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Data Visualisation

A Handbook for Data Driven Design

Andy Kirk

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First published 2016

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 Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers.

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

Contents List of Figures with Source Notes Acknowledgements About the Author INTRODUCTION P A R T A F O U N D A T IO N S

1 Defining Data Visualisation 2 Visualisation Workflow

P A R T B T H E H ID D E N T H IN K IN G 3 Formulating Your Brief 4 Working With Data 5 Establishing Your Editorial Thinking

P A R T C D E V E L O P IN G YO U R D E S IG N S O L U T IO N 6 Data Representation 7 Interactivity 8 Annotation 9 Colour 10 Composition

P A R T D D E V E L O P IN G YO U R C A P A B IL IT IE S 11 Visualisation Literacy

References Index

List of Figures with Source Notes 1 .1 A Definition for Data Visualisation 19 1 .2 Per Capita Cheese Consumption in the U.S., by Sarah Slobin (Fortune magazine) 20 1 .3 The Three Stages of Understanding 22 1 .4 – 6 Demonstrating the Process of Understanding 24–27 1 .7 The Three Principles of Good Visualisation Design 30 1 .8 Housing and Home Ownership in the UK, by ONS Digital Content Team 33 1 .9 Falling Number of Young Homeowners, by the Daily Mail 33 1 .1 0 Gun Deaths in Florida (Reuters Graphics) 34 1 .1 1 Iraq’s Bloody Toll, by Simon Scarr (South China Morning Post) 34 1 .1 2 Gun Deaths in Florida Redesign, by Peter A. Fedewa (@pfedewa) 35 1 .1 3 If Vienna would be an Apartment, by NZZ (Neue Zürcher Zeitung) [Translated] 45 1 .1 4 Asia Loses Its Sweet Tooth for Chocolate, by Graphics Department (Wall Street Journal) 45 2 .1 The Four Stages of the Visualisation Workflow 54 3 .1 The ‘Purpose Map’ 76 3 .2 Mizzou’s Racial Gap Is Typical On College Campuses, 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 in Wood, by Luis Carli (luiscarli.com) 79 3 .5 How Y’all, Youse and You Guys Talk, by Josh Katz (The New York Times) 80 3 .6 Spotlight on Profitability, by Krisztina Szücs 81 3 .7 Countries with the Most Land Neighbours 83 3 .8 Buying Power: The Families Funding the 2016 Presidential Election, by Wilson Andrews, Amanda Cox, Alicia DeSantis, Evan Grothjan, Yuliya Parshina-Kottas, Graham Roberts, Derek Watkins and Karen Yourish (The New 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 .1 0 OECD Better Life Index, by Moritz Stefaner, Dominikus Baur, Raureif GmbH 89 3 .1 1 Losing Ground, by Bob Marshall, The Lens, Brian Jacobs and Al Shaw (ProPublica) 89 3 .1 2 Grape Expectations, by S. Scarr, C. Chan, and F. Foo (Reuters Graphics) 91 3 .1 3 Keywords and Colour Swatch Ideas from Project about Psychotherapy Treatment in the Arctic 92 3 .1 4 An Example of a Concept Sketch, by Giorgia Lupi of Accurat 92 4 .1 Example of a Normalised Dataset 99 4 .2 Example of a Cross-tabulated Dataset 100 4 .3 Graphic Language: The Curse of the CEO, by David Ingold and Keith Collins (Bloomberg Visual Data), Jeff Green (Bloomberg News) 101 4 .4 US Presidents by Ethnicity (1789 to 2015) 114 4 .5 OECD Better Life Index, by Moritz Stefaner, Dominikus Baur, Raureif GmbH 116 4 .6 Spotlight on Profitability, by Krisztina Szücs 117 4 .7 Example of ‘Transforming to Convert’ Data 119 4 .8 Making Sense of the Known 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; Design credit: Quoctrung Bui (NPR) 136 5 .2 – 4 Why Peyton Manning’s Record Will Be Hard to Beat, by Gregor Aisch and Kevin Quealy (The New York Times) 138–140 C .1 Mockup Designs for ‘Poppy Field’, by Valentina D’Efilippo (design); Nicolas Pigelet (code); Data source: The Polynational War Memorial, 2014 (poppyfield.org) 146 6 .1 Mapping Records and Variables on to Marks and Attributes 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 Anita Rundles (Illustration) 155 6 .5 Lionel Messi: Games and Goals for FC Barcelona 156 6 .6 Image from the Home page of visualisingdata.com 156 6 .7 How the Insane Amount of Rain in Texas Could Turn Rhode Island Into a Lake, by Christopher Ingraham (The Washington Post) 156 6 .8 The 10 Actors with the Most Oscar Nominations but No Wins 161 6 .9 The 10 Actors who have Received the Most Oscar Nominations 162 6 .1 0 How Nations Fare in PhDs by Sex Interactive, by Periscopic; Research by Amanda Hobbs; Published in Scientific American 163 6 .1 1 Gender Pay Gap US, by David McCandless, Miriam Quick (Research) and Philippa Thomas (Design) 164 6 .1 2 Who Wins the Stanley Cup of Playoff Beards? by Graphics Department (Wall Street Journal) 165 6 .1 3 For These 55 Marijuana Companies, Every Day is 4/20, by Alex Tribou and Adam Pearce (Bloomberg Visual Data) 166 6 .1 4 UK Public Sector Capital Expenditure, 2014/15 167 6 .1 5 Global Competitiveness Report 2014–2015, by Bocoup and the World Economic Forum 168 6 .1 6 Excerpt from a Rugby Union Player Dashboard 169 6 .1 7 Range of Temperatures (°F) Recorded in the Top 10 Most Populated Cities During 2015 170 6 .1 8 This Chart Shows How Much More Ivy League Grads Make Than You, by Christopher Ingraham (The Washington Post) 171 6 .1 9 Comparing Critics Scores (Rotten Tomatoes) for Major Movie Franchises 172 6 .2 0 A Career in Numbers: Movies Starring Michael Caine 173 6 .2 1 Comparing the Frequency of Words Used in Chapter 1 of this Book 174 6 .2 2 Summary of Eligible Votes in the UK General Election 2015 175 6 .2 3 The Changing Fortunes of Internet Explorer and Google Chrome 176 6 .2 4 Literarcy Proficiency: Adult Levels by Country 177 6 .2 5 Political Polarization in the 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 .2 6 Finviz (www.finviz.com) 179 6 .2 7 This Venn Diagram Shows Where You Can Both Smoke Weed and Get a Same-Sex Marriage, by Phillip Bump (The Washington Post) 180 6 .2 8 The 200+ Beer Brands of SAB InBev, by Maarten Lambrechts for Mediafin: www.tijd.be/sabinbev (Dutch), www.lecho.be/service/sabinbev (French) 181 6 .2 9 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 .3 0 How Long Will We Live – And How Well? by Bonnie Berkowitz, Emily Chow and Todd Lindeman (The Washington Post) 183 6 .3 1 Crime Rates by State, by Nathan Yau 184 6 .3 2 Nutrient Contents – Parallel Coordinates, by Kai Chang (@syntagmatic) 185 6 .3 3 How the ‘Avengers’ Line-up Has Changed Over the Years, by Jon Keegan (Wall Street Journal) 186 6 .3 4 Interactive Fixture Molecules, by @experimental361 and @bootifulgame 187 6 .3 5 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, Christian E. Gunning, and John Armistead Selde 188 6 .3 6 The Global Flow of People, by Nikola Sander, Guy J. Abel and Ramon Bauer 189 6 .3 7 UK Election Results by Political Party, 2010 vs 2015 190 6 .3 8 The Fall and Rise of U.S. Inequality, in Two Graphs. Source: World Top Incomes Database; Design credit: Quoctrung Bui (NPR) 191 6 .3 9 Census Bump: Rank of the Most Populous Cities at Each Census, 1790–1890, by Jim Vallandingham 192 6 .4 0 Coal, Gas, Nuclear, Hydro? How Your State Generates Power. Source: U.S. Energy Information Administration, Credit: Christopher Groskopf, Alyson Hurt and Avie Schneider (NPR) 193 6 .4 1 Holdouts Find Cheapest Super Bowl Tickets Late in the Game, by Alex Tribou, David Ingold and Jeremy Diamond (Bloomberg Visual Data) 194 6 .4 2 Crude Oil Prices (West Texas Intermediate), 1985–2015 195 6 .4 3 Percentage Change in Price for Select Food Items, Since 1990, by Nathan Yau 196 6 .4 4 The Ebb and Flow of Movies: Box Office Receipts 1986–2008, by Mathew Bloch, Lee Byron, Shan Carter and Amanda Cox (The New York Times) 197 6 .4 5 Tracing the History of N.C.A.A. Conferences, by Mike Bostock, Shan Carter and Kevin Quealy (The New York Times) 198 6 .4 6 A Presidential Gantt Chart, by Ben Jones 199 6 .4 7 How the ‘Avengers’ Line-up Has Changed Over the Years, by Jon Keegan (Wall Street Journal) 200 6 .4 8 Native and New Berliners – How the S-Bahn Ring Divides the City, by Julius Tröger, André Pätzold, David Wendler (Berliner Morgenpost) and Moritz Klack (webkid.io) 201 6 .4 9 How Y’all, Youse and You Guys Talk, by Josh Katz (The New York Times) 202 6 .5 0 Here’s Exactly Where the Candidates Cash Came From, by Zach Mider, Christopher Cannon, and Adam Pearce (Bloomberg Visual Data) 203 6 .5 1 Trillions of Trees, by Jan Willem Tulp 204 6 .5 2 The Racial Dot Map. Image Copyright, 2013, Weldon Cooper Center for Public Service, Rector and Visitors of the University of Virginia (Dustin A. Cable, creator) 205 6 .5 3 Arteries of the City, by Simon Scarr (South China Morning Post) 206 6 .5 4 The Carbon Map, by Duncan Clark and Robin Houston (Kiln) 207 6 .5 5 Election Dashboard, by Jay Boice, Aaron Bycoffe and Andrei Scheinkman (Huffington Post). Statistical model created by Simon Jackman 208 6 .5 6 London is Rubbish at Recycling and Many Boroughs are Getting Worse, by URBS London using London Squared Map © 2015 www.aftertheflood.co 209 6 .5 7 Automating the Design of Graphical Presentations of Relational Information. Adapted from

McKinlay, J. D. (1986). ACM Transactions on Graphics, 5(2), 110–141. 213 6 .5 8 Comparison of Judging Line Size vs Area Size 213 6 .5 9 Comparison of Judging Related Items Using Variation in Colour (Hue) vs Variation in Shape 214 6 .6 0 Illustrating the Correct and Incorrect Circle Size Encoding 216 6 .6 1 Illustrating the Distortions Created by 3D Decoration 217 6 .6 2 Example of a Bullet Chart using Banding Overlays 218 6 .6 3 Excerpt from What’s Really Warming the World? by Eric Roston and Blacki Migliozzi (Bloomberg Visual Data) 218 6 .6 4 Example of Using Markers Overlays 219 6 .6 5 Why Is Her Paycheck Smaller? by Hannah Fairfield and Graham Roberts (The New York Times) 219 6 .6 6 Inside the Powerful Lobby Fighting for Your Right to Eat Pizza, by Andrew Martin and Bloomberg Visual Data 220 6 .6 7 Excerpt from ‘Razor Sales Move Online, Away From Gillette’, by Graphics Department (Wall Street Journal) 220 7 .1 US Gun Deaths, by Periscopic 225 7 .2 Finviz (www.finviz.com) 226 7 .3 The Racial Dot Map: Image Copyright, 2013, Weldon Cooper Center for Public Service, Rector and Visitors of the University of Virginia (Dustin A. Cable, creator) 227 7 .4 Obesity Around the World, by Jeff Clark 228 7 .5 Excerpt from ‘Social Progress Index 2015’, by Social Progress Imperative, 2015 228 7 .6 NFL Players: Height & Weight Over Time, by Noah Veltman (noahveltman.com) 229 7 .7 Excerpt from ‘How Americans Die’, by Matthew C. Klein and Bloomberg Visual Data 230 7 .8 Model Projections of Maximum Air Temperatures Near the Ocean and Land Surface on the June Solstice in 2014 and 2099: NASA Earth Observatory maps, by Joshua Stevens 231 7 .9 Excerpt from ‘A Swing of Beauty’, by Sohail Al-Jamea, Wilson Andrews, Bonnie Berkowitz and Todd Lindeman (The Washington Post) 231 7 .1 0 How Well Do You Know Your Area? by ONS Digital Content team 232 7 .1 1 Excerpt from ‘Who Old Are You?’, by David McCandless and Tom Evans 233 7 .1 2 512 Paths to the White House, by Mike Bostock and Shan Carter (The New York Times) 233 7 .1 3 OECD Better Life Index, by Moritz Stefaner, Dominikus Baur, Raureif GmbH 233 7 .1 4 Nobel Laureates, by Matthew Weber (Reuters Graphics) 234 7 .1 5 Geography of a Recession, by Graphics Department (The New York Times) 234 7 .1 6 How Big Will the UK Population be in 25 Years Time? by ONS Digital Content team 234 7 .1 7 Excerpt from ‘Workers’ Compensation Reforms by State’, by Yue Qiu and Michael Grabell (ProPublica) 235 7 .1 8 Excerpt from ‘ECB Bank Test Results’, by Monica Ulmanu, Laura Noonan and Vincent Flasseur (Reuters Graphics) 236 7 .1 9 History Through the President’s Words, by Kennedy Elliott, Ted Mellnik and Richard Johnson (The Washington Post) 237 7 .2 0 Excerpt from ‘How Americans Die’, by Matthew C. Klein and Bloomberg Visual Data 237 7 .2 1 Twitter NYC: A Multilingual Social City, by James Cheshire, Ed Manley, John Barratt, and Oliver O’Brien 238 7 .2 2 Killing the Colorado: Explore the Robot River, by Abrahm Lustgarten, Al Shaw, Jeff Larson, Amanda Zamora and Lauren Kirchner (ProPublica) and John Grimwade 238

7 .2 3 Losing Ground, by Bob Marshall, The Lens, Brian Jacobs and Al Shaw (ProPublica) 239 7 .2 4 Excerpt from ‘History Through the President’s Words’, by Kennedy Elliott, Ted Mellnik and Richard Johnson (The Washington Post) 240 7 .2 5 Plow, by Derek Watkins 242 7 .2 6 The Horse in Motion, by Eadweard Muybridge. Source: United States Library of Congress’s Prints and Photographs division, digital ID cph.3a45870. 243 8 .1 Titles Taken from Projects Published and Credited Elsewhere in This Book 248 8 .2 Excerpt from ‘The Color of Debt: The Black Neighborhoods Where Collection Suits Hit Hardest’, by Al Shaw, Annie Waldman and Paul Kiel (ProPublica) 249 8 .3 Excerpt from ‘Kindred Britain’ version 1.0 © 2013 Nicholas Jenkins – designed by Scott Murray, powered by SUL-CIDR 249 8 .4 Excerpt from ‘The Color of Debt: The Black Neighborhoods Where Collection Suits Hit Hardest’, by Al Shaw, Annie Waldman and Paul Kiel (ProPublica) 250 8 .5 Excerpt from ‘Bloomberg Billionaires’, by Bloomberg Visual Data (Design and development), Lina Chen and Anita Rundles (Illustration) 251 8 .6 Excerpt from ‘Gender Pay Gap US?’, by David McCandless, Miriam Quick (Research) and Philippa Thomas (Design) 251 8 .7 Excerpt from ‘Holdouts Find Cheapest Super Bowl Tickets Late in the Game’, by Alex Tribou, David Ingold and Jeremy Diamond (Bloomberg Visual Data) 252 8 .8 Excerpt from ‘The Life Cycle of Ideas’, by Accurat 252 8 .9 Mizzou’s Racial Gap Is Typical On College Campuses, by FiveThirtyEight 253 8 .1 0 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 War Memorial, 2012 254 8 .1 1 Twitter NYC: A Multilingual Social City, by James Cheshire, Ed Manley, John Barratt, and Oliver O’Brien 255 8 .1 2 Excerpt from ‘US Gun Deaths’, by Periscopic 255 8 .1 3 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-Share Alike 3.0 Unported license 265 9 .2 Colour Hue Spectrum 265 9 .3 Colour Saturation Spectrum 266 9 .4 Colour Lightness Spectrum 266 9 .5 Excerpt from ‘Executive Pay by the Numbers’, by Karl Russell (The New York Times) 267 9 .6 How Nations Fare in PhDs by Sex Interactive, by Periscopic; Research by Amanda Hobbs; Published in Scientific American 268 9 .7 How Long Will We Live – And How Well? by Bonnie Berkowitz, Emily Chow and Todd Lindeman (The Washington Post) 268 9 .8 Charting the Beatles: Song Structure, by Michael Deal 269 9 .9 Photograph of MyCuppa mug, by Suck UK (www.suck.uk.com/products/mycuppamugs/) 269 9 .1 0 Example of a Stacked Bar Chart Based on Ordinal Data 270 9 .1 1 Rim Fire – The Extent of Fire in the Sierra Nevada Range and Yosemite National Park, 2013: NASA Earth Observatory images, by Robert Simmon 270 9 .1 2 What are the Current Electricity Prices in Switzerland [Translated], by Interactive things for NZZ (the Neue Zürcher Zeitung) 271

9 .1 3 Excerpt from ‘Obama’s Health Law: Who Was Helped Most’, by Kevin Quealy and Margot Sanger-Katz (The New York Times) 272 9 .1 4 Daily Indego Bike Share Station Usage, by Randy Olson (@randal_olson) (http://www.randalolson.com/2015/09/05/visualizing-indego-bike-share-usage-patterns-in-philadelphia- part-2/) 272 9 .1 5 Battling Infectious Diseases in the 20th Century: The Impact of Vaccines, by Graphics Department (Wall Street Journal) 273 9 .1 6 Highest Max Temperatures in Australia (1st to 14th January 2013), Produced by the Australian Government Bureau of Meteorology 274 9 .1 7 State of the Polar Bear, by Periscopic 275 9 .1 8 Excerpt from Geography of a Recession by Graphics Department (The New York Times) 275 9 .1 9 Fewer Women Run Big Companies Than Men Named John, by Justin Wolfers (The New York Times) 276 9 .2 0 NYPD, Council Spar Over More Officers by Graphics Department (Wall Street Journal) 277 9 .2 1 Excerpt from a Football Player Dashboard 277 9 .2 2 Elections Performance Index, The Pew Charitable Trusts © 2014 278 9 .2 3 Art in the Age of Mechanical Reproduction: Walter Benjamin by Stefanie Posavec 279 9 .2 4 Casualties, by Stamen, published by CNN 279 9 .2 5 First Fatal Accident in Spain on a High-speed Line [Translated], by Rodrigo Silva, Antonio Alonso, Mariano Zafra, Yolanda Clemente and Thomas Ondarra (El Pais) 280 9 .2 6 Lunge Feeding, by Jonathan Corum (The New York Times); whale illustration by Nicholas D. Pyenson 281 9 .2 7 Examples of Common Background Colour Tones 281 9 .2 8 Excerpt from NYC Street Trees by Species, by Jill Hubley 284 9 .2 9 Demonstrating the Impact of Red-green Colour Blindness (deuteranopia) 286 9 .3 0 Colour-blind Friendly Alternatives to Green and Red 287 9 .3 1 Excerpt from, ‘Pyschotherapy in The Arctic’, by Andy Kirk 289 9 .3 2 Wind Map, by Fernanda Viégas and Martin Wattenberg 289 1 0 .1 City of Anarchy, by Simon Scarr (South China Morning Post) 294 1 0 .2 Wireframe Sketch, by Giorgia Lupi for ‘Nobels no degree’ by Accurat 295 1 0 .3 Example of the Small Multiples Technique 296 1 0 .4 The Glass Ceiling Persists Redesign, by Francis Gagnon (ChezVoila.com) based on original by S. Culp (Reuters Graphics) 297 1 0 .5 Fast-food Purchasers Report More Demands on Their Time, by Economic Research Service (USDA) 297 1 0 .6 Stalemate, by Graphics Department (Wall Street Journal) 297 1 0 .7 Nobels No Degrees, by Accurat 298 1 0 .8 Kasich Could Be The GOP’s Moderate Backstop, by FiveThirtyEight 298 1 0 .9 On Broadway, by Daniel Goddemeyer, Moritz Stefaner, Dominikus Baur, and Lev Manovich 299 1 0 .1 0 ER Wait Watcher: Which Emergency Room Will See You the Fastest? by Lena Groeger, Mike Tigas and Sisi Wei (ProPublica) 300 1 0 .1 1 Rain Patterns, by Jane Pong (South China Morning Post) 300 1 0 .1 2 Excerpt from ‘Pyschotherapy in The Arctic’, by Andy Kirk 301 1 0 .1 3 Gender Pay Gap US, by David McCandless, Miriam Quick (Research) and Philippa Thomas (Design) 301

1 0 .1 4 The Worst Board Games Ever Invented, by FiveThirtyEight 303 1 0 .1 5 From Millions, Billions, Trillions: Letters from Zimbabwe, 2005−2009, a book written and published by Catherine Buckle (2014), table design by Graham van de Ruit (pg. 193) 303 1 0 .1 6 List of Chart Structures 304 1 0 .1 7 Illustrating the Effect of Truncated Bar Axis Scales 305 1 0 .1 8 Excerpt from ‘Doping under the Microscope’, by S. Scarr and W. Foo (Reuters Graphics) 306 1 0 .1 9 Record-high 60% of Americans Support Same-sex Marriage, by Gallup 306 1 0 .2 0 Images from Wikimedia Commons, published under the Creative Commons Attribution-Share Alike 3.0 Unported license 308 1 1 .1 – 7 The Pursuit of Faster’ by Andy Kirk and Andrew 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 and Dad, the rest of my brilliant family and my super group of 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 are heroes and I hope this book does their rich talent 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 Yorkshire Police and the University of Leeds. He discovered data visualisation in early 2007 just at the time when he was shaping up his proposal for a Master’s (MA) Research Programme designed for members of staff at the University 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 he launched visualisingdata.com, a blogging platform that would chart the ongoing development 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, and collections of valuable references and resources. 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 public and private training events across the UK, Europe, North America, Asia, South Africa and Australia, reaching well over 3000 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 in 2013 and has been teaching a module on the Information Visualisation Master’s Programme since its inception. In January 2016, he began teaching a data visualisation module as part of the MSc in Business Analytics at the Imperial College Business School in London. 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 identifiable in any form of story.

One of these themes was ‘The Quest’. Booker describes this as revolving around a main protagonist who embarks on a 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 and the nature of data visualisation.

You are the central protagonist in this story in the role of the data visualiser. The journey you are embarking on involves a route along a design workflow where you will be faced with a wide range of different conceptual, practical and technical challenges. The start of this journey will be triggered by curiosity, which you will need to define in order to accomplish your goals. From this origin you will move forward to initiating and planning 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 you might share these with others. Only then will you be ready to take on the design stage. Here you will be faced with the prospect of handling a spectrum of different design options that will require creative and rational thinking to resolve most 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 when the 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 be your guide, helping you navigate efficiently through these difficult stages of your journey.

You will need to learn to be flexible and adaptable, capable of shifting your approach to suit the circumstances. This is important because there are plenty of potential villains lying in wait looking to derail progress. These are the forces that manifest through the imposition of restrictive creative constraints and the pressure created by 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 upon it.

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 and comforts will divert you away from the ideal path, so you will need to maintain clarity and focus.

The central conflict you will have to deal with is the notion that there is no perfect in data visualisation. 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 aiming for good enough will often indeed be good enough and fundamentally necessary.

In accomplishing the quest you will be rewarded with competency in data 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 than just 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 keep persevering with this journey the possibility of a happy ending will increase all the time.

I.2 W ho 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 in this book could (and, in several cases, do) exist as whole books in their own right.

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 through how these different ingredients, and many more, interact, influence and intersect to form the whole.

The decisions I have made in formulating this book‘s content have been shaped by my own process 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 your own knowledge of a subject when you have to explain it and teach it 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 data visualisation. I am not going to claim to belong to that latter cohort, but I have certainly been the former – a beginner – 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 be articulated and presented for me to develop my 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 practice of a visualisation design process. As I have alluded to in the opening, the central premise of this book is that the path to mastering data visualisation is achieved by making better decisions: effective choices, efficiently

made. The book’s central goal is to help develop your capability and confidence in facing these decisions.

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 establish its value proposition compared with other titles.

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 higher education who are interested in and need to learn about data visualisation.

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 good data visualisation practices. Where statistical capabilities are relevant so too is data visualisation.

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 importance of doing it well for both internal and external benefit. You might be a market researcher, a librarian 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 position and not directly involved in the creation of visualisation work, 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 decision points. You might be seeking generally to improve the sophistication of the language you use around commissioning visualisation work and to have a better way of expressing and evaluating work created for you.

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 some way to supporting these needs.

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 become apparent is that visualisation techniques are largely subject-matter neutral: a line chart that displays the ebb and flow of favourable opinion towards a politician involves the same techniques as using a line chart to show how a stock has changed in value over time or how peak temperatures have changed across a season in a given location. A line chart is a line chart, regardless of the subject matter. The context of the viewers (such as their needs and their knowledge) and the specific meaning that can be drawn will inevitably be unique to each setting, but the role of visualisation itself is adaptable and portable across 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 a discipline common to 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 contribute to fine-tuning their approaches.

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 bespoke and feature-rich outputs.

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 in this book should really echo the practices they are likely to be already observing.

As I have already touched on, data visualisation is a genuinely multidisciplinary field. The people who are active in this field or profession come from all backgrounds – everyone has a different 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 commonplace to more people.

That said, I think the following would be a fair outline of the type and shape of some of the most important prerequisite attributes for getting the most out of this book:

Strong numeracy is necessary as well as a familiarity with basic statistics. While it is reasonable to assume limited prior knowledge of data visualisation, there should be a strong desire to want to learn it. The demands of learning a craft like data visualisation take time and effort; 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 through just reading it cover to cover is over-promising and likely 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 for discovering and sharing answers will be at the heart 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 your artistry! 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 decision making and be prepared to have your creative thinking informed more

fundamentally by data rather than just 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 and some experience of working with data.

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 an instruction manual to fix a washing machine. It will help you to know what things to think about, when to 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-by- step 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, structure and scope.

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. Some tools are proprietary, others are open source; some are easier to learn, others require a great 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 a plateau: development and support decline, the community of users disperses and there is a certain expiry of value. Deprecation of syntax and functions in programming languages requires the perennial updating of 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 in the 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 in appropriate 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, Adobe Illustrator).

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 longer term to get the most out of the tools with which you develop strong skills.

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 flicking through those glossy ‘coffee table’ books as much as the next person; such books offer great inspiration and demonstrate some of the finest work in the 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 understanding what is behind the thinking that makes these amazing works succeed and others 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 above identified pages of note. I want to see creases where pages have been folded back or a double-page spread 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 distillation of what I have learned down the years.

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 scope of this particular text.

Take the science of visual perception, for example. There is no value in attempting to emulate what has already been covered by other books in greater depth and quality than I could achieve. Once you 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 realise that you have found yourself learning about what the very concept of light and sight is and at that point your brain begins to ache (well, mine does at least), especially when all you set out to discover was if a bar chart would be better than a pie chart.

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 the significance of otherwise robust ideas.

I want to remove the burden from you as a reader having to translate relevant theoretical discourse into applicable practice. Critical thinking will therefore be the watchword, 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 you could do) and a reliable approach for critically determining what choices you should make (the things you will do and why).

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 huge interest. However, again, this kind of content has already been superbly covered by other texts on more than enough occasions. Time to move on.

I am not going to spend time attempting to enlighten you about how we live in the age of ‘Big Data’ and how occupations related to data are or will be the ‘sexiest jobs’ of our time. The former is no longer news, the latter claim emerged from a single source. I do not want to bloat this book with the unnecessary reprising of topics that have been covered at length elsewhere. There is more valuable and useful content I want you to focus your time on.

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. Maybe even new 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 can only be a line in the sand.

Analysis vs Communication

A further important distinction to make concerns the subtle but significant difference between visualisations which are used for analysis and visualisations used for communication.

Before a visualiser can confidently decide what to communicate to others, he or she needs to have developed an 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.

Visualisation techniques used for analysis will be a key component of the journey 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 alone with visual analysis.

Exploratory data analysis is a huge and specialist subject in and of itself. In its most advanced form, working efficiently and effectively with large complex 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 in pure data science or statistical analysis you will likely require a deeper treatment of the exploratory data analysis topic than this book can reasonably offer. However, Chapter 4 will cover the essential elements in sufficient depth for the practical needs of most people working with data.

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 further digital resources, some of which are newly created, while others are curated references from the endless well of visualisation content on the Web. Included online (book.visualisingdata.com) will be:

a completed case-study project that demonstrates the workflow activities covered in this book, including full write-ups and all related digital materials; an extensive and up-to-date catalogue of over 300 data visualisation tools; a curated collection of tutorials and resources to help develop your confidence with some of the most common and valuable tools; practical exercises designed to embed the learning from each chapter; further reading resources to continue learning about the subjects covered in each chapter.

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, equip and inspire.

To challeng e 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 and viewpoints.

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 in the way that you have: where do you need to improve? What can you do 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 consuming visualisations with greater efficiency and effectiveness.

To enlig hten 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 the objective of enlightening is all about.

As you will discover, there is a lot on your plate, much to work through. It is not just about the visible end- product design decisions. Hidden beneath the surface are many contextual circumstances to weigh up, decisions about how best to prepare your data, choices around the multitude of viable ways of slicing those data up 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 and much more besides.

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 eq u ip is to ensure you have robust tactics for managing your way through the myriad options 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 make the challenge of undertaking data visualisation far less overwhelming, breaking down the overall prospect into smaller, more manageable task 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, when to think about them and how best to resolve all the thinking involved in any data-driven design challenge you meet.

To ins p ir e 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 much positive influence as possible. It should elevate your ambition and broaden your capability.

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 make it one of the most comprehensive, valuable and inspiring titles 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 you are reading now.

Each chapter opens with an introductory outline that previews the content to be covered and provides a bridge 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 the chapter.

Throughout the book you will see sidebar captions that will offer relevant references, aphorisms, good habits and practical tips from some of the most influential people in the field today.

Introduction

This introduction explains how I have attempted to make sense of the complexity of the subject, outlining the nature of the audience I am trying to reach, the key objectives, what topics the book will be covering and not covering, and how the content has been organised.

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. Chapter 1 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 data visualisation. Even if 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 introducing the key design workflow that you will be following.

C hapter 1: D efining D ata Visualisation

D efining d ata v is u alis atio n: outlining the components of thinking that make up the proposed definition for data visualisation. T he im p o r tance o f co nv ictio n: presenting three guiding principles of good visualisation design: trustworthy, accessible and elegant. D is tinctio ns and g lo s s ar y : explaining the distinctions and overlaps with other related disciplines and providing a glossary of terms used in this book to establish consistency of language.

C hapter 2: Visualisation W orkflow

T he im p o r tance o f p r o ces s : describing the data visualisation design workflow, what it involves and why a process approach is required. T he p r o ces s in p r actice: providing some useful tips, tactics and habits that transcend any particular stage of the process but will best prepare you for success with this 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, are somewhat out of sight in the final output; they are hidden beneath the surface but completely shape what is visible. These stages represent the often neglected contextual definitions, data wrangling and editorial challenges that are so critical to the success or otherwise of any visualisation work – they require a great deal of care and attention before you switch your attention to the design stage.

C hapter 3: Form ulating Your B rief

W hat is a b r ief? : describing the value of compiling a brief to help initiate, define and plan the requirements of your work. E s tab lis hing y o u r p r o ject’s co ntex t: 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. E s tab lis hing y o u r p r o ject’s v is io n: early considerations about the type of visualisation solution needed to achieve your aims and harnessing initial ideas about what this solution might look like.

C hapter 4: W orking W ith D ata

D ata liter acy : establishing a basic understanding with this critical literacy, providing some foundation understanding about datasets and data types and some observations about statistical literacy. D ata acq u is itio n: outlining the different origins of and methods for accessing your data. D ata ex am inatio n: approaches for acquainting yourself with the physical characteristics and meaning of your data. D ata tr ans fo r m atio n: optimising the condition, content and form of your data fully to prepare it for its analytical purpose. D ata ex p lo r atio n: developing deeper intimacy with the potential qualities and insights contained, and potentially hidden, within your data.

C hapter 5: Establishing Your Editorial Thinking

W hat is ed ito r ial think ing ? : defining the role of editorial thinking in data visualisation. T he influ ence o f ed ito r ial think ing : explaining how the different dimensions of editorial thinking influence design choices.

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 in this part of the book cover each of the five layers of the data visualisation anatomy. 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 beginning with an introduction to each design layer, an overview of the many different possible design options, followed by detailed guidance on the factors that influence your choices.

T he p r o d u ctio n cy cle: describing the cycle of development activities that take place during this stage, giving a context for how to work through the subsequent chapters in this part.

C hapter 6: D ata Representation

Intr o d u cing v is u al enco d ing : an overview of the essentials of data representation looking at the differences and relationships between visual encoding and chart types. C har t ty p es : a detailed repertoire of 49 different chart types, profiled in depth and organised by a taxonomy of chart families: categorical, hierarchical, relational, temporal, and spatial. Influ encing facto r s and co ns id er atio ns : presenting the factors that will influence the suitability of your data representation choices.

C hapter 7: Interactivity

T he featu r es o f inter activ ity :

Data adjustments: a profile of the options for interactively interrogating and manipulating data. View adjustments: a profile of the options for interactively configuring the presentation of data.

Influ encing facto r s and co ns id er atio ns : presenting the factors that will influence the suitability of your interactivity choices.

C hapter 8: Annotation

T he featu r es o f anno tatio n:

Project annotation: a profile of the options for helping to provide viewers with general explanations about your project. Chart annotation: a profile of the annotated options for helping to optimise viewers’ understanding your charts.

Influ encing facto r s and co ns id er atio ns : presenting the factors that will influence the suitability of your annotation choices.

C hapter 9: C olour

T he featu r es o f co lo u r :

Data legibility: a profile of the options for using colour to represent data. Editorial salience: a profile of the options for using colour to direct the eye towards the most relevant features of your data. Functional harmony: a profile of the options for using colour most effectively across the entire visualisation design.

Influ encing facto r s and co ns id er atio ns : presenting the factors that will influence the suitability of your colour choices.

C hapter 10: C om position

T he featu r es o f co m p o s itio n:

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.

Influ encing facto r s and co ns id er atio ns : presenting the factors that will influence the suitability of your composition choices.

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 – your role as a creator and your role as a viewer – and what you need to enhance your skills with both.

C hapter 11: Visualisation Literacy

V iew ing : L ear ning to s ee: learning about the most effective strategy for understanding visualisations in your role as a viewer rather than a creator. C r eating : T he cap ab ilities o f the v is u alis er : profiling the skill sets, mindsets and general attributes needed to master data visualisation design as a creator.

Part A Foundations

1 Defining Data Visualisation

This opening chapter will introduce you to the subject of data visualisation, defining what data visualisation is and is not. It will outline the different ingredients that make it such an interesting recipe and establish a foundation of understanding that will form a key reference for all of the decision making you are faced with.

Three core principles of good visualisation design will be presented that offer guiding ideals to help mould your convictions about distinguishing between effective and ineffective in data visualisation.

You will also see how data visualisation sits alongside or overlaps with other related disciplines, and some definitions about the use of language in this book will be established to ensure consistency in meaning across all chapters.

1.1 The Components of Understanding To set the scene for what is about to follow, I think it is important to start this book with a proposed definition for data visualisation (Figure 1.1). This definition offers a critical term of reference because its components and their meaning will touch on every element of content that follows in this book. Furthermore, as a subject that has many different proposed definitions, I believe it is worth clarifying my own view before going further:

F ig u r e 1 .1 A Definition for Data Visualisation

At first glance this might appear to be a surprisingly short definition: isn’t there more to data visualisation than that, you might ask? Can nine words sufficiently articulate what has already been introduced as an eminently complex and diverse discipline?

I have arrived at this after many years of iterations attempting to improve the elegance of my definition. In the past I have tried to force too many words and too many clauses into one statement, making it cumbersome and rather undermining its value. Over time, as I have developed greater clarity in my own convictions, I have in turn managed to establish greater clarity about what I feel is the real essence of this subject. The definition above is, I believe, a succinct and practically useful description of what the pursuit of visualisation is truly

about. It is a definition that largely informs the contents of this book. Each chapter will aim to enlighten you about different aspects of the roles of and relationships between each component expressed. Let me introduce and briefly examine each of these one by one, explaining where and how they will be discussed in the book.

Firstly, d ata, our critical raw material. It might appear a formality to mention data in the definition for, after all, we are talking about data visualisation as opposed to, let’s say, cheese visualisation (though visualisation of data using cheese has happened, see Figure 1.2), but it needs to be made clear the core role that data has in the design process. Without data there is no visualisation; indeed there is no need for one. Data plays the fundamental role in this work, so you will need to give it your undivided attention and respect. You will discover in Chapter 4 the importance of developing an intimacy with your data to acquaint yourself with its physical properties, its meaning and its potential qualities.

F ig u r e 1 .2 Per Capita Cheese Consumption in the US

Data is names, amounts, groups, statistical values, dates, comments, locations. Data is textual and numeric in format, typically held in datasets in table form, with rows of records and columns of different variables.

This tabular form of data is what we will be considering as the raw form of data. Through tables, we can look at the values contained to precisely read them as individual data points. We can look up values quite efficiently, scanning across many variables for the different records held. However, we cannot easily establish the comparative size and relationship between multiple data points. Our eyes and mind are not equipped to translate easily the textual and numeric values into quantitative and qualitative meaning. We can look at the data but we cannot really see it without the context of relationships that help us compare and contrast them effectively with other values. To derive understanding from data we need to see it represented in a different, visual form. This is the act of d ata r ep r es entatio n.

This word representation is deliberately positioned near the front of the definition because it is the quintessential activity of data visualisation design. Representation concerns the choices made about the form in which your data will be visually portrayed: in lay terms, what chart or charts you will use to exploit the brain’s visual perception capabilities most effectively.

When data visualisers create a visualisation they are representing the data they wish to show visually through combinations of marks and attributes. Marks are points, lines and areas. Attributes are the appearance properties of these marks, such as the size, colour and position. The recipe of these marks and their attributes,

along with other components of apparatus, such as axes and gridlines, form the anatomy of a chart.

In Chapter 6 you will gain a deeper and more sophisticated appreciation of the range of different charts that are in common usage today, broadening your visual vocabulary. These charts will vary in complexity and composition, with each capable of accommodating different types of data and portraying different angles of analysis. You will learn about the key ingredients that shape your data representation decisions, explaining the factors that distinguish the effective from the ineffective choices.

Beyond representation choices, the p r es entatio n of data concerns all the other visible design decisions that make up the overall visualisation anatomy. This includes choices about the possible applications of interactivity, features of annotation, colour usage and the composition of your work. During the early stages of learning this subject it is sensible to partition your thinking about these matters, treating them as isolated design layers. This will aid your initial critical thinking. Chapters 7–10 will explore each of these layers in depth, profiling the options available and the factors that influence your decisions.

However, as you gain in experience, the interrelated nature of visualisation will become much more apparent and you will see how the overall design anatomy is entirely connected. For instance, the selection of a chart type intrinsically leads to decisions about the space and place it will occupy; an interactive control may be included to reveal an annotated caption; for any design property to be even visible to the eye it must possess a colour that is different from that of its background.

The goal expressed in this definition states that data visualisation is about facilitating u nd er s tand ing . This is very important and some extra time is required to emphasise why it is such an influential component in our thinking. You might think you know what understanding means, but when you peel back the surface you realise there are many subtleties that need to be acknowledged about this term and their impact on your data visualisation choices. Understanding ‘understanding’ (still with me?) in the context of data visualisation is of elementary significance.

When consuming a visualisation, the viewer will go through a process of understanding involving three stages: perceiving, interpreting and comprehending (Figure 1.3). Each stage is dependent on the previous one and in your role as a data visualiser you will have influence but not full control over these. You are largely at the mercy of the viewer – what they know and do not know, what they are interested in knowing and what might be meaningful to them – and this introduces many variables outside of your control: where your control diminishes the influence and reliance on the viewer increases. Achieving an outcome of understanding is therefore a collective responsibility between visualiser and viewer.

These are not just synonyms for the same word, rather they carry important distinctions that need appreciating. As you will see throughout this book, the subtleties and semantics of language in data visualisation will be a recurring concern.

F ig u r e 1 .3 The Three Stages of Understanding

Let’s look at the characteristics of the different stages that form the process of understanding to help explain their respective differences and mutual dependencies.

Firstly, perceiving. This concerns the act of simply being able to read a chart. What is the chart showing you? How easily can you get a sense of the values of the data being portrayed?

Where are the largest, middle-sized and smallest values? What proportion of the total does that value hold? How do these values compare in ranking terms? To which other values does this have a connected relationship?

The notion of understanding here concerns our attempts as viewers to efficiently decode the representations of the data (the shapes, the sizes and the colours) as displayed through a chart, and then convert them into perceived values: estimates of quantities and their relationships to other values.

Interpreting is the next stage of understanding following on from perceiving. Having read the charts the viewer now seeks to convert these perceived values into some form of meaning:

Is it good to be big or better to be small? What does it mean to go up or go down? Is that relationship meaningful or insignificant? Is the decline of that category especially surprising?

The viewer’s ability to form such interpretations is influenced by their pre-existing knowledge about the portrayed subject and their capacity to utilise that knowledge to frame the implications of what has been read. Where a viewer does not possess that knowledge it may be that the visualiser has to address this deficit. They will need to make suitable design choices that help to make clear what meaning can or should be drawn from the display of data. Captions, headlines, colours and other annotated devices, in particular, can all be used to achieve this.

Comprehending involves reasoning the consequence of the perceiving and interpreting stages to arrive at a personal reflection of what all this means to them, the viewer. How does this information make a difference to what was known about the subject previously?

Why is this relevant? What wants or needs does it serve?

Has it confirmed what I knew or possibly suspected beforehand or enlightened me with new knowledge? Has this experience impacted me in an emotional way or left me feeling somewhat indifferent as a consequence? Does the context of what understanding I have acquired lead me to take action – such as make a decision or fundamentally change my behaviour – or do I simply have an extra grain of knowledge the consequence of which may not materialise until much later?

Over the page is a simple demonstration to further illustrate this process of understanding. In this example I play the role of a viewer working with a sample isolated chart (Figure 1.4). As you will learn throughout the design chapters, a chart would not normally just exist floating in isolation like this one does, but it will serve a purpose for this demonstration.

Figure 1.4 shows a clustered bar chart that presents a breakdown of the career statistics for the footballer Lionel Messi during his career with FC Barcelona.

The process commences with perceiving the chart. I begin by establishing what chart type is being used. I am familiar with this clustered bar chart approach and so I quickly feel at ease with the prospect of reading its display: there is no learning for me to have to go through on this occasion, which is not always the case as we will see.

I can quickly assimilate what the axes are showing by examining the labels along the x- and y-axes and by taking the assistance provided by colour legend at the top. I move on to scanning, detecting and observing the general physical properties of the data being represented. The eyes and brain are working in harmony, conducting this activity quite instinctively without awareness or delay, noting the most prominent features of variation in the attributes of size, shape, colour and position.

F ig u r e 1 .4 Demonstrating the Process of Understanding

I look across the entire chart, identifying the big, small and medium values (these are known as stepped magnitude judgements), and form an overall sense of the general value rankings (global comparison judgements). I am instinctively drawn to the dominant bars towards the middle/right of the chart, especially as I know this side of the chart concerns the most recent career performances. I can determine that the purple bar – showing goals – has been rising pretty much year-on-year towards a peak in 2011/12 and then there is a dip before recovery in his most recent season.

My visual system is now working hard to decode these properties into estimations of quantities (amounts of things) and relationships (how different things compare with each other). I focus on judging the absolute magnitudes of individual bars (one bar at a time). The assistance offered by the chart apparatus, such as the vertical axis (or y- axis) values and the inclusion of gridlines, is helping me more quickly estimate the quantities with greater assurance of accuracy, such as discovering that the highest number of goals scored was around 73.

I then look to conduct some relative higher/lower comparisons. In comparing the games and goals pairings I can see that three out of the last four years have seen the purple bar higher than the blue bar, in contrast to all the rest. Finally I look to establish proportional relationships between neighbouring bars, i.e. by how much larger one is compared with the next. In 2006/07 I can see the blue bar is more than twice as tall as the purple one, whereas in 2011/12 the purple bar is about 15% taller.

By reading this chart I now have a good appreciation of the quantities displayed and some sense of the relationship between the two measures, games and goals.

The second part of the understanding process is interpreting. In reality, it is not so consciously consecutive or delayed in relationship to the perceiving stage but you cannot get here without having already done the perceiving. Interpreting, as you will recall, is about converting perceived ‘reading’ into meaning. Interpreting is essentially about orientating your assessment of what you’ve read against what you know about the subject.

As I mentioned earlier, often a data visualiser will choose to – or have the opportunity to – share such insights via captions, chart overlays or summary headlines. As you will learn in Chapter 3, the visualisations that present this type of interpretation assistance are commonly described as offering an ‘explanatory’ experience. In this particular demonstration it is an example of an ‘exhibitory’ experience, characterised by the absence of any explanatory features. It relies on the viewer to handle the demands of interpretation without any assistance.

As you will read about later, many factors influence how well different viewers will be able to interpret a visualisation. Some of the most critical include the level of interest shown towards the subject matter, its relevance and the general inclination, in that moment, of a viewer to want to read about that subject through a visualisation. It is also influenced by the knowledge held about a subject or the capacity to derive meaning from a subject even if a knowledge gap exists.

Returning to the sample chart, in order to translate the quantities and relationships I extracted from the perceiving stage into meaning, I am effectively converting the reading of value sizes into notions of good or bad and comparative relationships into worse than or better than etc. To interpret the meaning of this data about Lionel Messi I can tap into my passion for and knowledge of football. I know that for a player to score over 25 goals in a season is very good. To score over 35 is exceptional. To score over 70 goals is frankly preposterous, especially at the highest level of the game (you might find plenty of players achieving these statistics playing for the Dog and Duck pub team, but these numbers have been achieved for Barcelona in La Liga, the Champions League and other domestic cup competitions). I know from watching the sport, and poring over statistics like this for 30 years, that it is very rare for a player to score remotely close to a ratio of one goal per game played. Those purple bars that exceed the height of the blue bars are therefore remarkable. Beyond the information presented in the chart I bring knowledge about the periods when different managers were in charge of Barcelona, how they played the game, and how some organised their teams entirely around Messi’s talents. I know which other players were teammates across different seasons and who might have assisted or hindered his achievements. I also know his age and can mentally compare his achievements with the traditional football career arcs that will normally show a steady rise, peak, plateau, and then decline.

Therefore, in this example, I am not just interested in the subject but can bring a lot of knowledge to aid me in interpreting this analysis. That helps me understand a lot more about what this data means. For other people they might be passingly interested in football and know how to read what is being presented, but they might not possess the domain knowledge to go deeper into the interpretation. They also just might not care. Now imagine this was analysis of, let’s say, an NHL ice hockey player (Figure 1.5) – that would present an entirely different challenge for me.

In this chart the numbers are irrelevant, just using the same chart as before with different labels. Assuming this was real analysis, as a sports fan in general I would have the capacity to understand the notion of a sportsperson’s career statistics in terms of games played and goals scored: I can read the chart (perceiving) that shows me this data and catch the gist of the angle of analysis it is portraying. However, I do not have sufficient domain knowledge of ice hockey to determine the real meaning and significance of the big–small, higher– lower value relationships. I cannot confidently convert ‘small’ into ‘unusual’ or ‘greater than’ into ‘remarkable’. My capacity to interpret is therefore limited, and besides I have no connection to the subject matter, so I am insufficiently interested to put in the effort to spend much time with any in-depth attempts at interpretation.

F ig u r e 1 .5 Demonstrating the Process of Understanding

Imagine this is now no longer analysis about sport but about the sightings in the wild of Winglets and Spungles (completely made up words). Once again I can still read the chart shown in Figure 1.6 but now I have absolutely no connection to the subject whatsoever. No knowledge and no interest. I have no idea what these things are, no understanding about the sense of scale that should be expected for these sightings, I don’t know what is good or bad. And I genuinely don’t care either. In contrast, for those who do have a knowledge of and interest in the subject, the meaning of this data will be much more relevant. They will be able to read the chart and make some sense of the meaning of the quantities and relationships displayed.

To help with perceiving, viewers need the context of scale. To help with interpreting, viewers need the context of subject, whether that is provided by the visualiser or the viewer themself. The challenge for you and I as data visualisers is to determine what our audience will know already and what they will need to know in order to possibly assist them in interpreting the meaning. The use of explanatory captions, perhaps positioned in that big white space top left, could assist those lacking the knowledge of the subject, possibly offering a short narrative to make the interpretations – the meaning – clearer and immediately accessible.

