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Data Visualisation: A Handbook For Data Driven Design
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act, 1988, this publication may be reproduced, stored or transmitted in any form, or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction, in accordance with the terms of licences issued by the Copyright LicensingAgency. Enquiries concerningreproductionoutsidethoseterms shouldbesent to thepublishers.
Library of Congress Control Number: 2015957322
British Library Cataloguing in Publication data
A catalogue record for this book is available from the British Library
ISBN 978-1-4739-1213-7
ISBN 978-1-4739-1214-4 (pbk)
Editor: Mila Steele
Editorial assistant: Alysha Owen
Production editor: Ian Antcliff
Marketing manager: Sally Ransom
Cover design: Shaun Mercier
Typeset by: C&M Digitals (P) Ltd, Chennai, India
Printed and bound in Great Britain by Bell and Bain Ltd, Glasgow
List of Figures withSourceNotes
Acknowledgements
About theAuthor
INTRODUCTION
PART A FOUNDATIONS
1 DefiningDataVisualisation
2 VisualisationWorkflow
PART B THE HIDDEN THINKING
3 FormulatingYourBrief
4 WorkingWithData
5 EstablishingYourEditorial Thinking
PART C DEVELOPING YOUR DESIGN SOLUTION
6 DataRepresentation
7 Interactivity
8 Annotation
9 Colour
10 Composition
PART D DEVELOPING YOUR CAPABILITIES
11 VisualisationLiteracy
References Index
List of Figures with Source Notes
1 1 ADefinitionforDataVisualisation19
1.2 PerCapitaCheeseConsumptionintheU.S., by SarahSlobin(Fortunemagazine) 20
1.3 TheThreeStages of Understanding22
1.4–6 DemonstratingtheProcess of Understanding24–27
1.7 TheThreePrinciples of GoodVisualisationDesign30
1 8 HousingandHomeOwnershipintheUK, by ONS Digital Content Team 33
1.9 FallingNumberof YoungHomeowners, by theDaily Mail 33
1.10 GunDeaths inFlorida(Reuters Graphics) 34
1.11 Iraq’s Bloody Toll, by SimonScarr(SouthChinaMorningPost) 34
1.12 GunDeaths inFloridaRedesign, by PeterA. Fedewa(@pfedewa) 35
1 13 If ViennawouldbeanApartment, by NZZ(NeueZürcherZeitung) [Translated] 45
1.14 AsiaLoses Its Sweet ToothforChocolate, by Graphics Department (Wall Street Journal) 45
2 1 TheFourStages of theVisualisationWorkflow 54
3.1 The‘PurposeMap’ 76
3.2 Mizzou’s Racial GapIs Typical OnCollegeCampuses, by FiveThirtyEight 77
3 3 Image taken from ‘Wealth Inequality in America’, by YouTube user ‘Politizane’ (www.youtube.com/watch?v=QPKKQnijnsM) 78
3 4 Dimensional Changes inWood, by Luis Carli (luiscarli com) 79
3.5 How Y’all, YouseandYouGuys Talk, by JoshKatz (TheNew York Times) 80
3.6 Spotlight onProfitability, by KrisztinaSzücs 81
3.7 Countries withtheMost LandNeighbours 83
3.8 BuyingPower: TheFamilies Fundingthe2016 Presidential Election, by WilsonAndrews, Amanda Cox, Alicia DeSantis, Evan Grothjan, Yuliya Parshina-Kottas, Graham Roberts, Derek Watkins and KarenYourish(TheNew York Times) 84
3.9 Image taken from ‘Texas Department of Criminal Justice’ Website (www.tdcj.state.tx.us/death row/dr executed offenders.html) 86
3.10 OECDBetterLifeIndex, by Moritz Stefaner, Dominikus Baur, Raureif GmbH89
3 11 LosingGround, by Bob Marshall, TheLens, BrianJacobs andAl Shaw (ProPublica) 89
3.12 GrapeExpectations, by S. Scarr, C. Chan, andF. Foo (Reuters Graphics) 91
3.13 Keywords and Colour Swatch Ideas from Project about Psychotherapy Treatment in the Arctic 92
3.14 AnExampleof aConcept Sketch, by GiorgiaLupi of Accurat 92
4 1 Exampleof aNormalisedDataset 99
4.2 Exampleof aCross-tabulatedDataset 100
4 3 Graphic Language: The Curse of the CEO, by David Ingold and Keith Collins (Bloomberg Visual Data), Jeff Green(BloombergNews) 101
4.4 US Presidents by Ethnicity (1789 to 2015) 114
4 5 OECDBetterLifeIndex, by Moritz Stefaner, Dominikus Baur, Raureif GmbH116
4.6 Spotlight onProfitability, by KrisztinaSzücs 117
4 7 Exampleof ‘Transformingto Convert’ Data119
4.8 MakingSenseof theKnown Knowns 123
4.9 What Good Marathons and Bad Investments Have in Common, by Justin Wolfers (The New
York Times) 124
5.1 The Fall and Rise of U.S. Inequality, in Two Graphs Source: World Top Incomes Database; Designcredit: QuoctrungBui (NPR) 136
5 2–4 Why PeytonManning’s RecordWill BeHardto Beat, by GregorAischandKevinQuealy (The New York Times) 138–140
C 1 Mockup Designs for ‘Poppy Field’, by ValentinaD’Efilippo (design); Nicolas Pigelet (code); Data source: ThePolynational WarMemorial, 2014 (poppyfield.org) 146
6.1 MappingRecords andVariables onto Marks andAttributes 152
6 2 List of Mark Encodings 153
6.3 List of Attribute Encodings 153
6 4 Bloomberg Billionaires, by Bloomberg Visual Data (Design and development), Lina Chen and AnitaRundles (Illustration) 155
6.5 Lionel Messi: Games andGoals forFCBarcelona156
6.20 ACareerinNumbers: Movies StarringMichael Caine173
6.21 ComparingtheFrequency of Words UsedinChapter1 of this Book 174
6.22 Summary of EligibleVotes intheUK General Election2015 175
6.23 TheChangingFortunes of Internet ExplorerandGoogleChrome176
6 24 Literarcy Proficiency: Adult Levels by Country 177
6.25 Political Polarization inthe American Public’, Pew Research Center, Washington, DC (February, 2015) (http://www.people-press.org/2014/06/12/political-polarization-in-the-american-public/) 178
6.26 Finviz (www.finviz.com) 179
6.27 This Venn Diagram Shows Where You Can Both Smoke Weed and Get a Same-Sex Marriage, by PhillipBump(TheWashington Post) 180
6.28 The 200+ Beer Brands of SAB InBev, by Maarten Lambrechts for Mediafin: www tijdbe/sabinbev (Dutch), www lecho be/service/sabinbev (French) 181
6.29 Which Fossil Fuel Companies are Most Responsible for Climate Change? by Duncan Clark and
Robin Houston (Kiln), published in the Guardian, drawing on work by Mike Bostock and Jason Davies 182
6.30 How Long Will We Live – And How Well? by Bonnie Berkowitz, Emily Chow and Todd Lindeman(TheWashington Post) 183
6.31 CrimeRates by State, by NathanYau184
6 32 Nutrient Contents – Parallel Coordinates, by Kai Chang(@syntagmatic) 185
6.33 How the ‘Avengers’ Line-up Has Changed Over the Years, by JonKeegan(Wall Street Journal) 186
6 34 InteractiveFixtureMolecules, by @experimental361 and@bootifulgame187
6.35 The Rise of Partisanship and Super-cooperators in the U.S. House of Representatives. Visualisation by Mauro Martino, authored by Clio Andris, David Lee, Marcus J Hamilton, Mauro Martino, ChristianE. Gunning, andJohnArmisteadSelde188
6.36 TheGlobal Flow of People, by NikolaSander, Guy J. Abel andRamonBauer189
6.37 UK ElectionResults by Political Party, 2010 vs 2015 190
6.38 The Fall and Rise of U.S. Inequality, in Two Graphs. Source: World Top Incomes Database; Designcredit: QuoctrungBui (NPR) 191
6.39 Census Bump: Rank of the Most Populous Cities at Each Census, 1790–1890, by Jim Vallandingham 192
