVE 93 94 The secon
6. ase of obvious errors remove them entirely If anything you should celebrate anomalies rather than removing them and be sure to investigate them for the interesting stories Conclusion Flight Patterns is a simple data visualization and this simplicity makes it compelling for several reasons For one thing the project reveals a map of our air transit system which is something that has never before been visualized publicly as far as I m aware Secondly the visualization is easy to understand even though it is made entirely from data the airports in the visualization create nodes that conform to our geographical conception of North America Figure 6 14 Likewise the densest flight paths fall over areas of high population just as we d expect Figure 6 14 A closeup on the southwest United States how many airports can you identify CHAPTER 6 FLIGHT PATTERNS A DEEP DIVE 101 Finally I find Flight Patterns compelling because it is comforting This is perhaps a strange emotion to associate with a map but by showing the orderliness of air trans port and by uncovering the mystery of how planes get from place to place Flight Patterns reveals a logical system that we are only a tiny part of when sitting in seat 16A at 34 000 feet It s comforting I think to see a system that works so well at such a high volume With over 200 000 flights in one day in the U S and Canada alone we truly have created roads in the sky every one
7. d striking feature of Flight Patterns is that it allows us to visualize the vast ness of the U S and Canadian air transportation system To me this is what makes data visualization so valuable We cannot grasp the totality of flight traffic in the U S and Canada by looking up at the sky or by seeing the raw numbers but we can under stand it through visualization Viewed together the flight paths show us more than the sum of their parts they show us a system and the system I believe is beautiful It reveals something not just about flight paths but about the geography of human pop ulations and more broadly of our species s clear desire to travel Techniques and Data Flight Patterns was created with Processing a programming language that is particularly suited for data visualization Once the flight data was procured always a critical step I wrote a simple Processing program to translate each data point s latitude and longitude into a 2D map on my computer screen Concurrently I added selective color to each point to indicate information such as altitude and aircraft model I then exported all of these images as TGA files The videos were a little trickier Showing the airplanes as moving dots failed to reveal the progress of each flight So instead I drew lines between each data point and after a set time interval 3 minutes or 5 minutes depending on the dataset I added a 4 black opacity layer over the entire map This mea
8. es and Drawbacks 000 329 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 noanoa 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 CONWIDULONS s a eaat os ied homes ke SS eS 369 HOCUS ioe a Sos dy uk Sy de eas cap ay ala sd ee chp Was cs de et a my 2 ae Cal a 375 x CONTENTS CHAPTER SIX Flight Patterns A Deep Dive Aaron Koblin with Valdean Klump THERE ARE ROADS IN THE SKY We can t see them but they are there distinct sharply defined avenues traversed by thousands of airplanes every day As individ ual observers we might never guess this was the case but plotting the raw flight data shows us otherwise Figure 6 1 Flight Patterns is a project I started in 2005 that visualizes civilian air traffic in the United States and Canada It exists in two mediums still imagery which trace
9. hel Monaghan Illustrator Robert Romano The O Reilly logo is a registered trademark of O Reilly Media Inc Beautiful Visualization the cover image and related trade dress are trademarks of O Reilly Media Inc Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks Where those designations appear in this book and O Reilly Media Inc was aware of a trademark claim the designations have been printed in caps or initial caps While every precaution has been taken in the preparation of this book the publisher and au thors assume no responsibility for errors or omissions or for damages resulting from the use of the information contained herein ISBN 978 1 449 37987 2 CONTENTS Preface i x26 5 ie Rad Hh as Re Ae ee aw aes we ee On Beauly 2 22 44 22k aes hee Ee PE OEE OE EA Noah lliinsky What Is Beauty Learning from the Classics How Do We Achieve Beauty Putting It Into Practice Conclusion Once Upon a Stacked Time Series Matthias Shapiro Question Visual Data Context Story Steps for Creating an Effective Visualization Hands on Visualization Creation Conclusion Jonathan Feinberg Wordle s Origins How Wordle Works Is Wordle Good Information Visualization How Wordle Is Actually Used Conclusion Acknowledgments References Color The Cinderella of Data Visualization Michael Driscoll Why Use Color in Data Graphics Luminosi
10. ing Your Own Conclusion References Finding Beautiful Insights in the Chaos of Social Network Visualizations Adam Perer Visualizing Social Networks Who Wants to Visualize Social Networks The Design of SocialAction Case Studies From Chaos to Beauty References Beautiful History Visualizing Wikipedia Martin Wattenberg and Fernanda Vi gas Depicting Group Editing History Flow in Action Chromogram Visualizing One Person at a Time Conclusion Turning a Table into a Tree Growing Parallel Sets into a Purposeful Project 00 5 Robert Kosara Categorical Data Parallel Sets Visual Redesign A New Data Model The Database Model Growing the Tree Parallel Sets in the Real World Conclusion References 144 144 151 156 156 156 157 160 162 166 173 175 175 184 186 191 194 195 197 199 200 202 203 204 204 CONTENTS vii 13 The Design of X by Y aoaaa ee 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 aoaaa ee ee ee 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