We are not quite finished, there is one stage left. The third part of the understanding process is comprehending. This is where I attempt to form some concluding reasoning that translates into what this analysis means for me. What can I infer from the display of data I have read? How do I relate and respond to the insights I have drawn out as through interpretation? Does what I’ve learnt make a difference to me? Do I know something more than I did before? Do I need to act or decide on anything? How does it make me feel emotionally?

F ig u r e 1 .6 Demonstrating the Process of Understanding

Through consuming the Messi chart, I have been able to form an even greater appreciation of his amazing career. It has surprised me just how prolific he has been, especially having seen his ratio of goals to games, and I am particularly intrigued to see whether the dip in 2013/14 was a temporary blip or whether the bounce back in 2014/15 was the blip. And as he reaches his late 20s, will injuries start to creep in as they seem to do for many other similarly prodigious young talents, especially as he has been playing relentlessly at the highest level since his late teens?

My comprehension is not a dramatic discovery. There is no sudden inclination to act nor any need – based on what I have learnt. I just feel a heightened impression, formed through the data, about just how good and prolific Lionel Messi has been. For Barcelona fanatics who watch him play every week, they will likely have already formed this understanding. This kind of experience would only have reaffirmed what they already probably knew.

And that is important to recognise when it comes to managing expectations about what we hope to achieve amongst our viewers in terms of their final comprehending. One person’s ‘I knew that already’ is another person’s ‘wow’. For every ‘wow, I need to make some changes’ type of reflection there might be another ‘doesn’t affect me’. A compelling visualisation about climate change presented to Sylvie might affect her significantly about the changes she might need to make in her lifestyle choices that might reduce her carbon footprint. For Robert, who is already familiar with the significance of this situation, it might have substantially less immediate impact – not indifference to the meaning of the data, just nothing new, a shrug of the shoulders. For James, the hardened sceptic, even the most indisputable evidence may have no effect; he might just not be receptive to altering his views regardless.

What these scenarios try to explain is that, from your perspective of the visualiser, this final stage of understanding is something you will have relatively little control over because viewers are people and people are complex. People are different and as such they introduce inconsistencies. You can lead a horse to water but you

cannot make it drink: you cannot force a viewer to be interested in your work, to understand the meaning of a subject or get that person to react exactly how you would wish.

Visualising data is just an agent of communication and not a guarantor for what a viewer does with the opportunity for understanding that is presented. There are different flavours of comprehension, different consequences of understanding formed through this final stage. Many visualisations will be created with the ambition to simply inform, like the Messi graphic achieved for me, perhaps to add just an extra grain to the pile of knowledge a viewer has about a subject. Not every visualisation results in a Hollywood moment of grand discoveries, surprising insights or life-saving decisions. But that is OK, so long as the outcome fits with the intended purpose, something we will discuss in more depth in Chapter 3.

Furthermore, there is the complexity of human behaviour in how people make decisions in life. You might create the most compelling visualisation, demonstrating proven effective design choices, carefully constructed with very a specific audience type and need in mind. This might clearly show how a certain decision really needs to be taken by those in the audience. However, you cannot guarantee that the decision maker in question, while possibly recognising that there is a need to act, will be in a position to act, and indeed will know how to act.

It is at this point that one must recognise the ambitions and – more importantly – realise the limits of what data visualisation can achieve. Going back again, finally, to the components of the definition, all the reasons outlined above show why the term to facilitate is the most a visualiser can reasonably aspire to achieve.

It might feel like a rather tepid and unambitious aim, something of a cop-out that avoids scrutiny over the outcomes of our work: why not aim to ‘deliver’, ‘accomplish’, or do something more earnest than just ‘facilitate’? I deliberately use ‘facilitate’ because as we have seen we can only control so much. Design cannot change the world, it can only make it run a little smoother. Visualisers can control the output but not the outcome: at best we can expect to have only some influence on it.

1.2 The Importance of Conviction The key structure running through this book is a data visualisation design process. By following this process you will be able to decrease the size of the challenge involved in making good decisions about your design solution. The sequencing of the stages presented will help reduce the myriad options you have to consider, which makes the prospect of arriving at the best possible solution much more likely to occur.

Often, the design choices you need to make will be clear cut. As you will learn, the preparatory nature of the first three stages goes a long way to securing that clarity later in the design stage. On other occasions, plain old common sense is a more than sufficient guide. However, for more nuanced situations, where there are several potentially viable options presenting themselves, you need to rely on the guiding value of good design principles.

‘I say begin by learning about data visualisation’s “black and whites”, the rules, then start looking for the greys. It really then becomes quite a personal journey of developing your conviction.’ Jorg e C am oes , D ata Vi s u ali zati on C ons u ltant

For many people setting out on their journey in data visualisation, the major influences that shape their early

beliefs about data visualisation design tend to be influenced by the first authors they come across. Names like Edward Tufte, unquestionably one of the most important figures in this field whose ideas are still pervasive, represent a common entry point into the field, as do people like Stephen Few, David McCandless, Alberto Cairo, and Tamara Munzner, to name but a few. These are authors of prominent works that typically represent the first books purchased and read by many beginners.

Where you go from there – from whom you draw your most valuable enduring guidance –will be shaped by many different factors: taste, the industry you are working in, the topics on which you work, the types of audiences you produce for. I still value much of what Tufte extols, for example, but find I can now more confidently filter out some of his ideals that veer towards impractical ideology or that do not necessarily hold up against contemporary technology and the maturing expectations of people.

‘My key guiding principle? Know the rules, before you break them.’ Greg or Ai s ch , Grap h i cs E d i tor, Th e New York T imes

The key guidance that now most helpfully shapes and supports my convictions comes from ideas outside the boundaries of visualisation design in the shape of the work of Dieter Rams. Rams was a German industrial and product designer who was most famously associated with the Braun company.

In the late 1970s or early 1980s, Rams was becoming concerned about the state and direction of design thinking and, given his prominent role in the industry, felt a responsibility to challenge himself, his own work and his own thinking against a simple question: ‘Is my design good design?’. By dissecting his response to this question he conceived 10 principles that expressed the most important characteristics of what he considered to be good design. They read as follows:

1. Good design is innovative. 2. Good design makes a product useful. 3. Good design is aesthetic. 4. Good design makes a product understandable. 5. Good design is unobtrusive. 6. Good design is honest. 7. Good design is long lasting. 8. Good design is thorough down to the last detail. 9. Good design is environmentally friendly.

10. Good design is as little design as possible.

Inspired by the essence of these principles, and considering their applicability to data visualisation design, I have translated them into three high-level principles that similarly help me to answer my own question: ‘Is my visualisation design good visualisation design?’ These principles offer me a guiding voice when I need to resolve some of the more seemingly intangible decisions I am faced with (Figure 1.7).

F ig u r e 1 .7 The Three Principles of Good Visualisation Design

In the book Will it Make the Boat Go Faster?, co-author Ben Hunt-Davis provides details of the strategies employed by him and his team that led to their achieving gold medal success in the Men’s Rowing Eight event at the Sydney Olympics in 2000. As the title suggests, each decision taken had to pass the ‘will it make the boat go faster?’ test. Going back to the goal of data visualisation as defined earlier, these design principles help me judge whether any decision I make will better aid the facilitation of understanding: the equivalence of ‘making the boat go faster’.

I will describe in detail the thinking behind each of these principles and explain how Rams’ principles map onto them. Before that, let me briefly explain why there are three principles of Rams’ original ten that do not entirely fit, in my view, as universal principles for data visualisation.

‘I’m always the fool looking at the sky who falls off the cliff. In other words, I tend to seize on ideas because I’m excited about them without thinking through the consequences of the amount of work they will entail. I find tight deadlines energizing. Answering the question of “what is the graphic trying to do?” is always helpful. At minimum the work I create needs to speak to this. Innovation doesn’t have to be a wholesale out- of-the box approach. Iterating on a previous idea, moving it forward, is innovation.’ Sarah Slobi n , Vi s u al Jou rnali s t

G o o d d es ig n is inno v ativ e: Data visualisation does not need always to be innovative. For the majority of occasions the solutions being created call upon the tried and tested approaches that have been used for generations. Visualisers are not conceiving new forms of representation or implementing new design techniques in every project. Of course, there are times when innovation is required to overcome a particular challenge; innovation generally materialises when faced with problems that current solutions fail to overcome. Your own desire for innovation may be aligned to personal goals about the development of your skills or through reflecting on previous projects and recognising a desire to rethink a solution. It is not that data visualisation is never about innovation, just that it is not always and only about innovation. G o o d d es ig n is lo ng las ting : The translation of this principle to the context of data visualisation can be taken in different ways. ‘Long lasting’ could be related to the desire to preserve the ongoing functionality of a digital project, for example. It is quite demoralising how many historic links you visit online only to find a project has now expired through a lack of sustained support or is no longer functionally supported on modern browsers. Another way to interpret ‘long lasting’ is in the durability of the technique. Bar charts, for example, are the old reliables of the field – always useful, always being used, always there when you need them (author wipes away a respectful tear). ‘Long lasting’ can also relate to avoiding the temptation of fashion or current gimmickry and having a timeless approach to design. Consider the recent design trend moving away from skeuomorphism and the emergence of so-called flat design. By the time this book is published there will likely be a new movement. ‘Long lasting’ could apply to the subject matter. Expiry in the relevance of certain angles of analysis or out-of-date data is inevitable in most of our work, particularly with subjects that concern current matters. Analysis about the loss of life during the Second

World War is timeless because nothing is now going to change the nature or extent of the underlying data (unless new discoveries emerge). Analysis of the highest grossing movies today will change as soon as new big movies are released and time elapses. So, once again, this idea of long lasting is very context specific, rather than being a universal goal for data visualisation. G o o d d es ig n is env ir o nm entally fr iend ly : This is, of course, a noble aim but the relevance of this principle has to be positioned again at the contextual level, based on the specific circumstances of a given project. If your work is to be printed, the ink and paper usage immediately removes the notion that it is an environmentally friendly activity. Developing a powerful interactive that is being hammered constantly and concurrently by hundreds of thousands of users puts an extra burden on the hosting server, creating more demands on energy supply. The specific judgements about issues relating to the impact of a project on the environment realistically reside with the protagonists and stakeholders involved.

A point of clarity is that, while I describe them as design principles, they actually provide guidance long before you reach the design thinking at the final stage of this workflow. Design choices encapsulate the critical thinking undertaken throughout. Think of it like an iceberg: the design is the visible consequences of lots of hidden preparatory thinking formed through earlier stages.

Finally, a comment is in order about something often raised in discussions about the principles for this subject: that is, the idea that visualisations need to be memorable. This is, in my view, not relevant as a universal principle. If something is memorable, wonderful, that will be a terrific by-product of your design thinking, but in itself the goal of achieving memorability has to be isolated, again, to a contextual level based on the specific goals of a given task and the capacity of the viewer. A politician or a broadcaster might need to recall information more readily in their work than a group of executives in a strategy meeting with permanent access to endless information at the touch of a button via their iPads.

Principle 1: Good Data Visualisation is Trustworthy

The notion of trust is uppermost in your thoughts in this first of the three principles of good visualisation design. This maps directly onto one of Dieter Rams’ general principles of good design, namely that good design is honest.

Trust vs Truth

This principle is presented first because it is about the fundamental integrity, accuracy and legitimacy of any data visualisation you produce. This should always exist as your primary concern above all else. There should be no compromise here. Without securing trust the entire purpose of doing the work is undermined.

There is an important distinction to make between trust and truth. Truth is an obligation. You should never create work you know to be misleading in content, nor should you claim something presents the truth if it evidently cannot be supported by what you are presenting. For most people, the difference between a truth and an untruth should be beyond dispute. For those unable or unwilling to be truthful, or who are ignorant of how to differentiate, it is probably worth putting this book away now: my telling you how this is a bad thing is not likely to change your perspective.

If the imperative for being truthful is clear, the potential for there being multiple different but legitimate versions of ‘truth’ within the same data-driven context muddies things. In data visualisation there is rarely a

singular view of the truth. The glass that is half full is also half empty. Both views are truthful, but which to choose? Furthermore, there are many decisions involved in your work whereby several valid options may present themselves. In these cases you are faced with choices without necessarily having the benefit of theoretical influence to draw out the right option. You decide what is right. This creates inevitable biases – no matter how seemingly tiny – that ripple through your work. Your eventual solution is potentially comprised of many well-informed, well-intended and legitimate choices – no doubt – but they will reflect a subjective perspective all the same. All projects represent the outcome of an entirely unique pathway of thought.

You can mitigate the impact of these subjective choices you make, for example, by minimising the amount of assumptions applied to the data you are working with or by judiciously consulting your audience to best ensure their requirements are met. However, pure objectivity is not possible in visualisation.

‘Every number we publish is wrong but it is the best number there is.’ An d rew D i lnott, C h ai r of th e UK Stati s ti cs Au th ori ty

Rather than view the unavoidability of these biases as an obstruction, the focus should instead be on ensuring your chosen path is trustworthy. In the absence of an objective truth, you need to be able to demonstrate that your truth is trustable.

Trust has to be earned but this is hard to secure and very easy to lose. As the translation of a Dutch proverb states, ‘trust arrives on foot and leaves on horseback’. Trust is something you can build by eliminating any sense that your version of the truth can be legitimately disputed. Yet, visualisers only have so much control and influence in the securing of trust. A visualisation can be truthful but not viewed as trustworthy. You may have done something with the best of intent behind your decision making, but it may ultimately fail to secure trust among your viewers for different reasons. Conversely a visualisation can be trustworthy in the mind of the viewer but not truthful, appearing to merit trust yet utterly flawed in its underlying truth. Neither of these are satisfactory: the latter scenario is a choice we control, the former is a consequence we must strive to overcome.

‘Good design is honest. It does not make a product appear more innovative, powerful or valuable than it really is. It does not attempt to manipulate the consumer with promises that cannot be kept.’ D i eter R am s , celebrated In d u s tri al D es i g ner

Let’s consider a couple of examples to illustrate this notion of trustworthiness. Firstly, think about the trust you might attach respectively to the graphics presented in Figure 1.8 and Figure 1.9. For the benefit of clarity both are extracted from articles discussing issues about home ownership, so each would be accompanied with additional written analysis at their published location. Both charts are portraying the same data and the same analysis; they even arrive at the same summary finding. How do the design choices make you feel about the integrity of each work?

F ig u r e 1 .8 Housing and Home Ownership in the UK (ONS)

Both portrayals are truthful but in my view the first visualisation, produced by the UK Office for National Statistics (ONS), commands greater credibility and therefore far more trust than the second visualisation, produced by the Daily Mail. The primary reason for this begins with the colour choices. They are relatively low key in the ONS graphic: colourful but subdued, yet conveying a certain assurance. In contrast, the Daily Mail’s colour palette feels needy, like it is craving my attention with sweetly coloured sticks. I don’t care for the house key imagery in the background but it is relatively harmless. Additionally, the typeface, font size and text colour feel more gimmicky in the second graphic. Once again, it feels like it is wanting to shout at me in contrast to the more polite nature of the ONS text. Whereas the Daily Mail piece refers to the ONS as the source of the data, it fails to include further details about the data source, which is included on the ONS graphic alongside other important explanatory features such as the subtitle, clarity about the yearly periods and the option to access and download the associated data. The ONS graphic effectively ‘shows all its workings’ and overall earns, from me at least, significantly more trust.

F ig u r e 1 .9 Falling Number of Young Homeowners (Daily Mail)

Another example about the fragility of trust concerns the next graphic, which plots the number of murders committed using firearms in Florida over a period of time. This frames the time around the enactment of the ‘Stand your ground’ law in the Florida. The area chart in Figure 1.10 shows the number of murders over time and, as you can see, the chart uses an inverted vertical y-axis with the red area going lower down as the number of deaths increases, with peak values at about 1990 and 2007. However, some commentators felt the inversion of the y-axis was deceptive and declared the graphic not trustworthy based on the fact they were perceiving the values as represented by an apparent rising ‘white mountain’. They mistakenly observed peak values around 1999 and 2005 based on them seeing these as the highest points. This confusion is caused by an effect known as figure-ground perception whereby a background form (white area) can become inadvertently recognised as the foreground form, and vice versa (with the red area seen as the background).

F ig u r e 1 .1 0 Gun Deaths in Florida

F ig u r e 1 .1 1 Iraq’s Bloody Toll

The key point here is that there was no intention to mislead. Although the approach to inverting the y-axis may not be entirely conventional, it was technically legitimate. Creatively speaking, the effect of dribbling blood was an understandably tempting metaphor to pursue. Indeed, the graphic attempts to emulate a notable infographic from several years ago showing the death toll during the Iraq conflict (Figure 1.11). In the case of the Florida graphic, on reflection maybe the data was just too ‘smooth’ to convey the same dribbling effect achieved in the Iraq piece. However, being inspired and influenced by successful techniques demonstrated by others is to be encouraged. It is one way of developing our skills.

F ig u r e 1 .1 2 Reworking of ‘Gun Deaths in Florida’

Unfortunately, given the emotive nature of the subject matter – gun deaths – this analysis would always attract a passionate reaction regardless of its form. In this case the lack of trust expressed by some was an unintended consequence of a single, innocent design: by reverting the y-axis to an upward direction, as shown in the reworked version in Figure 1.12, you can see how a single subjective design choice can have a huge influence on people’s perception.

The creator of the Florida chart will have made hundreds of perfectly sound visualisations and will make hundreds more, and none of them will ever carry the intent of being anything other than truthful. However, you can see how vulnerable perceived trust is when disputes about motives can so quickly surface as a result of the design choice made. This is especially the case within the pressured environment of a newsroom where you have only a single opportunity to publish a work to a huge and widespread audience. Contrast this setting with a graphic published within an organisation that can be withdrawn and reissued far more easily.

Trust Applies Throughout the Process

Trustworthiness is a pursuit that should guide all your decisions, not just the design ones. As you will see in the next chapter, the visualisation design workflow involves a process with many decision junctions – many paths down which you could pursue different legitimate options. Obviously, design is the most visible result of your decision making, but you need to create and demonstrate complete integrity in the choices made across the entire workflow process. Here is an overview of some of the key matters where trust must be at the forefront of your concern.

‘My main goal is to represent information accurately and in proper context. This spans from data reporting and number crunching to designing human-centered, intuitive and clear visualizations. This is my sole approach, although it is always evolving.’ Ken n ed y E lli ott, Grap h i cs E d i tor, Th e Wa shing ton Post

F o r m u lating y o u r b r ief: As mentioned in the discussion about the ‘Gun Crimes in Florida’ graphic, if you are working with potentially emotive subject matter, this will heighten the importance of demonstrating trust. Rightly or wrongly, your topic will be more exposed to the baggage of prejudicial opinion and trust will be precarious. As you will learn in Chapter 3, part of the thinking involved in ‘formulating your brief’ concerns defining your audience, considering your subject and establishing your early thoughts about the purpose of your work, and what you are hoping to achieve. There will be certain contexts that lend themselves to exploiting the emotive qualities of your subject and/or data but many others that will not. Misjudge these contextual factors, especially the nature of your audience’s needs, and you will jeopardise the trustworthiness of your solution. As I have shown, matters of trust are often outside of your immediate influence: cynicism, prejudice or suspicion held by viewers through their beliefs or opinions is a hard thing to combat or accommodate. In general, people feel comfortable with visualisations that communicate data in a way that fits with their world view. That said, at times, many are open to having their beliefs challenged by data and evidence presented through a visualisation. The platform and location in which your work is published (e.g. website or source location) will also influence trust. Visualisations encountered in already-distrusted media will create obstacles that are hard to overcome. W o r k ing w ith d ata: As soon as you begin working with data you have a great responsibility to be faithful to this raw material. To be transparent to your audience you need to consider sharing as much relevant information about how you have handled the data that is being presented to them:

How was it collected: from where and using what criteria? What calculations or modifications have you applied to it? Explain your approach. Have you made any significant assumptions or observed any special counting rules that may not be common? Have you removed or excluded any data? How representative it is? What biases may exist that could distort interpretations?

E d ito r ial think ing : Even with the purest of intent, your role as the curator of your data and the creator of its portrayal introduces subjectivity. When you choose to do one thing you are often choosing to not do something else. The choice to focus on analysis that shows how values have changed over time is also a decision to not show the same data from other viewpoints such as, for example, how it looks on a map. A decision to impose criteria on your analysis, like setting date parameters or minimum value thresholds, in order to reduce clutter, might be sensible and indeed legitimate, but is still a subjective choice.

‘Data and data sets are not objective; they are creations of human design. Hidden biases in both the collection and analysis stages present considerable risks [in terms of inference].’ Kate C rawford , Pri n ci p al R es earch er at M i cros oft R es earch N Y C

D ata r ep r es entatio n: A fundamental tenet of data visualisation is to never deceive the receiver. Avoiding possible misunderstandings, inaccuracies, confusions and distortions is of primary concern. There are many possible features of visualisation design that can lead to varying degrees of deception, whether intended or not. Here are a few to list now, but note that these will be picked up in more detail later:

The size of geometric areas can sometimes be miscalculated resulting in the quantitative values being disproportionately perceived. When data is represented in 3D, on the majority of occasions this represents nothing more than distracting – and distorting – decoration. 3D should only be used when there are legitimately three dimensions of data variables being displayed and the viewer is able to change his or her point of view to navigate to see different 2D perspectives. The bar chart value axis should never be ‘truncated’ – the origin value should always be zero – otherwise this approach will distort the bar size judgements. The aspect ratio (height vs width) of a line chart’s display is influential as it affects the perceived steepness of connecting lines which are key to reading the trends over time – too narrow and the steepness will be embellished; too wide and the steepness is dampened. When portraying spatial analysis through a thematic map representation, there are many different mapping projections to choose from as the underlying apparatus for presenting and orienting the geographical position of the data. There are many different approaches to flatten the spherical globe, translating it into a two-dimensional map form. The mathematical treatment applied can alter significantly the perceived size or shape of regions, potentially distorting their perception. Sometimes charts are used in a way that is effectively corrupt, like using pie charts for percentages that add up to more, or less, than 100%.

D ata p r es entatio n: The main rule here is: if it looks significant, it should be, otherwise you are either misleading or creating unnecessary obstacles for your viewer. The undermining of trust can also be caused by what you decline to explain: restricted or non-functioning features of interactivity.

Absent annotations such as introduction/guides, axis titles and labels, footnotes, data sources that fail to inform the reader of what is going on. Inconsistent or inappropriate colour usage, without explanation. Confusing or inaccessible layouts. Thoroughness in delivering trust extends to the faith you create through reliability and consistency in the functional experience, especially for interactive projects. Does the solution work and, specifically, does it work in the way it promises to do?

Principle 2: Good Data Visualisation is Accessible

This second of the three principles of good visualisation design helps to inform judgments about how best to facilitate your viewers through the process of understanding. It is informed by three of Dieter Rams’ general principles of good design:

2 Good design makes a product useful. 4 Good design makes a product understandable.

5 Good design is unobtrusive.

Reward vs Effort

The opening section of this chapter broke down the stages a viewer goes through when forming their understanding about, and from, a visualisation. This process involved a sequence of perceiving, interpreting and then comprehending. It was emphasised that a visualiser’s control over the viewer’s pursuit of understanding diminishes after each stage. The objective, as stated by the presented definition, of ‘facilitating’ understanding reflects the reality of what can be controlled. You can’t force viewers to understand, but you can smooth the way.

To facilitate understanding for an audience is about delivering accessibility. That is the essence of this principle: to remove design-related obstacles faced by your viewers when undertaking this process of understanding. Stated another way, a viewer should experience minimum friction between the act of understanding (effort) and the achieving of understanding (reward).

This ‘minimising’ of friction has to be framed by context, though. This is key. There are many contextual influences that will determine whether what is judged inaccessible in one situation could be seen as entirely accessible in another. When people are involved, diverse needs exist. As I have already discussed, varying degrees of knowledge emerge and irrational characteristics come to the surface. You can only do so much: do not expect to get all things right in the eyes of every viewer.

‘We should pay as much attention to understanding the project’s goal in relation to its audience. This involves understanding principles of perception and cognition in addition to other relevant factors, such as culture and education levels, for example. More importantly, it means carefully matching the tasks in the representation to our audience’s needs, expectations, expertise, etc. Visualizations are human-centred projects, in that they are not universal and will not be effective for all humans uniformly. As producers of visualizations, whether devised for data exploration or communication of information, we need to take into careful consideration those on the other side of the equation, and who will face the challenges of decoding our representations.’ Is abel M ei relles , Profes s or, OC AD Uni vers i ty ( Toronto)

That is not to say that attempts to accommodate the needs of your audience should just be abandoned, quite the opposite. This is hard but it is essential. Visualisation is about human-centred design, demonstrating empathy for your audiences and putting them at the heart of your decision making.

There are several dimensions of definition that will help you better understand your audiences, including establishing what they know, what they do not know, the circumstances surrounding their consumption of your work and their personal characteristics. Some of these you can accommodate, others you may not be able to, depending on the diversity and practicality of the requirements. Again, in the absence of perfection optimisation is the name of the game, even if this means that sometimes the least worst is best.

The Factors Your Audiences Influence

Many of the factors presented here will occur when you think about your project context, as covered in Chapter 3. For now, it is helpful to introduce some of the factors that specifically relate to this discussion about delivering accessible design.

S u b ject- m atter ap p eal: This was already made clear in the earlier illustration, but is worth logging again here: the appeal of the subject matter is a fundamental junction right at the beginning of the consumption experience. If your audiences are not interested in the subject – i.e. they are indifferent towards the topic or see no need or relevance to engage with it there and then – then they will not likely stick around. They will probably not be interested in putting in the effort to work through the process of understanding for something that might be ultimately irrelevant. For those to whom the subject matter is immediately appealing, they are significantly more likely to engage with the data visualisation right the way through.

‘Data visualization is like family photos. If you don’t know the people in the picture, the beauty of the composition won’t keep your attention.’ Z ach Gem i g n an i , C E O/Fou n d er of Ju i ce An alyti cs

Many of the ideas for this principle emerged from the Seeing Data visualisation literacy research project (seeingdata.org) on which I collaborated.

D y nam ic o f need : Do they need to engage with this work or is it entirely voluntary? Do they have a direct investment in having access to this information, perhaps as part of their job and they need this information to serve their duties? S u b ject- m atter k no w led g e: What might your audiences know and not know about this subject? What is their capacity to learn or potential motivation to develop their knowledge of this subject? A critical component of this issue, blending existing knowledge with the capacity to acquire knowledge, concerns the distinctions between complicated, complex, simple and simplified. This might seem to be more about the semantics of language but is of significant influence in data visualisation – indeed in any form of communication:

Complicated is generally a technical distinction. A subject might be difficult to understand because it involves pre-existing – and probably high-level – knowledge and might be intricate in its detail. The mathematics that underpinned the Moon landings are complicated. Complicated subjects are, of course, surmountable – the knowledge and skill are acquirable – but only achieved through time and effort, hard work and learning (or extraordinary talent), and, usually, with external assistance. Complex is associated with problems that have no perfect conclusion or maybe even no end state. Parenting is complex; there is no rulebook for how to do it well, no definitive right or wrong, no perfect way of accomplishing it. The elements of parenting might not be necessarily complicated – cutting Emmie’s sandwiches into star shapes – but there are lots of different interrelated pressures always influencing and occasionally colliding. Simple, for the purpose of this book, concerns a matter that is inherently easy to understand. It may be so small in dimension and scope that it is not difficult to grasp, irrespective of prior knowledge and experience. Simplified involves transforming a problem context from either a complex or complicated initial state to a reduced form, possibly by eliminating certain details or nuances.

Understanding the differences in these terms is vital. When considering your subject matter and the nature of your analysis you will need to assess whether your audience will be immediately able to understand what you are presenting or have the capacity to learn how to understand it. If it is a subject that is inherently complex or complicated, will it need to be simplified? If you are creating a graphic about taxation, will you need to strip it

down to the basics or will this process of simplification risk the subject being oversimplified? The final content may be obscured by the absence of important subtleties. Indeed, the audience may have felt sufficiently sophisticated to have had the capacity to work out and work with a complicated topic, but you denied them that opportunity. You might reasonably dilute/reduce a complex subject for kids, but generally my advice is don’t underestimate the capacity of your audience. Accordingly, clarity trumps simplicity as the most salient concern about data visualisation design.

‘Strive for clarity, not simplicity. It’s easy to “dumb something down,” but extremely difficult to provide clarity while maintaining complexity. I hate the word “simplify.” In many ways, as a researcher, it is the bane of my existence. I much prefer “explain,” “clarify,” or “synthesize.” If you take the complexity out of a topic, you degrade its existence and malign its importance. Words are not your enemy. Complex thoughts are not your enemy. Confusion is. Don’t confuse your audience. Don’t talk down to them, don’t mislead them, and certainly don’t lie to them.’ Am an d a Hobbs , R es earch er an d Vi s u al C on ten t E d i tor

W hat d o they need to k no w ? The million-dollar question. Often, the most common frustration expressed by viewers is that the visualisation ‘didn’t show them what they were most interested in’. They wanted to see how something changed over time, not how it looked on a map. If you were them what would you want to know? This is a hard thing to second-guess with any accuracy. We will be discussing it further in Chapter 5. U nfam iliar r ep r es entatio n: In the final chapter of this book I will cover the issue of visualisation literacy, discussing the capabilities that go into being the most rounded creator of visualisation work and the techniques involved in being the most effective consumer also. Many people will perhaps be unaware of a deficit in their visualisation literacy with regard to consuming certain chart types. The bar, line and pie chart are very common and broadly familiar to all. As you will see in Chapter 6, there are many more ways of portraying data visually. This deficit in knowing how to read a new or unfamiliar chart type is not a failing on the part of the viewer, it is simply a result of their lack of prior exposure to these different methods. For visualisers a key challenge lies with situations when the deployment of an uncommon chart may be an entirely reasonable and appropriate choice – indeed perhaps even the ‘simplest’ chart that could have been used – but it is likely to be unfamiliar to the intended viewers. Even if you support it with plenty of ‘how to read’ guidance, if a viewer is overwhelmed or simply unwilling to make the effort to learn how to read a different chart type, you have little control in overcoming this. T im e: At the point of consuming a visualisation is the viewer in a pressured situation with a lot at stake? Are viewers likely to be impatient and intolerant of the need to spend time learning how to read a display? Do they need quick insights or is there some capacity for them to take on exploring or reading in more depth? If it is the former, the immediacy of the presented information will therefore be a paramount requirement. If they have more time to work through the process of perceiving, interpreting and comprehending, this could be a more conducive situation to presenting complicated or complex subject matter – maybe even using different, unfamiliar chart types. F o r m at: What format will your viewers need to consume your work? Are they going to need work created for a print output or a digital one? Does this need to be compatible with a small display as on a smartphone or a tablet? If what you create is consumed away from its intended native format, such as viewing a large infographic with small text on a mobile phone, that will likely result in a frustrating experience for the viewer. However, how and where your work is consumed may be beyond your control. You can’t mitigate for every eventuality. P er s o nal tas tes : Individual preferences towards certain colours, visual elements and interaction

features will often influence (enabling or inhibiting) a viewer’s engagement. The semiotic conventions that visualisers draw upon play a part in determining whether viewers are willing to spend time and expend effort looking at a visualisation. Be aware though that accommodating the preferences of one person may not cascade, with similar appeal, to all, and might indeed create a rather negative reaction. A ttitu d e and em o tio n: Sometimes we are tired, in a bad mood, feeling lazy, or having a day when we are just irrational. And the prospect of working on even the most intriguing and well-designed project sometimes feels too much. I spend my days looking at visualisations and can sympathise with the narrowing of mental bandwidth when I am tired or have had a bad day. Confidence is an extension of this. Sometimes our audiences may just not feel sufficiently equipped to embark on a visualisation if it is about an unknown subject or might involve pushing them outside their comfort zone in terms of the demands placed on their interpretation and comprehension.

The Factors You C an Influence

Flipping the coin, let’s look at the main ways we, as visualisers, can influence (positively or negatively) the accessibility of the designs created. In effect, this entire book is focused on minimising the likelihood that your solution demonstrates any of these negative attributes. Repeating the mantra from earlier, you must avoid doing anything that will cause the boat to go slower.

‘The key difference I think in producing data visualisation/infographics in the service of journalism versus other contexts (like art) is that there is always an underlying, ultimate goal: to be useful. Not just beautiful or efficient – although something can (and should!) be all of those things. But journalism presents a certain set of constraints. A journalist has to always ask the question: How can I make this more useful? How can what I am creating help someone, teach someone, show someone something new?’ Lena Groeg er, Sci ence Jou rnali s t, D es i g n er an d D evelop er at ProPu bli ca

As you saw listed at the start of this section, the selected, related design principles from Dieter Rams’ list collectively include the aim of ensuring our work is useful, unobtrusive and understandable. Thinking about what not to do – focusing on the likely causes of failure across these aims – is, in this case, more instructive.

Y our Solution is Useless

You have failed to focus on relevant content. It is not deep enough. You might have provided a summary-level/aggregated view of the data when the audience wanted further angles of analysis and greater depth in the details provided. A complex subject was oversimplified. It is not fit for the setting. You created work that required too much time to make sense of, when immediate understanding and rapid insights were needed.

Y our Solution is Obtrusive

It is visually inaccessible. There is no appreciation of potential impairments like colour blindness and the display includes clumsily ineffective interactive features. Its format is misjudged. You were supposed to create work fit for a small-sized screen, but the solution created was too fine-detailed and could not be easily read. It has too many functions. You failed to focus and instead provided too many interactive options when the audience had no desire to put in a lot of effort interrogating and manipulating the display.

Y ou Solution is n ot Understan dable

Complex subject or complex analysis. Not explained clearly enough – assumed domain expertise, such as too many acronyms, abbreviations and technical language. Used a complex chart type. Not enough explanation of how to read the graphic or failure to consider if the audience would be capable of understanding this particular choice of chart type. Absent annotations. Insufficient details like scales, units, descriptions, etc.

Principle 3: Good Data Visualisation is Elegant

Elegance in design is the final principle of good visualisation design. This relates closely to the essence of three more of Dieter Rams’ general principles of good design:

3 Good design is aesthetic. 8 Good design is thorough down to the last detail. 10 Good design is as little design as possible.

W hat is Elegant D esign?

Elegant design is about seeking to achieve a visual quality that will attract your audience and sustain that sentiment throughout the experience, far beyond just the initial moments of engagement. This is presented as the third principle for good reason. Any choices you make towards achieving ‘elegance’ must not undermine the accomplishment of trustworthiness and accessibility in your design. Indeed, in pursuing the achievement of the other principles, elegance may have already arrived as a by-product of trustworthy and accessible design thinking. Conversely, the visual ‘look and feel’ of your work will be the first thing viewers encounter before experiencing the consequences of your other principle-led thinking. It therefore stands that optimising the perceived appeal of your work will have a great impact on your viewers.

The pursuit of elegance is elusive, as is its definition: what gives something an elegant quality? As we know, beauty is in the eye of the beholder, but how do we really recognise elegance when we are confronted by it?

When thinking about what the pursuit of elegance of means, the kind of words that surface in my mind are adjectives like stylish, dignified, effortless and graceful. For me, they capture the timelessness of elegance, certainly more so than fancy, cool or trendy, which seem more momentary. Elegance is perhaps appreciated more when it is absent from or not entirely accomplished in a design. If something feels cumbersome, inconsistent and lacking a sense of harmony across its composition and use of colour, it is missing that key ingredient of elegance.

‘When working on a problem, I never think about beauty. I think only how to solve the problem. But when I have finished, if the solution is not beautiful, I know it is wrong.’ R i ch ard Bu ckm i ns ter Fu ller, celebrated i n ven tor and vi s i onary

‘Complete is when something looks seamless, as if it took little effort to produce.’ Sarah Slobi n, Vi s u al Jou rn ali s t

When it feels like style over substance has been at the heart of decision-making, no apparent beauty can

outweigh the negatives of an obstructed or absent functional experience. While I’m loathe to dwell on forcing a separation in concern between form and function, as a beginner working through the design stages and considering all your options, functional judgements will generally need to be of primary concern. However, it is imperative that you also find room for appropriate aesthetic expression. In due course your experience will lead you to fuse the two perspectives together more instinctively.

In his book The Shape of Design, designer Frank Chimero references a Shaker proverb: ‘Do not make something unless it is both necessary and useful; but if it is both, do not hesitate to make it beautiful.’ In serving the principles of trustworthy and accessible design, you will have hopefully covered both the necessary and useful. As Chimero suggests, if we have served the mind, our heart is telling us that now is the time to think about beauty.

How D o You Achieve Elegance in D esign?

There are several components of design thinking that I believe directly contribute to achieving an essence of elegance.

‘“Everything must have a reason”… A principle that I learned as a graphic designer that still applies to data visualisation. In essence, everything needs to be rationalised and have a logic to why it’s in the design/visualisation, or it’s out.’ Stefan i e Pos avec, Inform ati on D es i g n er

E lim inate the ar b itr ar y : As with any creative endeavour or communication activity, editing is perhaps the most influential skill, and indeed attitude. Every single design decision you make – every dot, every pixel – should be justifiable. Nothing that remains in your work should be considered arbitrary. Even if there isn’t necessarily a scientific or theoretical basis for your choices, you should still be able to offer reasons for every thing that is included and also excluded. The reasons you can offer for design options being rejected or removed are just as important in evidence of your developing eye for visualisation design. Often you will find yourself working alone on a data visualisation project and will therefore need to demonstrate the discipline and competence to challenge yourself. Avoid going through the motions and don’t get complacent. Why present data on a map if there is nothing spatially relevant about the regional patterns? Why include slick interactive features if they really add no value to the experience? It is easy to celebrate the brilliance of your amazing ideas and become consumed by work that you have invested deeply in – both your time and emotional energy. Just don’t be stubborn or precious. If something is not working, learn to recognise when to not pursue it any further and then kill it. T ho r o u g hnes s : A dedicated visualiser should be prepared to agonise over the smallest details and want to resolve even the smallest pixel-width inaccuracies. The desire to treat your work with this level of attention demonstrates respect for your audience: you want them to be able to work with quality so pride yourself on precision. Do not neglect checking, do not cut corners, do not avoid the non-sexy duties, and never stop wanting to do better. S ty le: This is another hard thing to pin down, especially as the word itself can have different meanings for people, and especially when it has been somewhat ‘damaged’ by the age-old complaints around something demonstrating style over substance. Developing a style – or signature, as Thomas Clever suggests – is in many ways a manifestation of elegant design. The decisions around colour selection, typography and composition are all matters that influence your style. The development of a style preserves the consistency of your strongest design values, leaving room to respond flexibly to the nuances

of each different task you face. It is something that develops in time through the choices you make and the good habits you acquire.

‘You don’t get there [beauty] with cosmetics, you get there by taking care of the details, by polishing and refining what you have. This is ultimately a matter of trained taste, or what German speakers call fingerspitzengefühl (“finger-tip-feeling”)’. Oli ver R ei ch en s tei n, fou nd er of Inform ati on Arch i tects ( i A)

Many news and media organisations seek to devise their own style guides to help visualisers, graphics editors and developers navigate through the choppy waters of design thinking. This is a conscious attempt to foster consistency in approach as well as create efficiency. In these industries, the perpetual pressure of tight timescales from the relentless demands of the news cycle means that creating efficiency is of enormous value. By taking away the burden of having always to think from scratch about their choices, the visualisers in such organisations are left with more room to concern themselves with the fundamental challenge of what to show and not just get consumed by how to show it. The best styles will stand out as instantly recognisable: there is a reason why you can instantly pick out the work of the New York Times, National Geographic, Bloomberg, the Guardian, the Washington Post, the Financial Times, Reuters and the South China Morning Post. D eco r atio n s ho u ld b e ad d itiv e, no t neg ativ e: The decorative arts are historically considered to be an intersection of that which is useful and beauty, yet the term decoration when applied to data can often suggest a negative connotation of dressing it up using superfluous devices to attract people, but without any real substance. Visual embellishments are, in moderation and when discernibly deployed, effective devices for securing visual appeal and preserving communicated value. This is especially the case when they carry a certain congruence with the subject matter or key message, such as with the use of the different ground textures in the treemap displayed in Figure 1.13. In this graphic, Vienna is reduced to an illustrative 100m2 apartment and the floor plan presents the proportional composition of the different types of space and land in the city. This is acceptable gratuitousness because the design choices are additive, not negatively obstructive or distracting.

‘I suppose one could say our work has a certain “signature”. “Style” – to me – has a negative connotation of “slapped on” to prettify something without much meaning. We don’t make it our goal to have a recognisable (visual) signature, instead to create work that truly matters and is unique. Pretty much all our projects are bespoke and have a different end result. That is one of the reasons why we are more concerned with working according to values and principles that transcend individual projects and I believe that is what makes our work recognisable.’ Th om as C lever, C o- fou nd er C LE VE R °FR AN KE , a d ata d ri ven exp eri ences s tu d i o

F ig u r e 1 .1 3 If Vienna Would be an Apartment

Any design choices you make with the aim of enhancing appeal through novelty or fun need to support, not distract from, the core aim of facilitating understanding. Be led by your data and your audience, not your ideas. There should, though, always be room to explore ways of seeking that elusive blend of being fun, engaging and informative. The bar chart in Figure 1.14 reflects this: using Kit Kat-style fingers of chocolate for each bar and a foil wrapper background, it offers an elegant and appealing presentation that is congruent with its subject.

F ig u r e 1 .1 4 Asia Loses Its Sweet Tooth for Chocolate

Allow your personality to express itself in the times and places where such flair is supportive of the aims of facilitating understanding. After all, a singularity of style is a dull existence. As Groove Armada once sang: ‘If everybody looked the same, we’d get tired of looking at each other.’

N o t ab o u t m inim alis m : As expressed by Rams’ principle ‘Good design is as little design as possible’, elegant design achieves a certain invisibility: as a viewer you should not see design, you should see content. This is not to be confused with the pursuit of minimalism, which is a brutal approach that

strips away the arbitrary but then cuts deeper. In the context of visualisation, minimalism can be an unnecessarily savage and austere act that may be incongruous with some of the design options you may need to include in your work.

‘I’ve come to believe that pure beautiful visual works are somehow relevant in everyday life, because they can become a trigger to get people curious to explore the contents these visuals convey. I like the idea of making people say “oh that’s beautiful! I want to know what this is about!” I think that probably (or, at least, lots of people pointed that out to us) being Italians plays its role on this idea of “making things not only functional but beautiful”.’ Gi org i a Lu p i , C o- fou n d er and D es i g n D i rector at Accu rat

In ‘De architectura’, a thesis on architecture written around 15 BC by Marcus Vitruvius Pollio, a Roman architect, the author declares how the essence of quality in architecture is framed by the social relevance of the work, not the eventual form or workmanship towards that form. What he is stating here is that good architecture can only be measured according to the value it brings to the people who use it. In a 1624 translation of the work, Sir Henry Wooton offers a paraphrased version of one of Vitruvius’s most enduring notions that a ‘well building hath three conditions: firmness, commodity, and delight’, of which a further interpretation for today might be read as ‘sturdy, useful, and beautiful’. One can easily translate these further to fit with these principles of good visualisation design. Trustworthy is sturdy – it is robust, reliable, and has integrity. Useful is accessible – it can be used without undue obstruction. Beautiful is elegant – it appeals and retains attraction.

1.3 Distinctions and Glossary As in any text, consistency in the meaning of terms or language used around data visualisation is important to preserve clarity for readers. I began this chapter with a detailed breakdown of a proposed definition for the subject. There are likely to be many other terms that you either are familiar with or have heard being used. Indeed, there are significant overlaps and commonalities of thought between data visualisation and pursuits like, for example, infographic design.

As tools and creative techniques have advanced over the past decade, the traditional boundaries between such fields begin to blur. Consequently, the practical value of preserving dogmatic distinctions reduces accordingly. Ultimately, the visualiser tasked with creating a visual portrayal of data is probably less concerned about whether their creation will be filed under ‘data visualisation’ or ‘infographic’ as long as it achieves the aim of helping the audience achieve understanding.

Better people than me attach different labels to different works interchangeably, perhaps reflecting the fact that these dynamic groups of activities are all pursuing similar aims and using the same raw material – data – to achieve them. Across this book you will see plenty of references to and examples of works that might not be considered data visualisation design work in the purest sense. You will certainly see plenty of examples of infographics.

The traditional subject distinctions still deserve to be recognised and respected. People are rightfully proud of identifying with a discipline they have expertise or mastery in. And so, before you step into the design workflow chapters, it is worthwhile to spend a little time establishing clarifications and definitions for some of the related fields and activities so all readers are on the same page of understanding. Additionally, there is a glossary of the terms used that will help you more immediately understand the content of later chapters. It makes sense to position those clarifications in this chapter as well.

Distinctions

D ata v is : Just to start with one clarification. While the abbreviated term of data visualisation might be commonly seen as ‘data vis’ (or ‘data viz’; don’t get me started on the ‘z’ issue), and this is probably how all the cool kids on the street and those running out of characters on Twitter refer to it, I am sticking with the full Sunday name of ‘data visualisation’ or at the very least the shortened term ‘visualisation’. Info r m atio n v is u alis atio n: There are many who describe data visualisation as information visualisation and vice versa, myself included, without a great deal of thought for the possible differences. The general distinction, if there is any, tends to be down to one’s emphasis on the input material (data) or the nature of the output form (information). It is also common that information visualisation is used as the term to define work that is primarily concerned with visualising abstract data structures such as trees or graphs (networks) as well as other qualitative data (therefore focusing more on relationships rather than quantities). Info g r ap hics : The classic distinction between infographics and data visualisation concerns the format and the content. Infographics were traditionally created for print consumption, in newspapers or magazines, for example. The best infographics explain things graphically –systems, events, stories – and could reasonably be termed explanation graphics. They contain charts (visualisation elements) but may also include illustrations, photo-imagery, diagrams and text. These days, the art of infographic design continues to be produced in static form, irrespective of how and where they are published. Over the past few years there has been an explosion in different forms of infographics. From a purist perspective, this new wave of work is generally viewed as being an inferior form of infographic design and may be better suited to terms like info-posters or tower graphics (these commonly exist with a fixed-width dimension in order to be embedded into websites and social media platforms). Often these works will be driven by marketing intent through a desire to get hits/viewers, generally with the compromising of any real valuable delivery of understanding. It is important not to dismiss entirely the evident – if superficial – value of this type of work, as demonstrated by the occasionally incredible numbers for hits received. If your motive is ‘bums on seats’ then this approach will serve you well. However, I would question the legitimacy of attaching the term infographic to these designs and I sense the popular interest in these forms is beginning to wane. V is u al analy tics : Some people use this term to relate to analytical-style visualisation work, such as dashboards, that serve the role of operational decision support systems or provide instruments of business intelligence. Additionally, the term visual analytics is often used to describe the analytical reasoning and exploration of data facilitated by interactive tools. This aligns with the pursuit of exploratory data analysis that I will be touching on in Chapter 5. D ata ar t: Aside from the disputes over the merits of certain infographic work, data art is arguably the other discipline related to visualisation that stirs up the most debate. Those creating data art are often pursuing a different motive to pure data visualisation, but its sheer existence still manages to wind up many who perhaps reside in the more ‘purest’ visualisation camps. For data artists the raw material is still data but their goal is not driven by facilitating the kind of understanding that a data visualisation would offer. Data art is more about pursuing a form of self-expression or aesthetic exhibition using data as the paint and algorithms as the brush. As a viewer, whether you find meaning in displays of data art is entirely down to your personal experience and receptiveness to the open interpretation it invites. Info r m atio n d es ig n: Information design is a design practice concerned with the presentation of information. It is often associated with the activities of data visualisation, as it shares the underlying motive of facilitating understanding. However, in my view, information design has a much broader application concerned with the design of many different forms of visual communication, such as way-

finding devices like hospital building maps or in the design of utility bills. D ata s cience: As a field, data science is hard to define, so it is easier to consider this through the ingredients of the role of data scientists. They possess a broad repertoire of capabilities covering the gathering, handling and analysing of data. Typically this data is of a large size and complexity and originates from multiple sources. Data scientists will have strong mathematical, statistical and computer science skills, not to mention astute business experience and many notable ‘softer’ skills like problem solving, communication and presentation. If you find somebody with all these skills, tie them to a desk (legally) and never ever let them leave your organisation. D ata jo u r nalis m : Also known as data-driven journalism (DDJ), this concerns the increasingly recognised importance of having numerical, data and computer skills in the journalism field. In a sense it is an adaption of data visualisation but with unquestionably deeper roots in the responsibilities of the reporter/journalist. S cientific v is u alis atio n: This is another form of a term used by many people for different applications. Some give exploratory data analysis the label scientific visualisation (drawing out the scientific methods for analysing and reasoning about data). Others relate it to the use of visualisation for conceiving highly complex and multivariate datasets specifically concerning matters with a scientific bent (such as the modelling functions of the brain or molecular structures).