6.40 Coal, Gas, Nuclear, Hydro? How Your State Generates Power. Source: U.S. Energy Information Administration, Credit: ChristopherGroskopf, AlysonHurt andAvieSchneider(NPR) 193
6 41 Holdouts Find Cheapest Super Bowl Tickets Late in the Game, by Alex Tribou, David Ingold andJeremy Diamond(BloombergVisual Data) 194
6 42 CrudeOil Prices (West Texas Intermediate), 1985–2015 195
6.43 PercentageChangeinPriceforSelect FoodItems, Since1990, by NathanYau196
6.44 The Ebb and Flow of Movies: Box Office Receipts 1986–2008, by Mathew Bloch, Lee Byron, ShanCarterandAmandaCox (TheNew York Times) 197
6.45 Tracing the History of N.C.A.A. Conferences, by Mike Bostock, Shan Carter and Kevin Quealy (TheNew York Times) 198
6.46 APresidential Gantt Chart, by BenJones 199
6.47 How the ‘Avengers’ Line-up Has Changed Over the Years, by JonKeegan(Wall Street Journal) 200
6.48 Native and New Berliners – How the S-Bahn Ring Divides the City, by Julius Tröger, André Pätzold, DavidWendler(Berliner Morgenpost) andMoritz Klack (webkidio) 201
6.49 How Y’all, YouseandYouGuys Talk, by JoshKatz (TheNew York Times) 202
6.50 Here’s Exactly Where the Candidates Cash Came From, by Zach Mider, Christopher Cannon, andAdam Pearce(BloombergVisual Data) 203
6.51 Trillions of Trees, by JanWillem Tulp204
6 52 The Racial Dot Map Image Copyright, 2013, WeldonCooper Center for Public Service, Rector andVisitors of theUniversity of Virginia(DustinA. Cable, creator) 205
6.53 Arteries of theCity, by SimonScarr(South China Morning Post) 206
6.54 TheCarbonMap, by DuncanClark andRobinHouston(Kiln) 207
6.55 Election Dashboard, by Jay Boice, Aaron Bycoffe and Andrei Scheinkman (Huffington Post). Statistical model createdby SimonJackman208
6.63 Excerpt from What’s Really Warming the World? by Eric Roston and Blacki Migliozzi (BloombergVisual Data) 218
6.64 Exampleof UsingMarkers Overlays 219
6 65 Why Is Her Paycheck Smaller? by Hannah Fairfield and Graham Roberts (The New York Times) 219
6.66 Inside the Powerful Lobby Fighting for Your Right to Eat Pizza, by Andrew Martin and BloombergVisual Data220
6.67 Excerpt from ‘Razor Sales Move Online, Away From Gillette’, by Graphics Department (Wall Street Journal) 220
7.1 US GunDeaths, by Periscopic225
7.2 Finviz (www.finviz.com) 226
7.3 The Racial Dot Map: Image Copyright, 2013, Weldon Cooper Center for Public Service, Rector andVisitors of theUniversity of Virginia(DustinA. Cable, creator) 227
7 4 Obesity AroundtheWorld, by Jeff Clark 228
7.5 Excerpt from ‘Social Progress Index 2015’, by Social Progress Imperative, 2015 228
7 6 NFL Players: Height &Weight OverTime, by NoahVeltman(noahveltmancom) 229
7.7 Excerpt from ‘How Americans Die’, by Matthew C. KleinandBloombergVisual Data230
7.8 Model Projections of Maximum Air Temperatures Near the Ocean and Land Surface on the June Solsticein2014 and2099: NASAEarthObservatory maps, by JoshuaStevens 231
7.9 Excerpt from ‘A Swing of Beauty’, by Sohail Al-Jamea, Wilson Andrews, Bonnie Berkowitz and ToddLindeman(TheWashington Post) 231
7.10 How Well Do YouKnow YourArea? by ONS Digital Content team 232
7.11 Excerpt from ‘Who OldAreYou?’, by DavidMcCandless andTom Evans 233
7.12 512 Paths to theWhiteHouse, by MikeBostock andShanCarter(TheNew York Times) 233
7.13 OECDBetterLifeIndex, by Moritz Stefaner, Dominikus Baur, Raureif GmbH233
7 14 Nobel Laureates, by Matthew Weber(Reuters Graphics) 234
7.15 Geography of aRecession, by Graphics Department (TheNew York Times) 234
7.16 How BigWill theUK Populationbein25 Years Time? by ONS Digital Content team 234
7.17 Excerpt from ‘Workers’ Compensation Reforms by State’, by Yue Qiu and Michael Grabell (ProPublica) 235
7 18 Excerpt from ‘ECB Bank Test Results’, by MonicaUlmanu, LauraNoonanandVincent Flasseur (Reuters Graphics) 236
7.19 History Through the President’s Words, by Kennedy Elliott, Ted Mellnik and Richard Johnson (TheWashington Post) 237
7.20 Excerpt from ‘How Americans Die’, by Matthew C. KleinandBloombergVisual Data237
7 21 Twitter NYC: A Multilingual Social City, by James Cheshire, Ed Manley, John Barratt, and OliverO’Brien238
7 22 Killing the Colorado: Explore the Robot River, by Abrahm Lustgarten, Al Shaw, Jeff Larson, AmandaZamoraandLaurenKirchner(ProPublica) andJohnGrimwade238
7.23 LosingGround, by Bob Marshall, TheLens, BrianJacobs andAl Shaw (ProPublica) 239
7.24 Excerpt from ‘History Through the President’s Words’, by Kennedy Elliott, Ted Mellnik and RichardJohnson(TheWashington Post) 240
7 25 Plow, by Derek Watkins 242
7.26 The Horse in Motion, by Eadweard Muybridge. Source: United States Library of Congress’s Prints andPhotographs division, digital IDcph3a45870 243
8.1 Titles Takenfrom Projects PublishedandCreditedElsewhereinThis Book 248
8.2 Excerpt from ‘The Color of Debt: The Black Neighborhoods Where Collection Suits Hit Hardest’, by Al Shaw, AnnieWaldmanandPaul Kiel (ProPublica) 249
8.4 Excerpt from ‘The Color of Debt: The Black Neighborhoods Where Collection Suits Hit Hardest’, by Al Shaw, AnnieWaldmanandPaul Kiel (ProPublica) 250
8.5 Excerpt from ‘BloombergBillionaires’, by BloombergVisual Data(Designanddevelopment), Lina ChenandAnitaRundles (Illustration) 251
8 6 Excerpt from ‘Gender Pay Gap US?’, by David McCandless, Miriam Quick (Research) and PhilippaThomas (Design) 251
8.7 Excerpt from ‘Holdouts Find Cheapest Super Bowl Tickets Late in the Game’, by Alex Tribou, DavidIngoldandJeremy Diamond(BloombergVisual Data) 252
8.8 Excerpt from ‘TheLifeCycleof Ideas’, by Accurat 252
8 9 Mizzou’s Racial GapIs Typical OnCollegeCampuses, by FiveThirtyEight 253
8.10 Excerpt from ‘The Infographic History of the World’, Harper Collins (2013); by Valentina D’Efilippo (co-author and designer); James Ball (co-author and writer); Data source: The Polynational WarMemorial, 2012 254
8.11 Twitter NYC: A Multilingual Social City, by James Cheshire, Ed Manley, John Barratt, and OliverO’Brien255
8.12 Excerpt from ‘US GunDeaths’, by Periscopic255
8 13 Image taken from Wealth Inequality in America, by YouTube user ‘Politizane’ (www.youtube.com/watch?v=QPKKQnijnsM) 256
9.1 HSL Colour Cylinder: Image from Wikimedia Commons published under the Creative Commons Attribution-ShareAlike3.0 Unportedlicense265
9.2 ColourHueSpectrum 265
9 3 ColourSaturationSpectrum 266
9.4 ColourLightness Spectrum 266
9.5 Excerpt from ‘ExecutivePay by theNumbers’, by Karl Russell (TheNew York Times) 267
9.6 How Nations Fare in PhDs by Sex Interactive, by Periscopic; Research by Amanda Hobbs; PublishedinScientific American 268
9 7 How Long Will We Live – And How Well? by Bonnie Berkowitz, Emily Chow and Todd Lindeman(TheWashington Post) 268
9.8 ChartingtheBeatles: SongStructure, by Michael Deal 269
9.9 Photographof MyCuppamug, by Suck UK (www.suck.uk.com/products/mycuppamugs/) 269
9 11 Rim Fire – The Extent of Fire in the Sierra Nevada Range and Yosemite National Park, 2013: NASAEarthObservatory images, by Robert Simmon270