11. light paths over the north Atlantic appear jagged Figure 6 12 I opted to keep this data in the visual ization because it was important to show the flights coming from Europe I don t know why those errors are there They could indicate problems with the planes instru ments the processing of the ASDI or an error by the data supplier After fretting about it for a long time I decided to simply leave the data as it was Also when looking for the shortest flight I found that over 3 000 aircraft had reported their locations without ever departing the airport I kept these anomalies too CHAPTER 6 FLIGHT PATTERNS ADEEP DIVE 99 Figure 6 12 Flight paths over the north Atlantic show some anomalies in the data If you look carefully at the visualization you will notice some interesting features One obvious example is the restricted no fly zones over Nevada Figure 6 13 It doesn t appear as if these no fly zones are completely restricted though a tiny number of flights crossing this dark space are just discernable Figure 6 13 A closeup look at no fly zones in the southwest United States 100 BEAUTIFUL VISUALIZATION Every time you work with large organic datasets you will find errors and anomalies and I think it s important to consider how to handle them For each case I ask myself will I harm the integrity of the data by manipulating it If the answer is yes it s best to simply leave the data as it is or in the c
12. nt that older flight paths would fade into the background over time which helped to show the planes progress The data used in Flight Patterns is a processed version of the Aircraft Situation Display to Industry ASDI feed a record of all civilian flight paths that is published by the FAA The feed is available only to companies with ties to the aviation industry Thanks to my colleague Scott Hessels I received 28 hours worth of this flight data in 2005 My initial visualization was a contribution to the Celestial Mechanics project com pleted along with Gabriel Dunne at UCLA s Design Media Arts program The initial dataset I worked with was from March 19 20 2005 and includes 141 029 flights sampled every 3 minutes for a total of 6 871 383 data points Three years later in 2008 I worked with Wired magazine to obtain another dataset This data came from August 12 13 2008 and includes 205 514 flights sampled every minute for a total of 26 552 304 data points The data I received that was derived from the ASDI feed included the following infor mation for each data point See http processing org Civilian means all nonmilitary commercial and private flights tracked by the FAA BEAUTIFUL VISUALIZATION e Latitude e Longitude e Altitude e Aircraft manufacturer e Aircraft model e Timestamp e Flight number If you are interested in seeing some of the data yourself the FAA presently provides a sample of
13. s aircraft arriving and departing from U S and Canadian airports over a 24 hour period and video imagery which depicts the same data in motion In this chapter I ll show you some of these images and talk about the techniques I used to render them I ll also share some thoughts on why I find this project so compelling and why I hope you will as well All of the images in this chapter are available in high resolution online so if you find them intrigu ing I recommend that you visit my website to get a better look at them http www aaronkoblin com work flightpatterns On the site you may zoom in to the visualizations as well as view them in colors indicating aircraft altitude model and manufacturer You may also view videos of the flight data in motion 91 92 Figure 6 1 Flight Patterns a visualization of aircraft location data for airplanes arriving at and departing from U S and Canadian airports To begin with I want to draw your attention to what I believe are the two most strik ing features of the visualization The first is the tendency of airplanes to follow the exact same flight paths as other planes When I originally rendered the data I expected to see tight groupings of planes close to airports and a vast dispersion between them Instead I found the opposite flight paths between airports tend to cluster and then as the planes get closer to landing or departing their flight paths tend to disperse Figures
14. the ASDI feed in XML format at ttp www fly faa gov ASDI asdi html Color Flight Patterns does not use any complex mapmaking techniques simply plotting the data speaks for itself However color plays an important role in telling different stories using the same flight paths Figures 6 4 through 6 9 show some examples Figure 6 4 In this map color indicates altitude with pure white meaning the plane is at ground level CHAPTER 6 FLIGHT PATTERNS ADEEP DIVE 95 aid TU SOE Liga tied AiR GWW we ant eg yE wi l Figure 6 5 A closeup on the Atlanta airport clearly showing the layout of the runways again color indicates altitude Figure 6 6 In this map color is used to distinguish between different models of aircraft 96 BEAUTIFUL VISUALIZATION Figure 6 7 A map of a single aircraft model showing only flights on Embraer ERJ 145 regional jets Figure 6 8 Another map of a single aircraft model showing only flights on Boeing 737 jets CHAPTER 6 FLIGHT PATTERNS A DEEP DIVE 97 98 Figure 6 9 In this map separate colors show takeoffs and landings orange indicates a descending plane and blue indicates an ascending plane Motion In motion Flight Patterns reveals new pieces of information including aircraft direc tion and volume over time The visualization tracks flights from one evening to the next in order to show the country falling asleep and waking up the following day Figures 6 10 and 6 11
15. ty As a Means of Recovering Local Density Looking Forward What About Animation Methods Conclusion References and Further Reading 1 13 15 16 18 26 36 37 38 46 54 57 58 58 58 59 59 64 65 65 67 67 5 Mapping Information Redesigning the New York City Subway Map is ek Se ek ec eee EN Se Awe 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 anaana anaana aaan 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 12 References 122 8 Visualizing the U S Senate Social Graph 1991 2009 2c od ots Res ee a 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 10 11 12 The Big Picture Search and Discovery Todd Holloway The Visualization Technique YELLOWPAGES COM The Netflix Prize Creat
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