Glossary

The precision and consistency of language in this field can get caught up in a little too much semantic debate at times, but it is important to establish early on some clarity about its usage and intent in this book at least.

Roles and Term inology

P r o ject: For the purpose of this book, you should consider any data visualisation creation activity to be consistent with the idea of a project. Even if what you are working on is only seen as the smallest of visualisation tasks that hardly even registers on the bullet points of a to-do list, you should consider it a project that requires the same rigorous workflow process approach. V is u alis er : This is the role I am assigning to you – the person making the visualisation. It could be more realistic to use a term like researcher, analyst, creator, practitioner, developer, storyteller or, to be a little pretentious, visualist. Designer would be particularly appropriate but I want to broaden the scope of the role beyond just the design thinking to cover all aspects of this discipline. V iew er : This is the role assigned to the recipient, the person who is viewing and/or using your visualisation product. It offers a broader and better fit than alternatives such as consumer, reader, recipient or customer. A u d ience: This concerns the collective group of people to whom you are intending to serve your work. Within the audience there will be cohorts of different viewer types that you might characterise through distinct personas to help your thinking about serving the needs of target viewers. C o ns u m ing : This will be the general act of the viewer, to consume. I will use more active descriptions like ‘reading’ and ‘using’ when consuming becomes too passive and vague, and when distinctions are needed between reading text and using interactive features. C r eating : This will be the act of the visualiser, to create. This term will be mainly used in contrast with consuming to separate the focus between the act of the visualiser and the act of the viewer.

D ata Term inology

D ata is : I’m sorry ‘data are’ fans, but that’s just not how normal people speak. In this book, it’s going to be ‘data is’ all the way. Unless my editor disagrees, in which case you won’t even see this passage. R aw d ata: Also known as primary data, this is data that has not been subjected to statistical treatment or any other transformation to prepare it for usage. Some people have a problem with the implied ‘rawness’ this term claims, given that data will have already lost its purity having been recorded by some measurement instrument, stored, retrieved and maybe cleaned already. I understand this view, but am going to use the term regardless because I think most people will understand its intent. D atas et: A dataset is a collection of data values upon which a visualisation is based. It is useful to think of a dataset as taking the form of a table with rows and columns, usually existing in a spreadsheet or database. T ab u latio n: A table of data is based on rows and columns. The rows are the records – instances of things – and the columns are the variables – details about the things. Datasets are visualised in order to ‘see’ the size, patterns and relationships that are otherwise hard to observe. For the purpose of this book, I distinguish between types of datasets that are ‘normalised’ and others that are ‘cross-tabulated’. This distinction will be explained in context during Chapter 5. V ar iab les : Variables are related items of data held in a dataset that describe a characteristic of those records. It might be the names, dates of birth, genders and salaries of a department of employees. Think of variables as the different columns of values in a table, with the variable name being the descriptive label on the header row. There are different types of variables including, at a general level, quantitative (e.g. salary) and categorical (e.g. gender). A chart plots the relationship between different variables. For example, a bar chart might show the number of staff (with the size of bar showing the quantity) across different departments (one bar for each department or category). S er ies : A series of values is essentially a row (or column, depending on table layout) of related values in a table. An example of a series of values would be all the highest temperatures in a city for each month of the year. Plotting this on a chart, like a line chart, would produce a line for that city’s values across the year. Another line could be added to compare temperatures for another city thus presenting a further series of values. D ata s o u r ce: This is the term used to describe the origin of data or information used to construct the analysis presented. This is an important feature of annotation that can help gain trust from viewers by showing them all they need to know about the source of the data. B ig D ata: Big Data is characterised by the 3Vs – high volume (millions of rows of data), high variety (hundreds of different variables/columns) and high velocity (new data that is created rapidly and frequently, every millisecond). A database of bank transactions or an extract from a social media platform would be typical of Big Data. It is necessary to take out some of the hot air spouted about Big Data in its relationship with data visualisation. The ‘Bigness’ (one always feels obliged to include a capitalised B) of data does not fundamentally change the tasks one faces when creating a data visualisation, it just makes it a more significant prospect to work through. It broadens the range of possibilities, it requires stronger and more advanced technology resources, and it amplifies the pressures on time and resources. With more options the discipline of choice becomes of even greater significance.

Visualisation

C har t ty p e: Charts are individual, visual representations of data. There are many ways of representing your data, using different combinations of marks, attributes, layouts and apparatus: these combinations

form archetypes of charts, commonly reduced to simply chart types. There are some charts you might already be familiar with, such as the bar chart, pie chart or line chart, while others may be new to you, like the Sankey diagram, treemap or choropleth map. G r ap hs , char ts , p lo ts , d iag r am s and m ap s : Traditionally the term graph has been used to describe visualisations that display network relationships and chart would be commonly used to label common devices like the bar or pie chart. Plots and diagrams are more specifically attached to special types of displays but with no pattern of consistency in their usage. All these terms are so interchangeable that useful distinction no longer exists and any energy expended in championing meaningful difference is wasted. For the purpose of this book, I will generally stick to the term chart to act as the single label to cover all visualisation forms. In some cases, this umbrella label will incorporate maps for the sake of convenience even though they clearly have a unique visual structure that is quite different from most charts. By the way, the noise you just heard is every cartographer reading this book angrily closing it shut in outrage at the sheer audacity of my lumping maps and charts together. G r ap hic: The term graphic will be more apt when referring to visuals focused more on information- led explanation diagrams (infographics), whereas chart will be more concerned with data-driven visuals. S to r y telling : The term storytelling is often attached to various activities around data visualisation and is a contemporary buzzword often spread rather thinly in the relevance of its usage. It is a thing but not nearly as much a thing as some would have you believe. I will be dampening some of the noise that accompanies this term in the next chapter. F o r m at: This concerns the difference in output form between printed work, digital work and physical visualisation work. F u nctio n: This concerns the difference in functionality of a visualisation, whether it is static or interactive. Interactive visualisations allow you to manipulate and interrogate a computer-based display of data. The vast majority of interactive visualisations are found on websites but increasingly might also exist within apps on tablets and smartphones. In contrast, a static visualisation displays a single-view, non-interactive display of data, often presented in print but also digitally. A x es : Many common chart types (such as the bar chart and line chart) have axis lines that provide reference for measuring quantitative values or assigning positions to categorical values. The horizontal axis is known as the x-axis and the vertical axis is known as the y-axis. S cale: Scales are marks on axes that describe the range of values included in a chart. Scales are presented as intervals (10, 20, 30, etc.) representing units of measurement, such as prices, distances, years or percentages, or in keys that explain the associations between, for example, different sizes of areas or classifications of different colour attributes. L eg end : All charts employ different visual attributes, such as colours, shapes or sizes, to represent values of data. Sometimes, a legend is required to house the ‘key’ that explains what the different scales or classifications mean. O u tlier s : Outliers are points of data that are outside the normal range of values. They are the unusually large or small or simply different values that stand out and generally draw attention from a viewer – either through amazement at their potential meaning or suspicion about their accuracy. C o r r elatio n: This is a measure of the presence and extent of a mutual relationship between two or more variables of data. You would expect to see a correlation between height and weight or age and salary. Devices like scatter plots, in particular, help visually to portray possible correlations between two quantitative values.

Summary: Defining Data Visualisation

In this chapter you have learned a definition of data visualisation: ‘The representation and presentation of data to facilitate understanding.’ The process of understanding a data visualisation involves three stages, namely:

Perceiving: what can I see? Interpreting: what does it mean? Comprehending: what does it mean to me?

You were also introduced to the three principles of good visualisation design:

Good data visualisation is trustworthy. Good data visualisation is accessible. Good data visualisation is elegant.

Finally, you were presented with an array of descriptions and explanations about some of the key terms and language used in this field and throughout the book.

2 Visualisation W orkflow

Clear, effective and efficient thinking is the critical difference between a visualisation that succeeds and one that fails. You cannot expect just to land accidentally on a great solution. You have got to work for it.

In this chapter I will outline the data visualisation workflow that forms the basis of this book’s structure and content. This workflow offers a creative and analytical process that will guide you from an initial trigger that instigates the need for a visualisation through to developing your final solution.

You will learn about the importance of process thinking, breaking down the components of a visualisation design challenge into sequenced, manageable chunks. This chapter will also recommend some practical tips and good habits to ensure the workflow is most effectively adopted.

2.1 The Importance of Process As I have already established, the emphasis of this book is on better decision making. There are so many different things to think about when creating a data visualisation, regardless of whether the output will be the simplest of charts or the most ambitious of multi-faceted digital implementations.

The decisions you will face will inevitably vary in the weight of their significance. There will be some big choices – matters like defining your editorial angles and selecting the best fit chart type – and many seemingly small ones – such as picking the precise shade of grey for a chart’s axis labels. The process of creating a visualisation generally follows the Pareto principle, whereby 20% of decisions made have implications for about 80% of the final visible design. However, just because some decisions will appear more significant in the final output, as visualisers we need to attend to every single decision equally, caring about detail, precision and accuracy.

To repeat, one of the main mental barriers to overcome for those new to the field is to acknowledge that the pursuit of perfect in data visualisation is always unfulfilled. There are better and there are worse solutions, but there is no perfect. Perfect exists in an artificial vacuum. It is free of pressures, has no constraints. That is not real life. There will always be forces pushing and pulling you in different directions. There may be frustrating shortcomings in the data you will have to work with or limitations with your technical capabilities. As discussed, people – your audience members – introduce huge inconsistencies. They – we – are complex, irrational and primarily different. Accepting the absence of perfection helps us unburden ourselves somewhat from the constant nagging sense that we missed out on discovering the perfect solution. This can prove quite liberating.

That is not to say our ambitions need to be lowered. Quite the opposite. We should still strive for best, the absolute optimum solution given the circumstances we face. To achieve this requires improved effectiveness and efficiency in decision making. We need to make better calls, more quickly. The most reliable approach to achieving this is by following a design process.

The process undertaken in this book is structured around the following stages (Figure 2.1).

F ig u r e 2 .1 The Four Stages of the Visualisation Workflow

Here are a few observations about this process ahead of its commencement.

P r ag m atic: This process aims to provide a framework for thinking, rather than instructions for learning. As described in the Introduction, there are very few universal rules to rely upon. While the comfort provided by rules is what many might seek at the beginning of their learning journey, flexible pragmatism beats dogmatism in any situation. Useful rules do exist in visualisation but are often related to quite micro-level matters. I will come to discuss these in due course. R ed u cing the r and o m nes s o f y o u r ap p r o ach: The value of the process is that it guides your entry and closing points: where and how to begin your work as well as how and when it will be finished. When you are new to data visualisation, the sheer extent of things to think about can be quite an overwhelming prospect. This workflow approach aims to break down activities into a connected system of thinking that will help to organise and preserve the cohesiveness of your activities. The process incrementally leads you towards developing a solution, with each stage building on the last and informing the next. The core purpose of the approach is to give you a greater sense of the options that exist at each stage and provide you with better information with which to make your choices. P r o tect ex p er im entatio n: The systematic approach I am advocating in this book should not be seen as squeezing out the scope for creativity or eradicating any space for experimentation. It is natural to want to reduce wasted effort, but at the same time it is absolutely vital to seek opportunities – in the right places – for imagination to blossom. In reality, many of the projects you will work on will not necessarily rely on much creative input. There will be projects that have pressures on time – and a need to compromise on experimenting in favour of the desire for efficiency. There will be subjects or datasets that you work with that are just not congruent with overt creative thinking. It is about striking a balance, affording time on those activities that will bring the right blend of value to suit each context.

‘I truly feel that experimentation (even for the sake of experimentation) is important, and I would strongly encourage it. There are infinite possibilities in diagramming and visual communication, so we have much to explore yet. I think a good rule of thumb is to never allow your design or implementation to obscure the reader understanding the central point of your piece. However, I’d even be willing to forsake this, at times, to allow for innovation and experimentation. It ends up moving us all forward, in some way or another.’ Ken n ed y E lli ott, Grap h i cs E d i tor, Th e Wa shing ton Post

F acilitate ad ap tab ility and iter atio n: This workflow is characterised as a design process rather than a procedure. A good process should facilitate the adaptability and remove the inflexibility of a defined procedure of operation. Although the activities are introduced and presented in this book in a linear fashion, inevitably there is much iteration that takes place. There will be times when you will have to revisit decisions, maybe even redo activities in a completely different way given what you have discovered further down the line. If you make mistakes or bad calls – and everyone does – it is important to fail gracefully but recover quickly. You will need to be able to respond to changes in circumstances and accommodate their impact fast. A good process cushions the impact of situations

arising like this. T he fir s t o ccas io n, no t the las t: It is important to note that the tasks you face at each stage in the process will represent the first occasion you pay attention to these matters, but not the final occasion. There is something of a trickle-down effect here. Many of the concerns you will be faced with at the start of a challenge will likely continue to echo in your thoughts right through to the end. Some things are just not possible to close off that easily. Take the ongoing demands of profiling who your audiences are and what they might need. That thinking starts early and should actually never drop off your radar. The nature of the process gives you the best chance of keeping all the plates spinning that need to be spun, knowing which ones can be left to drop and when. A lw ay s the s am e p r o ces s : The range of visualisation challenges you will face in your career will vary. Even if you are producing the same work every month, no two projects will provide the same experience: just having an extra month of data means it has a new shape or size. It is different. Some projects you work on will involve fairly simple data, others will involve hugely complex data. In some cases you will have perhaps two hours or two days to deliver a solution, in others you might have two months. The key thing is that the process you follow will always require the same activities in the same sequence, regardless of the size, speed and complexity of your challenge. The main difference is that any extremes in the circumstances you face will amplify the stresses at each stage of the process and place greater demands on the need for thorough, effective and timely decision making. P ar titio ning y o u r m ind s et: Within each of the sequenced stages listed in Figure 2.1 there will be different demands on your mindset: sometimes you are thinking, sometimes you are doing, sometimes you are making. When you are working alone, especially, it is important to appreciate the activities that will require different mindsets:

Thinking: The duties here will be conceptual in nature, requiring imagination and judgment, such as formulating your curiosity, defining your audience’s needs, reasoning your editorial perspectives, and making decisions about viable design choices. Doing: These are practical tasks that will still engage the brain, obviously, but manifest themselves through more hands-on activities like sketching ideas and concepts, learning about a subject through research, gathering and handling your data. Making: These involve the constructive and technical activities that specifically relate to the production cycle as you face the challenge of translating promising, well-considered design concepts into effective, working solutions.

‘You need a design eye to design, and a non-designer eye to feel what you designed. As Paul Klee said, “See with one eye, feel with the other”.’ Oli ver R ei ch ens tei n, fou nd er of Inform ati on Arch i tects ( i A)

2.2 The Process in Practice Throughout this book I will call out key points of advice in the form of useful tips, tactics or good habits you should be looking to consider employing. Many of these have been informed by interviews with some of the brilliant people working in this field today. As you are about to commence the design workflow here are some pieces of advice that transcend any individual stage of the process.

M anag ing p r o g r es s and r es o u r ces : Good planning, time and resource management keep a process cohesive and progressing. They represent the lubricant. You will rarely have the luxury of working on a project that has no defined end date and so adhering to imposed or self-imposed

timescales is especially important. It is very easy to get swallowed up by the demands of certain activities, particularly those involved in the ‘working with data’ stage. Similarly the production cycle (which takes place during and beyond Part C), as you iterate between idea, prototype and construction, can at times appear never to have an end in sight. As one task is finished, another two always seem to appear. As you get closer to a deadline you will either sink or swim: for some the pressure of time is crippling; others thrive on the adrenaline it brings and their focus is sharpened as a result. Recognising the need to factor in time for some of the broader responsibilities – clerical tasks, arranging demo meetings and skype calls, file management and running backups – will prove hugely beneficial by the end. R o o m to think : On the theme of task duration and progress, it is important to build in the capacity to think. The notion of brain ‘states’ is relevant here, in particular the ‘alpha’ state which kicks in most commonly when we are particularly relaxed. Being in this state helps to heighten your imagination, learning and thought process. Apologies for the mental image but I do some of my most astute thinking in the shower or bath, and just before going to sleep at night. These are the occasions when I am most likely drifting into a relaxed alpha state and help me to contemplate most clearly the thoughts and ideas I might have. I find train or air travel achieves the same as does lying on a beach. Unfortunately in the latter scenario I just don’t care enough about work in that moment to note down my frequent genius ideas (what do you mean ‘which ones’?). If I have a task that will take two days of my time but the deadline is further away, I typically try to break down the time I give to it across smaller clusters of three to four hours spread across four days of activity in order to create sufficient opportunities for my brain to tick over during the intervening gaps and hopefully allow good ideas to ferment. H eu r is tics to s u p p o r t d ecis io ns : As I have discussed, there will be occasions when the best choice does not present itself, when time is pressurising you and when you will need to make a call. You might have to occasionally rely on heuristic techniques that help to speed up your decision-making at certain stages. Although this might seem an unsatisfactory tactic to consider, given the previously stated need to eliminate arbitrary choices, heuristics can remain consistent with this desire when they rely on educated, intuitive or common-sense judgements. As you develop your experience, the astuteness of such heuristic judgments will be increasingly reliable to fall back on when the need arises. P en and p ap er : The humble pairing of pen and paper will prove to be a real ally throughout your process. I will not over-sentimentally claim this is the most important tool combination because, unless you are producing artisan hand-drawn work, you will have other technical tools that would probably rise up the importance list. However, the point here is that capturing ideas and creating sketches are a critical part of your process. Do not rely on your memory; if you have a great idea sketch it down. This activity is never about artistic beauty. It does not need you to be an artist, it just needs you to get things out of your head and onto paper, particularly if you are collaborating with others. If you are incredibly fortunate to be so competent with a given tool that you find it more natural than using pen and paper to ‘sketch’ ideas quickly, then this is of course absolutely fine, as long as it is indeed the quickest medium to do so. N o te- tak ing : Whether this is via pen and paper, or in Word, or a Google doc, note-taking is a vital habit to get into. This is about preserving records of important details such as:

information about the sources of data you are using; calculations or manipulations you have applied to your data; assumptions you have made; data descriptions, particularly if explanations have been offered to use verbally by somebody who knows the data well; questions you have yet to get answers to; the answers you did get to your questions;

terminology, abbreviations, codes – things you need to remember the meaning and associations in your data; task lists and wish lists of features or ideas you would like to consider pursuing; issues or problems you can foresee; websites or magazines that you saw and gave you a bit of inspiration; ideas you have had or rejected.

Note-taking is easier said than done, and I am among the least natural note-takers to roam this Earth, but I have forced it into becoming a habit and a valuable one at that.

‘Because I speak the language of data, I can talk pretty efficiently with the experts who made it. It doesn’t take them long, even if the subject is new to me, for them to tell me any important caveats or trends. I also think that’s because I approach that conversation as a journalist, where I’m mostly there to listen. I find if you listen, people talk. (It sounds so obvious but it is so important.) I find if you ask an insightful question, something that makes them say “oh, that’s a good point,” the whole conversation opens up. Now you’re both on the same side, trying to get this great data to the public in an understandable way.’ Kati e Peek, D ata Vi s u ali zati on D es i g n er an d Sci en ce Jou rnali s t

C o m m u nicatio n: Communication is a two-way activity. Firstly, it is about listening to stakeholders (clients or colleagues) and your audience: what do they want, what do they expect, what ideas do they have? In particular, what knowledge do they have about your subject? Secondly, communication is about speaking to others: presenting ideas, updating on progress, seeking feedback, sharing your thoughts about possible solutions, and promoting and selling your work (regardless of the setting, you will need to do this). If you do not know the intimate details of your subject matter you will need to locate others who do: find smart people who know the subject better than you or find smart people who do not know the subject but are just smart. You cannot avoid the demands of communicating so do not hide behind your laptop – get out there and speak and listen to people who can help you. R es ear ch: Connected to the need for good communication is the importance of research. This is an activity that will exist as a constant, running along the spine of your process thinking. You cannot know everything about your subject, about the meaning of your data, about the relevant and irrelevant qualities it possesses. As you will see later, data itself can only tell us so much; often it just tells us where interesting things might exist, not what actually explains why they are interesting.

‘Research is key. Data, without interpretation, is just a jumble of words and numbers – out of context and devoid of meaning. If done well, research not only provides a solid foundation upon which to build your graphic/visualisation, but also acts as a source of inspiration and a guidebook for creativity. A good researcher must be a team player with the ability to think critically, analytically, and creatively. They should be a proactive problem solver, identifying potential pitfalls and providing various roadmaps for overcoming them. In short, their inclusion should amplify, not restrain, the talents of others.’ Am and a Hobbs , R es earch er and Vi s u al C onten t E d i tor

A ttentio n to d etail: Like note-taking, this will be something that might not be a natural trait for some but is so important. You cannot afford to carry any errors in your work. Start every project with that commitment. This is such an important ingredient to securing trust in your work. The process you are about to learn is greatly influenced by the concept of ‘aggregation of marginal gains’. You need to sweat the small stuff. Even if many of your decisions seem small and inconsequential, they deserve your full attention and merit being done right, always. You should take pride in the fine detail of your design

thinking, so embrace the need for checking and testing. If you are so immersed in your work that you become blind to it, get others to help – call on those same smart people you identified above. As someone who once published a graphic stating Iran’s population was around 80 billion and not 80 million, I know how one tiny mistake can cause the integrity of an entire project to crumble to the ground. You do not get a second chance at a first impression, somebody once said. I forget who, I wasn’t paying attention … M ak e it w o r k fo r y o u : The only way you will truly find out whether a process works for you is if you practise it, relentlessly. As I have stated, every project will be different even if only in small ways. However, if you just cannot get the approach presented in this book to fit your personality or purpose, modify it. We are all different. Do not feel like I am imposing this single approach. Take it as a proposed framework based on what has worked for me in the past. Bend it, stretch it, and make it work. As you become more experienced (and confident through having experienced many different types of challenges) the many duties involved in data visualisation design will become second nature, by which time you probably will no longer be aware of even observing a process. B e ho nes t w ith y o u r s elf: Feedback, editing, not doing certain things, are disciplines of the effective visualiser. Honesty with yourself is vital, especially as you are often working on a solo project but need so many different skill sets and mindsets. As I mentioned in the last section, preciousness or stubbornness that starts to impede on quality becomes destructive. Being blind to things that are not working, or not taking on board constructive feedback just because you have invested so much time in something, will prove to be the larger burden. Do not be afraid to kill things when they are not working. L ear n: Reflective learning is about looking back over your work, examining the output and evaluating your approach. What did you do well? What would you do differently? How well did you manage your time? Did you make the best decisions you could given the constraints that existed? Beyond private reflections, some of the best material about data visualisation on the Web comes from people sharing narratives about their design processes. Read how other people undertake their challenges. Maybe share your own? You will find you truly learn about something when you find the space to write about it and explain it to others. Write up your projects, present your work to others and, in doing so, that will force you to think ‘why did I do what I did?’.

‘No work is ever wasted. If it’s not working, let go and move on – it’ll come back around to be useful later.’ E m m a C oats , freelan ce Fi lm D i rector, form erly of Pi xar

Summary: Data Visualisation W orkflow In this chapter you were introduced to the design workflow, which involves four key stages:

1. Formulating your brief: planning, defining and initiating your project. 2. Working with data: going through the mechanics of gathering, handling and preparing your data. 3. Establishing your editorial thinking: defining what you will show your audience. 4. Developing your design solution: considering all the design options and beginning the production cycle.

Undertaking the activities in this workflow require you to partition your mindset:

Thinking: conceptual tasks, decision making. Doing: practical undertakings like sketching, visually examining data.

Making: technical duties like analysing data, constructing the solution.

Finally, you were presented with some general tips and tactics ahead of putting the process into practice:

This will be the first time you think about each of the stages and activities, not the last – visualisation design is as much about plate-spinning management as anything else. The importance of good project management to manage progress and resources cannot be over- emphasised. Create room to think: clear thinking helps with efficiency of effort. Pen and paper will prove to be one of your key tools. Note-taking is a habit worth developing. Communication is a two-way relationship: it is speaking and listening. Attention to detail is an obligation: the integrity of your work is paramount. Make the workflow work for you: practise and adapt the approach to suit you. Be honest with yourself, do not be precious and have the discipline not to do things, to kill ideas, to avoid scope-creep.

Part B The Hidden Thinking

3 Formulating Your Brief

In Chapter 2 you learnt about the importance of process, taking on data visualisation challenges using a design workflow to help you make good decisions. This third chapter initiates the process with formulating your brief.

The essence of this stage is to identify the context in which your work will be undertaken and then define its aims: it is the who, what, why, where, when and how. It can be as formal – and shared with others – or as informal an activity as you need to make it.

The first contextual task will be to consider why you are producing this data visualisation – what is its raison d’être? To answer this, you will need to define what triggered it (the origin curiosity) and what it is aiming to accomplish (the destination purpose). Recognising that no visualisation projects are ever entirely free of constraints or limitations, you will also need to identify the circumstances surrounding the project that will shape the scope and nature of the project you’re about to undertake.

Following these contextual definitions you will briefly switch your attention to consider a vision for your work. With the origin and intended destination mapped out you will be able to form an initial idea about what will be the best-fit type of design solution. You will be introduced to the purpose map, which provides a landscape of all the different types of visualisation you could pursue, helping you establish an early sense of what you should pursue. To wrap up the chapter, you will allocate some time to harness the instinctive thoughts you might have had about the ideas, images, keywords and inspirations that you feel could play a role in your work.

Collectively this work will provide you with a solid foundation from which to best inform all your subsequent workflow process stages.

3.1 W hat is a Brief? In its simplest form a brief represents a set of expectations and captures all the relevant information about a task or project. It is commonly associated with the parlance of project management or graphic design, but in data visualisation the need to establish clarity about the definitions and requirements of a project is just as relevant. This is about establishing the context of and vision for your work.

When you are working with clients or colleagues it will be in the interests of all parties to have a mutual understanding of the project’s requirements and some agreement over the key deliverables. In such situations you may have already been issued with some form of initial brief from these stakeholders. This could be as informal as an emailed or verbal request or as formal as a template-based briefing document. Irrespective of what has been issued you will get more value from compiling your own briefing document to ensure you have sufficient information to plan your upcoming work.

If you are not working for or with others – essentially pursuing work that you have initiated yourself – you clearly will not have been issued with any brief, but once again, it will be to your advantage to compile a brief for yourself. This does not have to be an overly burdensome or bureaucratic task. I use a simple checklist that is not only practically lightweight but also comprehensively helpful, comprising a series of question prompts

that I either answer myself or raise with those stakeholders with whom I am working.

For some beginners, this stage can feel somewhat frustrating. On the surface it sounds like a clerical prospect when what you really want is just to get on with the good stuff, like playing with the data and focusing on creativity. Understanding contextual matters, in particular before anything else, is too invaluable a practice to neglect. All the decisions that follow in this workflow will be shaped around the definitions you establish now. There may be changes but you will reap the benefits from gaining as much early clarity as possible.

3.2 Establishing Your Project’s Context

Defining Your O rigin Curiosity

A worthwhile data visualisation project should commence from the starting point of a curiosity. According to the dictionary definition, curiosity is about possessing ‘a strong desire to know or learn something’. This aligns perfectly with the goal of data visualisation, defined in Chapter 1 as being to facilitate understanding. By establishing a clear sense of where your project originated in curiosity terms, the primary force that shapes your decision making will be the desire to respond effectively to this expressed intrigue.

‘Be curious. Everyone claims she or he is curious, nobody wants to say “no, I am completely ‘uncurious’, I don’t want to know about the world”. What I mean is that, if you want to work in data visualisation, you need to be relentlessly and systematically curious. You should try to get interested in anything and everything that comes your way. Also, you need to understand that curiosity is not just about your interests being triggered. Curiosity also involves pursuing those interests like a hound. Being truly curious involves a lot of hard work, devoting time and effort to learn as much as possible about various topics, and to make connections between them. Curiosity is not something that just comes naturally. It can be taught, and it can be learned. So my recommendation is: develop your curiosity, educate yourself – don’t just wait for the world to come to you with good ideas. Pursue them.’ Profes s or Alberto C ai ro, Kni g h t C h ai r i n Vi s u al Jou rn ali s m , Un i vers i ty of M i am i , and Vi s u ali s ati on Sp eci ali s t

A visualisation process that lacks an initially articulated curiosity can lead to a very aimless solution. After all, what is it you are solving? What deficit in people’s understanding are you trying to address? Having the benefit of even just a broad motive can help you tremendously in navigating the myriad options you face.

The nature of the curiosity that surrounds your work will vary depending on where it originated and who it is serving. Consider these five scenarios where the characteristics differ sufficiently to offer different contextual challenges:

P er s o nal intrigue – ‘I wonder what …’ S tak eho ld er intrigue – ‘He/she needs to know …’ A u d ience intrigue – ‘They will need to know …’ A nticip ated intrigue – ‘They might be interested in knowing …’ P o tential intrigue – ‘There might be something interesting …’

Let’s work through an illustration of each of these scenarios to explain their differences and influences.

Firstly, there are situations where a project is instigated in response to a curiosity borne out of p er s o nal intr ig u e. An example of this type of situation can be found in the case-study project that I have published as

a digital companion to this book (book.visualisingdata.com) to help demonstrate the workflow process in practice. The project is titled ‘Filmographics’ and concerns the ebb and flow of the careers of different movie stars. You can find out more about it by visiting the book’s digital resources.

The reason I pursued this particular project was because, firstly, I have a passion for movies and, secondly, I had a particular curiosity about the emergence, re-emergence and apparent disappearance of certain actors. Expressed as a question, the core curiosity that triggered this project was: ‘What is the pattern of success or failure in the movie careers of a selection of notable actors?’

This initial question provided me with immediate clarity: the goal of the visualisation would be to deliver an ‘answer’ to this question, to help me better understand how the career patterns look for the different actors selected. In this case I am the originator of the curiosity and I am pursuing this project for my own interest.

Ultimately, whether this initially defined curiosity remains the same throughout the process does not really matter. Quite often one’s initial expression of curiosity shifts considerably once data has been gathered and analysed. When more research is carried out on the subject matter you become more roundly acquainted with the relevance (or otherwise) of the trigger enquiry. You might alter your pursuit when you realise there is something different – and potentially more interesting – to explore. You do not want to be anchored to an enquiry that no longer reflects the most relevant perspective but it does offer at least a clear starting point – an initial motive – from which the process begins.

Sometimes, the nature of the motivation for a personal intrigue-based curiosity is recognition of one’s ignorance about an aspect of a subject that should be known (a deficit in ‘available’ understanding) more than a defined interest in a subject that may not be known (possibly creating new understanding).

Let’s consider another scenario, still concerning movie-related subject matter, but to explain a different type of curiosity. Suppose you work for a movie studio and have been tasked by a casting director to compile a one- off report that will profile which actors are potentially the best option to cast in a major sci-fi movie that has just been given the green light to begin production. You have certain criteria to follow: they have to be female, aged 30-45, and must fit the description of ‘rising’ star. They must not have been in other sci-fi movies, nor can they have any of the ‘baggage’ that comes with being associated with huge flops. Their fees should be under $2 million. You go away, undertake the analysis, and compile a report showing the career paths of some of the most likely stars who fit the bill.

This scenario has not come about through your own personal curiosity but instead you are responding to the specific curiosity of the casting director. In undertaking this work you effectively inherit – take on – the curiosity of others. They have briefed you to find the data, analyse it, and then present the findings to them. This would be an example of curiosity born out of s tak eho ld er intr ig u e: work commissioned by a stakeholder who is also the target audience (or is the prominent party among the intended audience). There is no anticipation of interest here, rather it is known.

For the third scenario, you might work for a business involved in the analysis and commentary of the state of the movie industry. Let’s imagine your company specialises in producing a dashboard that is shared with a broad group of users comprising Hollywood executives, studio senior management and casting agents, among others. The dashboard profiles all aspects of the industry, covering current trends and the career fortunes of a wide range different actors, helping users to identify who is hot, who is not, who is emerging, who is declining, who will cost what, who scores well with different audiences, etc.

The various indicators of information you are compiling and presenting on the dashboard are based on the recognised needs of the professional curiosities these people (client users) will have about this subject matter (movie career statuses). Given the diverse permutations of the different measures included, not all the information provided will be of interest all the time to all who consume it, but it is provided and available as and when they do need it. This would be an example of curiosity born out of au d ience intr ig u e – shaped out of a combination of knowing what will be needed and reasonably anticipating what could be needed.

What you are working towards in situations like this is ensuring that all the relevant aspects of possible curiosity can be brought together in a single place to serve as many needs as possible. There are similarities here with the multitude of dials, displays and indicators in the cockpit of an aircraft. The pilot does not need all that information as an immediate priority all the time, but may need access to some of the information in a reactive sense should the situation arise. Additionally, this scenario may be typical of a varied and larger scale audience in contrast to the more bespoke nature of a stakeholder intrigue scenario. You will rarely if ever be able to serve 100% of the audience’s potential needs but you can certainly aspire to do your best.

Consider another similar scenario, but with a different setting used to illustrate a more subtle distinction. Suppose I am as a graphics editor working for a newspaper. One of the topics of current attention might concern the relatively late-career breakthrough of a certain actor, who has almost overnight moved from roles in relatively modest TV shows to starring in cinematic blockbusters. It is decided by the assignments editor that I will work on a graphic that examines the fortunes of this actor’s career alongside a selection of other actors to provide contrast or draw comparisons with.

On this occasion the trigger is not necessarily emerging from personal intrigue. I have essentially been issued with the requirements: even if I agree with the idea or had a similar thought myself, the organisational relationship decrees that others will instruct me about which tasks to work on. A stakeholder – the assignments editor (or others in the editorial hierarchy) – has determined that this is a topic of interest and worth exploring. However, in contrast to the stakeholder intrigue scenario, here the stakeholder is not the intended audience. It is not necessarily even a curiosity they have themselves. The motive for this work is likely driven by the machinations of current affairs: what is newsworthy and likely to be of some interest to readers? Therefore, the belief is that this analysis (looking at this actor’s career path compared with others) is aligned to the current entertainment news agenda.

‘The best piece of advice, which is “always be curious,” came from Steve Duenes, who has led the graphics team at the New York Times for more than a decade. Being curious covers the essence of journalism: question everything, never make assumptions, dig. You can’t make great visualizations without great information, so make sure your reporting leads you to visual stories that are interesting, surprising, significant.’ Han n ah Fai rfi eld , Sr. Grap h i cs E d i tor, Th e New York T imes

This would be an example of curiosity born out of anticip ated intr ig u e. The audience has not explicitly asked for this and does not necessarily need it. However, it is perceived to be relevant in the context of the news cycle and informed judgement has been used to anticipate there should be sufficient interest among the target audience about this topic. Sometimes you will work on projects where you have almost to imagine or assume what appetite exists among an audience rather than just respond to an expressed need.

Most of the projects I work on will be driven by stakeholders asking me to create a visualisation to communicate understanding to others (not necessarily them), as per the ‘audience’ and ‘anticipated’ intrigue scenarios. The secondary role of the ‘filmographics’ project, that I defined as emerging from and serving a

personal intrigue, will also be to pique the interest of other movie fans. Again, this is based on anticipated intrigue more than known audience intrigue.

The final scenario of curiosity goes back to our role as an individual. Let’s say I am interested in data visualisation and also interested in movies and I discover a clean dataset full of rich content about movies and actors. This sounds like a compelling opportunity to do something with it because I am convinced there will be some nuggets of insight locked inside. I might not have determined a specific curiosity yet, as my entry point, but I will be able to establish this later once I have had a closer look at what potential the data offers.

This would be a situation where curiosity is born out of p o tential intr ig u e – potential because I just do not know explicitly what it will be yet. Sometimes, in your subject of study or in the workplace, perhaps if you work with collections of survey results or findings from an experiment, you might find yourself with the opportunity to explore a dataset without any real prior sense of exactly what it is you are looking to get out of it. You are initially unclear about the precise angle of your enquiry but you will explore the data to acquaint yourself fully with its qualities and generally research the subject. From there you should have a better idea of a more specific curiosity you might pursue. In effect, this scenario would then switch into more personal intrigue (if it remains just for yourself) or anticipated intrigue (if you might share it with others).

This final scenario is the only one whereby the availability of and access to data would arrive before you have articulated a specific curiosity. In all the other scenarios outlined, the data you need will typically be sought as a response to the curiosity. Even in the lattermost scenario about potential intrigue, data itself does not just fall from the sky and into your lap(top). The sheer fact that you have a dataset to work with will be because somebody else, at an earlier moment in time, was interested in measuring an activity, recording it, and making the collected data available. That in itself could only have arisen from their own curiosity.

The potential intrigue type of curiosity might also extend to situations where you simply have a desire to practise your visualisation skills, experimenting and trying out new techniques with some sample data. In this scenario the incentive is more to learn from a new experience of working through a visualisation process and may not necessarily have the same drivers as when definable audiences exist

Why do these different scenarios of curiosity have such an important role to play? Firstly, they provide clarity about the angles of analysis that you might be pursuing. As you will see later, even in the smallest and seemingly simplest dataset, there are many possibilities for conducting different types of analysis. The burden of choosing is somewhat eased by knowing in advance what might be the most interesting and relevant analysis to focus on. Secondly, the different scenarios described all present slightly different characteristics in the dynamics of the people involved. Who are the stakeholders and what is their interest? Who are the intended recipients – the audience – and what is their interest? As you have already seen – and will keep seeing - the involvement of people creates such influential forces (good and bad) shaping your visualisation thinking. You therefore need to know about how those forces might materialise from the outset.

Identifying Your Project’s Circumstances

Defining your project’s circumstances involves identifying all the requirements and restrictions that are inherited by you, imposed on you or determined by you. These are the different pressure points that establish what you can or cannot pursue and what you should or should not pursue. Much of this contextual thinking is therefore associated with the aim of ambition management.

There are so many hidden variables and influences in a visualisation project that the end viewer never gets to see and often does not appreciate. It is natural for them to assess a project through the lens of an idealised context free of restriction, but there are always limitations, external influences and project-specific factors that affect the shape of the final work.

When starting a project you will find that not all the circumstances that could have an influence on your work will prove to be as identifiable, definable or fixed as you might like. Some things change. Some things can only be recognised once you’ve become a little more acquainted with the nature of your task. As I stated in the previous chapter, doing this activity now is only the first occasion you will be paying attention to these matters, not the last. Of course, the more you can define, the greater the clarity your subsequent decisions will be based upon. There are other stages where you can work with uncertainty but, ideally, not here. Your work needs to obtain as much focus as possible.

In order to design a tool, we must make our best efforts to understand the larger social and physical context within which it is intended to function.’ Bi ll Bu xton, C om p u ter Sci enti s t, D es i g n er and Au th or, Sk etching User Exp eriences

‘Context is key. You’ll hear that the most important quality of a visualisation is graphical honesty, or storytelling value, or facilitation of “insights”. The truth is, all of these things (and others) are the most important quality, but in different times and places. There is no singular function of visualisation; what’s important shifts with the constraints of your audience, goals, tools, expertise, and data and time available.’ Scott M u rray, D es i g n er

There are some factors that may not be relevant or do not have any predefined restrictions or set requirements. For example, you might not have any format restrictions (print vs digital, large size vs small size) to contend with, in which case it is entirely up to you how it evolves. Identifying that no format restrictions exist is as valuable as knowing when they do. It gives you control. You might decide there is merit in imposing a restriction yourself. You might appreciate some degree of focus by determining that your target output will be for a printed, poster-sized display.

People

S tak eho ld er s : In project situations where you have been requested/commissioned to do a visualisation by somebody else, it is helpful to establish an understanding of all the different players and their involvement. Defining stakeholders will help you anticipate what sort of experience you are going to go through, how enjoyable and smooth it might be, or how much friction and what obstacles might be involved. For starters, who is the ultimate customer? This might not be the person who has directly commissioned you, nor might it be somebody belonging to the intended audience, rather someone who has influence over the final work. They may not be decision makers, rather decision approvers. They are the people from whom you await the thumbs up. Stakeholders will have an influence on when work is of sufficient quality, in their eyes, to declare it as being on the right path or, ultimately, to signal the completion of the project. In my world, when you might be doing work as a contracted design consultant, they determine when I will get paid. As you have seen, stakeholders might also be the people from whom the origin curiosity emerged, so they will be especially invested in what it is you are able to produce.

Other stakeholders might have a smaller involvement or influence. Their role may be a positive one – offering advice and assistance with a specific domain challenge – or, in a minority of cases, a negative one – hindering progress by influencing design decisions beyond their remit and capability. In this case they become interferers. We don’t like interferers because they make life unnecessarily harder (especially, strangely enough, if they are nice people). A primary contact person, who will act as the liaison between parties, will be another important role to identify. If there are no stakeholders and the project is a solo pursuit there will be much more flexibility for you to dictate matters. You might even be more motivated to go ‘above and beyond’ if you are driven by a personal intrigue. Conversely there will be fewer channels of guidance and support. This is not to say that one situation is better, it just means they are different and this difference needs to be recognised early. A u d ience: What are the characteristics of your viewers? Several different attributes were defined in discussing the principle for ‘accessible design’ in Chapter 1. You are primarily trying to understand their relationship with the subject matter. How informed are they about a subject and what motivation might they have towards it – is it a passing interest or a definable need? What capacity might they have to make sense of the type of visualisations you may need to create (their graphical literacy)? How could their personal traits influence your design choices? You will never nor should you ever let the spinning plate of concern about your audience drop.

C onstraints

P r es s u r es : The primary pressure relates to timescales: how much time have you got to work through the full process and publish a completed solution? The difference in potential ambition between a project that is needed in two days compared with one that is needed in two months is clear. However, the real issue is the relationship between timescales and the estimated duration of your work. Two months might sound great but not if you have three months’ work to accomplish. Estimating project duration to any reliable degree is a difficult task. You need experience from working on a diverse range of projects that can inform your expectations for how long each constituent task could take. Even then, seemingly similar projects can end up with very different task durations as a result of the slightest changes in certain circumstances, such as the inclusion of an extra variable of data, or more significant changes like a previously print-only project requiring a bespoke digital interactive solution as well. In addition to project timescales, you will need to be aware of any other milestones that might have to be met. Work that you are producing for other stakeholders will often require you to present your ideas/progress at various stages. This is a good thing. It gives you the opportunity to check if you are in sync or discover if you have misunderstood certain needs. Note that it can be risky to present under- developed concepts to potentially inexperienced stakeholders who may not be able to extend their imagination to envision how the work will look when completed. Other pressures may exist in tangible terms through financial restrictions. What time can you afford to spend? This is not just associated with freelancing or studio work, it can be the same for research groups which have finite resources and need to use their time – and their costs – sensibly. It might also have an influence on occasions where you need to outsource parts of your work (e.g. paying for transcription services, third-party data sources) or make purchases (software, hardware, licences for photograph usage).

‘What is the LEAST this can be? What is the minimum result that will 1) be factually accurate, 2) present the core concepts of this story in a way that a general audience will understand, and 3) be readable on a variety of screen sizes (desktop, mobile, etc.)? And then I judge what else can be done based on the time I have.

Certainly, when we’re down to the wire it’s no time to introduce complex new features that require lots of testing and could potentially break other, working features.’ Alys on Hu rt, N ews Grap h i cs E d i tor, N PR , on d eali n g wi th ti m es cale p res s u res

Always note down your task durations so you can refine your estimates far better on future projects. These estimates are not just valuable for client work, you will need them to manage your own time regardless of the nature of the project.

The final pressure is slightly less tangible but comes in the form of what might be described as market influences. Sometimes you will find your work is competing for attention alongside other work. In this age of plenty, a desire to emulate the best or differentiate from the rest can prove to be a strong motive. For example, if you are working for a charitable organisation, how do you get your message across louder and more prominently than others? If you are working on an academic research project, how do you get your findings heard among all the other studies also looking to create an impact? It might be the internal dynamics within a student group or organisation or the broader competition across entire marketplace and industries, but regardless, considerations like this do introduce an extra ingredient to shape your thinking. R u les : These are relatively straightforward matters to define and are concerned with any design rules you need to know about and follow. These might be issues around:

Layout/size restrictions: Maximum size and specific shape restrictions might exist with graphics created for articles published in journals or the screen size dimensions for digital outputs that need to work on a tablet/smartphone. Are there printing resolution requirements around dpi (dots per inch)? The commonly used industry standard for printing is 300 dpi. Style guidelines: In many organisations (and with some media) there are often visual identity branding guides imposed on you that determine the colours, typeface and possibly logos that you need to include. If possible, try to push back on this because they can be unnecessarily restrictive and often the choices imposed are horribly ill-suited to data visualisation. Otherwise, you will have to abide by the style requirements dictated to you. Also check to see if you will need to include any logos. They may take up valuable space and you’ll need to think about their impact on the balance of your overall colour palette and composition. Functional restrictions: The potential requirement to create outputs that are compatible with certain browsers, versions of software or programming languages will be an important consideration to establish early.

C onsum ption

F r eq u ency : The issue of frequency concerns how often a particular project will be repeated and what its lifespan will be. It might be a regular (e.g. monthly report) or irregular (e.g. election polling graphic updated after each new release) product, in which case the efficiency and reproducibility of your data and design choices will be paramount. If it is a one-off, you will have freedom from this concern but you will have to weigh up the cost–benefit involved. Will there be any future benefits from reusing the techniques and thinking you put into this project? Can you afford to invest time and energy, for example, in programmatically automating certain parts of the creation process or will this be ultimately wasted if it is never reused? What is the trade-off between the amount of work to create it and the expiry of its relevance as time goes by – will it very quickly become out of date as new data ‘happens’? Maybe it is a one-off project in creation terms but is to be constantly fed by real-time data updates, in which

case the primary concern will be of functional robustness. S etting : This concerns the situation in which your work would be consumed. Firstly, this is judging whether the work is going to be consumed remotely or presented in person (in which case the key insights and explanations can be verbalised). Secondly, is the nature of the engagement one that needs to facilitate especially rapid understanding or does it lend itself to a more extended/prolonged engagement?

‘I like to imagine that I have a person sitting in front of me, and I need to explain something interesting or important about this data to them, and I’ve only got about 10 seconds to do it. What can I say, or show them, that will keep them from standing up and walking away?’ Bi ll R ap p , D ata Vi s u ali s ati on D es i g n er, d i s cu s s i ng an au d i ence s cenari o s etti ng h e concei ves i n h i s m i n d ’s eye

I keep four characteristic settings in mind when thinking about the situations in which my work will be consumed by viewers:

The boardroom: A setting characterised by there being limited time, patience or tolerance for what might be perceived as any delay in facilitating understanding: immediate insights required, key messages at a glance. The coffee shop: A more relaxed setting that might be compatible with a piece of work that is more involving and requiring of viewers to spend more time learning about the subject, familiarising themselves with how to read the display and discovering the (likely) many different parts of the content. The cockpit: The situation that relates to the instrumentation nature of a visualisation tool or dashboard. There is a need for immediate signals to stand out at a glance whilst also offering sufficient breadth and depth to serve the likely multitude of different potential interests. Another example might be the usage of a reference map that works on all levels of enquiry, from at a glance, high-level orientation through to in-depth detail to aid the operational needs of navigation. The prop: Here a visualisation plays the role of a supporting visual device to accompany a presenter’s verbal facilitation of the key understandings (via a talk) or an author’s written account of salient findings (report, article).

D eliverables

Q u antity : This concerns establishing the project’s workload prospect in terms of quantities. How many things am I making? How much, what type, what shape and what size? Is it going to involve a broad array of different angles of analysis or a much narrower and focused view of the data? What are the basic quantities of the outputs? Is it, for example, going to be about producing 12 different graphics for a varied slide deck or a 50-page report that will need two charts for each of the 20 questions in a survey and some further summaries? Perhaps its a web-based project with four distinct sections, each requiring four interactively adjustable views of data. It will not always be possible to determine such dimensions this early on in the process, but even by just establishing a rough estimate this can be helpful, especially for informing your estimate of the project’s likely duration. F o r m at: This concerns the output format: digital, print or physical. You will need a clear understanding of the specific format of the deliverables required to factor in how your design work will be affected:

Is it intended as a large poster-sized print or something for a standard A4-sized report? Will it exist as a website, a video, maybe even a tool or app?

Is the digital output intended for smartphone, tablet (which ones?) as well as desktop? What ppi (pixels per inch) or resolution will it ideally need to work with? Are you handing over to your stakeholder just the final design work or will you also be expected to provide all the background files that contributed to the final piece of work?