9 12 What aretheCurrent Electricity Prices inSwitzerland[Translated], by Interactivethings for NZZ (theNeueZürcherZeitung) 271
9.13 Excerpt from ‘Obama’s Health Law: Who Was Helped Most’, by Kevin Quealy and Margot Sanger-Katz (TheNew York Times) 272
9.14 Daily Indego Bike Share Station Usage, by Randy Olson (@randal olson) (http://www randalolsoncom/2015/09/05/visualizing-indego-bike-share-usage-patterns-in-philadelphiapart-2/) 272
9 15 Battling Infectious Diseases in the 20th Century: The Impact of Vaccines, by Graphics Department (Wall Street Journal) 273
9.16 Highest Max Temperatures in Australia (1st to 14th January 2013), Produced by the Australian Government Bureauof Meteorology 274
9.17 Stateof thePolarBear, by Periscopic275
9 18 Excerpt from Geography of aRecessionby Graphics Department (TheNew York Times) 275
9.19 FewerWomenRunBigCompanies ThanMenNamedJohn, by JustinWolfers (TheNew York Times) 276
9.20 NYPD, Council SparOverMoreOfficers by Graphics Department (Wall Street Journal) 277
9.23 Art intheAgeof Mechanical Reproduction: WalterBenjaminby StefaniePosavec279
9.24 Casualties, by Stamen, publishedby CNN 279
9.25 First Fatal Accident in Spain on a High-speed Line [Translated], by Rodrigo Silva, Antonio Alonso, Mariano Zafra, YolandaClementeandThomas Ondarra(El Pais) 280
9 26 Lunge Feeding, by Jonathan Corum (The New York Times); whale illustration by Nicholas D Pyenson281
9 27 Examples of CommonBackgroundColourTones 281
9.28 Excerpt from NYCStreet Trees by Species, by Jill Hubley 284
9.29 DemonstratingtheImpact of Red-greenColourBlindness (deuteranopia) 286
9 30 Colour-blindFriendly Alternatives to GreenandRed287
9.31 Excerpt from, ‘Pyschotherapy inTheArctic’, by Andy Kirk 289
9 32 WindMap, by FernandaViégas andMartinWattenberg289
10.1 City of Anarchy, by SimonScarr(South China Morning Post) 294
10.2 WireframeSketch, by GiorgiaLupi for‘Nobels no degree’ by Accurat 295
10.3 Exampleof theSmall Multiples Technique296
10.4 The Glass Ceiling Persists Redesign, by Francis Gagnon (ChezVoila.com) based on original by S. Culp(Reuters Graphics) 297
10.5 Fast-food Purchasers Report More Demands on Their Time, by Economic Research Service (USDA) 297
10.6 Stalemate, by Graphics Department (Wall Street Journal) 297
10.7 Nobels No Degrees, by Accurat 298
10 8 KasichCouldBeTheGOP’s ModerateBackstop, by FiveThirtyEight 298
10.9 On Broadway, by Daniel Goddemeyer, Moritz Stefaner, Dominikus Baur, and Lev Manovich 299
10.10 ER Wait Watcher: Which Emergency Room Will See You the Fastest? by Lena Groeger, Mike Tigas andSisi Wei (ProPublica) 300
10 11 RainPatterns, by JanePong(South China Morning Post) 300
10.12 Excerpt from ‘Pyschotherapy inTheArctic’, by Andy Kirk 301
10 13 Gender Pay Gap US, by David McCandless, Miriam Quick (Research) and Philippa Thomas (Design) 301
10.14 TheWorst BoardGames EverInvented, by FiveThirtyEight 303
10.15 From Millions, Billions, Trillions: Letters from Zimbabwe, 2005 2009, a book written andpublishedby CatherineBuckle(2014), tabledesignby Graham vandeRuit (pg. 193) 303
10 16 List of Chart Structures 304
10.17 IllustratingtheEffect of TruncatedBarAxis Scales 305
10 18 Excerpt from ‘DopingundertheMicroscope’, by S ScarrandW Foo (Reuters Graphics) 306
10.19 Record-high60% of Americans Support Same-sex Marriage, by Gallup306
10.20 Images from WikimediaCommons, publishedundertheCreativeCommons Attribution-Share Alike3 0 Unportedlicense308
11.1–7 ThePursuit of Faster’ by Andy Kirk andAndrew Witherley 318–324
Acknowledgements
This book has been made possible thanks to the unwavering support of my incredible wife, Ellie, and the endless encouragement from my Mum andDad, therest of my brilliant family andmy supergroupof friends.
From a professional standpoint I also need to acknowledge the fundamental role played by the hundreds of visualisation practitioners (no matter under what title you ply your trade) who have created such a wealth of brilliant work from which I have developed so many of my convictions and formed the basis of so much of the content in this book. The people and organisations who have provided me with permission to use their work areheroes andIhopethis book does theirrichtalent justice.
About the Author
Andy Kirk
is a freelance data visualisation specialist based in Yorkshire, UK. He is a visualisation design consultant, training provider, teacher, researcher, author, speaker and editor of the award-winning website visualisingdata.com
After graduating from Lancaster University in 1999 with a BSc (hons) in Operational Research, Andy held a variety of business analysis and information management positions at organisations including West YorkshirePoliceandtheUniversity of Leeds.
Hediscovereddatavisualisationinearly 2007 just at thetimewhenhewas shapinguphis proposal fora Master’s (MA) ResearchProgrammedesignedformembers of staff at theUniversity of Leeds.
On completing this programme with distinction, Andy’s passion for the subject was unleashed. Following his graduation in December 2009, to continue the process of discovering and learning the subject helaunchedvisualisingdata.com, abloggingplatform that wouldchart theongoingdevelopment of the data visualisation field Over time, as the field has continued to grow, the site too has reflected this, becoming one of the most popular in the field. It features a wide range of fresh content profiling the latest projects and contemporary techniques, discourse about practical and theoretical matters, commentary about key issues, andcollections of valuablereferences andresources
In 2011 Andy became a freelance professional focusing on data visualisation consultancy and training workshops Some of his clients include CERN, Arsenal FC, PepsiCo, Intel, Hershey, the WHO and McKinsey. At the time of writing he has delivered over 160 publicand private training events across the UK, Europe, NorthAmerica, Asia, SouthAfricaandAustralia, reachingwell over3000 delegates. In addition to training workshops Andy also has two academic teaching positions. He joined the highly respected Maryland Institute College of Art (MICA) as a visiting lecturer in2013 and has beenteaching amoduleontheInformationVisualisationMaster’s Programmesinceits inception InJanuary 2016, he began teaching a data visualisation module as part of the MSc in Business Analytics at the Imperial CollegeBusiness School inLondon.
Between 2014 and 2015 Andy was an external consultant on a research project called ‘Seeing Data’, funded by the Arts & Humanities Research Council and hosted by the University of Sheffield. This study explored the issues of data visualisation literacy among the general public and, among many things, helped to shape an understanding of the human factors that affect visualisation literacy and the effectiveness of design.
Introduction
I.1 The Quest Begins
In his book The Seven Basic Plots, author Christopher Booker investigated the history of telling stories. He examined the structures used in biblical teachings and historical myths through to contemporary storytelling devices used in movies and TV From this study he found seven common themes that, he argues, can be identifiableinany form of story.
One of these themes was ‘The Quest’. Booker describes this as revolving around a main protagonist who embarks ona journey to acquire a treasured object or reach an important destination, but faces many obstacles and temptations along the way. It is a theme that I feel shares many characteristics with the structure of this book andthenatureof datavisualisation
You are the central protagonist in this story in the role of the data visualiser. The journey you are embarking oninvolves aroutealong adesignworkflow whereyouwill befacedwithawiderangeof different conceptual, practical and technical challenges The start of this journey will be triggered by curiosity, which you will need to defineinorderto accomplishyourgoals. From this originyouwill moveforwardto initiatingandplanning your work, defining the dimensions of your challenge. Next, you will begin the heavy lifting of working with data, determining what qualities it contains and how youmight sharethesewithothers. Only thenwill yoube ready to take onthe designstage. Here you will be faced with the prospect of handling aspectrum of different designoptions that will requirecreativeandrational thinkingto resolvemost effectively
The multidisciplinary nature of this field offers a unique opportunity and challenge. Data visualisation is not an especially difficult capability to acquire, it is largely a game of decisions. Making better decisions will be your goal but sometimes clear decisions will feel elusive. There will be occasions whenthe best choice is not at all visible and others when there will be many seemingly equal viable choices. Which one to go with? This book aims to beyourguide, helpingyounavigateefficiently throughthesedifficult stages of yourjourney
Youwill need to learnto beflexibleand adaptable, capableof shifting your approachto suit thecircumstances. This is important because there are plenty of potential villains lying in wait looking to derail progress. These aretheforces that manifest throughtheimpositionof restrictivecreativeconstraints andthepressurecreatedby the relentless ticking clock of timescales. Stakeholders and audiences will present complex human factors through the diversity of their needs and personal traits These will need to be astutely accommodated Data, the critical raw material of this process, will dominate your attention. It will frustrate and even disappoint at times, as promises of its treasures fail to materialise irrespective of the hard work, love and attention lavished uponit
Your own characteristics will also contribute to a certain amount of the villainy. At times, you will find yourself wrestling with internal creative and analytical voices pulling against each other in opposite directions. Your excitably formed initial ideas will be embraced but will need taming. Your inherent tastes, experiences andcomforts will divert youaway from theideal path, so youwill needto maintainclarity andfocus.
The central conflict you will have to deal with is the notionthat there is no perfect indatavisualisation It is a field with very few ‘always’ and ‘ nevers ’ . Singular solutions rarely exist. The comfort offered by the rules that
instruct what is right and wrong, good and evil, has its limits. You can find small but legitimate breaking points with many of them. While you can rightly aspire to reach as close to perfect as possible, the attitude of aimingforgood enough will oftenindeedbegoodenoughandfundamentally necessary
Inaccomplishing the quest you will be rewarded with competency indata visualisation, developing confidence in being able to judge the most effective analytical and design solutions in the most efficient way. It will take time and it will need more thanjust reading this book. It will also require your ongoing effort to learn, apply, reflect and develop. Each new data visualisation opportunity poses a new, unique challenge. However, if you keepperseveringwiththis journey thepossibility of ahappy endingwill increaseall thetime.
I.2 Who is this Book Aimed at?
The primary challenge one faces when writing a book about data visualisation is to determine what to leave in and what to leave out. Data visualisation is big. It is too big a subject even to attempt to cover it all, in detail, in one book There is no single book to rule them all because there is no one book that can cover it all Each and every one of the topics covered by the chapters inthis book could (and, inseveral cases, do) exist as whole books intheirownright.
The secondary challenge when writing a book about data visualisation is to decide how to weave all the content together. Data visualisation is not rocket science; it is not an especially complicated discipline. Lots of it, as you will see, is rooted in common sense. It is, however, certainly a complex subject, a semantic distinction that will be revisited later. There are lots of things to think about and decide on, as well as many things to do and make. Creative and analytical sensibilities blend with artistic and scientific judgments. In one moment you might be checking the statistical rigour of your calculations, in the next deciding which tone of orange most elegantly contrasts with an 80% black. The complexity of data visualisation manifests itself throughhow thesedifferent ingredients, andmany more, interact, influenceandintersect to form thewhole
Thedecisions Ihavemadeinformulating this book‘s content havebeenshapedby my ownprocess of learning about, writing about and practising data visualisation for, at the time of writing, nearly a decade. Significantly – from the perspective of my own development – I have been fortunate to have had extensive experience designing and delivering training workshops and postgraduate teaching. I believe you only truly learn about yourownknowledgeof asubject whenyouhaveto explainit andteachit to others.
I have arrived at what I believe to be an effective and proven pedagogy that successfully translates the complexities of this subject into accessible, practical and valuable form. I feel well qualified to bridge the gap between the large population of everyday practitioners, who might identify themselves as beginners, and the superstar technical, creative and academic minds that are constantly pushing forward our understanding of the potential of datavisualisation. Iam not goingto claim to belongto that lattercohort, but Ihavecertainly been the former – abeginner – and most of my working hours are spent helping other beginners start their journey I know the things that I would have valued when I was starting out and I know how I would have wished them to bearticulatedandpresentedformeto developmy skills most efficiently
There is a large and growing library of fantastic books offering many different theoretical and practical viewpoints on the subject of data visualisation. My aim is to bring value to this existing collection of work by taking on a particular perspective that is perhaps under-represented in other texts – exploring the notion and practiceof avisualisationdesignprocess. As Ihavealludedto intheopening, thecentral premiseof this book is that thepathto mastering datavisualisationis achievedby making better decisions: effectivechoices, efficiently
made. Thebook’s central goal is to helpdevelopyourcapability andconfidenceinfacingthesedecisions.
Just as a single book cannot cover the whole of this subject, it stands that a single book cannot aim to address directly the needs of all people doing data visualisation. In this section I am going to run through some of the characteristics that shape the readers to whom this book is primarily targeted I will also put into context the content the book will and will not cover, and why. This will help manage your expectations as the reader and establishits valuepropositioncomparedwithothertitles.
Domain and Duties
The core audiences for whom this book has been primarily written are undergraduate and postgraduate-level students and early career researchers from social science subjects. This reflects a growing number of people in highereducationwho areinterestedinandneedto learnabout datavisualisation.
Although aimed at social sciences, the content will also be relevant across the spectrum of academic disciplines, from the arts and humanities right through to the formal and natural sciences: any academic duty where there is an emphasis on the use of quantitative and qualitative methods in studies will require an appreciation of gooddatavisualisationpractices Wherestatistical capabilities arerelevant so too is datavisualisation
Beyond academia, data visualisation is a discipline that has reached mainstream consciousness with an increasing number of professionals and organisations, across all industry types and sizes, recognising the importanceof doingit well forbothinternal andexternal benefit. Youmight beamarket researcher, alibrarian or a data analyst looking to enhance your data capabilities. Perhaps you are a skilled graphic designer or web developer looking to take your portfolio of work into a more data-driven direction Maybe you are in a managerial positionand not directly involved inthe creationof visualisationwork, but you need to coordinate or commission others who will be. You require awareness of the most efficient approaches, the range of options and the different key decisionpoints. You might be seeking generally to improve the sophisticationof the language you use around commissioning visualisation work and to have a better way of expressing and evaluatingwork createdforyou
Basically, anyone who is involved in whatever capacity with the analysis and visual communication of data as part of their professional duties will need to grasp the demands of data visualisation and this book will go someway to supportingtheseneeds
Subject Neutrality
One of the important aspects of the book will be to emphasise that data visualisation is a portable practice You will see a broad array of examples of work from different industries, covering very different topics. What will becomeapparent is that visualisationtechniques arelargely subject-matter neutral: alinechart that displays the ebb and flow of favourable opinion towards a politician involves the same techniques as using a line chart to show how astock has changed invalue over time or how peak temperatures have changed across aseasonin agivenlocation A linechart is alinechart, regardless of thesubject matter Thecontext of theviewers (suchas their needs and their knowledge) and the specific meaning that can be drawn will inevitably be unique to each setting, but theroleof visualisationitself is adaptableandportableacross all subject areas
Data visualisation is an entirely global concern, not focused on any defined geographic region. Although the English language dominates the written discourse (books, websites) about this subject, the interest in it and
visible output from across the globe are increasing at a pace. There are cultural matters that influence certain decisions throughout the design process, especially around the choices made for colour usage, but otherwise it is adisciplinecommonto all.