‘I love, love, love print. I feel there is something so special about having the texture and weight of paper be the canvas of the visualisation. It’s a privilege to be able to design for print these days, so take advantage of the strengths that paper offers – mainly, resolution and texture. Print has a lot more real estate than screen, allowing for very dense, information packed visualisations. I love to take this opportunity to build in multiple story strands, and let the reader explore on their own. The texture of paper can also play a role in enhancing the visualisation; consider how a design and colour choices might be different on a glossy magazine page versus the rougher surface of a newspaper.’ Jane Pong , D ata Vi s u ali s ati on D es i g ner, loves p ri nt ( I th i n k)

Resources

S k ills : What capabilities exist among those who will have a role to play in the design process? This might just be you, in which case what can you do and what can’t you do? What are you good at and not good at? If you have collaborators, what are the blend of competencies you collectively bring to the table? How might you allocate different roles and duties to optimise the use of your resources? To help assess your capabilities, and possibly those across any team you are part of, consider the breakdown presented in Chapter 11 in the ‘Seven hats’ section. T echno lo g y : As I have described already, there are myriad tools, applications and programming options in the data visualisation space, offering an array of different capabilities. No single package offers everything you will ever need but, inevitably, some offer more and others less. In order to complete the more advanced visualisation projects you will likely require a Swiss-Army-knife approach involving a repertoire of different technology options at each of the preparatory and development stages in this process. The software and technological infrastructure you have access to will have a great influence on framing the ambitions of your work. I will be sharing more information about tools in the digital resources that accompany this book.

‘The thing is, this world, especially the digital data visualization world, is changing rapidly: new technologies, new tools and frameworks are being developed constantly. So, you need to be able to adapt. But principles are much more timeless. If you know what you want to create, then using technology is just the means to create what you have in mind. If you’re too fixed on one type of technology, you may be out of a job soon. So, keep learning new technologies, but more importantly, know your principles, as they will allow you to make the right decisions.’ Jan W i llem Tu lp , D ata E xp eri ence D es i g ner

A final point to make about circumstances is to recognise the value, in many cases, of limitations and constraint. Often such restrictions can prove to be a positive influence. Consider the circumstances faced by Director Steven Spielberg while filming Jaw s. The early attempts to create a convincing-looking shark model proved to be so flawed that for much of the film’s scheduled production Spielberg was left without a visible shark to work with. Such were the diminishing time resources that he could not afford to wait for a solution to film the action sequences so he had to work with a combination of props and visual devices. Objects being disrupted, like floating barrels or buoys and, famously, a mock shark fin piercing the surface, were just some of the tactics he used to create the suggestion of a shark rather than actually show a shark. Eventually, a viable shark model was developed to serve the latter scenes but, as we all now know, in not being able to

show the shark for most of the film, the suspense was immeasurably heightened. This made it one of the most enduring films of its generation. The necessary innovation that emerged from the limited resources and increasing pressure led to a solution that surely transcended any other outcome had there been freedom from restrictions. The key message here is to embrace the constraints you face because they can heighten your creative senses and lead to successful, innovative solutions.

Defining Your Project’s Purpose

Identifying the curiosity that motivates your work establishes the project’s origin. The circumstances you have just considered will give you a sense of the different factors that will influence your experience on the project and shape your ambitions. The final component of contextual thinking is to consider your intended destination. What is it you specifically hope to accomplish with your visualisation? This involves articulating your project’s purpose.

You know now that the overriding goal is to facilitate understanding, that is non-negotiable, but the nature of this understanding may vary significantly. In Chapter 1, I described how – as viewers – we go through a process of understanding involving the stages of perceiving, interpreting and, finally, comprehending. The undertaking of the first stage of perceiving is largely controlled by the accessibility of the visualiser’s design choices. The second stage of interpreting (establishing meaning from a visualisation) will be influenced by the viewer’s capacity to derive meaning or by the visualiser providing explanatory assistance to help the viewer form this meaning. The final stage of comprehending is largely determined by the viewer alone as what something means to them is so uniquely shaped by their personal context: what they know or do not know, what their beliefs are and what their intentions are for acquiring this understanding.

This three-stage model of understanding helps demonstrate the importance of defining the purpose of a visualisation upfront. Some visualisations might aim to be quite impactive, attempting to shock or inspire viewers in order to persuade them about a need to change behaviour or make significant decisions. For example. you might be seeking to demonstrate visually compelling evidence of the impact of dietary factors like sugary drinks on the rise of obesity. The purpose might not just be to inform but actively to seek to make a difference, maybe targeting parents to change the foods they allow their kids to eat. To achieve this kind of outcome you might take a more emotive approach in the portrayal of your data to attract the audience’s attention in the first place and then strike home the powerful message in a way that resonates more deeply. Affecting people to this degree can be quite ambitious.

In a different context, you might not need to go this deep. Some projects may be more modestly designed to enlighten or simply inform viewers better about a subject, even if the acquired understanding is quite small. There might be recognition that the target viewers should (and maybe are better placed to) reach their own conclusions. Perhaps, if you were revealing the same type of dietary data to health professionals rather than to parents, you might only be serving to confirm what they already might know or at least suspect. They probably will not need convincing about the importance of the message, so the ambitions of the visualisation itself will be considerably different. To achieve the purpose of this project would likely lead to a very different design approach from the one in the previous scenario.

One size does not fit all. No single type of visualisation will be capable of delivering an experience whereby all flavours of understanding are facilitated. Articulating your purpose is your statement of intent: a necessary sense of focus to help inform your design choices and a potential measure to determine whether you accomplish your aims.

Defining your purpose before establishing your trigger curiosity is putting the cart before the horse. A project driven by curiosity is the purest basis for a visualisation project to commence and one most likely to be guided by the clearest thinking. It is the approach that fits best with the sequence of thinking outlined in this workflow. When the desired purpose drives decisions, visualisers can be overly focused on outputs and not inputs. As discussed in Chapter 4, you need to let your data do the talking, not force the data to do your talking. The pressure to reach the desired destination can impact artificially on the data, editorial and design decisions you make.

If you are working with colleagues or for clients who express their requirements from the perspective of an outcome- or purpose-led process, your skill as a visualiser will be to direct the discussions towards a more curiosity-led perspective. Sometimes you will find stakeholders who are primarily motivated by a desire to reach many viewers and their singular measure of success is purely the quantity of eyeballs that will peruse a piece of work. However, I would contest that this does not make it a viable motive for an effective data visualisation, where the measure of success is about facilitating understanding first and foremost. Loads of visitors and social media hits (likes, retweets, upvotes) are a wonderful bonus but should only be seen as a by-product of interest, not an indicator of effectiveness in and of itself. Those who seek a viral success story rarely achieve it because it is so hard to manufacture.

3.3 Establishing Your Project’s Vision

The ‘Purpose’ Map

In compiling definitions about the curiosity, circumstances and purpose, you have helped to initiate your process with a clear idea of the origin of your work, its likely desired destination, and some of the most influencing factors you will have to contend with along the way.

To supplement this contextual thinking you should take the opportunity to consider forming an initial vision for your work. The definition of vision is ‘the ability to think about or plan the future with imagination or wisdom’ and it has particular relevance for how we might foresee achieving the purpose we have stated.

There are many types of visualisation with many different characteristics. Two of the most significant concern the differences in tone and experience. Reflecting the diversity of visualisation work being produced, the ‘purpose map’ (Figure 3.1) offers a high-level view of this landscape shaped by different relationships across those two dimensions.

F ig u r e 3 .1 The ‘Purpose Map’

Certain types of visualisation will offer a better fit for your project. Their characteristics, in terms of experience and tone, will offer the right blend to best connect your origin curiosity with destination purpose. What you need to consider here is what can you envision being the most suitable type of visualisation that might be most capable of accomplishing what you intend.

While the more detailed design thinking won’t arrive until later in the workflow, even at this early stage it is instructive to put some thought into this matter. Let me explain sequentially the meaning of each of these dimensions and the significance of the different regions within this purpose map.

Experience

The horizontal dimension of this map concerns the experience of the visualisation:

How will it practically operate as a means of communication? Through what functional experience will understanding be achieved by the viewer?

Along this spectrum are three different states against which you may define your intentions: Explanatory, Exhibitory or Exploratory (for a mnemonic, think about this as being all about the EXs).

E x p lanato r y visualisations are found on the left side of the map. Explanatory in this context essentially means we – as visualisers – will provide the viewer with a visual portrayal of the subject’s data and will also take some responsibility to bring key insights to the surface, rather than leave the prospect of interpreting the meaning of the information entirely to the viewer. The visualiser here is attempting to assist with the viewers’ process of understanding as much as possible, in particular with the interpretation, drawing out the meaning of the data. The rightmost side of this explanatory region of the map (in the second column, more towards the middle of the map) might be considered the ‘mildest’ form of explanatory visualisation. Here you find

projects that include simple annotation devices like value labels or visual guides that direct the eye to help assist with the task of interpreting the data: the use of colour can be an immediate visual cue to help separate different features of a chart and captions might outline a key message or summary finding. An example of this kind of explanatory visualisation is seen in Figure 3.2, which was published in an article reporting on protests across US schools (in November 2015) regarding the underrepresentation of black students. Here you can see a scatter plot comparing the share of enrolled black students for different public research universities (in the vertical axis) with the share of the college-age black populations in the respective states. With protests beginning at the University of Missouri, the chart uses red to highlight this data point within the chart to enable comparison with other schools. Other notable schools are emphasised to draw out some of the main insights. Additionally, using encoded overlays, such as the trend line and dotted-line indicating proportional representation, the viewer is assisted beyond just perceiving the data to help them with the stage of interpretation: what does it mean to be higher or lower on this chart? Which locations are considered good/bad or typical/atypical?

F ig u r e 3 .2 Mizzou’s Racial Gap Is Typical On College Campuses

The best way to get your head around ‘explanatory’ visualisations is to consider how you would explain this display of analysis to a viewer if you were sat with that person in front of a screen or with a printout. What features would you be pointing out to them? Which values would you be pointing to as being the most interesting? What things would you not need to explain? The traits of a good explanatory visualisation will accommodate these types of important descriptions, which would otherwise be verbalised, within the design of the chart itself, making it ‘stand’ alone without the need for in-person explanation. Towards the leftmost region of the map this is where the experience is about generally more intensive attempts to enlighten an audience’s understanding of a subject. This could possibly be through the use of a narrative structured around a compelling sequence of information and/or a dramatic experience. The form of this type of work would be characterised by videos or presentations, or maybe an animated or motion graphic. Some term this ‘narrative’ visualisation. This is arguably where the most tangible demonstrations of visualisation through storytelling (more on this later) are found. An example that typifies this classification on the map would be characterised by this very powerful and popular video (Figure 3.3) about the issue of wealth inequality in the USA. It employs a semi-animated slideshow sequence to weave together the narrative and is accompanied by an effective and affective voiceover narrating the story.

F ig u r e 3 .3 Image taken from ‘Wealth Inequality in America’

Across all explanatory visualisations the visualiser will require sufficient knowledge (or the skill and capacity to acquire this) about the topic being shown in order to identify the most relevant, interesting and worthwhile insights to present to the viewer. Creating explanatory visualisations forces you to challenge how well you actually know a subject. If you cannot explain or articulate what is insightful, and why, to others, then this probably means you do not know the reasons yourself. Fundamentally, explanatory visualisations are the best-fit solution if the specific context dictates that saying nothing is not good enough; leaving viewers with a ‘so what?’ reaction would be seen as a failure, so in such cases a takeaway message(s) would need to be offered. E x p lo r ato r y visualisations differ from explanatory visualisations in that they are focused more on helping the viewer or – more specifically in this case – the user find their own insights. Almost universally, these types of works will be digital and interactive in nature. The ‘mildest’ forms of exploratory works are those that facilitate interrogation and manipulation of the data. You might be able to modify a view of the chart, perhaps by highlighting/filtering certain categories of interest, or maybe change data parameters and switch between different views. You might be able to hover over different features to reveal detailed annotations. All of these operations facilitate understanding to the extent of the perceiving stage. The task of interpreting and comprehending will largely be the responsibility of the viewer to form. This will be suitable if the intended audience have the necessary foundation knowledge for the subject and sufficient interest to translate the general and personal meaning. An example of this type of visualisation can be seen through the interactive project (Figure 3.4). It was developed to allow users to explore different measures concerning the dimension changes of wood, over time, across selected cities of the world.

F ig u r e 3 .4 Dimensional Changes in Wood

There are no captions, no indications of what is significant or insignificant, no assistance to form meaning through the use of colours or markers to emphasise ‘good’ or ‘bad’ values. This project is simply a visual window into the analysis of this data that lets users perceive the data values and interact with the different dimensions offered. To form meaning, it is open to them to determine what features of the data resonate with their existing interests, knowledge and needs. As you look more towards the rightmost edge of the purpose map you reach far deeper exploratory experiences. You might characterise visualisations here as facilitating a more participatory or contributory experience. The prospect of greater control, a deeper array of features and the possibility of contributing one’s own data to a visualisation can be very seductive. Users are naturally drawn to challenges like quizzes and projects that allow them to make sense of their place in the world (e.g. how does my salary compare with others; how well do I know the area where I live?) – they are simply too hard to resist! The huge success of the New York Times’ so-called ‘Dialect map’ (Figure 3.5), showing the similarity or otherwise of US dialects based on users’ responses to 25 questions, is just one example of a contemporary project employing this participatory approach to great effect.

F ig u r e 3 .5 How Y’all, Youse and You Guys Talk

The biggest obstacle to the success of an exploratory visualisation’s impact is the ‘so what?’ factor. ‘What do you want me to do with this project? Why is it relevant? What am I supposed to get out of this?’ If these are the reactions you are seeing expressed by your intended users then there is a clear disconnect between the intentions of your project and the experience (or maybe expectations) of the audiences using it. E x hib ito r y visualisations are found in the final separate ‘experience’ category within the latitude of the purpose map. They are characterised by being neither explicitly explanatory nor exploratory. With exhibitory visualisations the viewers have to do the work to interpret meaning, relying on their own capacity to make sense of the display of data (to perceive it) and the context of the subject-matter. As well as lacking explanatory qualities, they also do not offer scope for exploratory interrogation. I generally describe them as simply being visual displays of data. Think of this term in relation to exhibiting an artwork: it takes the interpretative capacity of the viewer to be able to understand the content of a display as well as the context of a display. When you look across the many different visualisations being published you will find that many projects mistakenly fall into the void of being exhibitory visualisation when they really need to be more supportively explanatory or functionally exploratory. So you might wonder what the value is of an exhibitory visualisation. Well, sometimes the setting for a visualisation does not need exploration or direct explanation. As I’ve stated, exhibitory projects rely entirely on and make assumptions about the capacity of and interest among the target audience. If you have a very specific audience whom you know to be sufficiently knowledgeable about the domain and the analysis you have provided, it might not need important insights to be surfaced in the way you would with an explanatory visualisation. An explanatory project will mainly be for audiences who do not have the knowledge, capacity or time to find for themselves the key features of meaning (through interpreting) alone. Furthermore, the extent of the analysis might be so narrow that there is no fundamental need to incorporate ways of manipulating and personalising the experience as you would see with exploratory visualisations.

F ig u r e 3 .6 Spotlight on Profitability

In Figure 3.6, the analysis of the top three profitable movies by genre and year is not interactive (and so does not enable any explorations), nor does it bring to the surface any observations about notable movies or conclusions about the relationship between movie ratings and takings. It is intended as an exhibitory experience – a visual display of this data – that lets you as a user draw your own conclusions, find your own shapes of interest, and look up the movies that you want to see data for. An exhibitory visualisation might also be a graphic that supports a written article or report. In and of itself it does not explain things in a stand-alone sense but instead exists as a visual prop for referencing. The written passages will therefore provide the explanatory narrative separate but still drawn from the supporting graphic. I mentioned earlier the scenario of sitting down with someone and explaining a chart to them from a printout or a screen. As I said, the key points verbalised in this setting would, for an explanatory piece, be directly incorporated within the graphic. Conversely, I might use an exhibitory visualisation in a presentation where my narrative, observations and gestures provide the explanatory experience – I perform these myself, in person – rather than these being incorporated within or around the chart(s). This would define the visualisation as exhibitory but presented in an explanatory setting. Two of the most famous visualisation-based presentations, Al Gore’s presentation in An Inconvenient Truth and Hans Rosling’s ‘Gapminder TEDtalk’, are excellent demonstrations of this.

One could argue that the Rosling talk was an explanatory presentation of an exploratory tool, but some of the main narrative was delivered against a more exhibitory animation of data.

Tone

The vertical dimension of the purpose map concerns the intended tone of the visualisation, with reading tone positioned towards the top and feeling tone towards the bottom. Whereas the experience dimension had two distinct and opposite sides (Explanatory vs Exploratory) with a pivot in the middle (Exhibitory), the tone dimension is much more of a continuum with subtle – and very subjective – variations between the two ends. What you are largely considering here is a judgement of the most suitable perceptual readability of your data.

Whereas the difference between types of experience can be quite distinct once you become familiar with the characteristics of each, defining tone is a slightly harder matter to nail down, especially as a beginner. The general question you are asking yourself is: through what tone of voice in my design will the purpose of this

project be accomplished? Let me elaborate by looking closely at the two ends of this continuum.

R ead ing to ne: At the top of the purpose map the tone of your visualisation design choices will be geared towards optimising the ease with which viewers can accurately estimate the magnitude of and relationships between values. There is emphasis on the efficiency of perceiving data. The reading tone would be your best-fit approach when the purpose of your work requires you to facilitate understanding with a high degree of precision and detail. This would also be relevant in situations when there is no need to seduce an audience through your aesthetic treatment. Furthermore, it suits the needs well when the subject matter does not inherently embody or merit any form of visual stimulation to convey the essence of the message more potently. The visual quality created with this tone might be considered rather utilitarian, formed around a style that feels consistent with adjectives like analytical, pragmatic, maybe even no-frills. Devices like bar charts, as you can see in Figure 3.7, are the poster boys for this type of display. As you will learn later, the perceptual accuracy enabled by using the size of a bar to represent quantitative values makes these charts extremely effective options for visually portraying data in a way that aids both general sense-making and accurate point-reading. That’s why they are so ubiquitous. Most of the visualisations you will ever produce will lean towards this reading end of the tonal continuum. Indeed, you might ask why would you ever seek to create anything but the most easily and accurately readable representations of data? Surely anything that compromises on this aim is undermining the principles of trustworthy and accessible design? Well, that’s why the definitions around purpose are so significant in their influence and why we need to appreciate other perspectives.

F ig u r e 3 .7 Countries with the Most Land Neighbours

‘There’s a strand of the data viz world that argues everything could be a bar chart. That’s possibly true but also possibly a world without joy.’ Am an d a C ox, E d i tor, T he Up shot

F eeling to ne: The lower end of this vertical dimension offers a contrasting tone of voice to that of reading. When I introduced in Chapter 1 the sequence of understanding – from perceiving to interpreting and through to comprehending – the illustration I gave was based on perceiving a bar chart. Here you could easily and confidently estimate the values portrayed by the bar sizes. Sometimes,

though, your aims will not necessarily fit with the singular desire to provide such a perceptually precise display: sometimes you might justify placing greater importance on the feeling of your data. At this side of the tonal spectrum there is more emphasis placed on determining the gist of the big, medium and small values and a general sense of the relationships that exist. Sometimes an ‘at-a-glance’, high-level view is the most suitable way to portray a subject’s values. Again, let me address the likely objections from those spitting their coffee at the very thought of any visualiser not giving the utmost priority to precision, efficiency and accuracy in their work. To appreciate why, on occasion, you might consider a different approach it is worth reflecting again on the motive for visualising data. Visual forms of data unquestionably offer a more revealing and more efficient way to understand the quantities and relationships that exist within data. It cannot be reasonably achieved either effectively or efficiently through non-visual forms. By visualising data you are looking for something more and something different from what, let’s say, a table of data can offer. The bar chart, by way of example, offers that. However, on occasion you might need something even more different than this. In the project illustrated in Figure 3.8, you will see excerpts from an analysis about the small number of families who have most financial clout when it comes to providing funding for presidential candidates. The data quantities are portrayed using Monopoly house pieces as a metaphor of wealth. The red houses represent the small number of families who have contributed nearly half of the initial campaign funding. The green pieces are representative of the total households in the US. You cannot count the pieces, you cannot even remotely estimate their relative proportions, but you get the gist of the scales involved as a proxy illustration of the remarkably disproportionate balance and power of wealth. Furthermore, this use of the Monopoly pieces is a symbolically strong metaphor as well as offering an appealing, almost playful approach to portraying the data.

F ig u r e 3 .8 Buying Power: The Families Funding the 2016 Presidential Election

There will be times when you will need to consider employing what might be described as aesthetic seduction: some way of creating an appealing form that attracts viewers and encourages them to engage with a subject they might not have otherwise found relevant. This could involve a novel visual ‘look’ that attracts – but also informs – or a functional feature that attracts – but also performs. The influence of fun cannot be underestimated here. I repeat, we are all humans with occasionally quite base needs. Sometimes viewers crave something that stirs a more upbeat and upfront emotional engagement.

Some may argue that viewers will be encouraged to engage with a visualisation if it is relevant to them, regardless of its appearance, otherwise they should not be considered part of the target audience. That is not

true, unfortunately. Perhaps in a business or operational setting, the needs of individuals, roles and groups are much more clear-cut and you can apply a binary perspective like that quite easily. Outside in the real world there are many more nuances. As a viewer your interest in a subject may not materialise until after viewers have engaged with a visualisation. It may be a consequence not the prerequisite. Had they not been somehow attracted to view it in the first place they might never have reached that point.

‘I love the idea of Edward Tufte’s assertion that “Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.” But I found that when I developed magazine graphics according to that philosophy, they were most often met with a yawn. The reality is that Scientific American isn’t required reading. We need to engage readers, as well as inform them. I try to do that in an elegant, and refined, and smart manner. To that end, I avoid illustrative details that distort the core concept. But I’m happy to include them if the topic could benefit from a welcoming gesture.’ Jen C h ri s ti ans en , Grap h i cs E d i tor at Scientific America n

‘On the one hand we had this great idea of doing something fun – animated lifts racing up and down buildings while the user was on the web page. But on the other hand this is The Financial Times and that carries with it a responsibility to do things in a certain way. So we spent time illustrating and designing to give the graphic high production values, and it was then presented alongside an excellent piece of journalism from our manufacturing correspondent. The result? An undeniably fun user experience, but delivered in such a way that met FT subscribers’ standards for high quality visuals and high quality journalism.’ Joh n Bu rn- M u rd och , Sen i or D ata Vi s u ali s ati on Jou rn ali s t at Fina ncia l T imes, h avi n g fu n vi s u ali s i n g th e s p eed of elevators i n s kys crap ers

On a similar note, sometimes you will be working with a subject – like wealth inequality, as we’ve just seen, or gun crime, as discussed earlier – that has the potential to stir strong emotions. Any visualisation of this data has to contend with decisions about how to handle the perpetual baggage of feeling that comes as standard. Depending on the purpose of your work there might be good reason to encapsulate and perhaps exploit these emotions through your visualisation in a way that arguably a bar chart simply may not be able to achieve. By embodying an emotional sensation (fear, shock, fun, power, inequity) through your display, you might be able to influence how your viewer experiences that most elusive stage of understanding, comprehending. The task of reasoning ‘what does this mean to me?’ is often a somewhat intangible notion but far less so when an emotional chord is struck. Behaviours can be changed. The making of decisions can be stirred. The taking of actions can be expedited. So long as the audience’s needs, interest and setting are aligned this can be an entirely suitable strategy.

For some in the visualisation field, this can be seen as manipulation and, to a certain degree it probably is. As long as you are still faithful to the underlying data and you have not achieved an outcome through superficial, artificial or deceptive means, I believe it is an entirely appropriate motive in the right circumstances. As ever, there is a balance to be struck and you must remind yourself of the influence of the design principles I introduced earlier to ensure that none of the choices you make hinder the overall goal of facilitating the type of understanding your context decrees.

It is important to note that any visualisation work that leans more towards ‘feeling’ is typically the exception and in a minority. However, a skilled visualisation practitioner needs to have an adaptive view. They need to be able to recognise and respond to those occasions when the purpose does support an exceptional approach and a compromise beyond just serving the most perpetually accurate and efficient reading of data is required.

The Purpose Map in Practice

A simple illustration of the role of the purpose map involves momentarily focusing on a rather grave subject: data about offender executions. In 2013, the State of Texas reached the unenviable milestone of having executed its 500th death-row prisoner since the resumption of capital punishment in 1982. At the time of this landmark I came across a dataset curated by the Texas Department of Criminal Justice and published on its website. This simply structured table of data (Figure 3.9) included striking information about the offenders, their offences and their final statements – a genuinely compelling source of data. Thinking about this subject and the dataset helps to frame the essence of what role this purpose map can play, especially in the tone dimension.

Since the milestone of 500 executions in 2013 the number has grown significantly. For the purpose of this illustration, for now we will consider the nature of this data as it was at the moment of this milestone.

F ig u r e 3 .9 Image taken from Texas Department of Criminal Justice Website

Imagine viewing this data from a high vantage point, like in a hot-air balloon. The big picture is that there are 500 prisoners who have been executed. That is the whole. Lowering the viewpoint, as you get a little closer, you might see a breakdown of race, showing 225 offenders were white, 187 black, 86 Hispanic and 2 defined as other. Lower still and you see that 4 offenders originated from Anderson County. Lower again reveals that 112 offenders referred to God in their last statement. Down to the lowest level – the closest vantage point – you see individuals and individual items of data, such as Charles Milton, convicted in Tarrant county, who was

aged 34 when executed on 25 June 1985.

The view of the data has travelled from a figurative perspective to a non-figurative one. The former is an abstraction of the data that effectively supresses the underlying phenomena being about people and translates – and maybe reduces it – into statistical quantities. People into numbers. The latter perspective concerns a more literal and realistic expression of what the data actually represents.

‘Data is a simplification – an abstraction – of the real world. So when you visualize data, you visualize an abstraction of the world.’ D r N ath an Y au , Stati s ti can and Au th or of Da ta Points

Going back to the discussion about judging tone, there are several different potential ways of portraying this executed offenders data depending on the purpose that has been defined.

Suppose you worked at the Texas Department of Criminal Justice as a member of staff responsible for conducting and reporting data analysis. You might be asked to analyse the resource implications of all offenders currently on death row, looking at issues around their cost ‘footprint’. In this case you might seek to strip away all the emotive qualities of the data and focus only on its statistical attributes. You would likely aim for a figurative or abstracted representation of the subject, reducing it to fundamental statistical quantities and high- level relationships. Your approach to achieve this would probably fit with the upper end of the tonal dimension, portraying your work with a utilitarian style that facilitates an efficient and precise reading of the data.

A different scenario may now involve your doing some visual work for a campaign group with a pro-capital- punishment stance. The approach might be to demonise the individuals, putting a human face to the offenders and their offences. The motive is to evoke sensation, shock and anger to get people to support this cause. Would a bar chart breakdown of the key statistics accomplish this in tone? Possibly not.

Another situation could see you working for a newspaper that had a particularly liberal viewpoint and was looking to publish a graphic to mark this sober milestone of 500 executions. You might avoid using the stern imagery of the offenders’ mug shots and instead focus on some of the human sentiments expressed in their last statements or on case studies of some of the extremely young offenders for whom life was perhaps never going to follow a positive path. To humanise or demonise the individuals involved in this dataset is possible because there is such richness and intimate levels of detail available from the data.

‘I have this fear that we aren’t feeling enough.’ C h ri s Jord an , Vi s u al Arti s t an d C u ltu ral Acti vi s t

It is worth reinforcing again that a figurative approach (reading) is typically what most of your work will involve and require. Only a small proportion will require a non-figurative (feeling) approach even with emotive subjects. The whole point about introducing you to the alternative perspective of the feeling tone is to prepare you for those occasions when the desired purpose of your work requires more of a higher-level grasp of data values or a deeper connection with subject matter through its data.

To complete this discussion, here are some final points to make about the purpose map to further clarify and frame its scope.

F o r m at: Firstly, it is important to stress that this map does not define format in terms of print, digital or physical. Exploratory visualisations will almost entirely be digital but exhibitory or explanatory

projects could be print or digital. F ir s t tho u g hts no t final co m m itm ent: Considering the definitions of experience and tone now simply represents the beginning of this kind of design thinking. As the workflow progresses you might change (or need to change) your mind and pursue an alternative course, especially when you get deeper into data work, the nature of which may reveal a better fit with a completely different type of solution. I will state again that in these early stages the things you will think about will be the first occasion on which you think about them but not the last. The benefit of starting this kind of thinking now is the increased focus it affords from any sense of eliminating potential types of visualisation from your concern that will have no relevance to your context. C o llectiv e v is u al q u ality : Decisions around tone may not be solely isolated to how data should be represented. There may be a broader sense of overall visual mood or ‘quality’ that you are trying to convey across the presentation design choices as well. As you will see, there are other media assets (photos, videos, illustrations, text) that could go towards achieving a certain tone for the project that does not necessarily directly influence the tone of the data. N o t ab o u t a s ing u lar lo catio n: Some projects will involve just a single chart and this makes it a far more straightforward prospect to inform your definition of its best-fit location on this purpose map. However, there will be other projects that you work on involving multiple chart assets, multiple interactions, different pages and deeper layers. So, when it comes to considering your initial vision through the purpose map dimensions, you may recognise separate definitions for each major elements. This will become much clearer as you get deeper into the project – and can actually identify the need for multiple assets. The mantra proposed by Ben Schneiderman, one of the most esteemed academics in this field – ‘Overview first, details on demand’ – informs the idea of thinking about different layers of readability and depth in your visualisation work accessed through interactivity. Some of the chart types that you will meet in Chapter 6 can only ever hope to deliver a gist of the general magnitude of values (the big, the small and the medium) and not their precise details. A treemap, for example, is never going to facilitate the detailed perceiving of values because it uses rectangular areas to represent data values and our perceptual system is generally quite poor at judging different area scales. Additionally, a treemap often comprises a breakdown of many categorical values within the same chart display, so it is very busy and densely packed. However, if you have the capability to incorporate interactive features that allow the user to enter via this first overview layer and then explore beneath the surface, maybe clicking on a shape to reveal a pop-up with precise value labels, you are opening up additional details. In effect you have moved your viewer’s readability up the tonal spectrum that began with more of a general feeling of data and then moved towards the reading of data as a result of the interactive operation. Sometimes a ‘gateway’ layer is required for your primary view, to seduce your audience or to provide a big-picture overview (feeling), and then you can let the audience move on to more perceptually precise displays of the data (reading) either through interaction or perhaps by advancing through pages in a report or slide-deck sequence.

In the Better Life Index, shown in Figure 3.10, the opening layer is based around a series of charts that look like flowers. This is attractive, intriguing and offers a nice, single-page, at-a-glance summary. The task of reading the petal sizes with any degree of precision is hard but that is not the intent of this first layer. The purpose is to get a balance between a form that attracts the user and a function that offers a general sense of where the big, medium and small values sit within the data. For those who want to read the values with more precision, they are only a click away (on the flowers) from viewing an alternative display using a bar chart to represent the same values.

F ig u r e 3 .1 0 OECD Better Life Index

F ig u r e 3 .1 1 Losing Ground

Increasingly there is a trend for projects to incorporate both explanatory and exploratory experiences into the same overall project – the term ‘explorable explanations’ has been coined to describe them. A project like ‘Losing Ground’ by ProPublica (Figure 3.11) is an example of this as it moves between telling a story about the disappearing coastline of Louisiana and enabling users to interrogate and adjust their

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view of the data at various milestone stages in the sequence.

Harnessing Ideas

The discussions so far in this chapter have involved practical reasoning. Before you move on to the immediate next stage of the design process – working with data – it can be valuable to briefly allow yourself the opportunity to harness your instinctive imagination.

Alongside your consideration of the purpose map, the other strand of thinking about ‘vision’ concerns the earliest seeds of any ideas you may have in mind for what this solution might comprise or even look like. These might be mental manifestations of ideas you have formed yourself or influenced or inspired by what you have seen elsewhere.

‘I focus on structural exploration on one side and on the reality and the landscape of opportunities in the other … I try not to impose any early ideas of what the result will look like because that will emerge from the process. In a nutshell I first activate data curiosity, client curiosity, and then visual imagination in parallel with experimentation.’ Santi ag o Orti z, fou n d er an d C h i ef D ata Offi cer at D ru m W ave, d i s cu s s i n g th e role – and ti m i ng – of form i n g i d eas and m en tal con cep ts

There are limits to the value of ideas and also to the role they are allowed to play, as I will mention shortly, but your instincts can offer a unique perspective if you choose to allow them to surface. If you have a naturally analytical approach to visualisation this activity might seem to be the wrong way round: how can legitimate ideas be formed until the data has been explored? I understand that, and it is a step that some readers will choose not to entertain until later in the process. However, do not rule it out, see if liberating your imagination now adds value to your analytical thinking later. There are several aspects to the concept and role of harnessing ideas that I feel are valuable to consider at this primary stage:

M ental v is u alis atio n: This concerns the other meaning of visualisation and is about embracing what we instinctively ‘see’ in our mind’s eye when we consider the emerging brief for our task. In Thinking Fast and Slow, by Daniel Kahneman, the author describes two models of thought that control our thinking activities. He calls these System 1 and System 2 thinking: the former is responsible for our instinctive, intuitive and metaphorical thoughts; the latter is much more ponderous, by contrast, much slower, and requiring of more mental effort when being called upon. System 1 thinking is what you want to harness right now: what are the mental impressions that form quickly and automatically in your mind when you first think about the challenge you’re facing? You cannot switch off System 1 thoughts. You will not be able to stop mental images formulating about what your mind’s eye sees when thinking about this problem instinctively. So, rather than stifling your natural mental habits, this earliest stage of the workflow process presents the best possible opportunity to allow yourself space to begin imagining. What colours do you see? Sometimes instinctive ideas are reflections of our culture or society, especially the connotations of colour usage. What shapes and patterns strike you as being semantically aligned with the subject? This can be useful not just to inspire but also possibly to obtain a glimpse into the similarly impulsive way the minds of your audience might connect with a subject when consuming the solution. For example, Figure 3.12 shows the size of production for different grape varieties across the wine industry. It uses a bubble chart to create the impression of a bunch of grapes. You can clearly see how this concept might have been formed in early sketches before the data even arrived, based on the mental

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visualisation of what the shape of a bunch of grapes looks like. It is consistent with the subject and offers an immediate metaphor that means any viewer looking at the work will immediately spot the connection between form and subject.

F ig u r e 3 .1 2 Grape expectations

K ey w o r d s : What terms of language come to mind when thinking about the subject or the phenomena of your data? Figure 3.13 shows some notes I made in capturing the instinctive keywords and colours that came to mind when I was forming early thoughts and ideas about a project to do with psychotherapy treatment in the Arctic. The words reflected the type of language I felt would be important to frame my design thinking, establishing a reference that could inform the tone of voice of my work. The colours were somewhat arbitrary and in the end I did not actually use them all, but they were indicative of the tones I was seeking. I did, however, see through my intention to avoid the blacks and blues (as they would carry unwelcome and clichéd connotations in this subject’s context).

F ig u r e 3 .1 3 Example of Keywords and Colour Swatch Ideas

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S k etching : As well as taking notes, sketching ideas is of great value to us here. I mentioned earlier that this is not about being a gifted artist but recognising the freedom and speed when extracting ideas from your mind onto paper. This is particularly helpful if you are working with collaborators and want a low-fidelity sketch for discussing plans, as well as in early discussions with stakeholders to understand better each others’ take on the brief. For some people, the most fluent and efficient way to ‘sketch’ is through their software application of choice rather than on paper.

‘I draw to freely explore possibilities. I draw to visually understand what I am thinking. I draw to evaluate my ideas and intuitions by seeing them coming to life on paper. I draw to help my mind think without limitations, without boundaries. The act of drawing, and the very fact we choose to stop and draw, demands focus and attention. I use drawing as my primary expression, as a sort of functional tool for capturing and exploring thoughts.’ Gi org i a Lu p i , C o- fou n d er and D es i g n D i rector at Accu rat

Regardless of whether your tool is the pen or the computer, just sketch your ideas with whatever is the most efficient and effective option given your time and confidence (see Figure 3.14). You will likely refine your sketches later on and, indeed, eventually you will move your attention completely away from pen and paper and onto the tools you are using to create the final work.

F ig u r e 3 .1 4 Example of a Concept Sketch, by Giorgia Lupi

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R es ear ch and ins p ir atio n: It is important to be sufficiently open to influence and inspiration from the world around you. Exposing your senses to different sources of reference both within and outside of visualisation can only help to broaden the range of solutions you might be able to conceive. Research the techniques that are being used around the visualisation field, look through books and see how others might have tackled similar subjects or curiosities (e.g. how they have shown changes over time on a map). Beyond visualisation consider any source of imagery that inspires you: colours, patterns, shapes, and metaphors from everyday life whose aesthetic qualities you just like. In addition to your notebook and sketch pad, start a scrapbook or project mood board that compiles the sources of inspiration you come across and helps you form ideas about the style, tone or essence of your project. They might not have immediate value for the current project you are working on but may materialise as useful for future work.

‘Recently taking up drawing has helped me better articulate the images I see in my mind, otherwise I still follow up on all different types of design and art outside information design/data visualisation. I try to look at things outside my field as often as I can to keep my mind fresh as opposed to only looking at projects from my field for inspiration.’ Stefan i e Pos avec, Inform ati on D es i g n er

‘Look at how other designers solve visual problems (but don’t copy the look of their solutions). Look at art to see how great painters use space, and organise the elements of their pictures. Look back at the history of infographics. It’s all been done before, and usually by hand! Draw something with a pencil (or pen … but NOT a computer!) Sketch often: The cat asleep. The view from the bus. The bus. Personally, I listen to music – mostly jazz – a lot.’ N i g el Holm es , E xp lan ati on Grap h i c D es i g n er, on i ns p i rati on s th at

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feed h i s ap p roach

‘It is easy to immerse yourself in a certain idea, but I think it is important to step back regularly and recognise that other people have different ways of interpreting things. I am very fortunate to work with people whom I greatly admire and who also see things from a different perspective. Their feedback is invaluable in the process.’ Jan e Pon g , D ata Vi s u ali s ati on D es i g n er

L im itatio n o f y o u r id eas : There are important limitations to acknowledge around the role of ideas. Influence and inspiration are healthy: the desire to emulate what others have done is understandable. Plagiarism, copying and stealing uncredited ideas are wrong. There are ambiguities in any creative discipline about the boundaries between influence and plagiarism, and the worlds of visualisation and infographic design are not spared that challenge. Being influenced by the research you do and the great work you see around the field is not stealing, but if you do incorporate explicit ideas influenced by others in your work, at the very least you should do the noble thing and credit the authors, or even better seek them out and ask them to grant you their approval. You do not have to credit William Playfair every time you use the bar chart, but there are certain unique visual devices that will be unquestionably deserving of attribution. Secondly, data is your raw material, your ideas are not. As you will see later, it is vital that you leave the main influence for your thinking to emerge from the type, size and meaning of your data. It may be that your ideas are ultimately incompatible with these properties of the data, in which case you will need to set these aside, and perhaps form new ones. Eventually you will need to evolve from ideas and sketched concepts to starting to develop a solution in your tool of choice. These early ideas and sparks of creativity are vital and they should be embraced, but do not be precious or stubborn, always maintain an open mind and recognise that they have a limited role. Try to ignore the voices in your head after a certain period! L im itatio n o f o ther s ’ id eas : Finally, there is the diplomatic challenge of being faced with the prospect of taking on board other people’s ideas. One of the greatest anxieties I face comes from working with stakeholders who are unequivocally and emphatically clear about what they think a solution should look like. Often your involvement in a project may arrive after these ideas have already been formed and have become the basis of the brief issued by the stakeholders to you (‘Can you make this, please?’). This is where your tactful but assured communicator’s skill set comes to the fore. The ideas presented may be reasonable and well intended but it is your responsibility to lead on the creation process and guide it away from an early concept that simply may not work out. You can take these idea on board but, as with the limitations of your own ideas, there will be other factors with a greater influence – the nature of the data, the type of curiosities you are pursuing, the essence of the subject matter and the nature of the audience, among many other things. These will be the factors that ultimately dictate whether any early vision of potential ideas ends up being of value.

Summary: Formulating Your Brief

Establishing Your Project’s C ontext

D efining Yo u r O r ig in C u r io s ity Why are we doing it: what type of curiosity has motivated the decision/desire to undertake this visualisation project?

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Personal intrigue: ‘I wonder what …’ Stakeholder intrigue: ‘He/she needs to know …’ Audience intrigue: ‘They need to know …’ Anticipated intrigue: ‘They might be interested in knowing …’ Potential intrigue: ‘There should be something interesting …’

C ir cu m s tances The key factors that will impact on your critical thinking and shape your ambitions:

People: stakeholders, audience. Constraints: pressures, rules. Consumption: frequency, setting. Deliverables: quantity, format. Resources: skills, technology.

D efining Yo u r P u r p o s e The ‘so what?’: what are we trying to accomplish with this visualisation? What is a successful ‘outcome’?

Establishing Your Project’s Vision

‘P u r p o s e M ap ’ Plotting your expectation of what will be the best-fit type of solution to facilitate the desired purpose:

What kind of experience? Explanatory, exhibitory or exploratory? What tone of voice will it offer? The efficiency and perceptibility of reading data vs the high-level, affective nature of feeling data?

H ar nes s ing Id eas What mental images, ideas and keywords instinctively come to mind when thinking about the subject matter of this challenge? What influence and inspiration can you source from elsewhere that might start to shape your thinking?

Tips and Tactics

Do not get hung up if you are struggling with some circumstantial factors. Certain things may change in definition, some undefined things will emerge, some defined things will need to be reconsidered, some things are just always open. Notes are so important to keep about any thoughts you have had that express the nature of your curiosity, articulation of purpose, any assumptions, things you know and do not know, where you might need to get data from, who are the experts, questions, things to do, issues/problems, wish lists … Keep a ‘scrapbook’ (digital bookmarks, print clippings) of anything and everything that inspires and influences you – not just data visualisations. Log your ideas and inspire yourself. This stage is about ambition management/skills – it is to your benefit that you treat it with the thoroughness it needs. The negative impact of any corners being cut here will be amplified later on.

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4 W orking W ith Data

In Chapter 3 the workflow process was initiated by exploring the defining matters around context and vision. The discussion about curiosity, framing not just the subject matter of interest but also a specific enquiry that you are seeking an answer to, in particular leads your thinking towards this second stage of the process: working with data.

In this chapter I will start by covering some of the most salient aspects of data and statistical literacy. This section will be helpful for those readers without any – or at least with no extensive – prior data experience. For those who have more experience and confidence with this topic, maybe through their previous studies, it might merely offer a reminder of some of the things you will need to focus on when working with data on a visualisation project.

There is a lot of hard work that goes into the activities encapsulated by ‘working with data’. I have broken these down into four different groups of action, each creating substantial demands on your time:

Data acquisition: Gathering the raw material. Data examination: Identifying physical properties and meaning. Data transformation: Enhancing your data through modification and consolidation. Data exploration: Using exploratory analysis and research techniques to learn.

You will find that there are overlapping concerns between this chapter and the nature of Chapter 5, where you will establish your editorial thinking. The present chapter generally focuses more on the mechanics of familiarisation with the characteristics and qualities of your data; the next chapter will build on this to shape what you will actually do with it.

As you might expect, the activities covered in this chapter are associated with the assistance of relevant tools and technology. However, the focus for the book will remain concentrated on identifying which tasks you have to undertake and look less at exactly how you will undertake these. There will be tool-specific references in the curated collection of resources that are published in the digital companion.

4.1 Data Literacy: Love, Fear and Loathing I frequently come across people in the field who declare their love for data. I don’t love data. For me it would be like claiming ‘I love food’ when, realistically, that would be misleading. I like sprouts but hate carrots. And don’t get me started on mushrooms.

At the very start of the book, I mentioned that data might occasionally prove to be a villain in your quest for developing confidence with data visualisation. If data were an animal it would almost certainly be a cat: it has a capacity to earn and merit love but it demands a lot of attention and always seems to be conspiring against you.

I love data that gives me something interesting to do analysis-wise and then, subsequently, also visually. Sometimes that just does not happen.

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I love data that is neatly structured, clean and complete. This rarely exists. Location data will have inconsistent place-name spellings, there will be dates that have a mixture of US and UK formats, and aggregated data that does not let me get to the underlying components.

You don’t need to love data but, equally, you shouldn’t fear data. You should simply respect it by appreciating that it will potentially need lots of care and attention and a shift in your thinking about its role in the creative process. Just look to develop a rapport with it, embracing its role as the absolutely critical raw material of this process, and learn how to nurture its potential.

For some of you reading this book, you might have interest in data but possibly not much knowledge of the specific activities involving data as you work on a visualisation design solution. An assumed prerequisite for anyone working in data visualisation is an appreciation of data and statistical literacy. However, this is not always the case. One of the biggest causes of failure in data visualisations – especially in relation to the principle I introduced about ‘trustworthy design’ – comes from a poor understanding of these numerate literacies. This can be overcome, though.

‘When I first started learning about visualisation, I naively assumed that datasets arrived at your doorstep ready to roll. Begrudgingly I accepted that before you can plot or graph anything, you have to find the data, understand it, evaluate it, clean it, and perhaps restructure it.’ M arci a Gray, Grap h i c D es i g ner

I discussed in the Introduction the different entry points from which people doing data visualisation work come. Typically – but absolutely not universally – those who join from the more creative backgrounds of graphic design and development might not be expected to have developed the same level of data and statistical knowledge than somebody from the more numerate disciplines. If you are part of this creative cohort and can identify with this generalisation, then this chapter will ease you through the learning process (and in doing so hopefully dispel any myth that it is especially complicated).

Conversely, many others may think they do not know enough about data but in reality they already do ‘get’ it – they just need to learn more about its role in visualisation and possibly realign their understanding of some of the terminology. Therefore, before delving further into this chapter’s tasks, there are a few ‘defining’ matters I need to address to cover the basics in both data and statistical literacy.

Data Assets and Tabulation Types

Firstly, let’s consider some of the fundamentals about what a dataset is as well as what shape and form it comes in.

When working on a visualisation I generally find there are two main categories of data ‘assets’: data that exist in tables, known as datasets; and data that exists as isolated values.

For the purpose of this book I describe this type of data as being raw because it has not yet been statistically or mathematically manipulated and it has not been modified in any other way from its original state.

Tabulated datasets are what we are mainly interested in at this point. Data as isolated values refers to data that exists as individual facts and statistical figures. These do not necessarily belong in, nor are they normally collected in, a table. They are just potentially useful values that are dispersed around the Web or across reports:

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individual facts or figures that you might come across during your data gathering or research stages. Later on in your work you might use these to inform calculations (e.g. applying a currency conversion) or to incorporate a fact into a title or caption (e.g. 78% of staff participated in the survey), but they are not your main focus for now.

Tabulated data is unquestionably the most common form of data asset that you will work with, but it too can exist in slightly different shapes and sizes. A primary difference lies between what can be termed normalised datasets (Figure 4.1) and cross-tabulated datasets (Figure 4.2).

A normalised dataset might loosely be described as looking like lists of data values. In spreadsheet parlance, you would see this as a series of columns and rows of data, while in database parlance it is the arrangement of fields and records. This form of tabulated data is generally the most detailed form of data available for you to work with. The table in Figure 4.1 is an example of normalised data where the columns of variables provide different descriptive values for each movie (or record) held in the table.

F ig u r e 4 .1 Example of a Normalised Dataset

Cross-tabulated data is presented in a reconfigured form where, instead of displaying raw data values, the table of cells contain the results of statistical operations (like summed totals, maximums, averages). These values are aggregated calculations formed from the relationship between two variables held in the normalised form of the data. In Figure 4.2, you will see the cross-tabulated result of the normalised table of movie data, now showing a statistical summary for each movie category. The statistic under ‘Max Critic Rating’ is formed from an aggregating calculation based on the ‘Critic Rating’ and ‘Category’ variables seen in Figure 4.1.

F ig u r e 4 .2 Example of a Cross-tabulated Dataset

Typically, if you receive data in an already cross-tabulated form, you do not have access to the original data.

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This means you will not be able to ‘reverse-engineer’ it back into its raw form, which, in turn, means you have reduced the scope of your potential analysis. In contrast, normalised data gives you complete freedom to explore, manipulate and aggregate across multiple dimensions. You may choose to convert the data into ‘cross- tabulated’ form but that is merely an option that comes with the luxury of having access to the detailed form of your data. In summary, it is always preferable, where possible, to work with normalised data.

Data Types

One of the key parts of the design process concerns understanding the different types of data (sometimes known as levels of data or scales of measurement). Defining the types of data will have a huge influence on so many aspects of this workflow, such as determining:

the type of exploratory data analysis you can undertake; the editorial thinking you establish; the specific chart types you might use; the colour choices and layout decisions around composition.