Level and Prerequisites
The coverage of this book is intended to serve the needs of beginners and those with intermediate capability. For most people, this is likely to be as far as they might ever need to go It will offer an accessible route for novices to start their learning journey and, for those already familiar with the basics, there will be content that will hopefully contributeto fine-tuningtheirapproaches
For context, I believe the only distinction between beginner and intermediate is one of breadth and depth of critical thinking rather than any degree of difficulty. The more advanced techniques in visualisation tend to be associated with the use of specific technologies for handling larger, complex datasets and/or producing more bespokeandfeature-richoutputs.
This book is therefore not aimed at experienced or established visualisation practitioners. There may be some new perspectives to enrich their thinking, some content that will confirm and other content that might constructively challenge their convictions. Otherwise, the coverage inthis book should really echo the practices they arelikely to bealready observing
As I have already touched on, data visualisation is a genuinely multidisciplinary field. The people who are active inthis field or professioncome from all backgrounds – everyone has adifferent entry point and nobody arrives with all constituent capabilities It is therefore quite difficult to define just what are the right type and level of pre-existing knowledge, skills or experiences for those learning about data visualisation. As each year passes, the savvy-ness of the type of audience this book targets will increase, especially as the subject penetrates more into the mainstream. What were seen as bewilderingly new techniques several years ago are now commonplaceto morepeople.
That said, I think the following would be a fair outline of the type and shape of some of the most important prerequisiteattributes forgettingthemost out of this book:
Strongnumeracy is necessary as well as afamiliarity withbasicstatistics.
While it is reasonable to assume limited prior knowledge of data visualisation, there should be a strong desireto want to learnit. Thedemands of learningacraft likedatavisualisationtaketimeandeffort; the capabilities will need nurturing through ongoing learning and practice. They are not going to be achieved overnight or acquired alone from reading this book. Any book that claims to be able magically to inject mastery throughjust readingit coverto coveris over-promisingandlikely to under-deliver. The best data visualisers possess inherent curiosity You should be the type of person who is naturally disposed to question the world around them or can imagine what questions others have. Your instinct fordiscoveringandsharinganswers will beat theheart of this activity.
There are no expectations of your having any prior familiarity with design principles, but a desire to embrace some of the creative aspects presented in this book will heighten the impact of your work. Unlock yourartistry!
If you are somebody with a strong creative flair you are very fortunate. This book will guide you through when and crucially when not to tap into this sensibility You should be willing to increase the rigour of your analytical decisionmaking and beprepared to haveyour creativethinking informed more
fundamentally by dataratherthanjust instinct.
A range of technical skills covering different software applications, tools and programming languages is not expected for this book, as I will explain next, but you will ideally have some knowledge of basic Excel andsomeexperienceof workingwithdata
I.3 Getting the Balance
Handbook vs Tutorial Book
The description of this book as being a ‘handbook’ positions it as being of practical help and presented in accessible form. It offers direction with comprehensive reference – more of a city guidebook for a tourist than aninstructionmanual to fix awashing machine It will helpyouto know what things to think about, whento think about them, what options exist and how best to resolve all the choices involved in any data-driven design.
Technology is the key enabler for working with data and creating visualisation design outputs Indeed, apart from a small proportion of artisan visualisation work that is drawn by hand, the reliance on technology to create visualisation work is an inseparable necessity For many there is a understandable appetite for step-bystep tutorials that help them immediately to implement data visualisation techniques via existing and new tools.
However, writing about data visualisation through the lens of selected tools is a bit of a minefield, given the diversity of technical options out there and the mixed range of skills, access and needs. I greatly admire those people who have authored tutorial-based texts because they require astute judgement about what is the right level, structureandscope.
The technology space around visualisation is characterised by flux. There are the ongoing changes with the enhancement of established tools as well as a relatively high frequency of new entrants offset by the decline of others. Sometools areproprietary, others areopensource; someareeasierto learn, others requireagreat deal of understanding before you can even consider embarking on your first chart. There are many recent cases of applications or services that have enjoyed fleeting exposure before reaching aplateau: development and support decline, the community of users disperses and there is a certain expiry of value. Deprecation of syntax and functions inprogramminglanguages requires theperennial updatingof skills
All of this perhaps paints a rather more chaotic picture than is necessarily the case but it justifies the reasons why this book does not offer teaching inthe use of any tools. While tutorials may be invaluable to some, they may also only be mildly interesting to others and possibly of no value to most Tools come and go but the craft remains. I believe that creating a practical, rather than necessarily a technical, text that focuses on the underlying craft of data visualisation with a tool-agnostic approach offers an effective way to begin learning about the subject inappropriate depth. The content should be appealing to readers irrespective of the extent of their technical knowledge (novice to advanced technicians) and specific tool experiences (e.g. knowledge of Excel, Tableau, AdobeIllustrator)
There is a role for all book types. Different people want different sources of insight at different stages in their development. If you are seeking a text that provides in-depth tutorials on a range of tools or pages of programmatic instruction, this one will not be the best choice. However, if you consult only tutorial-related
books, the chances are you will likely fall short on the fundamental critical thinking that will be needed in the longerterm to get themost out of thetools withwhichyoudevelopstrongskills.
To substantiate the book’s value, the digital companion resources to this book will offer a curated, up-to-date collection of visualisation technology resources that will guide you through the most common and valuable tools, helping you to gain a sense of what their roles are and where these fit into the design workflow. Additionally, there will be recommended exercises and many further related digital materials available for exploring.
Useful vs Beautiful
Another important distinction to make is that this book is not intended to be seen as a beauty pageant. I love flickingthroughthoseglossy ‘coffeetable’ books as muchas thenext person; suchbooks offergreat inspiration and demonstrate some of the finest work inthe field This book serves a very different purpose I believe that, as a beginner or relative beginner on this learning journey, the inspiration you need comes more from understandingwhat is behindthethinkingthat makes theseamazingworks succeedandothers not.
My desire is to make this the most useful text available, a reference that will spend more time on your desk than on your bookshelf. To be useful is to be used. I want the pages to be dog-eared. I want to see scribbles and annotated notes made across its pages and key passages underlined I want to see sticky labels peering out aboveidentified pages of note. Iwant to seecreases wherepages havebeenfolded back or adouble-pagespread that has been weighed down to keep it open. In time I even want its cover reinforced with wallpaper or wrapping paper to ensure its contents remain bound together There is every intention of making this an elegantly presented and packaged book but it should not be something that invites you to ‘look, but don’t touch’
Pragmatic vs Theoretical
The content of this book has been formed through many years of absorbing knowledge from all manner of books, generations of academic papers, thousands of web articles, hundreds of conference talks, endless online and personal discussions, and lots of personal practice. What I present here is a pragmatic translation and distillationof what Ihavelearneddowntheyears
It is not a deeply academic or theoretical book. Where theoretical context and reference is relevant it will be signposted as I do want to ground this book in as much evidenced-based content as possible; it is about judging what is going to add most value. Experienced practitioners will likely have an appetite for delving deeper into theoretical discourse and the underlying sciences that intersect in this field but that is beyond the scopeof this particulartext
Takethescienceof visual perception, forexample. Thereis no valueinattemptingto emulatewhat has already beencovered by other books ingreater depthand quality thanIcould achieve. Onceyou start peeling back the many different layers of topics like visual and cognitive science the boundaries of your interest and their relevance to data visualisation never seem to arrive. You get swallowed up by the depth of these subjects. You realisethat you havefound yourself learning about what thevery concept of light and sight is and at that point yourbrainbegins to ache(well, minedoes at least), especially whenall youset out to discoverwas if abarchart wouldbebetterthanapiechart.