In the simplest sense, data types are distinguished by being either qualitative or quantitative in nature. Beneath this distinction there are several further separations that need to be understood. The most useful taxonomy I have found to describe these different types of data is based on an approach devised by the psychologist researcher Stanley Stevens. He developed the acronym NOIR as a mnemonic device to cover the different types of data you may come to work with, particularly in social research: Nominal, Ordinal, Interval, and Ratio. I have extended this, adding onto the front a ‘T’ – for Textual – which, admittedly, somewhat undermines the grace of the original acronym but better reflects the experiences of handling data today. It is important to describe, define and compare these different types of data.

Textual (Qualitative)

Textual data is qualitative data and generally exists as unstructured streams of words. Examples of textual data might include:

‘Any other comments?’ data submitted in a survey. Descriptive details of a weather forecast for a given city. The full title of an academic research project. The description of a product on Amazon. The URL of an image of Usain Bolt’s victory in the 100m at the 2012 Olympics.

F ig u r e 4 .3 Graphic Language: The Curse of the CEO

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In its native form, textual data is likely to offer rich potential but it can prove quite demanding to unlock this. To work with textual data in an analysis and visualisation context will generally require certain natural language processing techniques to derive or extract classifications, sentiments, quantitative properties and relational characteristics.

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An example of how you can use textual data is seen in the graphic of CEO swear word usage shown in Figure 4.3. This analysis provides a breakdown of the profanities used by CEOs from a review of recorded conference calls over a period of 10 years. This work shows the two ways of utilising textual data in visualisation. Firstly, you can derive categorical classifications and quantitative measurements to count the use of certain words compared to others and track their usage over time. Secondly, the original form of the textual data can be of direct value for annotation purposes, without the need for any analytical treatment, to include as captions.

Working with textual data will always involve a judgement of reward vs effort: how much effort will I need to expend in order to extract usable, valuable content from the text? There are an increasing array of tools and algorithmic techniques to help with this transformational approach but whether you conduct it manually or with some degree of automation it can be quite a significant undertaking. However, the value of the insights you are able to extract may entirely justify the commitment. As ever, your judgment of the aims of your work, the nature of your subject and the interests of your audience will influence your decision.

N om inal (Qualitative)

Nominal data is the next form of qualitative data in the list of distinct data types. This type of data exists in categorical form, offering a means of distinguishing, labelling and organising values. Examples of nominal data might include:

The ‘gender’ selected by a survey participant. The regional identifier (location name) shown in a weather forecast. The university department of an academic member of staff. The language of a book on Amazon. An athletic event at the Olympics.

Often a dataset will hold multiple nominal variables, maybe offering different organising and naming perspectives, for example the gender, eye colour and hair colour of a class of school kids.

Additionally, there might be a hierarchical relationship existing between two or more nominal variables, representing major and sub-categorical values: for example, a major category holding details of ‘Country’ and a sub-category holding ‘Airport’; or a major category holding details of ‘Industry’ and a sub-category holding details of ‘Company Names’. Recognising this type of relationship will become important when considering the options for which angles of analysis you might decide to focus on and how you may portray them visually using certain chart types.

Nominal data does not necessarily mean text-based data; nominal values can be numeric. For example, a student ID number is a categorical device used uniquely to identify all students. The shirt number of a footballer is a way of helping teammates, spectators and officials to recognise each player. It is important to be aware of occasions when any categorical values are shown as numbers in your data, especially in order to understand that these cannot have (meaningful) arithmetic operations applied to them. You might find logic statements like TRUE or FALSE stated as a 1 and a 0, or data captured about gender may exist as a 1 (male), 2 (female) and 3 (other), but these numeric values should not be considered quantitative values – adding ‘1’ to ‘2’ does not equal ‘3’ (other) for gender.

Ordinal (Qualitative)

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Ordinal data is still categorical and qualitative in nature but, instead of there being an arbitrary relationship between the categorical values, there are now characteristics of order. Examples of nominal data might include:

The response to a survey question: based on a scale of 1 (unhappy) to 5 (very happy). The general weather forecast: expressed as Very Hot, Hot, Mild, Cold, Freezing. The academic rank of a member of staff. The delivery options for an Amazon order: Express, Next Day, Super Saver. The medal category for an athletic event: Gold, Silver, Bronze.

Whereas nominal data is a categorical device to help distinguish values, ordinal data is also a means of classifying values, usually in some kind of ranking. The hierarchical order of some ordinal values goes through a single ascending/descending rank from high or good values to low or bad values. Other ordinal values have a natural ‘pivot’ where the direction changes around a recognisable mid-point, such as the happiness scale which might pivot about ‘no feeling’ or weather forecast data that pivots about ‘Mild’. Awareness of these different approaches to ‘order’ will become relevant when you reach the design stages involving the classifying of data through colour scales.

Interval (Quantitative)

Interval data is the less common form of quantitative data, but it is still important to be aware of and to understand its unique characteristics. An interval variable is a quantitative and numeric measurement defined by difference on a scale but not by relative scale. This means the difference between two values is meaningful but an arithmetic operation such as multiplication is not.

The most common example is the measure for temperature in a weather forecast, presented in units of Celsius. The absolute difference between 15°C and 20°C is the same difference as between 5°C and 10°C. However, the relative difference between 5°C and 10°C is not the same as the difference between 10°C and 20°C (where in both cases you multiply by two or increase by 100%). This is because a zero value is arbitrary and often means very little or indeed is impossible. A temperature reading of 0°C does not mean there is no temperature, it is a quantitative scale for measuring relative temperature. You cannot have a shoe size or Body Mass Index of zero.

Ratio (Quantitative)

Ratio data is the most common quantitative variable you are likely to come across. It comprises numeric measurements that have properties of difference and scale. Examples of nominal data might include:

The age of a survey participant in years. The forecasted amount of rainfall in millimetres. The estimated budget for a research grant proposal in GBP (£). The number of sales of a book on Amazon. The distance of the winning long jump at the 2012 Olympics in metres.

Unlike interval data, for ratio data variables zero means something. The absolute difference in age between a 10 and 20 year old is the same as the difference between a 40 and 50 year old. The relative difference between a 10 and a 20 year old is the same as the difference between a 40 and an 80 year old (‘twice as old’).

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Whereas most of the quantitative measurements you will deal with are based on a linear scale, there are exceptions. Variables about the strength of sound (decibels) and magnitude of earthquakes (Richter) are actually based on a logarithmic scale. An earthquake with a magnitude of 4.0 on the Richter scale is 1000 times stronger based on the amount of energy released than an earthquake of magnitude 2.0. Some consider these as types of data that are different from ratio variables. Most still define them as ratio variables but separate them as non-linear scaled variables.

If temperature values were measured in kelvin, where there is an absolute zero, this would be considered a ratio scale, not an interval one.

Tem poral D ata

Time-based data is worth mentioning separately because it can be a frustrating type of data to deal with, especially in attempting to define its place within the TNOIR classification. The reason for this is that different components of time can be positioned against almost all data types, depending simply on what form your time data takes:

T ex tu al: ‘Four o’clock in the afternoon on Monday, 12 March 2016’

O r d inal: ‘PM’, ‘Afternoon’, ‘March’, ‘Q1’

Inter v al: ‘12’, ‘12/03/2016’, ‘2016’

R atio : ‘16:00’

Note that time-based data is separate in concern to duration data, which, while often formatted in structures such as hh:mm:ss, should be seen as a ratio measure. To work with duration data it is often useful to transform it into single units of time, such as total seconds or minutes.

D iscrete vs C ontinuous

Another important distinction to make about your data, and something that cuts across the TNOIR classification, is whether the data is discrete or continuous. This distinction is influential in how you might analyse it statistically and visually.

The relatively simple explanation is that discrete data is associated with all classifying variables that have no ‘in- between’ state. This applies to all qualitative data types and any quantitative values for which only a whole is possible. Examples might be:

Heads or tails for a coin toss. Days of the week. The size of shoes. Numbers of seats in a theatre.

In contrast, continuous variables can hold the value of an in-between state and, in theory, could take on any value between the natural upper and lower limits if it was possible to take measurements in fine degrees of

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detail, such as:

Height and weight. Temperature. Time.

One of the classifications that is hard to nail down involves data that could, on the TNOIR scale, arguably fall under both ordinal and ratio definitions based on its usage. This makes it hard to determine if it should be considered discrete or continuous. An example would be the star system used for rating a movie or the happiness rating. When a star rating value is originally captured, the likelihood is that the input data was discrete in nature. However, for analysis purposes, the statistical operations applied to data that is based on different star ratings could reasonably be treated either as discrete classifications or, feasibly, as continuous numeric values. For both star review ratings or happiness ratings decimal averages could be calculated as a way of formulating average score. (The median and mode would still be discrete.) The suitability of this approach will depend on whether the absolute difference between classifying values can be considered equal.

4.2 Statistical Literacy If the fear of data is misplaced, I can sympathise with anybody’s trepidation towards statistics. For many, statistics can feel complicated to understand and too difficult a prospect to master. Even for those relatively comfortable with stats, it is unquestionably a discipline that can easily become rusty without practice, which can also undermine your confidence. Furthermore, the fear of making mistakes with delicate and rule-based statistical calculations also depresses the confidence levels lower than they need to be.

The problem is that you cannot avoid the need to use some statistical techniques if you are going to work with data. It is therefore important to better understand statistics and its role in visualisation, as you must do with data. Perhaps you can make the problem more surmountable by packaging the whole of statistics into smaller, manageable elements that will dispel the perception of overwhelming complexity.

I do believe that it is possible to overstate the range and level of statistical techniques most people will need to employ on most of their visualisation tasks. The caveats are important as I know there will be people with visualisation experience who are exposed to a tremendous amount of statistical thinking in their work, but it is a relevant point.

It all depends, of course. From my experience, however, the majority of data visualisation challenges will generally involve relatively straightforward univariate and multivariate statistical techniques. Univariate techniques help you to understand the shape, size and range of quantitative values. Multivariate techniques help you to explore the possible relationships between different combinations of variables and variable types. I will describe some of the most relevant statistical operations associated with these techniques later in this chapter, at the point in your thinking where they are most applicable.

As you get more advanced in your work (and your confidence increases) you might have occasion to employ inference techniques. These include concepts such as data modelling and the use of regression analysis: attempting to measure the relationships between variables to explore correlations and (the holy grail) causations. Many of you will likely experience visualisation challenges that require an understanding of probabilities, testing hypotheses and becoming acquainted with terms like confidence intervals. You might use these techniques to assist with forecasting or modelling risk and uncertainty. Above and beyond that, you are

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moving towards more advanced statistical modelling and algorithm design.

It is somewhat dissatisfactory to allocate only a small part of this text to discussing the role of descriptive and exploratory statistics. However, for the scope of this book, and seeking to achieve a pragmatic balance, the most sensible compromise is just to flag up which statistical activities you might need to consider and where these apply. It can take years to learn about the myriad advanced techniques that exist and it takes experience to know when and how to deploy all the different methods.

There are hundreds of books better placed to offer the depth of detail you truly need to fulfil these activities and there is no real need to reinvent the wheel – and indeed reinvent an inferior wheel. That statistics is just one part of the visualisation challenge, and is in itself such a prolific field, further demonstrates the variety and depth of this subject.

4.3 Data Acquisition The first step in working with data naturally involves getting it. As I outlined in the contextual discussion about the different types of trigger curiosities, you will only have data in place before now if the opportunity presented by the data was the factor that triggered this work. You will recall this scenario was described as pursuing a curiosity born out of ‘potential intrigue’. Otherwise, you will only be in a position to know what data you need after having established your specific or general motivating curiosity. In these situations, once you have sufficiently progressed your thinking around ‘formulating your brief’, you will need to switch your thinking onto the task of acquiring your data:

What data do you need and why? From where, how, and by whom will the data be acquired? When can you obtain it?

W hat Data Do You Need?

Your primary concern is to ensure you can gather sufficient data about the subject in which you are interested to pursue your identified curiosity. By ‘sufficient’, I mean you will need to establish some general criteria in your mind for what data you do need and what data you do not need. There is no harm in getting more than you need at this stage but it can result in wasted efforts, waste that you would do well to avoid.

Let’s propose you have defined your curiosity to be ‘I wonder what a map of McDonald’s restaurant openings looks like over time?’. In this scenario you are going to try to find a source of data that will provide you with details of all the McDonald’s restaurants that have ever opened. A shopping list of data items would probably include the date of opening, the location details (as specific as possible) and maybe even a closing date to ensure you can distinguish between still operating and closed-down restaurants.

You will need to conduct some research, a perpetual strand of activity that runs throughout the workflow, as I explained earlier. In this scenario you might need first to research a bit of the history of McDonald’s restaurants to discover, for instance, when the first one opened, how many there are, and in which countries they are located. This will establish an initial sense of the timeframe (number of years) and scale (outlets, global spread) of your potential data. You might also discover significant differences between what is considered a restaurant and what is just a franchise positioned in shopping malls or transit hubs. Sensitivities around the qualifying

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criteria or general counting rules of a subject are important to discover, as they will help significantly to substantiate the integrity and accuracy of your work.

Unless you know or have been told where to find this restaurant data, you will then need to research from where the data might be obtainable. Will this type of information be published on the Web, perhaps on the commercial pages of McDonald’s own site? You might have to get in touch with somebody (yes, a human) in the commercial or PR department to access some advice. Perhaps there will be some fast-food enthusiast in some niche corner of the Web who has already gathered and made available data like this?

Suppose you locate a dataset that includes not just McDonald’s restaurants but all fast-food outlets. This could potentially broaden the scope of your curiosity, enabling broader analysis about the growth of the fast- food industry at large to contextualise MacDonald’s contribution to this. Naturally, if you have any stakeholders involved in your project, you might need to discuss with them the merits of this wider perspective.

Another judgement to make concerns the resolution of the data you anticipate needing. This is especially relevant if you are working with big, heavy datasets. You might genuinely want and need all available data. This would be considered full resolution – down to the most detailed grain (e.g. all details about all MacDonald’s restaurants, not just totals per city or country). Sometimes, in this initial gathering activity, it may be more practical just to obtain a sample of your data. If this is the case, what will be the criteria used to identify a sufficient sample and how will you select or exclude records? What percentage of your data will be sufficient to be representative of the range and diversity (an important feature we will need to examine next)? Perhaps you only need a statistical, high-level summary (total number of restaurants opened by year)?

The chances are that you will not truly know what data you want or need until you at least get something to start with and learn from there. You might have to revisit or repeat the gathering of your data, so an attitude of ‘what I have is good enough to start with’ is often sensible.

From W here, How and By W hom W ill the Data Be Acquired?

There are several different origins and methods involved in acquiring data, depending on whether it will involve your doing the heavy work to curate the data or if this will be the main responsibility of others.

C urated by You

This group of data-gathering tasks or methods is characterised by your having to do most of the work to bring the data together into a convenient digital form.

P r im ar y d ata co llectio n: If the data you need does not exist or you need to have full control over its provenance and collection, you will have to consider embarking on gathering ‘primary’ data. In contrast to secondary data, primary data involves you measuring and collecting the raw data yourself. Typically, this relates to situations where you gather quite small, bespoke datasets about phenomena that are specific to your needs. It might be a research experiment you have designed and launched for participants to submit responses. You may manually record data from other measurement devices, such as your daily weight as measured by your bathroom scales, or the number of times you interacted face-to-face with friends and family. Some people take daily photographs of themselves, their family members or their gardens, in order to stitch these back together eventually to portray stories of change. This data-gathering activity can be expensive in terms of both the time

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and cost. The benefit however is that you have carefully controlled the collection of the data to optimise its value for your needs.

M anu al co llectio n and d ata fo r ag ing : If the data you need does not exist digitally or in a convenient singular location, you will need to forage for it. This again might typically relate to situations where you are sourcing relatively small datasets. An example might be researching historical data from archived newspapers that were only published in print form and not available digitally. You might look to pull data from multiple sources to create a single dataset: for example, if you were comparing the attributes of a range of different cars and weighing up which to buy. To achieve this you would probably need to source different parts of the data you need from several different places. Often, data foraging is something you undertake in order to finish off data collected by other means that might have a few missing values. It is sometimes more efficient to find the remaining data items yourself by hand to complete the dataset. This can be somewhat time-consuming depending on the extent of the manual gathering required, but it does provide you with greater assurance over the final condition of the data you have collected.

E x tr acted fr o m p d f files : A special subset of data foraging – or a variation at least – involves those occasions when your data is digital but essentially locked away in a pdf file. For many years now reports containing valuable data have been published on the Web in pdf form. Increasingly, movements like ‘open data’ are helping to shift the attitudes of organisations towards providing additional, fully accessible digital versions of data. Progress is being made but it will take time before all industries and government bodies adopt this as a common standard. In the meantime, there are several tools on the market (free and proprietary) that will assist you in extracting tables of data from pdf files and converting these to more usable Excel or CSV formats.

Some data acquisition tasks may be repetitive and, should you possess the skills and have access to the necessary resources, there will be scope for exploring ways to automate these. However, you always have to consider the respective effort and ongoing worth of your approach. If you do go to the trouble of authoring an automation routine (of any description) you could end up spending more time on that than you would otherwise collecting by more manual methods. If it is going to be a regular piece of analysis the efficiency gains from your automation will unquestionably prove valuable going forward, but, for any one-off projects, it may not be ultimately worth it

W eb s cr ap ing ( als o k no w n as w eb har v es ting ) : This involves using special tools or programs to extract structured and unstructured items of data published in web pages and convert these into tabulated form for analysis. For example, you may wish to extract several years’ worth of test cricket results from a sports website. Depending on the tools used, you can often set routines in motion to extract data across multiple pages of a site based on the connected links that exist within it. This is known as web crawling. Using the same example (let’s imagine), you could further your gathering of test cricket data by programmatically fetching data back from the associated links pointing to the team line-ups. An important consideration to bear in mind with any web scraping or crawling activity concerns rules of access and the legalities of extracting the data held on certain sites. Always check – and respect – the terms of use before undertaking this.

C urated by Others

In contrast to the list of methods I have profiled, this next set of data-gathering approaches is characterised by other people having done most of the work to source and compile the data. They will make it available for you to access in different ways without needing the extent of manual efforts often required with the methods

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presented already. You might occasionally still have to intervene by hand to fine-tune your data, but others would generally have put in the core effort.

Is s u ed to y o u : On the occasions when you are commissioned by a stakeholder (client, colleague) you will often be provided with the data you need (and probably much more besides), most commonly in a spreadsheet format. The main task for you is therefore less about collection and more about familiarisation with the contents of the data file(s) you are set to work with.

D o w nlo ad fr o m the W eb : Earlier I bemoaned the fact that there are still organisations publishing data (through, for example, annual reports) in pdf form. To be fair, increasingly there are facilities being developed that enable interested users to extract data in a more structured form. More sophisticated reporting interfaces may offer users the opportunity to construct detailed queries to extract and download data that is highly customised to their needs.

S y s tem r ep o r t o r ex p o r t: This is related more to an internal context in organisations where there are opportunities to extract data from corporate systems and databases. You might, for example, wish to conduct some analysis about staff costs and so the personnel database may be where you can access the data about the workforce and their salaries.

‘Don’t underestimate the importance of domain expertise. At the Office for National Statistics (ONS), I was lucky in that I was very often working with the people who created the data – obviously, not everyone will have that luxury. But most credible data producers will now produce something to accompany the data they publish and help users interpret it – make sure you read it, as it will often include key findings as well as notes on reliability and limitations of the data.’ Alan Sm i th OBE , D ata Vi s u ali s ati on E d i tor, Fina ncia l T imes

T hir d - p ar ty s er v ices : There is an ever-increasing marketplace for data and many commercial services out there now offer extensive sources of curated and customised data that would otherwise be impossible to obtain or very complex to gather. Such requests might include very large, customised extracts from social media platforms like Twitter based on specific keywords and geo-locations.

A P I: An API (Application Programme Interface) offers the means to create applications that programmatically access streams of data from sites or services, such as accessing a live feed from Transport for London (TfL) to track the current status of trains on the London Underground system.

W hen Can the Data Be Acquired?

The issue of when data is ready and available for acquisition is a delicate one. If you are conducting analysis of some survey results, naturally you will not have the full dataset of responses to work with until the survey is closed. However, you could reasonably begin some of your analysis work early by using an initial sample of what had been submitted so far. Ideally you will always work with data that is as complete as possible, but on occasions it may be advantageous to take the opportunity to get an early sense of the nature of the submitted responses in order to begin preparing your final analysis routines. Working on any dataset that may not yet be complete is a risk. You do not want to progress too far ahead with your visualisation workflow if there is the real prospect that any further data that emerges could offer new insights or even trigger different, more interesting curiosities.

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4.4 Data Examination After acquiring your data your next step is to thoroughly examine it. As I have remarked, your data is your key raw material from which the eventual visualisation output will be formed. Before you choose what meal to cook, you need to know what ingredients you have and what you need to do to prepare them.

It may be that, in the act of acquiring the data, you have already achieved a certain degree of familiarity about its status, characteristics and qualities, especially if you curated the data yourself. However, there is a definite need to go much further than you have likely achieved before now. To do this you need to conduct an examination of the physical properties and the meaning of your data.

As you progress through the stages of this workflow, your data will likely change considerably: you will bring more of it in, you will remove some of it, and you will refine it to suit your needs. All these modifications will alter the physical makeup of your data so you will need to keep revisiting this step to preserve your critical familiarity.

Data Properties

The first part of familiarising yourself with with your data is to undertake an examination of its physical properties. Specifically you need to ascertain its type, size and condition. This task is quite mechanical in many ways because you are in effect just ‘looking’ at the data, establishing its surface characteristics through visual and/or statistical observations.

W hat To Look For?

The type and size of your data involve assessing the characteristics and amount of data you have to work with. As you examine the data you also need to determine its condition: how good is its quality and is it fit for purpose?

D ata ty p es : Firstly, you need to identify what data types you have. In gathering this data in the first place you might already have a solid appreciation about what you have before you, but doing this thoroughly helps to establish the attention to detail you will need to demonstrate throughout this stage. Here you will need to refer to the definitions from earlier in the chapter about the different types of data (TNOIR). Specifically you are looking to define each column or field of data based on whether it is qualitative (text, nominal, ordinal) or quantitative (interval, ratio) and whether it is discrete or continuous in nature. S iz e: Within each column or field you next need to know what range of values exist and what are the specific attributes/formats of the values held. For example, if you have a quantitative variable (interval or ratio), what is the lowest and the highest value? In what number format is it presented (i.e. how many decimal points or comma formatted)? If it is a categorical variable (nominal or ordinal), how many different values are held? If you have textual data, what is the maximum character length or word count? C o nd itio n: This is the best moment to identify any data quality and completeness issues. Naturally, unidentified and unresolved issues around data quality will come to bite hard later, undermining the scope and, crucially, trust in the accuracy of your work. You will address these issues next in the ‘transformation’ step, but for now the focus is on identifying any problems. Things to look out for may include the following:

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Missing values, records or variables – Are empty cells assumed as being of no value (zero/nothing) or no measurement (n/a, null)? This is a subtle but important difference. Erroneous values – Typos and any value that clearly looks out of place (such as a gender value in the age column). Inconsistencies – Capitalisation, units of measurement, value formatting. Duplicate records. Out of date – Values that might have expired in accuracy, like someone’s age or any statistic that would be reasonably expected to have subsequently changed. Uncommon system characters or line breaks. Leading or trailing spaces – the invisible evil! Date issues around format (dd/mm/yy or mm/dd/yy) and basis (systems like Excel’s base dates on daily counts since 1 January 1900, but not all do that).

How to Approach This?

I explained in the earlier ‘Data literacy’ section the difference in asset types (data that exists in tables and data that exists as isolated values) and also the difference in form (normalised data or cross-tabulated). Depending on the asset and form of data, your examination of data types may involve slightly different approaches, but the general task is the same. Performing this examination process will vary, though, based on the tools you are using. The simplest approach, relevant to most, is to describe the task as you would undertake it using Excel, given that this continues to be the common tool most people use or have the skills to use. Also, it is likely that most visualisation tasks you undertake will involve data of a size that can be comfortably handled in Excel.

‘Data inspires me. I always open the data in its native format and look at the raw data just to get the lay of the land. It’s much like looking at a map to begin a journey.’ Ki m R ees , C o- fou nd er, Peri s cop i c

As you go through this task, it is good practice to note down a detailed overview of what data you have, perhaps in the form of a table of data descriptions. This is not as technical a duty as would be associated with the creation of a data dictionary but its role and value are similar, offering a convenient means to capture all the descriptive properties of your various data assets.

Ins p ect and s can: Your first task is just to scan your table of data visually. Navigate around it using the mouse/trackpad, use the arrow keys to move up or down and left or right, and just look at all the data. Gain a sense of its overall dimension. How many columns and how many rows does it occupy? How big a prospect might working with this be? D ata o p er atio ns : Inspecting your data more closely might require the use of interrogation features such as sorting columns and doing basic filters. This can be a quick and simple way to acquaint yourself with the type of data and range of values. Going further, once again depending on the technology (and assuming you have normalised data to start with), you might apply a cross-tabulation or pivot table to create aggregated, summary views of different angles and combinations of your data. This can be a useful approach to also check out the unique range of values that exist under different categories as well as helping to establish how sub- categories may relate other categories hierarchically. This type of inspection will be furthered in the next step of the ‘working with data’ process when you will undertake deeper visual interrogations of the type, size and condition of your data. If you have multiple tables, you will need to repeat this approach for each one as well as determine how

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they are related collectively and on what basis. It could be that just considering one table as the standard template, representative of each instance, is sufficient: for example, if each subsequent table is just a different monthly view of the same activity. For so-called ‘Big Data’ (see the glossary definition earlier), it is less likely that you can conduct this examination work through relatively quick, visual observations using Excel. Instead it will need tools based around statistical language that will describe for you what is there rather than let you look at what is there. S tatis tical m etho d s : The role of statistics in this examination stage generally involves relatively basic quantitative analysis methods to help describe and understand the characteristics of each data variable. The common term applied to this type of statistical approach is univariate, because it involves just looking at one variable at a time (the best opportunity to perform the analysis of multiple variables comes later). Here are some different types of statistical analyses you might find useful at this stage. These are not the only methods you will ever need to use, but will likely prove to be among the most common:

Frequency counts: applied to categorical values to understand the frequency of different instances. Frequency distribution: applied to quantitative values to learn about the type and shape of the distribution of values. Measurements of central tendency describe the summary attributes of a group of quantitative values, including:

the mean (the average value); the median (the middle value if all quantities were arranged from smallest to largest); the mode (the most common value).

Measurements of spread are used to describe the dispersion of values above and below the mean: Maximum, minimum and range: the highest and lowest and magnitude of spread of values. Percentiles: the value below which x% of values fall (e.g. the 20th percentile is the value below which 20% of all quantitative values fall). Standard deviation: a calculated measure used to determine how spread out a series of quantitative values are.

Data Meaning

Irrespective of whether you or others have curated the data, you need to be discerning about how much trust you place in it, at least to begin with. As discussed in the ‘trustworthy design’ principle, there are provenance issues, inaccuracies and biases that will affect its status on the journey from being created to being acquired. These are matters you need to be concerned with in order to resolve or at least compensate for potential shortcomings.

Knowing more about the physical properties of your data does not yet achieve full familiarity with its content nor give you sufficient acquaintance with its qualities. You will have examined the data in a largely mechanical and probably quite detached way from the underlying subject matter. You now need to think a little deeper about its meaning, specifically what it does – and does not – truly represent.

‘A visualization is always a model (authored), never a mould (replica), of the real. That’s a huge responsibility.’ Paolo C i u ccarelli , Sci enti fi c D i rector of D ens i tyD es i g n R es earch Lab at

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Poli tecni co d i M i lan o

W hat Phenom enon?

Determining the meaning of your data requires that you recognise this is more than just a bunch of numbers and text values held in the cells of a table. Ask yourself, ‘What is it about? What activity, entity, instance or phenomenon does it represent?’.

One of the most valuable pieces of advice I have seen regarding this task came from Kim Rees, co-founder of Periscopic. Kim describes the process of taking one single row of data and using that as an entry point to learn carefully about what each value means individually and then collectively. Breaking down the separation between values created by the table’s cells, and then sticking the pieces back together, helps you appreciate the parts and the whole far better.

‘Absorb the data. Read it, re-read it, read it backwards and understand the lyrical and human-centred contribution.’ Kate M cLean , Sm ells cap e M ap p er and Sen i or Lectu rer Grap h i c D es i g n

You saw the various macro- and micro-level views applied to the context of the Texas Department for Criminal Justice executed offenders information in the previous chapter. The underlying meaning of this data – its phenomenon – was offenders who had been judged guilty of committing heinous crimes and had faced the ultimate consequence. The availability of textual data describing the offenders’ last statements and details of their crimes heightened the emotive potential of this data. It was heavy stuff. However, it was still just a collection of values detailing dates, names, locations, categories. All datasets, whether on executed offenders or the locations of MacDonald’s restaurants, share the same properties as outlined by the TNOIR data-type mnemonic. What distinguishes them is what these values mean.

What you are developing here is a more semantic appreciation of your data to substantiate the physical definitions. You are then taking that collective appreciation of what your data stands for to influence how you might decide to amplify or suppress the influence of this semantic meaning. This builds on the discussion in the last chapter about the tonal dimension, specifically the difference between figurative and non-figurative portrayals.

A bar chart (Figure 4.4) comprising two bars, one of height 43 and the other of height 1, arguably does not quite encapsulate the emotive significance of Barack Obama becoming the first black US president, succeeding the 43 white presidents who served before him. Perhaps a more potent approach may be to present a chronological display of 44 photographs of each president in order to visually contrast Mr Obama’s headshot in the final image in the sequence with the previous 43. Essentially, the value of 43 is almost irrelevant in its detail – it could be 25 or 55 – it is about there being ‘many’ of the same thing followed by the ‘one’ that is‘different’. That’s what creates the impact. (What will image number 45 bring? A further striking ‘difference’ or a return to the standard mould?)

F ig u r e 4 .4 US Presidents by Ethnicity (1789 to 2015)

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Learning about the underlying phenomena of your data helps you feel its spirit more strongly than just looking at the rather agnostic physical properties. It also helps you in knowing what potential sits inside the data – the qualities it possesses – so you are then equipped the best understanding of how you might want to portray it. Likewise it prepares you for the level of responsibility and potential sensitivity you will face in curating a visual representation of this subject matter. As you saw with the case study of the ‘Florida Gun Crimes’ graphic, some subjects are inherently more emotive than others, so we have to demonstrate a certain amount of courage and conviction in deciding how to undertake such challenges.

‘Find loveliness in the unlovely. That is my guiding principle. Often, topics are disturbing or difficult; inherently ugly. But if they are illustrated elegantly there is a special sort of beauty in the truthful communication of something. Secondly, Kirk Goldsberry stresses that data visualization should ultimately be true to a phenomenon, rather than a technique or the format of data. This has had a huge impact on how I think about the creative process and its results.’ Joh n N els on, C artog rap h er

C om pleteness

Another aspect of examining the meaning of data is to determine how representative it is. I have touched on data quality already, but inaccuracies in conclusions about what data is saying have arguably a greater impact on trust and are more damaging than any individual missing elements of data.

The questions you need to ask of your data are: does it represent genuine observations about a given phenomenon or is it influenced by the collection method? Does your data reflect the entirety of a particular phenomenon, a recognised sample, or maybe even an obstructed view caused by hidden limitations in the availability of data about that phenomenon?

Reflecting on the published executed offenders data, there would be a certain confidence that it is representative of the total population of executions but with a specific caveat: it is all the executed offenders under the jurisdiction of the Texas Department of Criminal Justice since 1982. It is not the whole of the executions

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conducted across the entire USA nor is it representative of all the executions that have taken place throughout the history of Texas. Any conclusions drawn from this data must be boxed within those parameters.

The matter of judging completeness can be less about the number of records and more a question of the integrity of the data content. This executed offenders dataset would appear to be a trusted and reliable record of each offender but would there/could there be an incentive for the curators of this data not to capture, for example, the last statements as they were explicitly expressed? Could they have possibly been in any way sanitised or edited, for example? These are the types of questions you need to pose. This is not aimless cynicism, it is about seeking assurances of quality and condition so you can be confident about what you can legitimately present and conclude from it (as well as what you should not).

Consider a different scenario. If you are looking to assess the political mood of a nation during a televised election debate, you might consider analysing Twitter data by looking at the sentiments for and against the candidates involved. Although this would offer an accessible source of rich data, it would not provide an entirely reliable view of the national mood. It could only offer algorithmically determined insights (i.e. through the process of determining the sentiment from natural language) of the people who have a Twitter account, are watching the debate and have chosen to tweet about it during a given timeframe.

Now, just because you might not have access to a ‘whole’ population of political opinion data does not mean it is not legitimate to work on a sample. Sometimes samples are astutely reflective of the population. And in truth, if samples were not viable then most of the world’s analyses would need to cease immediately.

A final point is to encourage you to probe any absence of data. Sometimes you might choose to switch the focus away from the data you have got towards the data you have not got. If the data you have is literally as much as you can acquire but you know the subject should have more data about it, then perhaps shine a light on the gaps, making that your story. Maybe you will unearth a discovery about the lack of intent or will to make the data available, which in itself may be a fascinating discovery. As transparency increases, those who are not stand out the most.

‘This is one of the first questions we should ask about any dataset: what is missing? What can we learn from the gaps?’ Jer Th orp , Fou n d er of Th e Offi ce for C reati ve R es earch

Any identified lack of completeness or full representativeness is not an obstacle to progress, it just means you need to tread carefully with regard to how you might represent and present any work that emerges from it. It is about caution not cessation.

Influence on Process

This extensive examination work gives you an initial – but thorough – appreciation of the potential of your data, the things it will offer and the things it will not. Of course this potential is as yet unrealised. Furthering this examination will be the focus of the next activity, as you look to employ more visual techniques to help unearth the as-yet-hidden qualities of understanding locked away in the data. For now, this examination work takes your analytical and creative thinking forward another step.

P u r p o s e m ap ‘to ne’: Through deeper acquaintance with your data, you will have been able to further consider the suitability of the potential tone of your work. By learning more about the inherent characteristics of the subject, this might help to confirm or redefine your intentions for adopting a

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utilitarian (reading) or sensation-based (feeling) tone. E d ito r ial ang les : The main benefit of exploring the data types is to arrive at an understanding of what you have and have not got to work with. More specifically, it guides your thinking towards what possible angles of analysis may be viable and relevant, and which can be eliminated as not. For example, if you do not have any location or spatial data, this rules out the immediate possibility of being able to map your data. This is not something you could pursue with the current scope of your dataset. If you do have time-based data then the prospect of conducting analysis that might show changes over time is viable. You will learn more about this idea of editorial ‘angle’ in the next chapter but let me state now it is one of the most important components of visualisation thinking. P hy s ical p r o p er ties influ ence s cale: Data is your raw material, your ideas are not. I stated towards the end of Chapter 3 that you should embrace the instinctive manifestations of ideas and seek influence and inspiration from other sources. However, with the shape and size of your data having such an impact on any eventual designs, you must respect the need to be led by your data’s physical properties and not just your ideas.

F ig u r e 4 .5 OECD Better Life Index

In particular, the range of values in your data will shape things significantly. The shape of data in the ‘Better Life Index’ project you saw earlier is a good example. Figure 4.5 presents an analysis of the quality of life across the 36 OECD member states. Each country is a flower comprising 11 petals with each representing a different quality of life indicator (the larger the petal, the better the measured quality of life). Consider this. Would this design concept still be viable if there were 20 indicators? Or just 3? How about if the analysis was for 150 countries? The connection between data range and chart design involves a discerning judgement about ‘fit’. You need to identify carefully the underlying shape of the data to be displayed and what tolerances this might test in the shape of the possible design concepts used.

‘My design approach requires that I immerse myself deeply in the problem domain and available data very early in the project, to get a feel for the unique characteristics of the data, its “texture” and the affordances it brings. It is very important that the results from these explorations, which I also discuss in detail with my clients, can influence the basic concept and main direction of the project. To put it in Hans Rosling’s words, you need to “let the data set change your mind set”.’ M ori tz Stefaner, Tru th & Beau ty Op erator

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Another relevant concern involves the challenge of elegantly handling quantitative measures that have hugely varied value ranges and contain (legitimate) outliers. Accommodating all the values into a single display can have a hugely distorting impact on the space it occupies. For example, note the exceptional size of the shape for Avatar in Figure 4.6, from the ‘Spotlight on profitability’ graphic you saw earlier. It is the one movie included that bursts through the ceiling, far beyond the otherwise entirely suitable 1000 million maximum scale value. As a single outlier, in this case, it was treated with a rather unique approach. As you can see, its striking shape conveniently trespasses onto the space offered by the two empty rows above. The result emphasises this value’s exceptional quality. You might seldom have the luxury of this type of effective resolution, so the key point to stress is always be acutely aware of the existence of ‘Avatars’ in your data.

F ig u r e 4 .6 Spotlight on Profitability

4.5 Data Transformation Having undertaken an examination of your data you will have a good idea about what needs to be done to ensure it is entirely fit for purpose. The next activity is to work on transforming the data so it is in optimum condition for your needs.

At this juncture, the linearity of a book becomes rather unsatisfactory. Transforming your data is something that will take place before, during and after both the examination and (upcoming) exploration steps. It will also continue beyond the boundaries of this stage of the workflow. For example, the need to transform data may only emerge once you begin your ‘editorial thinking’, as covered by the next chapter (indeed you will likely find yourself bouncing forwards and backwards between these sections of the book on a regular basis). As you get into the design stage you will constantly stumble upon additional reasons to tweak the shape and size of your data assets. The main point here is that your needs will evolve. This moment in the workflow is not going to be the only or final occasion when you look to refine your data.

Two important notes to share upfront at this stage. Firstly, in accordance with the desire for trustworthy

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design, any treatments you apply to your data need to be recorded and potentially shared with your audience. You must be able to reveal the thinking behind any significant assumptions, calculations and modifications you have made to your data.

Secondly, I must emphasise the critical value of keeping backups. Before you undertake any transformation, make a copy of your dataset. After each major iteration remember to save a milestone version for backup purposes. Additionally, when making changes, it is useful to preserve original (unaltered) data items nearby for easy rollback should you need them. For example, suppose you are cleaning up a column of messy data to do with ‘Gender’ that has a variety of inconsistent values (such as “M”, “Male”, “male”, “FEMALE”, “F”, “Female”). Normally I would keep the original data, duplicate the column, and then tidy up this second column of values. I have then gained access to both original and modified versions. If you are going to do any transformation work that might involve a significant investment of time and (manual) effort, having an opportunity to refer to a previous state is always useful in my experience.

There are four different types of potential activity involved in transforming your data: cleaning, converting, creating and consolidating.

T r ans fo r m to clean: I spoke about the importance of data quality (better quality in, better quality out, etc.) in the examination section when looking at the physical condition of the data. There’s no need to revisit the list of potential observations you might need to consider looking out for but this is the point where you will need to begin to address these. There is no single or best approach for how to conduct this task. Some issues can be addressed through a straightforward ‘find and replace’ (or remove) operation. Some treatments will be possible using simple functions to convert data into new states, such as using logic formulae that state ‘if this, do this, otherwise do that’. For example, if the value in the ‘Gender’ column is “M” make it “Male”, if the value is “MALE” make it “Male” etc. Other tasks might be much more intricate, requiring manual intervention, often in combination with inspection features like ‘sort’ or ‘filter’, to find, isolate and then modify problem values. Part of cleaning up your data involves the elimination of junk. Going back to the earlier scenario about gathering data about McDonald’s restaurants, you probably would not need the name of the restaurant manager, details of the opening times or the contact telephone number. It is down to your judgement at the time of gathering the data to decide whether these extra items of detail – if they were as easily acquirable as the other items of data that you really did need – may potentially provide value for your analysis later in the process. My tactic is usually to gather as much data as I can and then reject/trim later; later has arrived and now is the time to consider what to remove. Any fields or rows of data that you know serve no ongoing value will take up space and attention, so get rid of these. You will need to separate the wheat from the chaff to help reduce your problem. T r ans fo r m to co nv er t: Often you will seek to create new data values out of existing ones. In the illustration in Figure 4.7, it might be useful to extract the constituent parts of a ‘Release Date’ field in order to group, analyse and use the data in different ways. You might use the ‘Month’ and ‘Year’ fields to aggregate your analysis at these respective levels in order to explore within-year and across-year seasonality. You could also create a ‘Full Release Date’ formatted version of the date to offer a more presentable form of the release date value possibly for labeling purposes.

F ig u r e 4 .7 Example of Converted Data Transformation

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Extracting or deriving new forms of data will be necessary when it comes to handling qualitative ‘textual’ data. As stated in the ‘Data literacy’ section, if you have textual data you will generally always need to transform this into various categorical or quantitative forms, unless its role is simply to provide value as an annotation (such as a quoted caption or label). Some would argue that qualitative visualisation involves special methods for the representation of data. I would disagree. I believe the unique challenge of working with textual data lies with the task of transforming the data: visually representing the extracted and derived properties from textual data involves the same suite of representation options (i.e. chart types) that would be useful for portraying analysis of any other data types. Here is a breakdown of some of the conversions, calculations and extractions you could apply to textual data. Some of these tasks can be quite straightforward (e.g. Using the LEN function in Excel to determine the number of characters) while others are more technical and will require more sophisticated tools or programmes dedicated to handling textual data. Categorical conversions:

Identify keywords or summary themes from text and convert these into categorical classifications. Identify and flag up instances of certain cases existing or otherwise (e.g. X is mentioned in this passage). Identify and flag up the existence of certain relationships (e.g. A and B were both mentioned in the same passage, C was always mentioned before D). Use natural language-processing techniques to determine sentiments, to identify specific word types (nouns, verbs, adjectives) or sentence structures (around clauses and punctuation marks). With URLs, isolate and extract the different components of website address and sub-folder locations

Quantitative conversions:

Calculate the frequency of certain words being used. Analyse the attributes of text, such as total word count, physical length, potential reading duration. Count the number of sentences or paragraphs, derived from the frequency of different punctuation marks.

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Position the temporal location of certain words/phrases in relation to other words/phrases or compared to the whole (e.g. X was mentioned at 1m51s). Position the spatial location of certain words/phrases in relation to other words/phrases or compared to the whole.

A further challenge that falls under this ‘converting’ heading will sometimes emerge when you are working with data supplied by others in spreadsheets. This concerns the obstacles created when trying to analyse a data that has been formatted visually, perhaps in readiness for printing. If you receive data in this form you will need to unpack and reconstruct it into the normalised form described earlier, comprising all records and fields included in a single table.

Any merged cells need unmerging or removing. You might have a heading that is common to a series of columns. If you see this, unmerge it and replicate the same heading across each of the relevant columns (perhaps appending an index number to each header to maintain some differentiation). Cells that have visual formatting like background shading or font attributes (bold, coloured) to indicate a value or status are useful when observing and reading the data, but for analysis operations these properties are largely invisible. You will need to create new values in actual data form that are not visual (creating categorical values, say, or status flags like ‘yes’ or ‘no’) to recreate the meaning of the formats. The data provided to you – or that you create – via a spreadsheet does not need to be elegant in appearance, it needs to be functional. T r ans fo r m to cr eate: This task is something I refer to as the hidden cleverness, where you are doing background thinking to form new calculations, values, groupings and any other mathematical or manual treatments that really expand the variety of data available. A simple example might involve the need to create some percentage calculations in a new field, based on related quantities elsewhere within your existing data. Perhaps you have pairs of ‘start date’ and ‘end date’ values and you need to calculate the duration in days for all your records. You might use logic formula to assist in creating a new variable that summarises another – maybe something like (in language terms) IF Age < 18 THEN status = “Child”, ELSE status = “Adult”. Alternatively, you might want to create a calculation that standardised some quantities’ need to source base population figures for all the relevant locations in your data in order to convert some quantities into ‘per capita’ values. This would be particularly necessary if you anticipate wanting to map the data as this will ensure you are facilitating legitimate comparisons. T r ans fo r m to co ns o lid ate: This involves bringing in additional data to help expand (more variables) or append (more records) to enhance the editorial and representation potential of your project. An example of a need to expand your data would be if you had details about locations only at country level but you wanted to be able to group and aggregate your analysis at continent level. You could gather a dataset that holds values showing the relationships between country and continent and then add a new variable to your dataset against which you would perform a simple lookup operation to fill in the associated continent values. Consolidating by appending data might occur if you had previously acquired a dataset that now had more or newer data (specifically, additional records) available to bring it up to date. For instance, you might have started some analysis on music record sales up to a certain point in time, but once you’d actually started working on the task another week had elapsed and more data had become available. Additionally, you may start to think about sourcing other media assets to enhance your presentation options, beyond just gathering extra data. You might anticipate the potential value for gathering photos (headshots of the people in your data), icons/symbols (country flags), links to articles (URLs), or videos

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(clips of goals scored). All of these would contribute to broadening the scope of your annotation options. Even though there is a while yet until we reach that particular layer of design thinking, it is useful to start contemplating this as early possible in case the collection of these additional assets requires significant time and effort. It might also reveal any obstacles around having to obtain permissions for usage or sufficiently high quality media. If you know you are going to have to do something, don’t leave it too late – reduce the possibility of such stresses by acting early.

4.6 Data Exploration The examination task was about forming a deep acquaintance with the physical properties and meaning of your data. You now need to interrogate that data further – and differently – to find out what potential insights and qualities of understanding it could provide.

Undertaking data exploration will involve the use of statistical and visual techniques to move beyond looking at data and begin to start seeing it. You will be directly pursuing your initially defined curiosity, to determine if answers exist and whether they are suitably enlightening in nature. Often you will not know for sure whether what you initially thought was interesting is exactly that. This activity will confirm, refine or reject your core curiosity and perhaps, if you are fortunate, present discoveries that will encourage other interesting avenues of enquiry.

‘After the data exploration phase you may come to the conclusion that the data does not support the goal of the project. The thing is: data is leading in a data visualization project – you cannot make up some data just to comply with your initial ideas. So, you need to have some kind of an open mind and “listen to what the data has to say”, and learn what its potential is for a visualisation. Sometimes this means that a project has to stop if there is too much of a mismatch between the goal of the project and the available data. In other cases this may mean that the goal needs to be adjusted and the project can continue.’ Jan W i llem Tu lp , D ata E xp eri en ce D es i g n er

To frame this process, it is worth introducing something that will be covered in Chapter 5, where you will consider some of the parallels between visualisation and photography. Before committing to take a photograph you must first develop an appreciation of all the possible viewpoints that are available to you. Only then can you determine which of these is best. The notion of ‘best’ will be defined in the next chapter, but for now you need to think about identifying all the possible viewpoints in your data – to recognise the knowns and the unknowns.

W idening the Viewpoint: K nowns and Unknowns

At a news briefing in February 2002, the US Secretary of Defense, Donald Rumsfeld, delivered his infamous ‘known knowns’ statement:

Reports that say that something hasn’t happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – the ones we don’t know we don’t know. And if one looks throughout the history of our country and other free countries, it is the latter category that tend to be the difficult ones.

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There was much commentary about the apparent lack of elegance in the language used and criticism of the muddled meaning. I disagree with this analysis. I thought it was probably the most efficient way he could have articulated what he was explaining, at least in written or verbal form. The essence of Rumsfeld’s statement was to distinguish awareness of what is knowable about a subject (what knowledge exists) from the status of acquiring this knowledge. There is a lot of value to be gained from using this structure (Figure 4.8) to shape your approach to thinking about data exploration.

The known knowns are aspects of knowledge about your subject and about the qualities present in your data that you are aware of – you are aware that you know these things. The nature of these known knowns might mean you have confidence that the origin curiosity was relevant and the available insights that emerged in response are suitably interesting. You cannot afford to be complacent, though. You will need to challenge yourself to check that these curiosities are still legitimate and relevant. To support this, you should continue to look and learn about the subject through research, topping up your awareness of the most potentially relevant dynamics of the subject, and continue to interrogate your data accordingly.

Additionally, you should not just concentrate on this potentially quite narrow viewpoint. As I mentioned earlier, it is important to give yourself as broad a view as possible across your subject and its data to optimise your decisions about what other interesting enquiries might be available. This is where you need to consider the other quadrants in this diagram.

F ig u r e 4 .8 Making Sense of the Known Knowns

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On occasion, though I would argue rarely, there may be unknown knowns, things you did not realise you knew or perhaps did not wish to acknowledge that you knew about a subject. This may relate to previous understandings that have been forgotten, consciously ignored or buried. Regardless, you need to acknowledge these.

For the knowledge that has yet to be acquired – the known unknowns and the even more elusive unknown unknowns – tactics are needed to help plug these gaps as far, as deep and as wide as possible. You cannot possibly achieve mastery of all the domains you work with. Instead, you need to have the capacity and be in position to turn as many unknowns as possible into knowns, and in doing so optimise your understanding of a subject. Only then will you be capable of appreciating the full array of viewpoints the data offers.

To make the best decisions you first need to be aware of all the options. This activity is about broadening your awareness of the potentially interesting things you could show – and could say – about your data. The resulting luxury of choice is something you will deal with in the next stage.

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Exploratory Data Analysis

As I have stated, the aim throughout this book is to create a visualisation that will facilitate understanding for others. That is the end goal. At this stage of the workflow the deficit in understanding lies with you. The task of addressing the unknowns you have about a subject, as well as substantiating what knowns already exist, involves the use of exploratory data analysis (EDA). This integrates statistical methods with visual analysis to offer a way of extracting deeper understanding and widening the view to unlock as much of the potential as possible from within your data.

The chart in Figure 4.9 is a great demonstration of the value in combining statistical and visual techniques to understand your data better. It shows the results of nearly every major and many minor (full) marathon from around the world. On the surface, the distribution of finishing times reveals the common bell shape found in plots about many natural phenomenon, such as the height measurements of a large group of people. However, when you zoom in closer the data reveals some really interesting threshold patterns for finishing times on or just before the three-, four- and five-hour marks. You can see that the influence of runners setting themselves targets, often rounded to the hourly milestones, genuinely appeared to affect the results achieved.

F ig u r e 4 .9 What Good Marathons and Bad Investments Have in Common

Although statistical analysis of this data would have revealed many interesting facts, these unique patterns were only realistically discoverable through studying the visual display of the data. This is the essence of EDA but there is no instruction manual for it. As John Tukey, the father of EDA, described: ‘Exploratory data analysis is an attitude, a flexibility, and a reliance on display, not a bundle of techniques’. There is no single path to undertaking this activity effectively; it requires a number of different technical, practical and conceptual capabilities.

Ins tinct o f the analy s t: This is the primary matter. The attitude and flexibility that Turkey describes are about recognising the importance of the analyst’s traits. Effective EDA is not about the tool. There are many vendors out there pitching their devices as the magic option where we just have to ‘point and click’ to uncover a deep discovery. Technology inevitably plays a key role in facilitating this endeavour but the value of a good analyst cannot be underestimated: it is arguably more influential than

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the differentiating characteristics between one tool and the next. In the absence of a defined procedure for conducting EDA, an analyst needs to possess the capacity to recognise and pursue the scent of enquiry. A good analyst will have that special blend of natural inquisitiveness and the sense to know what approaches (statistical or visual) to employ and when. Furthermore, when these traits collide with a strong subject knowledge this means better judgments are made about which findings from the analysis are meaningful and which are not. R eas o ning : Efficiency is a particularly important aspect of this exploration stage. The act of interrogating data, waiting for it to volunteer its secrets, can take a lot of time and energy. Even with smaller datasets you can find yourself tempted into trying out myriad combinations of analyses, driven by the desire to find the killer insight in the shadows.

‘At the beginning, there’s a process of “interviewing” the data – first evaluating their source and means of collection/aggregation/computation, and then trying to get a sense of what they say – and how well they say it via quick sketches in Excel with pivot tables and charts. Do the data, in various slices, say anything interesting? If I’m coming into this with certain assumptions, do the data confirm them, or refute them?’ Alys on Hu rt, N ews Grap h i cs E d i tor, N PR

Reasoning is an attempt to help reduce the size of the prospect. You cannot afford to try everything. There are so many statistical methods and, as you will see, so many visual means for seeing views of data that you simply cannot expect to have the capacity to try to unleash the full exploratory artillery. EDA is about being smart, recognising that you need to be discerning about your tactics.

In academia there are two distinctions in approaches to reasoning – deductive and inductive – that I feel are usefully applied in this discussion:

Deductive reasoning is targeted: You have a specific curiosity or hypothesis, framed by subject knowledge, and you are going to interrogate the data in order to determine whether there is any evidence of relevance or interest in the concluding finding. I consider this adopting a detective’s mindset (Sherlock Holmes). Inductive reasoning is much more open in nature: You will ‘play around’ with the data, based on your sense or instinct about what might be of interest, and wait and see what emerges. In some ways this is like prospecting, hoping for that moment of serendipity when you unearth gold.

In this exploration process you ideally need to accommodate both approaches. The deductive process will focus on exploring further targeted curiosities, the inductive process will give you a fighting chance of finding more of those slippery ‘unknowns’, often almost by accident. It is important to give yourself room to embark on these somewhat less structured exploratory journeys.

I often think about EDA in the context of a comparison with the challenge of a ‘Where’s Wally?’ visual puzzle. The process of finding Wally feels somewhat unscientific. Sometimes you let your eyes race around the scene like a dog who has just been let out of the car and is torpedoing across a field. However, after the initial burst of randomness, perhaps subconsciously, you then go through a more considered process of visual analysis. Elimination takes place by working around different parts of the scene and sequentially declaring ‘Wally-free’ zones. This aids your focus and strategy for where to look next. As you then move across each mini-scene you are pattern matching, looking out for the giveaway characteristics of the boy wearing glasses, a red-and-white- striped hat and jumper, and blue trousers.

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The objective of this task is clear and singular in definition. The challenge of EDA is rarely that clean. There is a source curiosity to follow, for sure, and you might find evidence of Wally somewhere in the data. However, unlike the ‘Where’s Wally?’ challenge, in EDA you have the chance also to find other things that might change the definition of what qualifies as an interesting insight. In unearthing other discoveries you might determine that you no longer care about Wally; finding him no longer represents the main enquiry.

Inevitably you are faced with a trade-off between spare capacity in time and attention and your own internal satisfaction that you have explored as many different angles of enquiry as possible.

C har t ty p es : This is about seeing the data from all feasible angles. The power of the visual means that we can easily rely on our pattern-matching and sense-making capabilities – in harmony with contextual subject knowledge – to make observations about data that appear to have relevance. The data representation gallery that you will encounter in Chapter 6 presents nearly 50 different chart types, offering a broad repertoire of options for portraying data. The focus of the collection is on chart types that could be used to communicate to others. However, within this gallery there are also many chart types that help with pursuing EDA. In each chart profile, indications are given for those chart types that be particularly useful to support your exploratory activity. As a rough estimate, I would say about half of these can prove to be great allies in this stage of discovery. The visual methods used in EDA do not just involve charting, they also involve selective charting – smart charting, ‘smarting’ if you like? (No, Andy, nobody likes that). Every chart type presented in the gallery includes helpful descriptions that will give you an idea of their role and also what observations – and potential interpretations – they might facilitate. It is important to know now that the chart types are organised across five main families (categorical, hierarchical, relational, temporal, and spatial) depending on the primary focus of your analysis. The focus of your analysis will, in turn, depend on the types of data you have and what you are trying to see.

‘I kick it over into a rough picture as soon as possible. When I can see something then I am able to ask better questions of it – then the what-about-this iterations begin. I try to look at the same data in as many different dimensions as possible. For example, if I have a spreadsheet of bird sighting locations and times, first I like to see where they happen, previewing it in some mapping software. I’ll also look for patterns in the timing of the phenomenon, usually using a pivot table in a spreadsheet. The real magic happens when a pattern reveals itself only when seen in both dimensions at the same time.’ Joh n N els on, C artog rap h er, on th e valu e of vi s u ally exp lori n g h i s d ata

R es ear ch: I have raised this already but make no apology for doing so again so soon. How you conduct research and how much you can do will naturally depend on your circumstances, but it is always important to exploit as many different approaches to learning about the domain and the data you are working with. As you will recall, the middle stage of forming understanding – interpreting – is about viewers translating what they have perceived from a display into meaning. They can only do this with domain knowledge. Similarly, when it comes to conducting exploratory analysis using visual methods, you might be able to perceive the charts you make, but without possessing or acquiring sufficient domain knowledge you will not know if what you are seeing is meaningful. Sometimes the consequence of this exploratory data analysis will only mean you have become better acquainted with specific questions and more defined curiosities about a subject even if you possibly do not yet have any answers. The approach to research is largely common sense: you explore the places (books, websites) and consult the people (experts, colleagues) that will collectively give you the best chance of getting accurate answers

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to the questions you have. Good communication skills, therefore, are vital – it is not just about talking to others, it is about listening. If you are in a dialogue with experts you will have to find an approach that allows you to understand potentially complicated matters and also cut through to the most salient matters of interest. S tatis tical m etho d s : Although the value of the univariate statistical techniques profiled earlier still applies here, what you are often looking to undertake in EDA is multivariate analysis. This concerns testing out the potential existence of a correlation between quantitative variables as well as determining the possible causation variables – the holy grail of data analysis. Typically, I find statistical analysis plays more of a supporting role during much of the exploration activity rather than a leading role. Visual techniques will serve up tangible observations about whether data relationships and quantities seem relevant, but to substantiate this you will need to conduct statistical tests of significance. One of the main exceptions is when dealing with large datasets. Here the first approach might be more statistical in nature due to the amount of data obstructing rapid visual approaches. Going further, algorithmic approaches – using techniques like machine learning – might help to scale the task of statistically exploring large dimensions of data – and the endless permutations they offer. What these approaches gain in productivity they clearly lose in human quality. The significance of this should not be underestimated. It may be possible to take a blended approach where you might utilise machine learning techniques to act as an initial battering ram to help reduce the problem, identifying the major dimensions within the data that might hold certain key statistical attributes and then conducting further exploration ‘by hand and by eye’. N o thing s : What if you have found nothing? You have hit a dead end, discovering no significant relationships and finding nothing interesting about the shape or distribution of your data. What do you do? In these situations you need to change your mindset: nothing is usually something. Dead ends and discovering blind alleys are good news because they help you develop focus by eliminating different dimensions of possible analysis. If you have traits of nothingness in your data or analysis –gaps, nulls, zeroes and no insights – this could prove to be the insight. As described earlier, make the gaps the focus of your story. There is always something interesting in your data. If a value has not changed over time, maybe it was supposed to – that is an insight. If everything is the same size, that is the story. If there is no significance in the quantities, categories or spatial relationships, make those your insights. You will only know that these findings are relevant by truly understanding the context of the subject matter. This is why you must make as much effort as possible to convert your unknowns into knowns.

‘My main advice is not to be disheartened. Sometimes the data don’t show what you thought they would, or they aren’t available in a usable or comparable form. But [in my world] sometimes that research still turns up threads a reporter could pursue and turn into a really interesting story – there just might not be a viz in it. Or maybe there’s no story at all. And that’s all okay. At minimum, you’ve still hopefully learned something new in the process about a topic, or a data source (person or database), or a “gotcha” in a particular dataset – lessons that can be applied to another project down the line.’ Alys on Hu rt, N ews Grap h i cs E d i tor, N PR

N o t alw ay s need ed : It is important to couch this discussion about exploration in pragmatic reality. Not all visualisation challenges will involve much EDA. Your subject and your data might be immediately understandable and you may have a sufficiently broad viewpoint of your subject (plenty of known knowns already in place). Further EDA activity may have diminishing value. Additionally, if you

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are faced with small tables of data this simply will not warrant multivariate investigation. You certainly need to be ready and equipped with the capacity to undertake this type of exploration activity when it is needed, but the key point here is to judge when.

Summary: W orking with Data This chapter first introduced key foundations for the requisite data literacy involved in visualisation, specifically the importance of the distinction between normalised and cross-tabulated datasets as well as the different types of data (using the TNOIR mnemonic):

Textual (qualitative): e.g. ‘Any other comments?’ data submitted in a survey. Nominal (qualitative): e.g. The ‘gender’ selected by a survey participant. Ordinal (qualitative): e.g. The response to a survey question, based on a scale of 1 (unhappy) to 5 (very happy). Interval (quantitative): e.g. The shoe size of a survey participant. Ratio (quantitative): e.g. The age of a survey participant in years.

You then walked through the four steps involved in working with data:

A cq u is itio n Different sources and methods for getting your data.

Curated by you: primary data collection, manual collection and data foraging, extracted from pdf, web scraping (also known as web harvesting). Curated by others: issued to you, downloaded from the Web, system report or export, third-party services, APIs.

E x am inatio n Developing an intimate appreciation of the characteristics of this critical raw material:

Physical properties: type, size, and condition. Meaning: phenomenon, completeness.

T r ans fo r m atio n Getting your data into shape, ready for its role in your exploratory analysis and visualisation design:

Clean: resolve any data quality issues. Create: consider new calculations and conversions. Consolidate: what other data (to expand or append) or other assets could be sought to enhance your project?

E x p lo r atio n Using visual and statistical techniques to see the data’s qualities: what insights does it reveal to you as you deepen your familiarity with it?

Tips and Tactics

Perfect data (complete, accurate, up to date, truly representative) is an almost impossible standard to reach (given the presence of time constraints) so your decision will be when is good enough, good enough: when do diminishing returns start to materialise?

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Do not underestimate the demands on your time; working with data will always be consuming of your attention and effort:

Ensure you have built plenty of time into your handling of this data stage. Be patient and persevere. Be disciplined: it is easy to get swallowed up in the potential hunt for discovering things from your data, attempting to explore every possible permutation.

If your data does not already have a unique identifier it is often worth creating one to track your data preparation process. This is especially helpful if you need to preserve or revert to a very specific ordering of your data (e.g. if the rows have been carefully arranged in order to undertake cross-row calculations like cumulative or sub-totals). Clerical tasks like file management are important: maintain backups of each major iteration of data, employ good file organisation of your data and other assets, and maintain logical naming conventions. Data management practices around data security and privacy will be important in the more sensitive/confidential cases. Keep notes about where you have sourced data, what you have done with it, any assumptions or counting rules you have applied, ideas you might have for transforming or consolidating, issues/problems, things you do not understand. To learn about your data, its meaning and the subject matter to which it relates, you should build in time to undertake research in order to equip yourself suitably with domain knowledge. Anticipate and have contingency plans for the worst-case scenarios for data, such as the scarcity of data availability, null values, odd distributions, erroneous values, long values, bad formatting, data loss. Communicate. If you do not know anything about your data, ask: do not assume or stay ignorant. And then listen: always pay attention to key information. Attention to detail is of paramount importance at this stage, so get into good habits early and do not cut corners. Maintain an open mind and do not get frustrated. You can only work with what you have. If it is not showing what you expected or hoped for, you cannot force it to say something that is simply not there. Exploratory Data Analysis is not about design elegance. Do not waste time making your analysis ‘pretty’, it only needs to inform you.

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5 Establishing Your Editorial Thinking

It is very easy to introduce every chapter with claims that each of these reached is the important stage but you have really now reached a critical juncture. This is the place in the process where you need to start to commit to a definitive pathway.

The data you gathered during Chapter 4 was shaped by your trigger curiosity. You may have found qualities in the data that you feel reveal relevant insights in response to that pursuit. Alternatively, through exploring your data and researching your subject, you may have discovered new enquiries that might actually offer more interesting perspectives.

Ahead of commencing the design and development of your solution you need to decide what you are actually going to do with this data: what are you going to show your audience? This is where editorial thinking becomes important. In my view it is one of the most defining activities that separates the best visualisers from the rest, possibly even more so than technical talent or design flair.

In this this chapter you will learn about what editorial thinking means, the role it plays, what decisions you need to make and how you might do so.

5.1 W hat is Editorial Thinking? You will have noticed the common thread of curiosity that weaves its way through the preparatory activities of this workflow process. From the opening curiosity that initiated your work, you then effectively sought, gathered and became acquainted with your data in pursuit of some kind of answer. In this third stage, you will need to make some decisions. The essence of editorial thinking is demonstrating a discerning eye for what you are going to portray visually to your audience; the matter of how follows next. This stage is the critical bridge between your data work and your design work.

In the first chapter I described how a single context can hold several legitimate views of the truth. The glass that is half full of water is also half empty. It is also half full of air. Its water contents might be increasing or decreasing. Depending on your perspective, there are several legitimate ways of portraying this situation. In a nutshell, editorial thinking is about deciding which of the many viable perspectives offered by your data you will decide to focus on.

To translate this to data visualisation, assume you have data that breaks down total organisational spend across many geographic regions over time. Your profiling of your audience has already informed your thinking that the main interest is in how this has changed over time. But at this point, having looked at the data closely, you have found some really interesting patterns in the spatial analysis. What are you going to do? Are you going to show your audience how this spend compares by region on a map, having now established that this might be of interest to them, or are you going to focus on still showing how it has changed over time by region? Perhaps you could show both. Do you need to show all the regions and include all the available time periods or just focus on some specific key moments? You have got to decide what you are going to do because you are about to face the task of picking chart types, deciding on a layout, possible interactivity, and many other presentation matters.

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When trying to explain the role of editorial thinking I find it helpful to consider some of the parallels that exists between data visualisation and photography, or perhaps more specifically, photojournalism. By translating into data visualisation some of the decisions involved in taking a photograph, you will find useful perspectives to help shape your editorial thinking. In turn this will have a huge bearing on the design choices that follow. There are three particular perspectives to consider: angle, framing and focus.

‘A photo is never an objective reflection, but always an interpretation of reality. I see data visualization as sort of a new photojournalism – a highly editorial activity.’ M ori tz Stefaner, Tru th & Beau ty Op erator

Angle

Think of a chart as being a photograph of data. As with a photograph, in visualisation you cannot show everything at once. A panoramic 360° view of data is impossible to display at any moment and certainly not through the window of a single chart. You must pick an angle.

‘When the data has been explored sufficiently, it is time to sit down and reflect – what were the most interesting insights? What surprised me? What were recurring themes and facts throughout all views on the data? In the end, what do we find most important and most interesting? These are the things that will govern which angles and perspectives we want to emphasise in the subsequent project phases.’ M ori tz Stefaner, Tru th & Beau ty Op erator

In photography the angle would be formed by the position from where you are standing when taking a shot. In visualisation this relates to the angle of analysis you intend to show: what are you measuring and by which dimension(s) are you breaking it down? Are you going to show how product sales have changed over time, or how sales look organised by regional hierarchically or how they compare on a map and over time? There are many different angles you could choose. You could also choose to show data from multiple different angles using several charts presented together. Your key consideration in determining each angle is whether it is relevant and sufficient.

‘It requires the discipline to do your homework, the ability to quiet down your brain and be honest about what is interesting.’ Sarah Slobi n , Vi s u al Jou rnali s t

R elev ant: Why is it worth providing a view of your data from this angle and not another one? Why is this angle of analysis likely to offer the most relevant and compelling window into the subject for your intended audience? Is it still relevant in light of the context of the origin curiosity – that is, have definitions evolved since familiarising yourself with the data, learning about its potential qualities as well as researching the subject at large?

The judgement of relevance would be similar to the notion of newsworthiness in journalism. In that context, terms like timeliness, proximity, novelty, human interest and current prominence are all ingredients that shape what ultimately becomes news content. The ecosystem in which your work is consumed is likely to be much narrower in size and diversity than it is for a newspaper, for example. Issues of human interest and novelty will seldom have a bearing on your judgement of relevance. Therefore, I believe it is realistic to reduce the list of factors that shape your thinking about relevance to three:

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What does your intended audience want or need to know? The various characteristics of your audience’s profile, matters discussed in Chapter 1 (accessible design) and Chapter 3 (contextual circumstances), should provide a good sense of this. Sometimes, you can simply ask the members of your intended audience: you might know who they are personally or at least be able to gather information about their needs. On other occasions, with a larger audience, you might need to consider creating personas: a small number of imagined identities that may be demographically representative of the types of viewer you expect to target. Ask yourself, if you were them, what would you want to know? What makes something relevant in your context? Part of your judgement will be to consider whether relevance is a product of the normal or the exceptional; often the worthiness of an item of news is based on it being exceptional rather than going through the repeated reporting of normality. Reciting the famous journalistic aphorism, you need to determine if you are reporting news of ‘dog bites man!’ or ‘man bites dog!’. A lack of relevance is a curse that strikes a lot of visualisation work. What you often see is evidence of data that has been worked up into a visual output just because it is available and just because visual things are appealing. There is almost a scattergun approach in hoping that someone, somewhere will find a connection to justify it as relevant. What do you want your audience to know? You might have the control to decide. Although you respect the possible expressed needs of your audience you might actually be better placed to determine what is truly relevant. Depending on the context, and your proximity to the subject and its data, you might have the autonomy to dictate on what it is you want to say, more so than what you think the audience want to see. Indeed, that audience may not yet know or be sufficiently domain aware to determine for itself what is relevant or otherwise.

S u fficient: This is about judging how many angles you need. If a chart (generally) offers a single angle into your data, is that sufficiently representative of what you wish to portray? As I said earlier, you cannot show everything in one chart. Maybe you need multiple charts offering a blend of different angles of analysis to sufficiently represent the most interesting dimensions of the subject matter. Perhaps showing a view of your data over time needs to be supplemented by a spatial view to provide the context for any interpretations.

It is easy to find yourself being reluctant to commit to just a singular choice of angle. Even in a small dataset, there are typically multiple possible angles of analysis you could conduct. It is often hard to ignore the temptation of wanting to include multiple angles to serve more people’s interests.

It is important not to fall into the trap of thinking that if you throw more and more additional angles of analysis into your work you will automatically enrich that work. Just because you have 100 photographs of your holiday, that does not mean you should show me them all. When I reflect on some of the work I have created down the years, I wish I had demonstrated better selection discipline – a greater conviction to exclude angles – to avoid additional content creeping in just because it was available. I often found it far too easy to see everything as being potentially interesting. And I still do (it’s the curse of the analyst). The real art is to find just enough of those angles that respond to the core essence of your – or your inherited – curiosity.

‘I think this is something I’ve learned from experience rather than advice that was passed on. Less can often be more. In other words, don’t get carried away and try to tell the reader everything there is to know on a subject. Know what it is that you want to show the reader and don’t stray from that. I often find myself asking others “do we need to show this?” or “is this really necessary?” Let’s take it out.’ Si m on Scarr, D ep u ty Head of Grap h i cs , Th om s onR eu ters

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Framing

The next perspective to define about your editorial thinking contributes to the refinement of the angles you have selected. This concerns framing decisions. In photographic parlance this relates to choices about the field of view: what will be included inside the frame of the photograph and what will be left out?

Just like a photographer, a visualiser must demonstrate careful judgement about what to show, what not to show, and how to show it. This is effectively a filtering decision concerned with which data to include and exclude:

All category values, or just a select few? All quantitative values or just those over a certain threshold? All data or just those between a defined start and end date period?

Naturally, the type and extent of the framing you might need to apply will be influenced by the nature of your trigger curiosity, as well as factors like the complexity of the subject matter and the amount of data available to show. Further considerations like the setting (need rapid insights or OK for deeper, more prolonged engagement?) and output format will also have a bearing on this matter.

One of the key motives of framing is to remove unnecessary clutter – there is only so much that can be accommodated in a single view before it becomes too busy, too detailed, and too small in resolution. There is only so much content your audience will likely be willing and able to process. Inevitably, a balance must be struck to find the most representative view of your content. If you zoom in, filtering away too much of the content, it might hide the important context required for perceiving values. Conversely, if you avoid filtering your content you may fail to make visible the most salient discoveries.

Focus

The third component of editorial thinking concerns what you might choose to focus on. This is not a function of filtering – that is the concern of framing – it is about emphasising what is more important in contrast to what is less important.

The best photographs are able to balance light and colour, not just setting the mood of a situation but illuminating key elements within the frame that help to create depth. They provide a sense of visual hierarchy through their depth as well as the sizing and arrangement of each form.

What needs to be brought into view in the foreground, left in the mid-ground, and maybe relegated to the background simply for context or orientation? What needs to be bigger and more prominent and what can be less so?

Whereas framing judgements were about reducing clutter, this is about reducing noise. If everything in a visualisation is shouting, nothing is heard; if everything is in the foreground, nothing stands out; if everything is large, nothing is dominant.

Decisions about focus primarily concern the development of explanatory visualisations, because creating such a focus – surfacing insights through the astute use of colour or annotated accentuation – is a key purpose for that type of experience. Beyond colour, focus can be achieved through composition choices such as the way

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elements are more prominently sized and located or the way contents are positioned within a view.

5.2 The Influence of Editorial Thinking It is important to ground this discussion by explaining practically how these editorial perspectives will apply to your workflow process and, in particular, influence your design thinking.

I described a chart as being like a photograph of the data, displaying a visual answer to a data-driven curiosity. Determining the choice of chart (technically, ‘data representation’) is just one part of the overall anatomy of a data visualisation. There are choices to be made about four other design layers, namely features of interactivity, annotation, colour and composition.

Your decisions across this visualisation design anatomy are influenced, in a large way, by the editorial definitions you have will make about angle, framing and focus. They might not lead directly or solely to the final choices – there are many other factors to consider, as you have seen – but they will signpost the type of editorial qualities the visualisation will need to accommodate. Let’s look at two illustrations of the connection between editorial and design thinking to explain this.

Example 1: The Fall and Rise of us Inequality

The first example (Figure 5.1) is a chart taken from an article published in the ‘Planet Money: The Economy Explained’ section of the US-based National Public Radio (NPR) website. The article is titled ‘The Fall and Rise of U.S. Inequality in 2 Graphs’. As the title suggests the full article includes two charts, but I just want to focus on the second one for the purpose of this illustration.

F ig u r e 5 .1 The Fall and Rise of U.S. Inequality, in Two Graphs

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Editorial Perspectives

Let’s assess the editorial perspectives of angle, framing and focus as demonstrated by this work.

A ng le: The main angle of analysis can be expressed as: ‘What is the relationship between two quantitative measures (average income for the bottom 90% and for the top 1% of earners) and how has this changed over time (year)?’. This angle would be considered relevant because the relationship between the haves and the have-nots is a key indicator of wealth distribution. It is a topical and suitable choice of analysis to include with any discussion about inequality in the USA. As I mentioned there is a second chart presented so it would be reasonable to say that the two sufficiently cover the necessary angles to support the article. F r am ing : The parameters that define the inclusion and exclusion of data in the displayed analysis

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involve filters for time period (1917 to 2012) and country (just for the USA). The starting point of the data commencing from 1917 may reflect a simple arbitrary cut-off point or a significant milestone in the narrative. More likely, it probably represents the earliest available data. One always has a basic desire to always want every chart to include the most up-to-date view of data. While it only reaches as far forward in time as 2012 (despite publication in 2015) the analysis is of such historical depth that it should be considered suitably representative of the subject matter. To just focus on the USA is entirely understandable. F o cu s : The visualisation includes a ‘time slider’ control that allows users to move the focus incrementally through each year, colouring each consecutive yearly marker for emphasis. The colours are organised into three classifications to draw particular attention to two main periods of noticeably different relationships between the two quantitative measures.

Influence on D esign C hoices

How do these identified editorial perspectives translate directly into design thinking? As you will learn in Chapters 6–10 any visualisation comprises five layers of design. Let’s have a look at how they might be influenced by editorial thinking.

D ata r ep r es entatio n: The angle is what fundamentally shapes the data representation approach. In lay terms, it determines which chart type is used. In this example, the defined angle is to show the relationship between two quantitative measures over time (average income for bottom 90% vs. top 1% of earners). A suitable chart type to portray this visually is the scatter plot (as selected). As you will learn in the next chapter, the scatter plot belongs to the ‘relational’ family of chart types. Given there was also a dimension of time expressed in this angle, a chart type from the ‘temporal’ family of charts could have been used but with the main emphasis being on showing the relationships the scatter plot was the better choice. The framing perspective defines what data will be included in the chosen chart: only data for the USA and the time period 1917–2012 is displayed. Inter activ ity : As you will discover in Chapter 7, the role of interactivity is to enable adjustments to what data is displayed and how it is displayed. The sole feature of interactivity in this project is offered through the ‘time slider’ control, which sequences the unveiling of the data points year by year in either a manual or automated fashion. The inclusion of such interactivity can be influenced by the editorial decisions concerning focus: unveiling the yearly values sequences the emphasis on the position – and emerging pattern – of each consecutive value. A nno tatio n: The primary chart annotations on show here are the two arrows and associated captions, drawing attention to the two prominent patterns that support the general fall and then rise of inequality. Again, the inclusion of the captions would be a consequence of editorial thinking (focus) determining these respective patterns in the data should be emphasised to the viewer. C o lo u r : As you will learn about in Chapter 9, one of the key applications of colour is to support editorial salience – how to emphasise content and direct the eye. As before, editorial focus would influence the decision to deploy four colour states within the chart: a default colour to show all points at the start of the animation and then three different emerging colours to separate the three clustered groups visually. Note that the final colour choices of red, green and orange tones are not directly informed by editorial thinking, as the identified value of using four different ones to draw out the focus is what drives this choice. C o m p o s itio n: This concerns all of the physical layout, shape and size decisions. In this example, the dimensions of editorial thinking have had limited influence over the composition choices. Although,

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recognising again that there are two charts in the full article, the focus perspective would have likely informed the decision to sequence the ordering of the charts: what made better sense to go first or last and why?

Example 2: W hy Peyton Manning’s Record W ill Be Hard to Beat

In this second example, published on ‘TheUpshot’ section of the New York Times website, there are three charts presented in an article titled ‘Why Peyton Manning’s Record Will Be Hard to Beat’. Here I will look at all three charts.

Editorial Perspectives

Again, let’s assess the editorial perspectives of angle, framing and focus as demonstrated by this work.

F ig u r e 5 .2 Why Peyton Manning’s Record Will Be Hard to Beat

A ng le: The first chart (Figure 5.2) displays the angle of analysis expressed as ‘How have quantitative values (NFL touchdown passes) broken down by category (quarterbacks) changed over time (year)?’. This analysis was relevant at the time due to the significance of Peyton Manning setting a new record for NFL quarterback touchdown passes, an historic moment and, according to the article, ‘evidence of how much the passing game has advanced through the history of the game’. Inspired by this achievement, the question posed by this article overall is whether the record will ever be bettered – which would have likely been the origin curiosity that drove the visualisation project in the first place. The article was time relevant because the record had just been achieved. On its own, this analysis would be deemed insufficient to support the overarching enquiry, as evidenced by the inclusion of two further charts that we will look at shortly. F r am ing : The parameters that define the inclusion and exclusion framing relate to the time period (1930 to 19 October 2014) and qualifying quantitative threshold (minimum of 30 touchdown passes). It is representative of the truth at the moment of production (i.e. up to 19 October 2014) though clearly the data would no longer be up to date as soon as the next round of games took place. The judgment of the 30 touchdown passes threshold would either be informed by knowledge of the sport

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(and 30 TDs being a common measure) or more likely influenced by the shape of the data for every quarterback, indicating that it was a logical cut-off value. F o cu s : The chart emphasises the record holder as well as the other current players in order to orientate the significance of the achievement and to highlight other contemporary players who could have a chance of pursuing this record. It also emphasises previous record holders or noted players to show just how special the new record is. If you want to know the achievements of any other player, their career ‘lines’ and values come into focus through mouseover-driven interactivity.

F ig u r e 5 .3 Why Peyton Manning’s Record Will Be Hard to Beat

In the second chart (Figure 5.3), the same definitions stand for the angle and framing, but the focus has changed. This chart shows the same angle of analysis as seen in the first chart but is now composed of several small repeated charts, each one focusing on the career trajectories of a selected previous record holder.

F o cu s : Colour is used to emphasise the previous record-holding players’ career lines with an illuminating background banding used to display the duration/era of their record standing. Value labels show the number of touchdowns achieved.

The final chart (Figure 5.4) has many similarities with the first chart. Once again it maintains the same consistent definition for framing and it has the same focus as the first chart but now there is a subtle difference in angle.

A ng le: This is now expressed as: ‘How have cumulative quantitative values (NFL touchdown passes) broken down by category (quarterbacks) changed over time (age)?’. The difference is the time measure being about age, not year. This is relevant as it provides an alternative view of the time measure, switching year for age to continue pursuing the curiosity over how long Manning’s record might last. More specifically it enquires if ‘the quarterback who will surpass Manning’s record is playing today?’. Incidentally, as the article concludes, it is going to be a very difficult record to beat.

F ig u r e 5 .4 Why Peyton Manning’s Record Will Be Hard to Beat

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Influence on D esign C hoices

Now, let’s switch the viewpoint again and look at how this visualisation’s design choices are directly informed by the editorial thinking.

D ata r ep r es entatio n: As I have stated, the angle and framing dimensions are hugely influential in the reasoning of chart type requirements. In each of the charts used we are being shown different perspectives around the central theme of how touchdown passes have changed over time for each qualifying quarterback. A line chart showing cumulative values for all the players was the most appropriate way of portraying this. Naturally, the line chart belongs to the ‘temporal’ family of chart types. Alternative angles of analysis may have explored the relationship angle between the measures of age and total touchdown passes. A scatter plot would have been ideal to display that angle, but the inclusion of the cumulative touchdown passes statistic, as portrayed using the line, made for a much more striking display of the trajectories. Inter activ ity : The only feature of interaction determined necessary here is achieved through a mouseover event in the first and third charts to reveal the names and total passes for any of the players who are presented as grey lines. This serves the interests of viewers who want to identify these background data values for ‘everyone else’. By introducing value labels only through interactivity it also means the busy-ness of labelling all values by default could be elegantly – and wisely – avoided. A nno tatio n: This interactive labelling is a joint decision concerned with annotation. Elsewhere, the decision to include permanent annotated labels in each chart for category (player) and value (touchdown passes) provides emphasis in the first and third charts on the career achievements of Peyton Manning, the other current quarterbacks, and previous record holders. The second chart only labels the respective record holders who are the subject of each separate display. C o lo u r : The approach to creating focus is further achieved with colour. In the main chart, emphasis is again drawn to Peyton Manning’s line, as the record holder (thick blue line), other current players (highlighted with a blue line) as well as previous record holders or noted players (dark grey line). For the

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second chart the light-blue coloured banding draws out the period of the records held by selected players down the years. This really helps the viewer to perceive the duration of their records. C o m p o s itio n: The further influence of the editorial decisions for focus would be seen through the sequencing of the charts in the article. Given the rigid dimensions of space in which the article exists, the decision to order the charts in the way they are presented will have been informed by the desired narrative that was required to present analysis to support the articulated statement in the title.

A closing point to make here is that the influence of editorial thinking does not just flow forwards into the design stages. Although presented as separate, consecutive stages, ‘working with data’ and ‘editorial thinking’ are strongly related and quite iterative: working with data influences your editorial perspectives; and your editorial perspectives in turn may influence activities around working with data. In the earlier stages of your development it is useful to create this sequential distinction in activities but in reality there will be much toing and froing. The data transformation activity, in particular, is essentially the key wormhole that links these two stages. Editorial definitions may trigger the need for more data to be gathered about the specific subject matter or some consolidation in detail to support the desired angles of analysis and the framing dimensions. The acquisition of new data will always then trigger a need to repeat the data examination activity. Editorial definitions might also influence the need for further calculations, groupings or general modifications to refine its preparedness for displaying the analysis.

Summary: Establishing Your Editorial Thinking In this chapter you learnt about the three perspectives that underpin your editorial thinking.

A n gle

Must be relevant in its potential interest for your audience. Must have sufficient quantities to cover all relevant views – but no more than required.

Framin g

Applying filters to your data to determine the inclusion and exclusion criteria. Framing decisions must provide access to the most salient content but also avoid any distorting of the view of the data.

Focus

Which features of the display to draw particular attention to? How to organise the visibility and hierarchy of the content?

Tips and Tactics

Data shapes the story, not the other way round: maintain this discipline throughout your work. If your data was especially riddled with gaps, perhaps consider making this the story: inverting attention towards the potential consequence, cause and meaning behind these gaps? There is always something interesting in your data: you just might not be equipped with sufficient domain knowledge to know this or it may not be currently relevant. Get to know the difference between relevant and irrelevant by researching and learning more about your subject.

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Communication: ask people better placed than you, who might have the subject knowledge, about what is truly interesting and relevant. A good title will often express the main curiosity or angle of analysis from the outset, giving viewers a clear idea about what the visualisation that follows will aim to answer or reveal.

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Part C Developing Your Design Solution

T he P r o d u ctio n C y cle

Within the four stages of the design workflow there are two distinct parts. The first three stages, as presented in Part B of this book, were described as ‘The Hidden Thinking’ stages, as they are concerned with undertaking the crucial behind-the-scenes preparatory work. You may have completed them in terms of working through the book’s contents, but in visualisation projects they will continue to command your attention, even if that is reduced to a background concern.

You have now reached the second distinct part of the workflow which involves developing your design solution. This stage follows a production cycle, commencing with rationalising design ideas and moving through to the development of a final solution.

The term cy cle is appropriate to describe this stage as there are many loops of iteration as you evolve rapidly between conceptual, practical and technical thinking. The inevitability of this iterative cycle is, in large part, again due to the nature of this pursuit being more about optimisation rather than an expectation of achieving that elusive notion of perfection. Trade-offs, compromises, and restrictions are omnipresent as you juggle ambition and necessary pragmatism.

How you undertake this stage will differ considerably depending on the nature of your task. The creation of a relatively simple, single chart to be slotted into a report probably will not require the same rigour of a formal production cycle that the development of a vast interactive visualisation to be used by the public would demand. This is merely an outline of the most you will need to do – you should edit, adapt and participate the steps to fit with your context.

There are several discrete steps involved in this production cycle:

Conceiving ideas across the five layers of visualisation design. Wireframing and storyboarding designs. Developing prototypes or mock-up versions. Testing. Refining and completing. Launching the solution.

Naturally, the specific approach for developing your design solution (from prototyping through to launching) will vary hugely, depending particularly on your skills and resources: it might be an Excel chart, or a Tableau dashboard, an infographic created using Adobe Illustrator, or a web-based interactive built with the D3.js library. As I have explained in the book’s introduction, I’m not going to attempt to cover the myriad ways of implementing a solution; that would be impossible to achieve as each task and tool would require different instructions.

For the scope of this book, I am focusing on taking you through the first two steps of this cycle – conceiving ideas and wireframing/storyboarding. There are parallels here with the distinctions between architecture (design) and engineering (execution) – I’m effectively chaperoning you through to the conclusion of your design thinking.

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To fulfil this, Part C presents a detailed breakdown of the many design options you will face when conceiving your visualisation design and provides you with an appreciation of the key factors that will influence the actual choices you make. The next few chapters are therefore concerned with the design thinking involved with each of these five layers of the visualisation design anatomy, namely:

Chapter 6: Data representation Chapter 7: Interactivity Chapter 8: Annotation Chapter 9: Colour Chapter 10: Composition

The sequencing of these layers is deliberate, based on the need to prioritise your attention: what will be included and how will it appear. Initially, you will need to make decisions about what choices to make around data representation (charts), interactivity and annotation. These are the layers that result in visible design content or features being included in your work. You will then complete your design thinking by making decisions about the appearance of these visible components, considering their colour and composition.

C o nceiv ing : This will cover all your initial thinking across the various layers of design covered in the next few chapters. The focus here is on conceiving ideas based on the design options that seem to fit best with the preparatory thinking that has gone before during the first three stages. As you fine-tune your emerging design choices the benefit of sketching re-emerges, helping you articulate your thoughts into a rough visual form. As mentioned in Chapter 3, for some people the best approach involves sketching with the pen, for others it is best expressed through the medium of technical fluency. Whichever approach suits you best, it is helpful to start to translate your conceptual thinking into visual thinking, particularly when collaborating. This sketching might build on your instinctive sketched concepts from stage 1, but you should now be far better informed about the realities of your challenge to determine what is relevant and feasible.

‘I tend to keep referring back to the original brief (even if it’s a brief I’ve made myself) to keep checking that the concepts I’m creating tick all the right boxes. Or sometimes I get excited about an idea but if I talk about it to friends and it’s hard to describe effectively then I know that the concept isn’t clear enough. Sometimes just sleeping on it is all it takes to separate the good from the bad! Having an established workflow is important to me, as it helps me cover all the bases of a project, and feel confident that my concept has a sound logic.’ S tefanie P o s av ec, Info r m atio n D es ig ner

W ir efr am ing and s to r y b o ar d ing : Wireframing involves creating a low-fidelity illustration of the potential layout for those solutions that will generally occupy a single page of space, such as a simple interactive visualisation or an infographic. There is no need to be too precise just yet, you are simply mapping out what will be on your page/screen (charts, annotations), how they will be arranged and what things (interactive functions) it will do. If your project is going to require a deeper architecture, like a complex interactive, or will comprise sequenced views, like presentations, reports or animated graphics, each individual wireframe view will be weaved together using a technique called storyboarding. This maps out the relationships between all the views of your content to form an overall visual structure. Sometimes you might approach things the other way round, beginning with a high-level storyboard to provide a skeleton structure within which you can then form your more detailed thinking about the specific wireframe layouts within each page or view.

P r o to ty p es / m o ck - u p s : Whereas wireframing and storyboarding are characterised by the creation of low-fi ‘blueprints’, the development of mock-ups (for example, Figure C.1) or prototypes (the terms tend to be used

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interchangeably) involves advancing your decisions about the content and appearance of your proposed solution. This effectively leads to the development of a first working version that offers a reasonably close representation of what the finished product might look like.

F ig u r e C .1 Mockup designs for ‘Poppy Field’

T es ting : Once you have an established prototype version, you must then seek to have it tested. Firstly, you do this ‘internally’ (i.e. by you or by collaborators/colleagues) to help iron out any obvious immediate problems. In software development parlance, this would be generally consistent with alpha testing. Naturally, beta follows alpha and this is where you will seek others to test it, evaluate it, and feedback on it. This happens regardless of the output format; it doesn’t need to be a digital, interactive project to merit being tested. There will naturally be many different aspects to your proposed solution that will need checking and evaluating. The three principles of good visualisation design that I presented earlier offer a sensible high-level structure to guide this testing:

Trustworthy design testing concerns assessing the reliability of the work, in terms of the integrity of its content and performance. Are there any inaccuracies, mistakes or even deceptions? Are there any design choices that could lead to misunderstandings? Any aspects in how the data has been calculated or counted that could undermine trust? If it is a digital solution, what is the speed of loading and are there any technical bugs or errors? Is it suitably responsive and adaptable in its use across different platforms? Try out various user scenarios: multiple and concurrent users, real-time data, all data vs sample data, etc. Ask the people testing your solution to try to break it so you can find and resolve any problems now. Accessible design testing relates to how intuitive or sufficiently well explained the work is. Do they understand how to read it and what all the encodings mean? Is the viewer provided with a sufficient level of assistance that would be required as per the characteristics of the intended audience? Can testers find the answers to the questions you intended them to find and quickly enough? Can they find answers to the questions they think are most relevant? Elegant design testing relates to questions such as: Is the solution suitably appealing in design? Are there any features which are redundant or superfluous design choices that are impeding the process of using the solution?

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Who you invite to test your work will vary considerably from one project to the next but generally you will have different possible people to consider participating in this task:

Stakeholders: the ultimate customers/clients/colleagues who have commissioned the work may need to be included in this stage, if not for full testing then at least to engage them in receiving initial concept feedback. Recipients: you might choose a small sample of your target audience and invite those viewers to take part in initial beta testing. Critical friends: peers/team/colleagues with suitable knowledge and appreciation about the design process may offer a more sophisticated capacity to test out your work. You: sometimes (often) it may ultimately be down to you to undertake the testing, through either lack of access to other people or most typically a simple lack of time. To accomplish this effectively you have to find a way almost to detach yourself from the mindset of the creator and occupy that of the viewer: you need to see the wood and the trees.

The timing of when to seek feedback through testing/evaluation will vary across different contexts again. Sometimes the pressure from stakeholders who request to see progress will determine this. Otherwise, you will need to judge carefully the right moment to do so. You don’t want to get feedback when it is too late to change or you have invested too much effort creating a prototype that might require widespread changes in approach. Likewise, it can be risky showing far-too-undercooked concepts to stakeholders or testers when they might not have the capacity to realise this is just an early indication of the direction of travel. The least valuable form of testing feedback is when pedantic stakeholders spend time pointing out minutiae that of course need correcting but have no significance at this stage. No-one comes away with anything of value from this kind of situation.

‘We can kid ourselves that we are successful in what we “want” to achieve, but ultimately an external and critical audience is essential. Feedback comes in many forms; I seek it, listen to it, sniff it, touch it, taste it and respond.’ K ate M cL ean, S m ells cap e M ap p er and S enio r L ectu r er G r ap hic D es ig n

R efining and co m p leting : Based on the outcome of your testing process, this will likely trigger a need to revisit some of the issues that have emerged and resolve them satisfactorily. Editing your work involves:

correcting issues; stripping away the superfluous content; checking and enhancing preserved content; adding extra degrees of sophistication to every layer of your design; improving the consistency and cohesion of your choices; double-checking the accuracy of every component.

As your work heads towards a state of completion your mindset will need to shift from a micro-level checking back to a macro-level assessment of whether you have truly delivered against the contextual requirements and purpose of your project.

In any creative process a visualiser is faced with having to declare work as being complete. Judging this can be quite a tough call to make in many projects. As I have discussed plenty of times, your sense of ‘finished’ often needs to be based on when you have reached the status of good enough. While the presence of a looming deadline (and at times increasingly agitated stakeholders) will sharpen the focus, often it comes down to a

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fingertip sense of when you feel you are entering the period of diminishing returns, when the refinements you make no longer add sufficient value for the amount of effort you invest in making them.

‘You know you’ve achieved perfection in design, not when you have nothing more to add, but when you have nothing more to take away.’ A nto ine d e S aint- E x u p ér y , W r iter , P o et, A r is to cr at, Jo u r nalis t, and P io neer ing A v iato r

‘Admit that nothing you create on a deadline will be perfect. However, it should never be wrong. I try to work by a motto my editor likes to say: No Heroics. Your code may not be beautiful, but if it works, it’s good enough. A visualisation may not have every feature you could possibly want, but if it gets the message across and is useful to people, it’s good enough. Being “good enough” is not an insult in journalism – it’s a necessity.’ L ena G r o eg er , S cience Jo u r nalis t, D es ig ner and D ev elo p er at P r o P u b lica

‘It was intimidating to release to the public a self-initiated project on such a delicate subject considering some limitation with content and data source. But I came to appreciate that it’s OK to offer a relevant way of looking at the subject, rather than provide a beginning-to-end conclusion.’ V alentina D ’efilip p o , Info r m atio n D es ig ner , d is cu s s ing her ‘P o p p y F ield ’ p r o ject that lo o k ed at the his to r y o f w o r ld co nflicts and the r es u lting lo s s o f life

L au nching : The nature of launching work will again vary significantly based, as always, on the context of your challenge. It may simply be emailing a chart to a colleague or you might be presenting your work to an audience. For other cases it could be a graphic going to print for a newspaper or involve an anxious go-live moment with the launch of a digital project on a website, to much fanfare and public anticipation. Whatever the context of your ‘launch’ stage, there are a few characteristic matters to bear in mind – these will not be relevant to all situations but over time you might need to consider their implications for your setting:

Are you ready? Regardless of the scope of your work, as soon as you declare work completed and published you are at the mercy of your decisions. You are no longer in control of how people will interpret your work and in what way they will truly use it. If you have particularly large, diverse and potentially emotive subject matter, you will need to be ready for the questions and scrutiny that might head in your direction. Communicating your work is a big deal. The need to publicise and sell its benefits is of particular relevance if you have a public-facing project (you might promote it strongly or leave it as a slow burner that spreads through ‘word of mouth’). For more modest and personal audiences you might need to consider directly presenting your work to these groups, coaching them through what it offers. This is particularly necessary on those occasions when you may be using a less than familiar representation approach. What ongoing commitment exists to support the work? This clearly refers to specific digital projects. Do you have to maintain a live data feed? Will it need to sustain operations with variable concurrent visitors? What happens if it goes viral – have you got the necessary infrastructure? Have you got the luxury of ongoing access to the skill sets required to keep this project alive and thriving? Will you need to revise, update and rerelease the project? As I discussed in the contextual circumstances, will you need to replicate this work on a repeated basis? What can you do to make the reproduction as seamless as possible? What is the work’s likely shelf life? Does it have a point of expiry after which it could be archived or even killed? How might you digitally preserve it beyond its useful lifespan?

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6 Data Representation

In this chapter you will explore in detail the first, and arguably the most significant, layer of the visualisation design anatomy: data representation. This is concerned with deciding in what visual form you wish to show your data.

To really get under the skin of data representation, we are going to look at it from both theoretical and pragmatic perspectives. You will start by learning about the building blocks of visual encoding, the real essence of this discipline and something that underpins all data representation thinking. Whereas visual encoding is perhaps seen as the purist ‘bottom-up’ viewpoint, the ‘top-down’ perspective possibly offers more pragmatic value by framing your data representation thinking around the notion of chart types. For most people facing up to this stage of data representation, this is conceptually the more practical entry point from which to shape their decisions.

To substantiate your understanding of this design layer you will take a tour through a gallery of 49 different chart type options, reflecting the many common and useful techniques being used to portray data visually in the field today. This gallery will then by supplemented by an overview of the key influencing factors that will inform and determine the choices you make.