An important reason for giving greater weight to pragmatism is because of people: people are the makers, the stakeholders, the audiences and the critics in data visualisation. Although there are a great deal of valuable research-driven concepts concerning data visualisation, their practical application can be occasionally at odds with the somewhat sanitised and artificial context of the research methods employed To translate them into real-world circumstances can sometimes be easier said than done as the influence of human factors can easily distort thesignificanceof otherwiserobust ideas
I want to remove the burden from you as a reader having to translate relevant theoretical discourse into applicablepractice. Critical thinking will thereforebethewatchword, equipping you with the independence of thought to decide rationally for yourself what the solutions are that best fit your context, your data, your message and your audience. To do this you will need an appreciation of all the options available to you (the different things youcould do) and areliableapproachfor critically determining what choices you should make (thethings youwill do andwhy).
Contemporary vs Historical
This book is not going to look too far back into the past. We all respect the ancestors of this field, the great names who, despite primitive means, pioneered new concepts in the visual display of statistics to shape the foundations of the field being practised today The field’s lineage is decorated by the influence of William Playfair’s first ever bar chart, Charles Joseph Minard’s famous graphic about Napoleon’s Russian campaign, Florence Nightingale’s Coxcomb plot and John Snow’s cholera map. These are some of the totemic names and classic examples that will always be held up as the ‘firsts’. Of course, to many beginners in the field, this historical context is of hugeinterest. However, again, this kindof content has already beensuperbly coveredby othertexts onmorethanenoughoccasions Timeto moveon
Iam not going to spendtimeattempting to enlightenyouabout how weliveintheageof ‘Big Data’ andhow occupations related to dataareor will bethe‘sexiest jobs’ of our time. Theformer is no longer news, thelatter claim emerged from a single source. I do not want to bloat this book with the unnecessary reprising of topics that havebeencoveredat lengthelsewhere. Thereis morevaluableanduseful content Iwant youto focus your timeon
The subject matter, the ideas and the practices presented here will hopefully not date a great deal. Of course, many of the graphic examples included in the book will be surpassed by newer work demonstrating similar concepts as the field continues to develop However, their worth as exhibits of a particular perspective covered in the text should prove timeless. As more research is conducted in the subject, without question there will be new techniques, new concepts, new empirically evidenced principles that emerge Maybeevennew rules There will be new thought-leaders, new sources of reference, new visualisers to draw insight from. New tools will be created, existing tools will expire. Some things that are done and can only be done by hand as of today may become seamlessly automated in the near future That is simply the nature of a fast-growing field This book canonly bealineinthesand.
Analysis vs Communication
A further important distinction to make concerns the subtle but significant difference between visualisations whichareusedforanalysis andvisualisations usedforcommunication
Beforeavisualisercanconfidently decidewhat to communicateto others, heorsheneeds to havedevelopedan intimate understanding of the qualities and potential of the data. This is largely achieved through exploratory data analysis. Here, the visualiser and the viewer are the same person. Through visual exploration, different interrogations can be pursued ‘ on the fly’ to unearth confirmatory or enlightening discoveries about what insights exist.
Visualisationtechniques usedforanalysis will beakey component of thejourney towards creating visualisation for communication but the practices involved differ. Unlike visualisation for communication, the techniques used for visual analysis do not have to be visually polished or necessarily appealing. They are only serving the purpose of helping you to truly learn about your data When a data visualisation is being created to communicate to others, many careful considerations come into play about the requirements and interests of the intended or expected audience. This has a significant influence on many of the design decisions you make that do not exist alonewithvisual analysis.
Exploratory data analysis is a huge and specialist subject in and of itself. In its most advanced form, working efficiently and effectively withlargecomplex data, topics like‘machine learning’, using self-learning algorithms to help automate and assist in the discovery of patterns in data, become increasingly relevant. For the scope of this book the content is weighted more towards methods and concerns about communicating data visually to others If your role is inpure data science or statistical analysis you will likely require a deeper treatment of the exploratory dataanalysis topicthanthis book canreasonably offer. However, Chapter4 will cover the essential elements insufficient depthforthepractical needs of most peopleworkingwithdata
Print vs Digital
The opportunity to supplement the print version of this book with an e-book and further digital companion resources helps to cushion the agonising decisions about what to leave out. This text is therefore enhanced by access to furtherdigital resources, someof whicharenewly created, whileothers arecuratedreferences from the endless well of visualisationcontent ontheWeb. Includedonline(book.visualisingdata.com) will be:
acompletedcase-study project that demonstrates theworkflow activities coveredinthis book, including full write-ups andall relateddigital materials; anextensiveandup-to-datecatalogueof over300 datavisualisationtools; a curated collection of tutorials and resources to help develop your confidence with some of the most commonandvaluabletools; practical exercises designedto embedthelearningfrom eachchapter; furtherreadingresources to continuelearningabout thesubjects coveredineachchapter
I.4 Objectives
Before moving on to an outline of the book’s contents, I want to share four key objectives that I hope to accomplish for you by the final chapter. These are themes that will run through the entire text: challenge, enlighten, equipandinspire
To challenge you I will be encouraging you to recognise that your current thinking about visualisation may need to be reconsidered, both as a creator and as a consumer. We all arrive in visualisation from different subject and domain origins and with that comes certain baggage and prior sensibilities that can distort our
perspectives I will not be looking to eliminate these, rather to help you harness and align them with other traits andviewpoints.
I will ask you to relentlessly consider the diverse decisions involved in this process. I will challenge your convictions about what you perceive to be good or bad, effective or ineffective visualisation choices: arbitrary choices will be eliminated from your thinking. Even if you are not necessarily a beginner, I believe the content you read in this book will make you question some of your own perspectives and assumptions. I will encourage you to reflect on your previous work, asking you to consider how and why you have designed visualisations intheway that youhave: wheredo youneedto improve? What canyoudo better?
It is not just about creating visualisations, I will also challenge your approach to reading visualisations. This is not something you might usually think much about, but there is an important role for more tactical approaches to consumingvisualisations withgreaterefficiency andeffectiveness.
To enlighten you will be to increase your awareness of the possibilities in data visualisation As you begin your discovery of data visualisation you might not be aware of the whole: you do not entirely know what options exist, how they are connected and how to make good choices. Until you know, you don’t know –that is what theobjectiveof enlighteningis all about.
As you will discover, there is a lot on your plate, much to work through. It is not just about the visible endproduct designdecisions. Hiddenbeneaththesurfacearemany contextual circumstances to weighup, decisions about how best to prepare your data, choices around the multitude of viable ways of slicing those dataup into different angles of analysis. That is all before you even reach the design stage, where you will begin to consider the repertoire of techniques for visually portraying your data – the charts, the interactive features, the colours andmuchmorebesides.
This book will broaden your visual vocabulary to give you more ways of expressing your data visually. It will enhance the sophistication of your decision making and of visual language for any of the challenges you may face.
To equip is to ensureyouhaverobust tactics formanagingyourway throughthemyriadoptions that exist in data visualisation. The variety it offers makes for a wonderful prospect but, equally, introduces the burden of choice. This book aims to makethechallengeof undertakingdatavisualisationfarless overwhelming, breaking downtheoverall prospect into smaller, moremanageabletask chunks
The structure of this book will offer a reliable and flexible framework for thinking, rather than rules for learning. It will lead to better decisions. With an emphasis on critical thinking you will move away from an over-reliance on gut feeling and taste. To echo what I mentioned earlier, its role as a handbook will help you know what things to think about, whento think about them andhow best to resolveall thethinkinginvolved inany data-drivendesignchallengeyoumeet
To inspire is to give you more than just a book to read. It is the opening of a door into a subject to inspire you to step further inside. It is about helping you to want to continue to learn about it and expose yourself to as muchpositiveinfluenceas possible It shouldelevateyourambitionandbroadenyourcapability
It is a book underpinned by theory but dominated by practical and accessible advice, including input from some of the best visualisers in the field today. The range of print and digital resources will offer lots of supplementary material including tutorials, further reading materials and suggested exercises. Collectively this
will hopefully makeit oneof themost comprehensive, valuableandinspiringtitles out there.
I.5 Chapter
Contents
The book is organised into four main parts (A, B, C and D) comprising eleven chapters and preceded by the ‘Introduction’ sections youarereadingnow.
Each chapter opens with anintroductory outline that previews the content to be covered and provides abridge between consecutive chapters. In the closing sections of each chapter the most salient learning points will be summarised and some important, practical tips and tactics shared As mentioned, online there will be collections of practical exercises and further reading resources recommended to substantiate the learning from thechapter.
Throughout the book you will see sidebar captions that will offer relevant references, aphorisms, good habits andpractical tips from someof themost influential peopleinthefieldtoday.
Introduction
This introductionexplains how Ihaveattempted to makesenseof thecomplexity of thesubject, outlining the nature of the audience I am trying to reach, the key objectives, what topics the book will be covering and not covering, andhow thecontent has beenorganised
Part A: Foundations
Part A establishes the foundation knowledge and sets up a key reference of understanding that aids your thinking across the rest of the book. Chapter1 will be the logical starting point for many of you who are new to the field to help you understand more about the definitions and attributes of datavisualisation. Evenif you are not a complete beginner, the content of the chapter forms the terms of reference that much of the remaining content is based on. Chapter 2 prepares you for the journey through the rest of the book by introducingthekey designworkflow that youwill befollowing
Chapter 1: Defining Data Visualisation
Defining data visualisation: outlining the components of thinking that make up the proposed definitionfordatavisualisation
The importance of conviction: presenting three guiding principles of good visualisation design: trustworthy, accessibleandelegant.
Distinctions and glossary: explaining the distinctions and overlaps with other related disciplines andprovidingaglossary of terms usedinthis book to establishconsistency of language.
Chapter 2: Visualisation Workflow
The importance of process: describing the data visualisation design workflow, what it involves andwhy aprocess approachis required.
The process in practice: providing someuseful tips, tactics andhabits that transcendany particular stageof theprocess but will best prepareyouforsuccess withthis activity.
Part B: The Hidden Thinking
Part B discusses the first three preparatory stages of the data visualisation design workflow ‘The hidden thinking’ title refers to how these vital activities, that have a huge influence over the eventual design solution, aresomewhat out of sight inthefinal output; they arehiddenbeneaththesurfacebut completely shapewhat is visible. Thesestages represent theoftenneglectedcontextual definitions, datawranglingandeditorial challenges that are so critical to the success or otherwise of any visualisation work – they require a great deal of care and attentionbeforeyouswitchyourattentionto thedesignstage
Chapter 3: Formulating Your Brief
What is a brief?: describing the value of compiling a brief to help initiate, define and plan the requirements of yourwork
Establishing your project’s context: defining the origin curiosity or motivation, identifying all the key factors and circumstances that surround your work, and defining the core purpose of your visualisation.
Establishing your project’s vision: early considerations about the type of visualisation solution neededto achieveyouraims andharnessinginitial ideas about what this solutionmight look like
Chapter 4: Working With Data
Data literacy: establishing abasicunderstanding withthis critical literacy, providing somefoundation understandingabout datasets anddatatypes andsomeobservations about statistical literacy
Data acquisition: outliningthedifferent origins of andmethods foraccessingyourdata.
Data examination: approaches foracquainting yourself withthephysical characteristics andmeaning of yourdata.
Data transformation: optimising the condition, content and form of your data fully to prepare it forits analytical purpose
Data exploration: developing deeper intimacy with the potential qualities and insights contained, andpotentially hidden, withinyourdata.
Chapter 5: Establishing Your Editorial Thinking
What is editorial thinking?: definingtheroleof editorial thinkingindatavisualisation. The influence of editorial thinking: explaining how the different dimensions of editorial thinkinginfluencedesignchoices.
Part C: Developing Your Design Solution
Part C is the main part of the book and covers progression through the data visualisation design and production stage. This is where your concerns switch from hidden thinking to visible thinking. The individual chapters inthis part of thebook covereachof thefivelayers of thedatavisualisationanatomy They are treated as separate affairs to aid the clarity and organisation of your thinking, but they are entirely interrelated matters and the chapter sequences support this. Within each chapter there is a consistent structure beginningwithanintroductionto eachdesignlayer, anoverview of themany different possibledesignoptions, followedby detailedguidanceonthefactors that influenceyourchoices.
The production cycle: describing the cycle of development activities that take place during this stage, givingacontext forhow to work throughthesubsequent chapters inthis part.
Chapter 6: Data Representation
Introducing visual encoding: an overview of the essentials of data representation looking at the differences andrelationships betweenvisual encodingandchart types.
Chart types: a detailed repertoire of 49 different chart types, profiled in depth and organised by a taxonomy of chart families: categorical, hierarchical, relational, temporal, andspatial
Influencing factors and considerations: presenting the factors that will influence the suitability of yourdatarepresentationchoices.
Editorial salience: a profile of the options for using colour to direct the eye towards the most relevant features of yourdata
Functional harmony: a profile of the options for using colour most effectively across the entire visualisationdesign
Influencing factors and considerations: presenting the factors that will influence the suitability of yourcolourchoices.
Chapter 10: Composition
The features of composition:
Project composition: a profile of the options for the overall layout and hierarchy of your visualisation design.
Chart composition: a profile of the options for the layout and hierarchy of the components of your charts.
Influencing factors and considerations: presenting the factors that will influence the suitability of yourcompositionchoices.
Part D: Developing Your Capabilities
Part D wraps up the book’s content by reflecting on the range of capabilities required to develop confidence and competence with data visualisation. Following completion of the design process, the multidisciplinary nature of this subject will now be clearly established. This final part assesses the two sides of visualisation literacy – yourroleas acreatorandyourroleas aviewer– andwhat youneedto enhanceyourskills withboth
Chapter 11: Visualisation Literacy
Viewing: Learning to see: learning about the most effective strategy for understanding visualisations inyourroleas aviewerratherthanacreator
Creating: The capabilities of the visualiser: profiling the skill sets, mindsets and general attributes neededto masterdatavisualisationdesignas acreator.
Another random document with no related content on Scribd:
They lived in squalid filth, with the great sheets of tobacco in the bed clothing, under the beds, mixed up with stale food, put in the corners of the dirty room.
We got a bill through to put a stop to that kind of work, and the court of appeals declared it unconstitutional. Friends, there was this gem in the opinion.
The court said that it could not permit the legislature to interfere with the sanctity of home. [Laughter. A voice: “No home at all.”]
TAKES WORLD OF TIME.
It was not home at all, as you say, that squalid 16-foot room where those families lived day in and day out and worked day in and day out; and yet the court forbade us to try and improve those conditions, to try and make the conditions of tenement-house life so that it would be possible for decent men and decent women to live decently there and bring up their children as American citizens should be brought up.
Friends, it is idle for any man to ask me to sit unmoved and without protest when a court makes a decision like that [cries of “Good” and applause], and it is a waste of time to tell me, as Mr. Taft did in his speech, that I am laying an impious hand on the ark of the covenant when I try to secure the reversal of such a decision. [Laughter and applause.]
Now wait. I have only just begun. [Applause.] I am sorry for you, but I will get through as quick as I can. [Voices: “Go on, go on.”]
THE LAW THAT WAS PASSED.