6.1 Introducing Visual Encoding As introduced in the opening chapter, data representation is the act of giving visual form to your data. As viewers, when we are perceiving a visual display of data we are decoding the various shapes, sizes, positions and colours to form an understanding of the quantitative and categorical values represented. As visualisers, we are doing the reverse through visual encoding, assigning visual properties to data values. Visual encoding forms the basis of any chart or map-based data representation, along with the components of chart apparatus that help complete the chart display.

There are many different ways of encoding data but these always comprise combinations of two different properties, namely marks and attributes. Marks are visible features like dots, lines and areas. An individual mark can represent a record or instance of data (e.g. your phone bill for a given month). A mark can also represent an aggregation of records or instances (e.g. a summation of individual phone charges to produce the bill for a given month). A set of marks would therefore represent a set of records or instances (e.g. the 12 monthly phone bills for 2015).

Attributes are variations applied to the appearance of marks, such as the size, position, or colour. They are used to represent the values held by different quantitative or categorical variables against each record or instance (or, indeed, each aggregation). If you had 12 marks, one for each phone bill during 2015, you could use the size attribute of each mark to represent the various phone bill totals.

Figure 6.1 offers a more visual illustration. In the dataset there are six records, one for each record listed. ‘Gender’ is a categorical variable and ‘Years Since First Movie’ is a quantitative variable. ‘Male’ and ‘43’ are the specific values of these variables associated with Harrison Ford. In the associated chart, each actor from the table is represented by the mark of a line (or bar). This represents their record or instance in the table. Harrison Ford’s bar is proportionally sized in scale to represent the 43 years since his first movie and is coloured purple

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to distinguish his gender as ‘Male’. Each of the five other actors similarly has a bar sized according to the years since their first movie and coloured according to their gender.

F ig u r e 6 .1 Illustration of Visual Encoding

The objective of visual encoding is to find the right blend of marks and attributes that most effectively will portray the angle of analysis you wish to show your viewers. The factors that shape your choice and define the notion of what is considered ‘effective’ are multiple and varied in their influence. Before getting on to there, let’s take a closer look at the range of different marks and attributes that are commonly found in the data representation toolkit.

It is worth noting upfront that while the organisation of the ‘attributes’, in particular, suggests a primary role, several can be deployed to encode both categorical (nominal, ordinal) variables and quantitative variables. Furthermore, as you see in the bar chart in Figure 6.1, combinations of several attributes are often applied to marks (such as colour and size) to encode multiple values.

Although beyond the scope of this book, there are techniques being developed in the field exploring the use of non-visual senses to portray data, using variations in properties for auditory (sound), haptic (touch), gustatory (taste) and olfactory (smell) senses.

F ig u r e 6 .2 List of Mark Encodings

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F ig u r e 6 .3 List of Attribute Encodings

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Grasping the basics of visual encoding and its role in data visualisation is one of the fundamental pillars of understanding this discipline. However, when it comes to the reality of considering your data representation options you do not necessarily need to always approach things from this somewhat bottom-up perspective. For most people’s needs when creating a data visualisation it is more pragmatic (and perhaps more comprehensible) to think about data representation from a top-down perspective in the shape of chart types.

If marks and attributes are the ingredients, a chart ‘type’ is the recipe offering a predefined template for displaying data. Different chart types offer different ways of representing data, each one comprising unique combinations of marks and attributes onto which specific types of data can be mapped.

Recall that I am using chart type as the all-encompassing term, though this is merely a convenient singular label to cover any variation of map, graph, plot and diagram based around the representation of data.

Let’s work through a few examples to illustrate the relationship between some selected chart types demonstrating different combinations of marks and attributes.

To begin with Figure 6.4, visualises the recent fortunes of the world’s billionaires. The display shows the relative ranking of each profiled billionaire in the rich list, grouping them by the different sectors of industry in which they have developed their wealth. This data is encoded using the point mark and two attributes of position. The point in this deployment is depicted using small caricature face drawings representative of each individual – effectively unique symbols to represent the distinct ‘category’ of each different billionaire. Note that these are points, as distinct from area marks, because their size is constant and insignificant in terms of any quantitative implication. The position in the allocated column signifies the industry the individuals are associated with, while the vertical position signifies the rank (higher position = higher rank towards number 1).

For reference, this is considered a derivative of the univariate scatter plot, which usually shows the dispersal of a range of absolute values rather than rank.

F ig u r e 6 .4 Bloomberg Billionaires

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As seen in Chapter 1, the clustered bar chart in Figure 6.5 displays a series of line marks (normally described as bars). There are 11 pairs of bars, one for each of the football seasons included in the aggregated analysis. The attribute of colour is used to distinguish the bars between the two quantitative measures displayed: blue is for ‘games’, purple is for ‘goals’. The size dimension of ‘height’ (the widths are constant) along the y-axis scale then represents the quantitative values associated with each season and each measure.

Figure 6.6 is called a bubble chart and displays a series of geometric area marks to represent the top 100 blog posts on my website based on their popularity over the previous 100 days. Each circle represents an individual post and is sized to show the quantitative value of ‘total visits’ and then coloured according to the seven different post categories I use to organise my content.

F ig u r e 6 .5 Lionel Messi: Games and Goals for FC Barcelona

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F ig u r e 6 .6 Image from the home page of visualisingdata.com

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F ig u r e 6 .7 How the Insane Amount of Rain in Texas Could Turn Rhode Island Into a Lake

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Figure 6.7 demonstrates the use of the form, which is more rarely used. My advice is that it should remain that way as it is hard for us to judge scales of volume in 2D displays. However, it can be of merit when values are extremely diverse in size as in this good example. The chart displayed contextualises the amount of water that had flowed into Texas reservoirs in the 30 days up to 27 May 2015. The size (volume) of a cube is used to display the amount of rain, with 8000 small cubes representing 1000 acre-feet of water (43,560,000 cubic feet or 1233.5 mega litres) to create the whole (8 million acre-feet), which is then compared against the heights of the Statue of Liberty and what was then the world’s tallest building, the Burj Khalifa, to orient in height terms at least.

6.2 Chart Types For many people, creating a visualisation involves using tools that offer chart menus: you might select a chart type and then ‘map’ the records and variables of data against the marks and attributes offered by that particular

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chart type. Different tools will offer the opportunity to work with a different range of chart types, some with more than others.

As you develop your capabilities in data visualisation and become more ‘expressive’ – trying out unique combinations of marks and attributes – your approach might lean more towards thinking about representation from a bottom-up perspective, considering the visual encodings you wish to deploy and arriving at a particular chart type as the destination rather than an origin. This will be especially likely if you develop or possess a talent for creating visualisations through programming languages.

As the field has matured over the years, and a greater number of practitioners have been experimenting with different recipes of marks and attributes, there is now a broad range of established chart types. Once again I hesitate to use the universal label of chart type (some mapping techniques are not chart types per se) but it will suffice. While all of us are likely to be familiar with the ‘classic three’ – namely, the bar, pie and line chart – there are many other chart type options to consider.

To acquaint you with a broader repertoire of charting options, over the coming pages I present you with a gallery. This offers a curated collection of some of the common and useful chart types being used across the field today. This gallery aims to provide you with a valuable reference that will directly assist your judgements, helping you to pick (conceptually, at least) from a menu of options.

I have attempted to assign each chart to one of five main families based on their primary analytical purpose. What type of angle of analysis does each one principally show? Using the five-letter mnemonic CHRTS this should provide a useful taxonomy for organising your thinking about which chart or charts to use for your data representation needs.

I know what you’re thinking: ‘well that’s a suspiciously convenient acronym’! Honestly, if it was as intentional as that I would have tried harder to somehow crowbar in an ‘A’ family. OK, I did spend a lot of time, but I couldn’t find it and it’s now my life’s ambition to do so. Only then will my time on this planet have been truly worthwhile. In the meantime, CHRTS is close enough. Besides, vowels are hugely overrated.

Each chart type presented is accompanied by an array of supporting details that will help you fully acquaint yourself with the role and characteristics of each option.

A few further comments about what this gallery provides:

The primary name used to label each chart type as well as some further alternative names that are often used An indication of which CHRTS family each chart belongs to, based on their specific primary role, as well as a sub-family definition for further classification

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An indicator for each chart type to show which ones I consider to be most useful for undertaking Exploratory Data Analysis (the black magnifying glass symbol) An indicator for whether I believe a chart would typically require interactive features to offer optimum usability (the black cursor symbol) A description of the chart’s representation: what it shows and what encodings (marks, attributes) it is comprised of A working example of the chart type in use with a description of what it specifically shows A ‘how to read’ guide, advising on the most effective and efficient approach to making sense of each chart type and what features to look out for Presentation tips offering guidance on some of the specific choices to be considered around interactivity, annotation, colour or composition design ‘Variations and alternatives’ offer further derivatives and chart ‘siblings’ to consider for different purposes

E x clu s io ns : It is by no means an exhaustive list: the vast permutations of different marks and attributes prevents any finite limit to how one might portray data visually. I have, however, consciously excluded some chart types from the gallery mainly because they were not different enough from other charts that have been profiled in detail. I have mentioned charts that represent legitimate derivatives of other charts where necessary but simply did not deem it worthy to assign a whole page to profile them separately. The Voronoi treemap, for example, is really just a circular treemap that uses different algorithms to arrange its constituent pieces. While the construction task is different, its usage is not. The waterfall chart is a single stacked bar chart broken down into sequenced stages. Inclu s io ns : I have wrestled with the rights and wrongs of including some chart types, unquestionably. The radar chart, for example, has many limitations and flaws but is not entirely without merit if deployed in a very specific way and only for certain contexts. By including profiles of partially flawed charts like these I am using the gallery as much to signpost their shortcomings so that you know to use them sparingly. There will be some purists gathering in angry mobs and foaming at the mouth in reaction to the audacity of my including the pie chart and word cloud. These have limited roles, absolutely, but a role nonetheless. Put down your pitchforks, return to your homes and have a good read of my caveats. Rather than being the poacher of all bad stuff, I think a gamekeeper role is equally important. Although I have excluded several charts on grounds of demonstrating only a slight variation on profiled charts, there are some types included that do exhibit only small derivations from other charts (such as the bar chart and the clustered bar, or the scatter plot and the bubble plot). In these cases I felt there was sufficient difference in their practical application, and they were in common usage, to merit their separate inclusion, despite sharing many similarities with other profiled siblings.

‘Interestingly, visualisations of textual data are not as developed as one would expect. There is a great need for such visualisations given the amount of textual information we generate daily, from social media to news media and so on, not to mention all the materials generated in the past and that are now digitally available. There are opportunities to contribute to the research efforts of humanists as well as social scientists by devising ways to represent not only frequencies of words and topics, but also semantic content. However, this is not at all trivial.’ Is abel M ei relles , Profes s or, OC AD Uni vers i ty ( Toronto) , d i s cu s s i ng one of th e m an y rem ai ni ng u nknowns i n vi s u ali s ati on

C ateg o r ical co m p ar is o ns : All chart types can feasibly facilitate comparisons between categories, so

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why have a separate C family? Well, the distinction is that those charts belonging to the H, R, T and S families offer an additional dimension of analysis as well as providing comparison between categories. D u al fam ilies : Some charts do not fit just into a single family. Showing connected relationships (e.g. routes or flows) on a map is ticking the requirements across at least two or family groups (Relational, Spatial). In each case I have tried to best-fit the family classifications around the primary angle of analysis portrayed by each chart – what is the most prominent aspect that characterises each representation technique. T ex t v is u alis atio n: As I noted in the discussion about data types, when it comes to working with textual-based data you are almost always going to need to perform some transformation, maybe through value extraction or by applying a statistical technique. The text itself can otherwise largely function only as an annotated device. Chart types used to visualise text actually visualise the properties of text. For example, the word cloud visualises the quantitative frequency of the use of words: text might be the subject, but categories (words) and their quantities (counts) are the data mappings. Varieties of network diagrams might show the relationship between word usage, such as the sequence of words used in sentences (word trees), but these are still only made possible through some quantitative, categorical or semantic property being drawn from the original text. D as hb o ar d : These methods are popular in corporate settings or any context where you wish to create instrumentation that offers both at-a-glance and detailed views of many different analytical and information monitoring dimensions. Dashboards are not a unique chart type themselves but rather should be considered projects that comprise multiple chart types from across the repertoire of options presented in the gallery. Some of the primary demands of designing dashboards concern editorial thinking (what angles to show and why) and composition choices (how to get it all presented in a unified page layout). S m all m u ltip les : This is an invaluable technique for visualising data but not necessarily a chart type per se and, once again, more a concern for about editorial thinking and composition design. Small multiples involve repeated display of the same chart type but with adjustments to the framing of the data in each panel. For example, each panel may show the same angle of analysis but for different categories or different points in time. Small multiples are highly valued because they exploit the capabilities of our visual perception system when it comes to comparing charts in a simultaneous view, overcoming our weakness at remembering and recalling chart views when consumed through animated sequences or across different pages. A no te ab o u t ‘s to r y telling ’: Storytelling is an increasingly popular term used around data visualisation but I feel it is often misused and misunderstood, which is quite understandable as we all have different perspectives. I also feel it is worth clarifying my take on what I believe storytelling means practically in data visualisation and especially in this discussion about data representation, which is where it perhaps most logically resides in terms of how it is used. Stories are constructs based on the essence of movement, change or narrative. A line chart shows how a series of values have changed over a temporal plane. A flow map can reveal what relationships exist across a spatial plane between two points separated by distance – they may be evident of a journey. However, aside from the temporal and spatial families of charts, I would argue that no other chart family realistically offers this type of construct in and of itself. The only way to create a story from other types of charts is to incorporate a temporal dimension (video/slideshow) or provide a verbal/written narrative that itself involves a dimension of time through the sequence of its delivery. For example, a bar chart alone does not represent a story, but if you show a ‘before’ and ‘after’ pair of bar charts side by side or between slides, you have essentially created ‘change’ through sequence. If you

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show a bar chart with a stack on top of it to indicate growth between two points in time, well, you have added a time dimension. A network diagram shows relationships, but stood alone this is not a story – its underlying structure and arrangement are in abstract space. Just as you do when showing friends a photograph from your holiday, you might use this chart as a prop to explain how relationships between some of the different entities presented are significant. Making the chart a prop allows you to provide a narrative. In this case it is the setting and delivery that are consistent with the notion of storytelling, not the chart itself. I made a similar observation about the role of exhibitory visualisations used as props within explanatory settings. A further distinction to make is between stories as being presented and stories as being interpreted. The famous six-word story ‘for sale: baby shoes, never worn’ by Ernest Hemingway is not presented as a story, the story is triggered in our mind when we dissect this passage and start to infer meaning, implication and context. The imagined bar chart I mentioned earlier in the book that could show the 43 white presidents and 1 black president is only presenting a story if it is accompanied by an explanatory narrative (in which case the chart was again really just a prop) or if you understand the meaning of the significance of this statistic without this description and are able to form the story in your own mind.

C harts C om parisons

B ar chart

ALSO KN OW N AS Column chart, histogram (wrongly)

R E PR E SE N TATION D E SC R IPTION

A bar chart displays quantitative values for different categories. The chart comprises line marks (bars) – not rectangular areas – with the size attribute (length or height) used to represent the quantitative value for each category.

E X AM PLE Comparing the number of Oscar nominations for the 10 actors who have received the most nominations without actually winning an award.

Fi g u re 6.8 The 10 Actors with the Most Oscar Nominations but No Wins

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HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the axes so you know with which categorical value each bar is associated and what the range of the quantitative values is (min to max). Think about what high and low values mean: is it ‘good’ to be large or small? Glance across the entire chart to locate the big, small and medium bars and perform global comparisons to establish the high-level ranking of biggest > smallest. Identify any noticeable exceptions and/or outliers. Perform local comparisons between neighbouring bars, to identify larger than and smaller than relationships and estimate the relative proportions. Estimate (or read, if labels are present) the absolute values of specific bars of interest. Where available, compare the quantities against annotated references such as targets, forecast, last year, average, etc.

PR E SE N TATION TIPS

AN N OTATION : Chart apparatus devices like tick marks and gridlines, in particular, can be helpful to increase the accuracy of the reading of the quantitative values. If you have axis labels you should not need direct labels on each bar – this will lead to label overload, so generally decide between one or the other.

VAR IATION S & ALTE R N ATIVE S

A variation in the use of bar charts is to show changes over time. You would use a bar chart when the focus is on individual quantitative values over time rather than (necessarily) the trend/change between points, for which a line-chart would be best. ‘Spark bars’ are mini bar charts that aim to occupy only a word’s length amount of space. They are often seen in dashboards where space is at a premium and there is a desire to optimise the density of the display. To show further categorical subdivisions, you might consider the ‘clustered bar chart’ or a ‘stacked bar chart’ if there is a part-to-whole angle. ‘Dot plots’ offer a particularly useful alternative to the bar chart for situations where you have to show large quantitative values with a

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narrow range of differences.

C harts C om parisons

C lustered bar chart

ALSO KN OW N AS Clustered column chart, paired bar chart

R E PR E SE N TATION D E SC R IPTION

A clustered bar chart displays quantitative values for different major categories with additional categorical dimensions included for further breakdown. The chart comprises line marks (bars) – not rectangular areas – with the size attribute (length or height) used to represent the quantitative value for each category and colours used to distinguish further categorical dimensions.

E X AM PLE Comparing the number of Oscar nominations with the number of Oscar awards for the 10 actors who have received the most nominations.

Fi g u re 6.9 The 10 Actors who have Received the Most Oscar Nominations

HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the axes so you know with which categorical value each bar is associated and what the range of the

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quantitative values is (min to max). Learn about the colour associations to understand what sub-categories the bars within each cluster represent. Glance across the entire chart to locate the big, small and medium bars and perform global comparisons to establish the high-level ranking of biggest > smallest. Identify any noticeable exceptions and/or outliers. Perform local comparisons within clusters to identify the size relationship (which is larger and by how much?) and estimate (or read, if labels are present) the absolute values of specific bars of interest.

PR E SE N TATION TIPS

C OM POSITION : The quantitative value axis should always start from the origin value of zero: a bar should be representative of the true, full quantitative value, nothing more, nothing less, otherwise the perception of bar sizes will be distorted when comparing relative sizes. If your categorical clusters involve a breakdown of more than three bars, it becomes a little too busy, so you might therefore consider giving each cluster its own separate bar chart and using small multiples to show a chart for each major category. Sometimes one bar might be slightly hidden behind the other, implying a before and after relationship, often when space is at a premium – just do not hide too much of the back bar. There is no significant difference in perception between vertical or horizontal bars though horizontal layouts tend to make it easier to accommodate and read the category labels for each bar. The individual bars should be positioned adjacent to each other with a noticeable gap and then between each cluster to help direct the eye towards the clustering patterns first and foremost. Where possible try to make the categorical sorting meaningful.

VAR IATION S & ALTE R N ATIVE S

Clustered bar charts are also sometimes used to show how two associated sub-categories have changed over time (like the Lionel Messi bar chart discussed in Chapter 1). Alternatives would include the ‘dot plot’ or, if you have just two categories forming the clusters and these categories have a binary state (male, female or yes %, no %), the ‘back-to-back bar chart’ would be effective.

C harts C om parisons

D ot plot

ALSO KN OW N AS Dot chart

R E PR E SE N TATION D E SC R IPTION

A dot plot displays quantitative values for different categories. In contrast to the bar chart, rather than using the size of a bar, point marks (typically circles but any ‘symbol’ is legitimate) are used with the position along a scale indicating the quantitative value for each category. Sometimes an area mark is used to indicate one value through position and another value through size. Additional categorical dimensions can be accommodated in the same chart by including additional marks differentiated by colour or symbol.

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E X AM PLE Comparing the number and percentage of PhDs awarded by gender across different academic subjects.

Fi g u re 6.10 How Nations Fare in PhDs by Sex

HOW TO R E AD IT & W HAT TO LOOK FOR

For single-series dot plots (i.e. just one dot per row), look at the axes so you know with which categorical value each row is associated and what the range of the quantitative values is (min to max). Where you have multiple series dot plots (i.e. more than one dot), establish what the different colours/symbols represent in terms of categorical breakdown. Glance across the entire chart to locate the big, small and medium values and perform global comparisons to establish the high-level ranking of biggest > smallest. Identify any noticeable exceptions and/or outliers. Where you have multiple series look across each series of dot values separately and then perform local comparisons within rows to identify the relative position of each dot, observing the gaps, big and small. Estimate the absolute values of specific dots of interest. Where available, compare the quantities against annotated references such as targets, forecast, last year, average, etc.

PR E SE N TATION TIPS

AN N OTATION : Chart apparatus devices like tick marks and gridlines, in particular, can be helpful to increase the accuracy of the reading of the quantitative values.

C OM POSITION : Given that the quantitative value axis does not need to commence from a zero origin it is important to label clearly the axis values when the baseline is not commencing from a minimum of zero. There is no significant difference in perception between vertical or horizontal arrangement though horizontal layouts tend to make it easier to accommodate and read the category labels for each row. Where possible try to make the categorical sorting meaningful, maybe organising values in ascending/descending size order.

VAR IATION S & ALTE R N ATIVE S

Alternatives would include the ‘bar chart’, to show the size of quantitative values for different categories. The ‘connected dot plot’ would be used to focus on the difference between two measures. The ‘univariate scatter

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plot’ would be used to show the range of multiple values across categories, to display the diversity and distribution of values.

C harts C om parisons

C onnected D ot Plot

ALSO KN OW N AS Barbell chart, dumb-bell chart

R E PR E SE N TATION D E SC R IPTION

A connected dot plot displays absolute quantities and quantitative differences between two categorical dimensions for different major categories. The display is formed by two points (normally circles but any ‘symbol’ is legitimate) to mark the quantitative value positions for two comparable categorical dimensions. There is a row of connected dots for each major category. Colour or difference in symbol is generally used to distinguish these points. Joining the two points together is a connecting line which effectively represents the ‘delta’ (difference) between the two values.

E X AM PLE Comparing the typical salaries for women and men across a range of different job categories in the US.

Fi g u re 6.11 Gender Pay Gap US

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HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the axes so you know with which major categorical values each row is associated and what the range of the quantitative values is (min to max). Determine which dots resemble which categorical dimension (could be colour, symbol or a combination) and see if there is any meaning behind the colouring of the connecting bars. Think about what the quantitative values mean to determine whether it is a good thing to be higher or lower. Glance across the entire chart to locate the big, small and medium connecting bars in each direction. Perform global comparisons to establish the high-level ranking of biggest > smallest differences as well as the highest and lowest values. There may be deliberate sorting of the display based on one of the quantitative measures. Identify any noticeable exceptions and/or outliers. Estimate (or read, if labels are present) the absolute values, direction and size of differences for specific categories of interest.

PR E SE N TATION TIPS

AN N OTATION : Chart apparatus devices like tick marks and gridlines, in particular, can be helpful to increase the accuracy of the reading of the quantitative values. Consider labelling categories adjacent to the plotted points rather than next to the axis line (and possibly far away from the values) to make it easier for the reader to understand the category–row association.

C OLOUR TIPS: Colour may be used to indicate and emphasise the directional basis of the connecting line differences.

C OM POSITION : If the two plotted measures are very similar, and the point markers effectively overlap, you will need to decide which should be positioned on top. As the representation of the quantitative values is through position along a scale and not size (it is the difference that is sized, not the absolutes) the quantitative axis does not need to have a zero origin. However, a zero origin can be helpful to establish the scale of the differences. Where possible try to make the sorting meaningful using any one of the three quantitative measures to optimise the layout.

VAR IATION S & ALTE R N ATIVE S

Variations in the use of the ‘connected dot plot’ would show before and after analysis between two points in time, possibly using the ‘arrow chart’ to indicate the direction of change explicitly. Similarly, the ‘carrot chart’ uses line width tapering to indicate direction, the fatter end the more recent values. The ‘univariate scatter plot’ would be used to show the range of multiple values across categories, to display the diversity and distribution of values rather than comparing differences between values.

C harts C om parisons

Pictogram

ALSO KN OW N AS Isotype chart, pictorial bar chart, stacked shape chart, tally chart

R E PR E SE N TATION D E SC R IPTION

A pictogram displays quantitative values for different major categories with additional categorical

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dimensions included for further breakdown. In contrast with the bar chart, rather than using the size of a bar, quantities of point marks, in the form of symbols or pictures, are stacked to represent the quantitative value for each category. Each point may be representative of one or many quantitative units (e.g. a single shape may represent 1000 people) but note that, unless you use symbol portions, you will not be able to represent decimals. Pictograms may be used to offer a more emotive (humanising or more light-hearted) display than a bar can offer. Additional categorical dimensions can be accommodated in the same chart by using marks differentiated by variations in colour, symbol or picture. Always ensure the markers used are as intuitively recognisable as possible and consider minimising the variety as this makes it cognitively harder for the viewer to identify associations easily and make sense of the quantities.

E X AM PLE Comparing the number of players with different facial hair types across the four teams in the NHL playoffs in 2015.

Fi g u re 6.12 Who Wins the Stanley Cup of Playoff Beards?

HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the major categorical axis to establish with which category each row is associated. Establish the mark associations to understand what categorical dimensions each colour/shape variation represents. Glance across the entire chart to locate the big, small and medium stacks of shapes and perform global comparisons to establish the high-level ranking of biggest > smallest. Identify any noticeable exceptions and/or outliers. Perform local comparisons between neighbouring categories, to identify larger than and smaller than relationships and estimate the relative proportions. Estimate (or read, if labels are present) the absolute values of specific groups of markers of interest.

PR E SE N TATION TIPS

AN N OTATION : The choice of symbol/ picture should be as recognisably intuitive as possible and locate any legends as close as possible to the display.

C OLOUR TIPS: Maximise the variation in marker by using different combinations in both colour and shape, rather than just variation of one attribute.

C OM POSITION : If the quantities of markers exceed a single row, try to make the number of units per row logically ‘countable’, such as displaying in groups of 5, 10 or 100. To aid readability, make sure there is a sufficiently noticeable gap between rows, otherwise sometimes the eye struggles to form the distinct clusters of shapes for each category displayed. Where possible try to make the categorical sorting meaningful, maybe organising values in ascending/descending size order.

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VAR IATION S & ALTE R N ATIVE S

Extending the idea of using repeated quantities of representative symbols, some applications take this further by using large quantities of individual symbols to get across the feeling of magnitude and scale. When showing a part-to-whole relationship, the ‘waffle chart’ can use simple symbol devices to differentiate the constituent parts of a whole.

C harts C om parisons

Proportional shape chart

ALSO KN OW N AS Area chart (wrongly)

R E PR E SE N TATION D E SC R IPTION

A proportional shape chart displays quantitative values for different categories. The chart is based on the use of different area marks, one for each category, sized in proportion to the quantities they represent. By using the quadratic dimension of area size rather than the linear dimension of bar length or dot position, the shape chart offers scope for displaying a diverse range of quantitative values within the same chart. Typically the layout is quite free-form with no baseline or central gravity binding the display together.

E X AM PLE Comparing the market capitalisation ($) of companies involved in the legal sale of marijuana across different industry sectors.

Fi g u re 6.13 For These 55 Marijuana Companies, Every Day is 4/20

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HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the shapes and their associated labels so you know with what major categorical values each is associated. If there are only direct labels, find the largest shape to establish its quantitative value as the maximum and do likewise for the smallest – this will help calibrate the size judgements. Otherwise, if it exists, acquaint yourself with the size key. Glance across the entire chart to locate the big, small and medium shapes and perform global comparisons to establish the high-level ranking of biggest > smallest. Identify any noticeable exceptions and/or outliers. Perform local comparisons between neighbouring shapes to identify larger than and smaller than relationships and estimate the relative proportions. Estimate (or read, if labels are present) the absolute values of specific shapes of interest.

PR E SE N TATION TIPS

AN N OTATION : Sometimes a quantitative size key will be included rather than direct labelling (usually when there are many shapes and limited empty space) though direct labels will help overcome some of the limitations of judging area size. You will have to decide how to handle label positioning for those shapes with exceptionally small sizes.

C OLOUR TIPS: Colours are not fundamentally necessary to encode category (the position/separation of different shapes achieves that already) but they can be useful as redundant encodings to make the category even more immediately distinguishable.

C OM POSITION : Estimating and comparing the size of areas with accuracy is not as easy as it is for judging bar length or dot position, so only use this chart type if you have a diverse range of quantitative values. The geometric accuracy of the size calculations is paramount. Mistakes are often made, in particular, with circle size calculations: it is the area you are modifying, not the diameter/radius. Arrangement approaches vary: sometimes you see the shapes anchored to a common baseline (bottom or central alignment) while on other occasions they might just ‘float’. If you use an organic shape, like a human figure, to represent different quantities you need to adjust the entire shape area, not just the height. Often the approach for this type of display is to treat the figure as a rudimentary rectangular shape. Sometimes the volume of a shape is used rather than area to represent quantitative values (especially if there are almost exponentially different values to show) but this increases the perceptual difficulty in estimating and comparing values. Where possible try to make the categorical sorting meaningful, maybe organising values in ascending/descending size order.

VAR IATION S & ALTE R N ATIVE S

The ‘bubble chart’ uses clusters of sized bubbles to compare categorical values and, sometimes, to represent part-to-whole analysis. The ‘nested shape chart’ might include secondary, smaller area sizes nested within each shape to display local part-to-whole relationships.

C harts C om parisons

B ubble chart

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ALSO KN OW N AS Circle packing diagram

E X AM PLE Comparing the Public sector capital expenditure (£ million) on services by function of the UK Government during 2014/15.

R E PR E SE N TATION D E SC R IPTION

A bubble chart displays quantitative values for different major categories with additional categorical dimensions included for further breakdown. It is based on the use of circles, one for each category, sized in proportion to the quantities they represent. Sometimes several separate clusters may be used to display further categorical dimensions, otherwise the colouring of each circle can achieve this. It is similar in concept to the proportional shape chart but differs through the typical layout being based on clustering, which therefore also enables it as a device for showing part-to-whole relationships as well.

Fi g u re 6.14 UK Public Sector Capital Expenditure, 2014/15

HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the shapes and their associated labels so you know with what major categorical values each is associated, noting any size and colour legends to assist in forming associations. If there are multiple clusters, learn about the significance of the grouping/separation in each case. If there are direct labels, find the largest shape to establish its quantitative value as the maximum and do likewise for the smallest – this will help calibrate other size judgements. Glance across the entire chart to locate the big, small and medium shapes and perform global comparisons to establish the high-level ranking of biggest > smallest. Identify any noticeable exceptions and/or outliers. Perform local comparisons between neighbouring shapes to identify larger than and smaller than relationships and estimate the relative proportions. Estimate (or read, if labels are present) the absolute values of specific shapes of interest. If there are multiple clusters, note the general relative size and number of members in each case.

PR E SE N TATION TIPS

INTERACTIVITY: Bubble charts may often be accompanied by interactive features that let users select or mouseover individual circles to reveal annotated values for the quantity and category.

AN N OTATION : If interactivity is not achievable, a quantitative size key should be included or direct labelling; the latter may make the display busy (and be hard to fit into smaller circles) but will help overcome

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some of the limitations of judging area size.

C OLOUR TIPS: Colours are sometimes used as redundant encodings to make the quantitative sizes even more immediately distinguishable.

C OM POSITION : Estimating and comparing the size of areas with accuracy is not as easy as it is for judging bar length or dot position, so only use this chart type if you have a diverse range of quantitative values. The use of this chart will primarily be about facilitating a gist, a general sense of the largest and smallest values. The geometric accuracy of the circle size calculations is paramount. Mistakes are often made with circle size calculations: it is the area you are modifying, not the diameter/radius. If you wish to make your bubbles appear as 3D spheres you are essentially no longer representing quantitative values through the size of a geometric area mark; rather the mark will be a ‘form’ and so the size calculation will be based on volume, not area. There is no categorical or quantitative sorting applied to the layout of the bubble chart, instead the tools that offer these charts will generally use a layout algorithm that applies a best-fit clustering to arrange the circles radially about a central ‘gravity’ force.

VAR IATION S & ALTE R N ATIVE S

When the collection of quantities represents a whole, this evolves into a chart known as a ‘circle packing diagram’ and usually involves many parts that pack neatly into a circular layout representing the whole. Another variation of the packing diagram is when the adjacency between circle ‘nodes’ indicates a connected relation, offering a variation of the node–link diagram for showing networks of relationships. The bubble plot also uses differently sized circles but the position in each case is overlaid onto a scatter plot structure, based on two dimensions of further quantitative variables. Removing the size attribute (and effectively replacing area with point mark) you could simply use the quantity of points clustered together for different categories to create a ’tally chart’.

C harts C om parisons

Radar chart

ALSO KN OW N AS Filled radar chart, star chart, spider diagram, web chart

E X AM PLE Comparing the global competitive scores (out of 7) across 12 ‘pillars’ of performance for the United Kingdom.

R E PR E SE N TATION D E SC R IPTION

A radar chart shows values for three or more different quantitative measures in the same display for, typically, a single category. It uses a radial (circular) layout comprising several axes emerging from the centre-like spokes on a wheel, one for each measure. The quantitative values for each measure are plotted through position along each scale and then joined by connecting lines to form a unique geometric shape. Sometimes this shape is then filled with colour. A radar chart should only be considered in situations where the cyclical ordering (and neighbourly pairings) has some significance (such as data that might be plotted around the face of a clock or compass) and when the quantitative scales are the same (or similar) for each

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axis. Do not plot values for multiple categories on the same radar chart, but use small multiples formed of several radar charts instead.

Fi g u re 6.15 Global Competitiveness Report 2014—2015

HOW TO R E AD IT & W HAT TO LOOK FOR

Look around the chart and acquaint yourself with the quantitative measure represented by each axis and note the sequencing of the measures around the display. Is there any significance in this arrangement that can assist in interpreting the overall shape? Note the range of values along each independent axis so you understand what positions along the scales mean in a value sense for each measure. Scan the shape to locate the outliers both towards the outside (larger values) and inside (smaller values) of the scales. It is more important to pay attention to the position of values along an axis than the nature of the connecting lines between axes, unless the axis scales are consistent or at least if the relative position along the scale has the same implied meaning. If the variable sequencing has cyclical relevance, the spiking, bulging or contracting shape formed will give you some sense of the balance of values. Perform local comparisons between neighbouring axes to identify larger than and smaller than relationships. Estimate (or read, if labels are present) the absolute values of specific shapes of interest.

PR E SE N TATION TIPS

AN N OTATION : The inclusion of visible annotated features like axis lines, tick marks, gridlines and value labels can naturally aid the readability of the radar chart. Gridlines are only relevant if there are common scales across each quantitative variable. If so, the gridlines must be presented as straight lines, not concentric arcs, because the connecting lines joining up the values are themselves straight lines.

C OLOUR TIPS: Often the radar shapes are filled with a colour, sometimes with a degree of

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transparency to allow the background apparatus to be partially visible.

C OM POSITION : The cyclical ordering of the quantitative variables has to be of optimum significance as the connectors and shape change for every different ordering permutation. This will have a major impact on the readability and meaning of the resulting chart shape. As the axes will be angled all around the radial display, you will need to make sure all the associated labels are readable (i.e. not upside down or at difficult angles).

VAR IATION S & ALTE R N ATIVE S

A ‘polar chart’ is an alternative to the radar chart that removes some of the main shortcomings caused by connecting lines in the radar chart. If you have consistent value scales across the different quantitative measures, a ‘bar chart’ or ‘dot plot’ would be a better alternative. While not strictly a variation, ‘parallel coordinates’ display a similar technique for plotting several independent quantitative measures in the same chart. The main difference is that parallel coordinates use a linear layout and can accommodate many categories in one display.

C harts C om parisons

Polar chart

ALSO KN OW N AS Coxcomb plot, polar area plot

R E PR E SE N TATION D E SC R IPTION

A polar chart shows values for three or more different quantitative measures in the same display. It uses a radial (circular) layout comprising several equal-angled circular sectors like slices of a pizza, one for each measure. In contrast to the radar chart (which uses position along a scale), the polar chart uses variation in the size of the sector areas to represent the quantitative values. It is, in essence, a radially plotted bar chart. Colour is an optional attribute, sometimes used visually to indicate further categorical dimensions. A polar chart should only be considered in situations where the cyclical ordering (and neighbourly pairings) has some significance (such as data that might be plotted around the face of a clock or compass) and when the quantitative scales are the same (or similar) for each axis.

E X AM PLE Comparing the quantitative match statistics across 14 different performance measures for a rugby union player.

Fi g u re 6.16 Excerpt from a Rugby Union Player Dashboard

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HOW TO R E AD IT & W HAT TO LOOK FOR

Look around the chart and acquaint yourself with the quantitative measures each sector represents and note the sequencing of the measures around the display. Is there any significance in this arrangement that can assist in interpreting the overall shape? Note the range of values included on the quantitative scale and acquaint yourself with any colour associations. Glance across the entire chart to locate the big, small and medium sectors and perform global comparisons to establish the high-level ranking of biggest > smallest. Identify any noticeable exceptions and/or outliers. Perform local comparisons between neighbouring variables to identify the order of magnitude and estimate the relative sizes. Estimate (or read, if labels are present) the absolute values of specific sectors of interest. Where available, compare the quantities against annotated references such as targets, forecast, last year, average, etc. If there is significance behind the sequencing of the variables, look out for any patterns that emerge through spiking, bulging or contracting shapes.

PR E SE N TATION TIPS

AN N OTATION : The inclusion of visible annotated features like tick marks and value labels can naturally aid the readability of the polar chart. Gridlines are only relevant if there are common scales across each quantitative variable. If so, the gridlines must be presented as arcs reflecting the outer shape of each sector. Connecting lines joining up the values are themselves straight lines. Each sector typically uses the same quantitative scale for each quantitative measure but, on the occasions when this is not the case, each axis will require its own, clear value scale.

C OLOUR TIPS: Often polar chart sectors are filled with a meaningful colour, sometimes with a degree of transparency to allow the background apparatus to be partially visible.

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C OM POSITION : The cyclical ordering of the quantitative variables has to be of some significance to legitimise the value of the polar chart over the bar chart. As the sectors will be angled all around the radial display, you will need to make sure all the associated labels are readable (i.e. not upside down or at difficult angles). The quantitative values represented by the size of the sectors need to be carefully calculated. It is the area of the sector, not the radius length, that will be modified to portray the values accurately. If you make maximum quantitative value equivalent to the largest sector area, all other sector sizes can be calculated accordingly. Knowing how many different quantitative variables you are showing means you can easily calculate the angle of any given sector. The quantitative measure axes should always start from the origin value of zero: a sector should be representative of the true, full quantitative value, nothing more, nothing less, otherwise the perception of size will be distorted when comparing relative sizes.

VAR IATION S & ALTE R N ATIVE S

Unless the radial layout provides meaning through the notion of a ‘whole’ or through the cyclical arrangement of measures, you might be best using a ‘bar chart’. Variations in approach tend to see modifications in the sector shape with measure values represented by individual bars lengths or, in the example of the Better Life Index project, through variations in ‘petal’ sizes.

C harts D istributions

Range C hart

ALSO KN OW N AS Span chart, floating bar chart, barometer chart

R E PR E SE N TATION D E SC R IPTION

A range chart displays the minimum to maximum distribution of a series of quantitative values for different categories. The display is formed by a bar, one for each category, with the lower and upper position of the bars shaped by the minimum and maximum quantitative values in each case. The resulting bar lengths thus represent the range of values between the two limits.

E X AM PLE Comparing the highest and lowest temperatures (°F) recorded across the top 10 most populated cities during 2015.

Fi g u re 6.17 Range of Temperatures Recorded in Top 10 Most Populated Cities (2015)

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HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the axes so you know with what major categorical values each range bar is associated and what the range of the quantitative values is (min to max). Glance across the entire chart to locate the big, small and medium bars and perform global comparisons to establish the high-level ranking of biggest > smallest differences as well as the highest and lowest values. Identify any noticeable exceptions and/or outliers. Perform local comparisons between neighbouring bars, to identify larger than and smaller than relationships and estimate the relative proportions. There may be deliberate sorting of the display based on one of the quantitative measures. Estimate (or read, if labels are present) the absolute values of specific bars of interest. Where available, compare the quantities against annotated references such as targets, forecast, last year, average, etc.

PR E SE N TATION TIPS

AN N OTATION : Chart apparatus devices like tick marks and gridlines, in particular, can be helpful to increase the accuracy of the reading of the quantitative values. If you have axis labels you may not need direct labels on each bar – this will be lead to label overload, so generally decide between one or the other.

C OM POSITION : The quantitative value axis does not need to commence from zero, unless it means something significant to the interpretation, as the range of values themselves does not necessarily start from zero and the focus is more on the range and difference between the outer values. There is no significant difference in perception between vertical or horizontal layouts, though the latter tend to make it easier to accommodate and read the category labels. Where possible, try to make the categorical sorting meaningful, maybe organising values in ascending/descending size order.

VAR IATION S & ALTE R N ATIVE S

‘Connected dot plots’ will also emphasise the difference between two selected measure values (as opposed to min/max) or where the underlying data is a change over time between two observations. ‘Band charts’ will often be used to show how the range of data values has changed over time, displaying the minimum and maximum bands at each time unit. These are often used in displays like weather forecasts.

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C harts D istributions

B ox-and-whisker plot

ALSO KN OW N AS Box plot

R E PR E SE N TATION D E SC R IPTION

A box-and-whisker plot displays the distribution and shape of a series of quantitative values for different categories. The display is formed by a combination of lines and point markers to indicate (through position and length), typically, five different statistical measures. Three of the statistical values are common to all plots: the first quartile (25th percentile), the second quartile (or median) and the third quartile (75th percentile) values. These are displayed with a box (effectively a wide bar) positioned and sized according to the first and third quartile values with a marker indicating the median. The remaining two statistical values vary in definition: usually either the minimum and maximum values or the 10th and 90th percentiles. These statistical values are represented by extending a line beyond the bottom and top of the main box to join with a point marker indicating the appropriate position. These are the whiskers. A plot will be produced for each major category.

E X AM PLE Comparing the distribution of annual earnings 10 years after starting school for graduates across the eight Ivy League schools.

Fi g u re 6.18 Ranking the Ivies

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HOW TO R E AD IT & W HAT TO LOOK FOR

Begin by looking at the axes so you know with which category each plot is associated and what the range of quantitative values is (min to max). Establish the specific statistics being displayed, by consulting any legends or descriptions, especially in order to identify what the ‘whiskers’ are representing. Glance across the entire chart to locate the main patterns of spread, identifying any common or noticeably different patterns across categories. Look across the shapes formed for each category to learn about the dispersal of values: starting with the median, then observing the extent and balance of the ‘box’ (the interquartile range between the 25th and 75th percentiles) and then check the ‘whisker’ extremes. Is the shape balanced or skewed around the median? Is the interquartile range wide or narrow? Are the whisker extremes far away from the edges of the box? Then return to comparing shapes across all categories to identify more precisely any interesting differences or commonalities for each of the five statistical measures.

PR E SE N TATION TIPS

AN N OTATION : If you have axis labels you may not need direct labels on each bar – this will lead to label overload, so generally decide between one or the other.

C OM POSITION : The quantitative value axis does not need to commence from zero, unless it means something significant to the interpretation, as the range of values themselves do not necessarily start from zero and the focus is on the statistical properties between the outer values. There is no significant difference in perception between vertical or horizontal box-and-whisker plots, though horizontal layouts tend to make it easier to accommodate and read the category labels. Try to keep a noticeable gap between plots to enable greater clarity in reading. When you have several or many plots in the same chart, where possible try to make the categorical sorting meaningful, maybe organising values in ascending/descending order based on

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the median value.

VAR IATION S & ALTE R N ATIVE S

Variations involve reducing the number of statistical measures included in the display by removing the whiskers to just show the 25th and 75th percentiles through the lower and upper parts of the box. The ‘candlestick chart’ (or OHLC chart) involves a similar approach and is often used in finance to show the distribution and milestone values of stock performances during a certain time frame (usually daily), plotting the opening, highest, lowest and closing prices, using colour to indicate an up or down trend.

C harts D istributions

Univariate scatter plot

ALSO KN OW N AS 1D scatter plot, jitter plot

R E PR E SE N TATION D E SC R IPTION

A univariate scatter plot displays the distribution of a series of quantitative values for different categories. In contrast to the box-and-whisker plot, which shows selected statistical values, a univariate scatter plot shows all values across a series. For each category, a range of points (typically circles but any ‘symbol’ is legitimate) are used to mark the position along the scale of the quantitative values. From this you can see the range, the outliers and the clusters and form an understanding about the general shape of the data.

E X AM PLE Comparing the distribution of average critics score (%) from the Rotten Tomatoes website for each movie released across a range of different franchises and movie theme collections.

Fi g u re 6.19 Comparing Critics Scores for Major Movie Franchises

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HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the axes so you know what each scatter row/column relates to in terms of which category it is associated with and what the range of the quantitative values is (min to max). If colour has been used to emphasise or separate different marks, establish what the associations are. Also, learn about how the design depicts multiple marks on the same value – these may appear darker or indeed larger. Glance across the entire chart to observe the main patterns of clustering and identify any noticeable exceptions and/or outliers across all categories. Then look more closely at the patterns within each scatter to learn about each category’s specific dispersal of values. Look for empty regions where no quantitative values exist. Estimate the absolute values of specific dots of interest. Where available, compare the quantities against annotated references such as the average or median.

PR E SE N TATION TIPS

AN N OTATION : Chart apparatus devices like gridlines can be helpful to increase the accuracy of the reading of the quantitative values. Direct labelling is normally restricted to including values for specifically noteworthy points only.

C OLOUR : Colour may be used to establish focus of certain points and/or distinction between different sub-category groups to assist with interpretation. When several points have the exact same value you might need to use unfilled or semi-transparent filled circles to facilitate a sense of value density.

C OM POSITION : The representation of the quantitative values is based on position and not size, therefore the quantitative axis does not need to have a zero origin. There is no significant difference in perception between vertical or horizontal arrangement, though horizontal layouts tend to make it easier to accommodate and read the category labels. Where possible try to make the categorical sorting meaningful, maybe organising values in ascending/descending size order.

VAR IATION S & ALTE R N ATIVE S

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To overcome occlusion caused by plotting several marks at the same value, a variation of the univariate scatter plot may see the points replaced by geometric areas (like circles), where the position attribute is used to represent a quantitative value along a scale and the size attribute is used to indicate the frequency of observations of similar value. Adding a second quantitative variable axis would lead to the use of a ’scatter plot’.

C harts D istributions

Histogram

ALSO KN OW N AS Bar chart (wrongly)

R E PR E SE N TATION D E SC R IPTION

A histogram displays the frequency and distribution for a range of quantitative groups. Whereas bar charts compare quantities for different categories, a histogram technically compares the number of observations across a range of value ‘bins’ using the size of lines/bars (if the bins relate to values with equal intervals) or the area of rectangles (if the bins have unequal value ranges) to represent the quantitative counts. With the bins arranged in meaningful order (that effectively form ordinal groupings) the resulting shape formed reveals the overall pattern of the distribution of observations.

E X AM PLE Comparing the distribution of movies released over time starring Michael Caine across five- year periods based on the date of release in the US.

Fi g u re 6.20 A Career in Numbers: Movies Starring Michael Caine

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HOW TO R E AD IT & W HAT TO LOOK FOR

Begin by looking at the axes so you know what the chart depicts in terms of the categorical bins and the range of the quantitative values (zero to max). Glance across the entire chart to establish the main pattern. Is it symmetrically shaped, like a bell or pyramid (around a median or average value)? Is it skewed to the left or right? Does it dip in the middle and peak at the edges (known as bimodal)? Does it have several peaks and troughs? Maybe it is entirely random in its pattern? All these characteristics of ‘shape’ will inform you about the underlying distribution of the data.

PR E SE N TATION TIPS

AN N OTATION : Chart apparatus devices like tick marks and gridlines in particular can be helpful to increase the accuracy of the reading of the quantitative values. Axis labels more than direct value labels tend to be used so as not to crowd the shape of the histogram.

C OM POSITION : Unlike the bar chart there should be no (or at most a very thin) gap between bars to help the collective shape of the frequencies emerge. The sorting of the quantitative bins must be in ascending order so that the reading of the overall shape preserves its meaning. The number of value bins and the range of values covered by each have a prominent influence over the appearance of the histogram and the usefulness of what it might reveal: too few bins may disguise interesting nuances, patterns and outliers; too many bins and the most interesting shapes may be abstracted by noise above signal. There is no singular best approach, the right choice simply arrives through experimentation and iteration.

VAR IATION S & ALTE R N ATIVE S

For analysis that looks at the distribution of values across two dimensions, such as the size of populations

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for age across genders, a ‘back-to-back histogram’ (with male on one side, female on the other), also commonly known as a ‘violin plot’ or ‘population pyramid’, is a useful approach to see and compare the respective shapes. A ‘box-and-whisker plot’ reduces the distribution of values to five key statistical measures to describe key dimensions of the spread of values.