Then we passed a law providing that in factories there should be safeguards over all the dangerous machinery, and a girl working in a factory had her arm taken off above the elbow by an unprotected flywheel. She sued and recovered damages, and the court of appeals of the State of New York threw out her case, and there was
another gem in their opinion They said that they would not permit the legislature to interfere with the liberty of that girl to work amidst dangerous machinery. [Laughter.]
Now you can see yourselves—you can look up. I will refer anyone who wants to look it up to the exact decisions where this language occurred.
The liberty of that girl was protected by the court—the liberty; she had the liberty to starve or to work under the conditions offered her, and she had no other liberty. [Applause.]
DREADFUL DENIAL OF JUSTICE.
And I hold that it was a dreadful denial of justice to prevent that poor girl from recovering for the accident due to the criminal carelessness of her employer, and I, for one, will never rest when there is a decision like that on the books until we have got it repealed. [“Good! Good!” and cheers.]
I could give you 20 such cases. I will only give you 2 more—only 2 more.
Then in the next case that came up that I refer to we passed a law, modeled on your Massachusetts law, a law forbidding women to be worked in factories more than 10 hours a day or after 9 o’clock at night.
Friends, personally, I thought that was an utterly insufficient law. I felt that it did not go nearly far enough. Your Massachusetts law prohibits them from working more than 9 hours a day and after 6 o’clock at night.
Ours did not go as far. It was with reluctance that I could make myself accept it at all. I did simply because it was the best we could get at that time.
At that time girls and women in factories, in sweatshops, were being worked 12, 14, sometimes 16 hours a day and until 10, 11, and 12 at night.
THE COURT’S BARRIER.
The Massachusetts court held that your law was constitutional; but our law, which did not go as far, the Court of Appeals of the State of New York held as unconstitutional, and there was this further gem in the language—the court said that there had been altogether too much legislation of this kind in the United States and the time had come for the court fearlessly to oppose the barrier of its judgment against it.
I love that word “fearlessly.” [Prolonged cheering.] Those amiable, well-meaning, excellently intentioned judges, who, doubtless, did not in their whole circle of acquaintances know one woman who was working in a factory or a sweatshop, said that they would stand “fearlessly” for what?
“Fearlessly” for the right of the big factory owner and the small sweatshop owner to earn their money by working women, haggard, hour after hour, day after day, night after night, in the factory and sweatshop.
Now, friends, do you know what my proposal is—this revolutionary proposal of mine [laughter], this proposal of mine that they say represents socialism and anarchy?
FOR THE 10,000,000.
My proposal is this: That in a case like that, the 10,000,000 people of the State of New York, to whom those 6 or 7 elderly, worthy, wellmeaning men had said that they could not have justice—that those 10,000,000 people, after a time amply sufficient in which to come to a sober judgment, should be permitted to vote for themselves as to whether the Constitution, which they themselves had made, had been correctly interpreted by your court here in Massachusetts and the Court of Appeals in the State of New York. [Prolonged cheering.]
And if in a case like that the people are not intelligent enough to vote and say what they themselves meant when they made the
Constitution, they are not intelligent enough to have made the Constitution in the first place. [Applause.]
THE COMPENSATION ACT.
One more case, only one more—two, perhaps. [Laughter.] We passed in New York a workmen’s compensation act.
The Supreme Court of the United States, interpreting exactly the same language in the Constitution of the United States as the court of appeals interpreted in the constitution of the State of New York, and in reference to a practically exactly similar bill, decided that that law was constitutional.
The Court of Appeals of the State of New York decided that the Supreme Court of the United States was wrong [laughter] and that the law was not constitutional.
Now, I want you to listen to this sentence, because I haven’t seen this fact brought out by any man yet.
“UNCONSTITUTIONALITY.”
At this moment in the State of New York if a New York brakeman is injured on a train going straight through across New York, from Boston to Chicago, he has a right to recover, it is constitutional for him to recover [laughter] for the injury.
But if his brother is working on a trolley in the same city in which he is injured on the railroad train, or if he is working on a railway train that only goes from Albany to Buffalo, then it becomes unconstitutional for him to recover. [Laughter and applause.]
ANARCHY OR JUSTICE.
Friends, it is idle to tell me that I have not the right to protest that the United States Supreme Court was right and the court of appeals wrong [applause]; and if I am an anarchist, I am an anarchist in
company with the Supreme Court of the United States. [Applause and cheers.]
All I ask is that, in that kind of a case, the ten millions of people of New York State shall not be balked of justice, but, after ample time for due deliberation on their part, shall be permitted to vote in this case whether the Supreme Court of the United States and the State courts of a dozen other States, like Iowa and Washington, are right, or whether the amiable elderly gentlemen of the New York Court of Appeals are right.
I ask whether it be true that justice can not be given to the man crippled in industry in New York, although it can be given to him in the Nation, and in Iowa and in Washington, and in every civilized country abroad; whether it be true that the man can not have justice done him in New York, or whether we shall say to the judges of the Court of Appeals of New York, “No; we stand by the doctrines laid down by the Supreme Court of the Nation,” and given utterance to by the Massachusetts judge, Mr. Justice Holmes, in the Oklahoma bank case. “We say that the people have the right, in the exercise of the police power of the general-welfare clause of the Constitution”—I am using Mr. Holmes’s words, not mine—“that the people have the right to decide such cases in accordance with the common standard of morality, with the preponderance opinion of the citizenship of the community, and that the courts have to respect such opinion as the popular opinion as to what morality demands.” [Applause.]
I hold that if it be true—if it be true—that in this country fishers and plowmen and choppers are to constitute a State, it must be true that they shall be permitted to impose their ideas of common justice, of common decency, upon their public servants, and that the public servants are in very fact the servants and not the masters of the community [Applause.]
THE BATTLE FOR JUSTICE.
Now, friends, I ask you of Massachusetts to stand in the front of the battle for justice and for righteousness as I have outlined. I wish
that I could make the men who are best off in the community—the big corporation men, the big bankers, merchants, railroad men— understand that in this fight for justice we ought to have the right to expect them to lead.
FOR THE RIGHT.
Surely, friends, surely men of Massachusetts, if we are true to the Massachusetts ideals of the past, we will expect those to whom much has been given to take the lead in striving to get justice for their fellows to whom less has been given. [Applause.]
I want the men who are well off to give justice now because it is right, and not to wait till they have given justice simply because they fear longer to deny it. Justice! Let it come, because we believe in it. Let it not be forced upon us because we are afraid to deny it.
Nine-tenths of wisdom is being wise in time, and the proper way to conserve all of the present system of our civilization that is best worth conserving is to alter that which causes a heavy strain to come upon the rest.
I ask, at least, that we decline to commit ourselves to a policy of foolish Bourbonism; that instead of denying the need of any change in our laws, in our social and industrial system, to meet the changing needs of the times, we take the lead in making every change that is necessary in order to make our constitutions and the body of our law enacted under the Constitution instruments for justice as between man and man, instruments for getting justice for the average citizenship of the American Republic. [Applause.]
Friends, now you have heard me to-night and you can judge for yourselves. Am I preaching anarchy? Am I preaching socialism? [Cries of “No.”]
I am preaching elementary justice.
MISSOURI AND MASSACHUSETTS.
And now I ask you of Massachusetts to respond to the appeal contained in this telegram to me from Missouri, which says that the State convention of Missouri, representing the State, has refused all compromise and has sent a delegation to Chicago standing straight for me [great cheering] and for the cause that I represent.
And I ask you people of Massachusetts, you men of Massachusetts, on next Tuesday to put yourselves beside Illinois and Pennsylvania and Maine in the lead of the movement. [Great applause.]
OTranscriber’s Notes:
Punctuation and spelling inaccuracies were silently corrected
Archaic and variable spelling has been preserved.
Variations in hyphenation and compound words have been preserved
*** END OF THE PROJECT GUTENBERG EBOOK ADDRESS OF HON. THEODORE ROOSEVELT, DELIVERED AT BOSTON, MASS., SATURDAY, APRIL 27, 1912 ***
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