C harts D istributions

W ord cloud

ALSO KN OW N AS Tag cloud

R E PR E SE N TATION D E SC R IPTION

A word cloud shows the frequency of individual word items used in textual data (such as tweets, comments) or documents (passages, articles). The display is based around an enclosed cluster of words with the font (not the word length) sized according to the frequency of usage. In modifying the size of font this is effectively increasing the area size of the whole word. All words have a different shape and size so this can make it quite difficult to avoid the prominence of long words, irrespective of their font size. Word clouds are therefore only useful when you are trying to get a quick and rough sense of some of the dominant keywords used in the text. They can be an option for working with qualitative data during the data exploration stage, more so as a means for reporting analysis to others.

E X AM PLE Comparing the frequency of words used in Chapter 1 of this book.

Fi g u re 6.21 Word Cloud of the Text from Chapter 1

HOW TO R E AD IT & W HAT TO LOOK FOR

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The challenge with reading word clouds is to avoid being drawn to the length and/or area of a word – they are simply attributes of the word, not a meaningful representation of frequency. It is the size of the font that you need to focus on. Scan the display to spot the larger text showing the more frequently used words. Consider any words of specific interest to see if you can find them; if they are not significantly visible, that in itself could be revealing. While most word cloud generators will dismiss many irrelevant words, you might still need to filter out perceptually the significance of certain dominantly sized text.

PR E SE N TATION TIPS

IN TE R AC TIVITY : Interactive features that let users interrogate, filter and scrutinise the words in more depth, perhaps presenting examples of their usage in a passage, can be quite useful to enhance the value of a word cloud.

AN N OTATION : While the absolutes are generally of less interest than relative comparisons, to help viewers get as much out of the display as possible a simple legend explaining how the font size equates to frequency number can be useful.

C OLOUR : Colours may be used as redundant encoding to accentuate further the larger frequencies or categorically to create useful visual separation.

C OM POSITION : The arrangement of the words within a word cloud is typically based on a layout process. Although not random, this will generally prioritise the placement of words to occupy optimum collective space that preserves an overall shape (with essentially a central gravity) over and above any arrangement that might better enable direct comparison.

VAR IATION S & ALTE R N ATIVE S

The alternative approach would be to use any other method in this categorical family of charts that would more usefully display the counts of text, such as a bar chart.

C harts Part-to-whole

Pie chart

ALSO KN OW N AS Pizza chart

R E PR E SE N TATION D E SC R IPTION

A pie chart shows how the quantities of different constituent categories make up a whole. It uses a circular display divided into sectors for each category, with the angle representing each of the percentage proportions. The resulting size of the sector (in area terms) is a spatial by-product of the angle applied to each part and so offers an additional means for judging the respective values. The role of a pie chart is primarily about being able to compare a part to a whole than being able to compare one part to another part. They therefore work best when there are only two or three parts included. There are a few important rules for pie charts. Firstly, the total percentage values of all sector values must be 100%; if the aggregate is greater than or less than 100% the chart will be corrupted. Secondly, the whole has to be meaningful – often

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people just add up independent percentages but that is not what a pie chart is about. Finally, the category values must represent exclusive quantities; nothing should be counted twice or overlap across different categories. Despite all these warnings, do not be afraid of the pie chart – just use it with discretion.

E X AM PLE Comparing the proportion of eligible voters in the 2015 UK election who voted for the Conservative Party, for other parties and who did not vote.

Fi g u re 6.22 Summary of Eligible Votes in the UK General Election 2015

HOW TO R E AD IT & W HAT TO LOOK FOR

Begin by establishing which sectors relate to what categories. This may involve referring to a colour key legend or through labels directly adjacent to the pie. Quickly scan the pie to identify the big, medium and small sectors. Notice if there is any significance behind the ordering of the parts. Unless there are value labels, you next will attempt to judge the individual sector angles. This usually involves mentally breaking the pie into 50% halves (180°) or 25% quarters (90°) and using those guides to perceptually measure the category values. Comparing parts against other parts with any degree of accuracy will only be possible once you have formed estimates of the individual sector sizes. If you are faced with the task of judging the size of many parts it is quite understandable if you decide to give up quite soon.

PR E SE N TATION TIPS

AN N OTATION : The use of local labelling for category values can be useful but too many labels can become cluttered, especially when attempting to label very small angled sectors.

C OLOUR : Colour is generally vital to create categorical separation and association of the different sectors so aim to use the difference in colour hue and not colour saturation to maximise the visible difference.

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C OM POSITION : Positioning the first slice at the vertical 12 o’clock position gives a useful baseline to help judge the first sector angle value. The ordering of sectors using descending values or ordinal characteristics helps with the overall readability and allocation of effort. Do not consider using gratuitous decoration (like 3D, gradient colours, or exploding slices).

VAR IATION S & ALTE R N ATIVE S

Sometimes a pie chart has a hole in the centre and is known as a ‘doughnut chart’, continuing the food- related theme. The function is exactly the same as a pie but the removal of the centre, often to accommodate a labelling property, removes the possibility of the reader judging the angles at the origin. One therefore has to derive the angles from the resulting arc lengths. If you want to display multiple parts (more than three) the bar chart will be a better option and, for many parts, the ‘treemap’ is best. Depending on the allocated space, a ‘stacked bar chart’ may provide an alternative to the pie. Unlike most chart types, the pie chart does not work well in the form of small multiples (unless there is only a single part being displayed). A ‘nested shape chart’, typically based on embedded square or circle areas, enables comparison across a series of one-part-to-whole relationships based on absolute numbers, rather than percentages, where the wholes may vary in size.

C harts Part-to-whole

W affle chart

ALSO KN OW N AS Square pie, unit chart, 100% stacked shape chart

R E PR E SE N TATION D E SC R IPTION

A waffle chart shows how the quantities of different constituent categories make up a whole. It uses a square display usually representing 100 point ‘cells’ through a 10 × 10 grid layout. Each constituent category proportion is displayed through colour-coding a proportional number of cells. Difference in symbol can also be used. The role of the waffle chart is to simplify the counting of proportions in contrast to the angle judgements of the pie chart, though the display is limited to rounded integer values. This is easier when the grid layout facilitates quick recognition of units of 10. As with the pie chart, the waffle chart works best when you are showing how a single part compares to the whole and perhaps offers greater visual impact when there are especially small percentages of a whole. Rather than just colouring in the grid cells, sometimes different symbols will be used to associate with different categories. For example, you might see figures or gender icons used to show the makeup of a given sample population.

E X AM PLE Comparing the proportion of total browser usage for Internet Explorer and Chrome across key milestone moments.

Fi g u re 6.23 The Changing Fortunes of Internet Explorer and Google Chrome

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HOW TO R E AD IT & W HAT TO LOOK FOR

Begin by establishing how the different shapes or colours are associated with different categories. Assess the grid layout to understand the dimension of the chart and the quantity of cell ‘units’ forming the display (e.g. is it a 10 x 10 grid?). Quickly scan the chart to identify the big, medium and small sectors. Notice if there is any significance behind the ordering of the parts. Unless there are value labels, you will need to count/estimate the number of units representing each category value. Comparing parts against other parts will only be possible once you have established the individual part sizes. If several related waffle charts are shown, possibly for different categories or points in time, identify the related colours/shapes in each chart and establish the patterns of size between and across the various charts, looking for trends, declines and general differences.

PR E SE N TATION TIPS

AN N OTATION : Direct labelling can become very cluttered and hard to incorporate elegantly without the need for long arrows.

C OLOUR : Borders around each square cell are useful to help establish the individual units, but do not make the borders too thick to the point where they dominate attention.

C OM POSITION : Always start each row of values from the same side, for consistency and to make it easier for people to estimate the values. When you have several parts in the same waffle chart, where possible try to make the categorical sorting meaningful, maybe organising values in ascending/descending size order or based on a logical categorical order.

VAR IATION S & ALTE R N ATIVE S

Sometimes the waffle chart approach is used to show stacks of absolute unit values and indeed there are overlaps in concept between this variation in the waffle chart and potential applications of the pictogram. Aside from the pie chart, a ‘nested shape chart’ will provide an alternative way of showing a part-to-whole relationship while also occupying a squarified layout.

C harts Part-to-whole

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Stacked bar chart

ALSO KN OW N AS

R E PR E SE N TATION D E SC R IPTION

A stacked bar chart displays a part-to-whole breakdown of quantitative values for different major categories. The percentage proportion of each categorical dimension or ‘part’ is represented by separate bars, distinguished by colour, that are sized according to their proportion and then stacked to create the whole. Sometimes the whole is standardised to represent 100%, at other times the whole will be representative of absolute values. Stacked bar charts work best when the parts are based on ordinal dimensions, which enables ordering of the parts within the stack to help establish the overall shape of the data. If the parts are representative of nominal data, it is best to keep the number of constituent categories quite low, as estimating the size of individual stacked parts when there are many becomes quite hard.

E X AM PLE Comparing the percentage of adults (16–65 year olds) achieving different proficiency levels in literacy across different countries.

Fi g u re 6.24 Literarcy Proficiency: Adult Levels by Country

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HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the axes so you know with what major categorical values each bar is associated and what the quantitative values are, determining if it is a 100% stacked bar or an absolute stacked bar (in which case identify the min and the max). Establish the colour association to understand what categories the bars within each stack represent. Glance across the entire chart. If the categorical data is ordinal, and the sorting/colour of the stacks is intuitive, you should be able to derive meaning from the overall balance of colour patterns, especially where any annotated gridlines help to guide your value estimation. If the categorical data is nominal, seek to locate the dominant colours and the least noticeable ones. Comparing across different stacked bars is made harder by the lack of a common baseline for anything other than the bottom stack on the zero baseline (and for 100% stacked bars, those final ones at the top) and so a general sense of magnitude will be your focus. Study closer the constituent parts within each stack to establish the high-level ranking of biggest > smallest. Estimate (or read, if labels are present) the absolute values of specific stacked parts of interest.

PR E SE N TATION TIPS

AN N OTATION : Direct value labelling can become very cluttered when there are many parts or stacks and you are comparing several different major categories. You might be better with a table if that is your aim. Definitely include value axis labels with logical intervals and it is very helpful to annotate, through gridlines, key units such as the 25%, 50% and 75% positions when based on a 100% stacked bar chart.

C OLOUR : If you are representing categorical ordinal data, colour can be astutely deployed to give a sense of the general balance of values within the whole, but this will only work if their sorting arrangement within the stack is logically applied. For categorical nominal data, ensure the stacked parts have sufficiently

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different colours so that their distinct bar lengths can be efficiently observed.

C OM POSITION : Across the main categories, once again consider the optimum sorting option, maybe organising values in ascending/descending size order or based on a logical categorical order. Judging the size of the stacks with accuracy is harder for those that are not on the zero baseline, so maybe consider which ones are of most importance to be more easily read and place those on the baseline.

VAR IATION S & ALTE R N ATIVE S

The main alternative would be to use ‘multi-panel bar charts’, where separate bar charts each include just one ’stack’/part and they are then repeated for each subsequent constituent category. In the world of finance the ‘waterfall chart’ is a common approach based on a single stacked bar broken up into individual elements, almost like a step-by-step narrative of how the components of income look on one side and then how the components of expenditure look on the other, with the remaining space representing the surplus or deficit. Like their unstacked siblings, stacked bar charts can also be used to show how categorical composition has changed over time.

C harts Part-to-whole

B ack-to-back bar chart

ALSO KN OW N AS Paired bar chart

R E PR E SE N TATION D E SC R IPTION

A back-to-back bar chart displays a part-to-whole breakdown of quantitative values for different major categories. As with any bar chart, the length of a bar represents a quantitative proportion or absolute value for each part and across all major categories. In contrast to the stacked bar chart, where the constituent bars are simply stacked to form a whole, in a back-to-back bar chart the constituent parts are based on diverging categorical dimensions with a ‘directional’ essence such as yes/no, male/female, agree/disagree. The values for each dimension are therefore presented on opposite sides of a shared zero baseline to help reveal the shape and contrast differences across all major categories.

E X AM PLE Comparing the responses to a survey question asking for opinions about ‘the government collection of telephone and Internet data as part of anti-terrorism efforts’ across different demographic categories.

Fi g u re 6.25 Political Polarization in the American Public

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HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the axes so you know with which major categorical values each bar is associated and what the range of the quantitative values is (min to max). Establish what categorical dimensions are represented by the respective sides of the display and any colour associations. Glance across the entire chart to locate the big, small and medium bars and perform global comparisons to establish the high-level ranking of biggest > smallest. Repeat this for each side of the display, noticing any patterns of dominance of larger values on either side. Identify any noticeable exceptions and/or outliers. Perform local comparisons for each category value to estimate the relative sizes (or read, if labels are present) of each bar.

PR E SE N TATION TIPS

AN N OTATION : Chart apparatus devices like tick marks and gridlines in particular can be helpful to increase the accuracy of the reading of the quantitative values.

C OLOUR : The bars either side of the axis do not need to be coloured but often are to create further visual association.

VAR IATION S & ALTE R N ATIVE S

Back-to-back bar charts facilitate a general sense of the shape of diverging categorical dimensions. However, if you want to facilitate direct comparison, a ‘clustered bar chart’ showing adjacent bars helps to compare respective heights more precisely. For analysis that looks at the distribution values across two dimensions, such as the size of populations for age across genders, a ‘back-to-back histogram’ (with male on one side, female on the other), also commonly known as a ‘violin plot’ or ‘population pyramid’, is a useful approach to

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see and compare the respective shapes. Some back-to-back applications do not show a part-to-whole relationship but simply compare quantities for two categorical values. Further variations may appear as ‘back-to-back area charts’ showing mutual change over time for two contrasting states.

C harts Part-to-whole

Treem ap

ALSO KN OW N AS Heat map (wrongly)

R E PR E SE N TATION D E SC R IPTION

A treemap is an enclosure diagram providing a hierarchical display to show how the quantities of different constituent parts make up a whole. It uses a contained rectangular layout (often termed ‘squarified’) representing the 100% total divided into proportionally sized rectangular tiles for each categorical part. Colour can be used to represent an additional quantitative measure, such as an indication of amount of change over a time period. The absolute positioning and dimension of each rectangle is organised by an underlying tiling algorithm to optimise the overall space usage and to cluster related categories into larger rectangle-grouped containers. Treemaps are most commonly used, and of most value, when there are many parts to the whole but they are only valid if the constituent units are legitimately part of the same ‘whole’.

E X AM PLE Comparing the relative value of and the daily performance of stocks across the S&P 500 index grouped by sectors and industries.

Fi g u re 6.26 FinViz: Standard and Poor’s 500 Index

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HOW TO R E AD IT & W HAT TO LOOK FOR

Look at the high-level groupings to understand the different containing arrangements and establish what the colour association is. Glance across the entire chart to seek out the big, small and medium individual rectangular sizes and perform global comparisons to establish a general ranking of biggest > smallest values. Also identify the largest through to smallest container group of rectangles. If the colour coding is based on quantitative variables, look out for the most eye-catching patterns at the extreme end of the scale(s). If labels are provided (or offered through interactivity), browse around the display looking for categories and values of specific interest. As with any display based on the size of the area of a shape, precise reading of values is hard to achieve and so it is important to understand that treemaps can only aim to provide a single-view gist of the properties of the many components of the whole.

PR E SE N TATION TIPS

IN TE R AC TIVITY : Typically, a treemap will be presented with interactive features to enable selection/mouseover events to reveal further annotated details and/or drill-down navigation.

AN N OTATION : Group/container labels are often allocated a cell of space but these are not to be read as proportional values. Effective direct value labelling becomes difficult as the rectangles get smaller, so often only the most prominent values might be annotated. Interactive features will generally offer visibility of the relevant labels where possible.

C OLOUR : Colour can also be used to provide further categorical grouping distinction if not already assigned to represent a quantitative measure of change.

C OM POSITION : As the tiling algorithm is focused on optimising the dimensions and arrangement of the rectangular shapes, treemaps may not always be able to facilitate much internal sorting of high to low values. However, generally you will find the larger shapes appear in the top left of each container and work outwards towards the smaller constituent parts.

VAR IATION S & ALTE R N ATIVE S

A variation of the treemap sees the rectangular layout replaced by a circular one and the rectangular tiles replaced by organic shapes. These are known as ‘Voronoi treemaps’ as the tiling algorithm is informed by a

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Voronoi tessellation. The ‘circle packing diagram’, a variation of the ‘bubble chart’, similarly shows many parts to a whole but uses a non-tessellating circular shape/layout. The ‘mosaic plot’ or ‘Marimekko chart’ is similar in appearance to a treemap but, in contrast to the treemap’s hierarchical display, presents a detailed breakdown of quantitative value distributions across several categorical dimensions, essentially formed by varied width stacked bars.

C harts Part-to-whole

Venn diagram

ALSO KN OW N AS Set diagram, Euler diagram (wrongly)

R E PR E SE N TATION D E SC R IPTION

A Venn diagram shows collections of and relationships between multiple sets. They typically use round or elliptical containers to represent all different ‘membership’ permutations to include all independent and intersecting containers. The size of the contained area is (typically) not important: what is important is in which containing region a value resides, which may be represented through the mark of a text label or ‘point’.

E X AM PLE Comparing sets of permutations for legalities around marijuana usage and same-sex marriage across states of the USA.

Fi g u re 6.27 This Venn Diagram Shows Where You Can Both Smoke Weed and Get a Same-Sex Marriage

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HOW TO R E AD IT & W HAT TO LOOK FOR

To read a Venn diagram firstly establish what the different containers are representative of in terms of their membership. Assess the membership of the intersections (firstly ‘all’, then ‘partial’ intersections when involving more than two sets) then work outwards towards the independent container regions where values are part of one set but not part of others. Occasionally there will be a further grouping state outside of the containers that represents values that have no membership with any set at all.

PR E SE N TATION TIPS

AN N OTATION : Unless you are using point markers to represent membership values, clear labels are vital to indicate how many or which elements hold membership with each possible set combination.

C OLOUR : Colour is often used to create more immediate distinction between the intersections and independent parts or members of each container.

C OM POSITION : As the attributes of size and shape of the containers are of no significance there is more flexibility to manipulate the display to fit the number of sets around the constraint of real estate you are facing and to get across the set memberships you are attempting to show. The complexity of creating containers to accommodate all combinations of intersection and independence states increases as the number of sets increases, especially to preserve all possible combinations of intersections between and independencies from all sets. As the number of sets increases, the symmetry of shape reduces and the circular containers are generally replaced with ellipses. While it is theoretically possible to exceed four and five set diagrams, the ability of readers to make sense of the displays diminishes and so they commonly involve only two or three different sets.

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VAR IATION S & ALTE R N ATIVE S

A common variation or alternative to the Venn (but often mistakenly called a Venn) is the ‘Euler diagram’. The difference is that an Euler diagram does not need to present all possible intersections with and independencies from all sets. A different approach to visualising sets (especially larger numbers) can be achieved using the ‘UpSet’ technique.

C harts Hierarchies

D endrogram

ALSO KN OW N AS Node–link diagram, layout tree, cluster tree, tree hierarchy

R E PR E SE N TATION D E SC R IPTION

A dendrogram is a node–link diagram that displays the hierarchical relationship across multiple tiers of categorical dimensions. It displays a hierarchy based on multi-generational ‘parent-and-child’ relationships. Starting from a singular origin root node (or ‘parent’) each subsequent set of constituent ‘child’ nodes, a tier below and represented by points, is connected by lines (curved or straight) to indicate the existence of a relationship. Each constituent node may have further sub-constituencies represented in the same way, continuing down through to the lowest tier of detail. Each ‘generational’ tier is presented at the same relative distance from the origin. The layout can be based on either a linear tree structure (typically left to right) or radial tree (outwards from the centre).

E X AM PLE Showing a breakdown of the 200+ beer brands belonging to SAB InBev across different countries grouped by continent.

Fi g u re 6.28 The 200+ Beer Brands of SAB InBev

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HOW TO R E AD IT & W HAT TO LOOK FOR

Reading a dendrogram will generally be a highly individual experience based on your familiarity with the subject and your interest in exploring certain hierarchical pathways. The main focus of attention will likely be to find the main clusters from where most constituent parts branch out and to contrast these with the thinner, lighter paths comprising fewer parts. Work left to right (linear) or in to out (radial) through the different routes that stoke your curiosity.

PR E SE N TATION TIPS

AN N OTATION : With labelling required for each node, depending on the number of tiers and the amount of nodes, the size of the text will need to be carefully considered to ensure readability and minimise the effect of clutter.

C OLOUR : Colour would be an optional choice for accentuating certain nodes or applying some further visual categorisation.

C OM POSITION : There are several different layout options to display tree hierarchies like the dendrogram. The common choice is a cluster layout based on the ‘Reingold–Tilford’ tree algorithms that offers a tidying and optimisation treatment for the efficiency of the arrangement of the nodes and connections. The sequencing of sub-constituencies under each node could be logically arranged in some

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more meaningful way than just alphabetical, though the cataloguing nature of A–Z may suit your purpose. The choice of a linear or radial tree structure will be informed largely by the space you have to work in as well as by the cyclical or otherwise nature of the content in your data. The main issue is likely to be one of legibility if and when you have numerous layers of divisions and many constituent parts to show in a single view.

VAR IATION S & ALTE R N ATIVE S

More advanced applications of dendrograms are used to present hierarchical clustering (in fields such as computational biology) and apply more quantitative meaning to the length of the links and the positioning of the nodes. The ‘tree hierarchy diagram’ offers a similar tree structure but introduces quantitative attributes to the nodes using area marks, such as circles, sized according to a quantitative value. An alternative approach to the dendrogram could involve a ‘linear bracket’. This might show hierarchical structures for data-related sporting competitions with knock-out format. The outer nodes would be the starting point representing all the participating competitors/teams. Each subsequent tier would represent those participants who progressed to the next round, continuing through to the finalists and eventual victors.

C harts Hierarchies

Sunburst

ALSO KN OW N AS Adjacency diagram, icicle chart, multi-level pie chart

E X AM PLE Showing a breakdown of the types of companies responsible for extracting different volumes of carbon-based fuels through various activities.

R E PR E SE N TATION D E SC R IPTION

A sunburst chart is an adjacency diagram that displays the hierarchical and part-to-whole relationships across multiple tiers of categorical dimensions. In contrast to the dendrogram, the sunburst uses layers of concentric rings, one layer for each generational tier. Each ring layer is divided into parts based on the constituent categorical dimensions at that tier. Each part is represented by a different circular arc section that is sized (in length; width is constant) according to the relative proportion. Starting from the centre ‘parent’ tier, the outward adjacency of the constituent parts of each tier represents the ‘parent-and-child’ hierarchical composition.

Fi g u re 6.29 Which Fossil Fuel Companies are Most Responsible for Climate Change?

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HOW TO R E AD IT & W HAT TO LOOK FOR

Reading a sunburst chart will be a highly individual experience based on your familiarity with the subject and your interest in exploring certain hierarchical pathways. The main focus of attention will likely be to find the largest arc lengths, representing the largest single constituent parts, and those layers or tiers with the most constituent parts. Work from the centre outwards through the different routes that stoke your curiosity. Depending on the deployment of colour, this may help you identify certain additional categorical patterns.

PR E SE N TATION TIPS

IN TE R AC TIVITY : Often interactive mouseover/selection events are the only way to reveal the annotations here.

AN N OTATION : Labelling can be quite difficult to fit into the narrow spaces afforded by small proportion ‘parts’. If interactivity is not an option you may decide to label only those parts that can accommodate the text space.

C OLOUR : Colours are often used to achieve further categorical distinction.

C OM POSITION : Sometimes the parent–child (and other generational) relationships could be legitimately reversed, so decisions need to be made about the best hierarchy sequencing to suit the

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curiosities of the audience. The sequencing of sub-constituencies under each node could also be logically arranged in a meaningful way, more so than just alphabetical, unless the cataloguing nature of A–Z ordering suits your purpose.

VAR IATION S & ALTE R N ATIVE S

Where the sunburst chart uses a radial layout, the ‘icicle chart‘ uses a vertical, linear layout starting from the top and moving downwards. The choice of a linear or radial tree structure will be informed largely by the space you have to work in as well as by the legitimacy of the cyclical nature of the content in your data. A variation on the sunburst chart would be the ‘ring bracket’. This might show a reverse journey for hierarchical data based on something like sporting competitions with knock-out formats. The outer concentric partitions would represent the participant competitors/teams at the start of the process. The length of these arc line parts would be equally distributed across all constituent parts with each subsequent tier representing ‘participants’ who progress forward to the next ‘round’, continuing through to the finalists and eventual victors in the centre.

C harts C orrelations

Scatter plot chart

ALSO KN OW N AS Scatter graph

R E PR E SE N TATION D E SC R IPTION

A scatter plot displays the relationship between two quantitative measures for different categories. Scatter plots are used to explore visually the potential existence, extent or absence of a significant relationship between the plotted variables. The display is formed by points (usually a dot or circle), representing each category and plotted positionally along quantitative x- and y-axes. Sometimes colour is used to distinguish categorical dimensions across all the points. Scatter plots do not work too well if one or both of the quantitative measures has limited variation in value as this especially causes problems of ‘occlusion’, whereby multiple instances of the similar values are plotted on top of each other and essentially hidden from the reader.

E X AM PLE Exploring the relationship between life expectancy and the percentage of healthy years across all countries.

Fi g u re 6.30 How Long Will We Live — And How Well?

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HOW TO R E AD IT & W HAT TO LOOK FOR

Learn what each quantitative axis relates to and make a note of the range of values in each case (min to max). Look at what category or observation each plotted value on the chart refers to and look up any colour associations being used for categorical distinction. Scan the chart looking for the existence of any diagonal trends that might suggest a linear correlation between the variables, or note the complete absence of any pattern, to mean no correlation. Annotations will often assist in determining the significance of any patterns like this. Identify any clusters of points and also look at the gaps, which can be just as revealing. Some of the most interesting observations come from individual outliers standing out separately from others. Look out for any patterns formed by points with similar categorical colour. One approach to reading the ‘meaning’ of the plotted positions involves trying to break down the chart area into a 2 × 2 grid translating what marks positioned in those general areas might mean – which corner is ‘good’ or ‘bad’ to be located in? Remember that ruling out significant relationships can be just as useful as ruling them in.

PR E SE N TATION TIPS

AN N OTATION : Gridlines can be useful to help make the value estimates clearer and reference lines (such as a trend line of best fit) might aid interpretation. It is usually hard to make direct labelling of all values work well. Firstly, it can be tricky making it clear which value relates to which point, especially when several points may be clustered together. Secondly, it creates a lot of visual clutter. Labelling choices should be based on values that are of most interest to include editorially unless interactive features enable annotations to be revealed through selection or mouseover events. If possible, you might consider putting a number inside the marker to indicate a count of the number of points at the same position if this occurs.

C OLOUR : If colours are being used to distinguish the different categories, ensure these are as visibly different as possible. On the occasion where multiple values may be plotted close to or on top of each other, you might need to use semi-transparency to enable overlapping of points to build up a recognisably darker colour compared to other points, indicating an underlying stack of values at the same location on the chart.

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C OM POSITION : As the encoding of the plotted point values is based on position along an axis, it is not necessary to start the axes from a zero baseline, so just make the scale ranges as representative as possible of the range of values being plotted. Ideally a scatter plot will have a 1:1 aspect ratio (equally as tall as it is wide), creating a squared area to help patterns surface more evidently. If one quantitative variable (e.g. weight) is likely to be affected by the other variable (e.g. height), it is general practice to place the former on the y-axis and the latter on the x-axis. If you have to use a logarithmic quantitative scale on either or both axes, you need to make this clear to readers so they avoid making incorrect conclusions from the resulting patterns (that might imply correlation if the values were linear, for example).

VAR IATION S & ALTE R N ATIVE S

A ‘ternary plot’ is a variation of the scatter plot through the inclusion of a third quantitative variable axis. The ‘bubble plot’ also incorporates a third quantitative variable, this time through encoding the size of a geometric shape (replacing the point marker). A ‘scatter plot matrix’ involves a single view of multiple scatter plots presenting different combinations of plotted quantitative variables, used to explore possible relationships among larger multivariate datasets. A ‘connected scatter plot’ compares the shifting state of two quantitative measures over time.

C harts C orrelations

B ubble plot

ALSO KN OW N AS Bubble chart

R E PR E SE N TATION D E SC R IPTION

A bubble plot displays the relationship between three quantitative measures for different categories. Bubble plots are used visually to explore the potential existence, extent or absence of a significant relationship between the plotted variables. In contrast to the scatter plot, the bubble plot plots proportionally sized circular areas, for each category, across two quantitative axes with the size representing a third quantitative measure. Sometimes colour is used to distinguish categorical dimensions across all the shapes.

E X AM PLE Exploring the relationship between rates of murders, burglaries (per 100,000 population) and population across states of the USA.

Fi g u re 6.31 Crime Rates by State

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HOW TO R E AD IT & W HAT TO LOOK FOR

Learn what each quantitative axis relates to and make a note of the range of values in each case (min to max). Look at what category or observation each plotted value on the chart refers to. Establish the quantitative size associations for the bubble areas and look up any colour associations being used for categorical distinction. Scan the chart looking for the existence of any diagonal trends that might suggest a linear correlation between the variables, or note the complete absence of any pattern, to mean no correlation. Annotations will often assist in determining the significance of any patterns like this. Identify any clusters of points and also look at the gaps, which can be just as revealing. Some of the most interesting observations come from individual outliers standing out separately from others. Look out for any patterns formed by points with similar categorical colour. What can you learn about the distribution of small, medium or large circles: are they clustered together in similar regions of the chart or quite randomly scattered? One approach to reading the ‘meaning’ of the plotted positions involves trying to break down the chart area into a 2 × 2 grid translating what marks positioned in those general areas might mean – which corner is ‘good’ or ‘bad’ to be located in? Remember that ruling out significant relationships can be just as useful as ruling them in. Estimating and comparing the size of areas is not as easy as it is for judging bar length or dot position. This means that the use of this chart type will primarily be about facilitating a gist – a general sense of the hierarchy of the largest and smallest values.

PR E SE N TATION TIPS

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C OLOUR : If colours are being used to distinguish the different categories, ensure these are as visibly different as possible. When a circle has a large value its size will often overlap in spatial terms with other values. The use of outline borders and semi-transparent colours helps with the task of avoiding occlusion (visually hiding values behind others).

C OM POSITION : As the encoding of the plotted area marker values is based on position along an axis, it is not necessary to start the axes from a zero baseline – just make the scale ranges as representative as possible of the range of values being plotted. Make sensible decisions about how large to make the maximum bubble size; this will usually require trial and error experimentation to find the right balance. Ideally a bubble plot will have a 1:1 aspect ratio (equally as tall as it is wide), creating a squared area to help patterns surface more evidently. If one quantitative variable (e.g. weight) is likely to be affected by the other variable (e.g. height), it is general practice to place the former on the y-axis and the latter on the x-axis. Geometric accuracy of the circle size calculations is paramount, since mistakes are often made with circle size calculations: it is the area you are modifying, not the diameter/radius. If you wish to make your bubbles appear as 3D spheres you are essentially no longer representing quantitative values through the size of a geometric area mark, rather the mark will be a ‘form’ and so the size calculation will be based on volume, not area.

VAR IATION S & ALTE R N ATIVE S

If the third quantitative variable is removed, the display would just become a ‘scatter plot’. Variations on the bubble plot might see the use of different geometric areas as the markers, maybe introducing extra meaning from the underlying data through the shape, size and dimensions used.

C harts C orrelations

Parallel coordinates

ALSO KN OW N AS Parallel sets

R E PR E SE N TATION D E SC R IPTION

Parallel coordinates display multiple quantitative measures for different categories in a single display. They are used visually to explore the relationships and characteristics of multi-dimensional, multivariate data. Parallel coordinates are based on a series of parallel axes representing different quantitative measures with independent axis scales. The quantitative values for each measure are plotted and then connected to form a single line. Each connected line represents a different category record. Colour may be used to differentiate further categorical dimensions. As more data is added the collective ’shape’ of the data emerges and helps to inform the possibility of relationships existing among the different measures. Parallel coordinates look quite overwhelming but remember that they are almost always only used to assist in exploratory work of large and varied datasets, more so than being used for explanatory presentations of data. Generally the greater the number of measures, the more difficult the task of making sense of the underlying patterns will be, so be discerning in your choice of which variables to include. This method does not work for showing categorical (nominal) measures nor does it really offer value with the inclusion of low-range, discrete quantitative variables used (e.g. number of legs per human). Patterns will mean very little when intersecting with such

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axes (they may be better deployed as a filtering parameter or a coloured categorical separator).

E X AM PLE Exploring the relationship between nutrient contents for 14 different attributes across 1,153 different items of food.

Fi g u re 6.32 Nutrient Contents — Parallel Coordinates

HOW TO R E AD IT & W HAT TO LOOK FOR

Look around the chart and acquaint yourself with what each quantitative measure axis represents. Also note what kind of sequencing of measure has been used: are neighbouring measures significantly paired? Note the range of values along each independent axis so you understand what positions along the scales represent and can determine what higher and lower positions mean. If colour has been used to group related records then identify what these represent. Scan the overall mass of lines to identify any major patterns. Study the patterns in the space between each pair of adjacent axes. This is where you will really see the potential presence or absence of, and nature of, relationships between measures. The main patterns to identify involve the presence of parallel lines (showing consistent relationships), lines converging in similar directions (some correlation) and then complete criss-crossing (negative relationship). Look out for any associations in the patterns across colour groupings. Remember that ruling out significant relationships can be just as useful as ruling them in.

PR E SE N TATION TIPS

IN TE R AC TIVITY : Parallel coordinates are particularly useful when offered with interactive features, such as filtering techniques, enabling the user to interrogate and manipulate the display to facilitate visual exploration. Additionally, the option to rearrange the sequence of the measures can be especially useful.

AN N OTATION : The inclusion of visible annotated features like axis lines, tick marks, gridlines and value labels can naturally aid the readability of the data but be aware of the impact of clutter.

C OLOUR : When you are plotting large quantities of records, inevitably there will be over-plotting and this might disguise the real weight of values, so the variation in the darkness of colour can be used to establish density of observations.

C OM POSITION : The ordering of the quantitative variables has to be of optimum significance as the connections between adjacent axes will offer the main way of seeing the local relationships: the patterns will change for every different ordering permutation. Remember that the line directions connecting records are often inconsequential in their meaning unless neighbouring measures have a common scale and similar

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meaning: the connections are more about establishing commonality of pattern across records, rather than there being anything too significant behind the absolute slope direction/length.

VAR IATION S & ALTE R N ATIVE S

The ‘radar chart’ has similarities with parallel coordinates in that they include several independent quantitative measures in the same chart but on a radial layout and usually only showing data for one record in the same display. A variation on the parallel coordinate would be the ‘Sankey diagram’, which displays categorical composition and quantitative flows between different categorical dimensions or ‘stages’.

C harts C orrelations

Heat m ap

ALSO KN OW N AS Matrix chart, mosaic plot

R E PR E SE N TATION D E SC R IPTION

A heat map displays quantitative values at the intersection between two categorical dimensions. The chart comprises two categorical axes with each possible value presented across the row and column headers of a table layout. Each corresponding cell is then colour-coded to represent a quantitative value for each combination of category pairing. It is not easy for the eye to determine the exact quantitative values represented by the colours, even if there is a colour scale provided, so heat maps mainly facilitate a gist of the order of magnitude.

E X AM PLE Exploring the connections between different Avengers characters appearing in the same Marvel comic book titles between 1963 and 2015.

Fi g u re 6.33 How the ‘Avengers’ Line-up Has Changed Over the Years

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HOW TO R E AD IT & W HAT TO LOOK FOR

Learn what each categorical dimension relates to and make a note of the range of values in each case, paying attention to the significance of any ordering. Establish the quantitative value associations for the colour scales, usually found via a legend. Glance across the entire chart to locate the big, small and medium shades (generally darker = larger) and perform global comparisons to establish the high-level ranking of biggest > smallest. Scan across each row and/or column to see if there are specific patterns associated with either set of categories. Identify any noticeable exceptions and/or outliers. Perform local comparisons between neighbouring cell’s areas, to identify larger than and smaller than relationships and estimate the relative proportions. Estimate (or read, if labels are present) the absolute values of specific colour scales of interest.

PR E SE N TATION TIPS

AN N OTATION : Direct value labelling is possible, otherwise a clear legend to indicate colour associations will suffice.

C OLOUR : Sometimes multiple different colour hues may be used to subdivide the quantitative values into further distinct categorical groups. Decisions about how many colour-scale levels and what intervals each relates to in value ranges will affect the patterns that emerge. There is no single right answer – you will arrive at it largely through trial and error/experimentation – but it is important to consider, especially when you have a diverse distribution of values.

C OM POSITION : Logical sorting (and maybe even sub-grouping) of the categorical values along each axis will aid readability and may help surface key relationships.

VAR IATION S & ALTE R N ATIVE S

A ‘radial heat map’ offers a structure variation whereby the table may be portrayed using a circular layout. As with any radial display this is only really of value if the cyclical ordering means something for the subject matter. A variation would see colour shading replaced by a measure of pattern density, using a scale of ‘packedness’ to indicate increasing quantitative values. An alternative approach would be the ‘matrix chart’ using size of a shape to indicate the quantitative or a range of point marker to display categorical

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characteristics.

C harts C onnections

M atrix chart

ALSO KN OW N AS Table chart

R E PR E SE N TATION D E SC R IPTION

A matrix chart displays quantitative values at the intersection between two categorical dimensions. The chart comprises two categorical axes with each possible value presented across the row and column headers of a table layout. Each corresponding cell is then marked by a geometric shape with its area sized to represent a quantitative value and colour often used visually to distinguish a further categorical dimension. While they are most commonly seen using circles, you can use other proportionally sized shapes.

E X AM PLE Exploring the perceived difficulty of fixtures across the season for teams in the premier league 2013–14.

Fi g u re 6.34 Interactive Fixture Molecules

HOW TO R E AD IT & W HAT TO LOOK FOR

Learn what each categorical dimension relates to and make a note of the range of values in each case, paying attention to the significance of any ordering. Establish the quantitative size associations for the area marks and look up any colour associations being used, both usually found via a legend. Glance across the entire chart to locate the big, small and medium areas and perform global comparisons to establish the high-level ranking of biggest > smallest. Scan across each row and/or column to see if there are specific patterns associated with either set of categories. Identify any noticeable exceptions and/or outliers. Perform local

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comparisons between neighbouring circular areas, to identify larger than and smaller than relationships and estimate the relative proportions. Estimate (or read, if labels are present) the absolute values of specific geometric areas of interest.

PR E SE N TATION TIPS

AN N OTATION : Direct value labelling is possible, otherwise be sure to include a clear size legend. Normally this will be more than sufficient as the reader may simply be looking to get a gist of the order of magnitude.

C OLOUR : If colours are being used to distinguish the different categories, ensure these are as visibly different as possible.

C OM POSITION : If there are large outlier values there may be occasions when the size of a few circles outgrows the cell it occupies. You might editorially decide to allow this, as the striking shape may create a certain impact, otherwise you will need to limit the largest quantitative value to be represented by the maximum space available within the table’s cell layout. Logical sorting (and maybe even sub-grouping) of the categorical values along each axis will aid readability and may help surface key relationships. The geometric accuracy of the circle size calculations is paramount. Mistakes are often made with circle size calculations: it is the area you are modifying, not the diameter/radius.

VAR IATION S & ALTE R N ATIVE S

A variation may be to remove the quantitative attribute of the area marker, replacing it with a point marker to represent a categorical status to indicate simply a yes/no observation through the presence/absence of a point or through the quantity of points to represent a total. An application of this might be in calendar form whereby a marker in a date cell indicates an instance of something. It could also employ a broader range of different categorical options; in practice any kind of marker (symbol, colour, photograph) could be used to show a characteristic of the relationship at each coordinate cell. An alternative might be the ‘heat map’ which colour-codes the respective cells to indicate a relationship based on a quantitative measure.

C harts C onnections

N ode–link diagram

ALSO KN OW N AS Network diagram, graph, hairballs

R E PR E SE N TATION D E SC R IPTION

Node–link diagrams display relationships through the connections between categorical ‘entities’. The entry- level version of this type of diagram displays entities as nodes (represented by point marks and usually including a label) with links or edges (represented by lines) depicting the existence of connections. The connecting lines will often display an attribute of direction to indicate the influencer relationship. In some versions a quantitative weighting is applied to the show relationship strength, maybe through increased line width. Replacing point marks with a geometric shape and using attributes of size and colour is a further

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variation. Often the complexity seen in these displays is merely a reflection of the underlying complexity of the subject and/or system upon which the data is based, so oversimplifying can compromise the essence of such content.

E X AM PLE Exploring the connections of voting patterns for Democrats and Republicans across all members of the US House of Representatives from 1949 to 2012.

Fi g u re 6.35 The Rise of Partisanship and Super-cooperators in the U.S.

HOW TO R E AD IT & W HAT TO LOOK FOR

The first thing to consider is what entity each node (point or circular area) represents and what the links mean in relationship terms. There may be several other properties to acquaint yourself with, including attributes like the size of the node areas, the categorical nature of colouring, and the width and direction of the connections. Across the graph you will mainly be seeking out the clusters that show the nodes with the most relationships (representative of influencers or hubs) and those without (including outliers). Small networks will generally enable you to look closely at specific nodes and connections and easily see the emerging relationships. When datasets are especially large, consisting of thousands of nodes and greater numbers of mutual connections, the displays can seem overwhelmingly cluttered and will be too dense to make many detailed observations at node–link level. Instead, just relax and know that your readability will be about a higher level sense-making of the clusters/hubs and main outliers.

PR E SE N TATION TIPS

IN TE R AC TIVITY : Node–link diagrams are particularly useful when offered with interactive features, enabling the user to interrogate and manipulate the display to facilitate visual exploration. The option to apply filters to reduce the busy-ness of the visual and enable isolation of individual node connections helps users to focus on specific parts of the network of interest.

AN N OTATION : The extent of annotated features tends to be through the inclusion of value labels for each entity. Accommodating the relative word sizes on each node can be difficult to achieve with real elegance (once again that is where interactivity adds value, through the select/mouseover event to reveal the label).

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C OLOUR : Aside from the possible categorical colouring of each node, decisions need to be made about the colour of the connecting lines, especially on deciding how prominent these links will be in contrast to the nodes.

C OM POSITION : Composition decisions are where most of the presentation customisation exists. There are several common algorithmic treatments used to compute custom arrangements to optimise network displays, such as force-directed layouts (using the physics of repulsion and springs to amplify relationships) and simplifying techniques (such as edge bundling to aggregate/summarise multiple similar links).

VAR IATION S & ALTE R N ATIVE S

There are many derivatives of the node–link diagram, as explained, based on variations in the use of different attributes. ‘Hive plots’ and ‘BioFabric’ offer alternative approaches based on replacing nodes with vertices.

C harts C onnections

C hord diagram

ALSO KN OW N AS Radial network diagram, arc diagram (wrongly)

R E PR E SE N TATION D E SC R IPTION

A chord diagram displays relationships through the connections between and within categories. They are formed around a radial display with different categories located around the edge: either as individual nodes or proportionally sized segments (arcs) of the circumference according to a part-to-whole breakdown. Emerging inwards from each origin position are curved lines that join with other related categorical locations around the edge. The connecting lines are normally proportionally sized according to a quantitative measure and a directional or influencing relationship is often indicated. The perceived readability of the chord diagram will always be influenced by the quantity and range of values being plotted. Small networks will enable a reader to look closely at specific categories and their connections to see the emerging relationships easily; larger systems will look busy through the network of lines but they can still provide windows into complex networks of influence. Often the complexity seen in these displays is merely a reflection of the underlying complexity of the subject and/or system upon which the data is based, so oversimplifying can compromise the essence of such content.

E X AM PLE Exploring the connections of migration between and within 10 world regions based on estimates across five-year intervals between 1990 and 2010.

Fi g u re 6.36 The Global Flow of People

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HOW TO R E AD IT & W HAT TO LOOK FOR

First determine how categories are displayed around the circumference, either as nodes or part-to-whole arcs, and identify each one individually. Consider the implication of the radial sorting of these categorical values and, if based on part-to-whole sizes, establish a sense of the largest > smallest arc lengths. Colour- coding may be applied to the categories so note any associations. Look inside the display to determine what relationships the connecting lines represent and check for any directional significance. Look closer at the tangled collection of lines criss-crossing this space, noting the big values (usually through line weight or width) and the small ones. Avoid being distracted by the distance a line travels, which is just a by-product of the outer categorical arrangement: a long connecting line is just as significant a relationship as a short one. For this reason, pay close attention to any connecting lines that have very short looping distances to adjacent categories. Are there any patterns of lines heading towards or leaving certain categories?

PR E SE N TATION TIPS

IN TE R AC TIVITY : Chord diagrams are particularly useful when offered with interactive features, enabling the user to interrogate and manipulate the display to facilitate visual exploration. The option to apply filters to reduce the busy-ness of the visual and enable isolation of individual node connections helps users to focus on specific parts of the network of interest.

AN N OTATION : Annotated features tend to be limited to value labelling of the categories around the circumference and, occasionally, directly onto the base or ends of the connecting lines (usually just those that are large enough to accommodate them).

C OLOUR : Aside from the categorical colouring of each node, decisions need to be made about the colour of the connecting lines, especially on deciding how prominent these links will be in contrast to the

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nodes. Sometimes the connections will match the origin or destination colours, or they will combine the two (with a start and end colour to match the relationship).

C OM POSITION : The main arrangement decisions come through sorting, firstly by generating as much logical meaning from the categorical values around the edge of the circle and secondly by deciding on the sorting of the connecting lines in the z-dimension – if many lines are crossing, there is a need to think about which will be on top and which will be below. Showing the direction of connections can be difficult as there is so little room for manoeuvring many more visual attributes, such as arrows or colour changes. One common, subtle solution is to pull the destination join back a bit, leaving a small gap between the connecting line and the destination arc. This then contrasts with connecting lines that emerge directly from the categorical arcs, showing it is their origin.

VAR IATION S & ALTE R N ATIVE S

The main alternatives would be to consider variations of the ‘node–link diagram’ or, specifically, the ‘arc diagram’, which offers a further variation on the theme of networked displays, placing all the nodes along a baseline and forming connections using semi-circular arcs, rather than using a graph or radial layout.

C harts C onnections

Sankey diagram

ALSO KN OW N AS Alluvial diagram

R E PR E SE N TATION D E SC R IPTION

Sankey diagrams display categorical composition and quantitative flows between different categorical dimensions or ‘stages’. The most common contemporary form involves a two-sided display, with each side representing different (but related) categorical dimensions or different states of the same dimension (such as ‘before and after’). On each side there is effectively a stacked bar chart displaying proportionally sized and differently coloured (or spaced apart) constituent parts of a whole. Curved bands link each side of the display to represent connecting categories (origin and destination) with the proportionally sized band (its thickness) indicating the quantitative nature of this relationship. Some variations involve multiple stages and might present attrition through the diminution size of subsequent stacks. Traditionally the Sankey has been used as a flow diagram to visualise energy or material usage across engineering processes. It is closely related to the ‘alluvial diagram’, which tends to show changes in composition and flow over time, but the Sankey label is often applied to these displays also.

E X AM PLE Exploring the seat changes among political parties between the 2010 and 2015 UK General Elections.

Fi g u re 6.37 UK Election Results by Political Party, 2010 vs 2015

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HOW TO R E AD IT & W HAT TO LOOK FOR

Based on the basic two-sided version of the Sankey diagram, look down both sides of the chart to learn what states are represented and what the constituent categories are. Pay close attention to the categorical sorting and pick out the large and small values on each side. Then look at the connecting lines, making observations about the largest and narrowest bands and noting any that seem to be mostly redistributed into a different category compared to those that just join with the same. Notice any small break-off bands that seem to cross the height of the whole chart, perhaps representing a more dramatic change or diversion between states. As with most network-type visualisations, the perceived readability of the Sankey diagram will always be influenced by the quantity and range of values being plotted, as well as the number of different states presented.

PR E SE N TATION TIPS

IN TE R AC TIVITY : Sankey diagrams are particularly useful when offered with interactive features, enabling the user to interrogate and manipulate the display to facilitate visual exploration. The option to apply filters to reduce the busy-ness of the visual and enable isolation of individual node connections helps users to focus on specific parts of the network of interest.

AN N OTATION : Annotated features tend to be limited to value labelling of the categories that make up each ‘state’ stack.

C OLOUR : Colouring is often used visually to indicate the categories of the connecting bands, though it can get a little complicated when trying to combine a sense of change through an origin category colour blending with a destination category colour when there has been a switch.

C OM POSITION : The main arrangement decisions come through sorting, firstly by generating as much

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logical meaning from the categorical values within the stacks and, secondly, by deciding on the sorting of the connecting lines in the z-dimension – if many lines are crossing, there is a need to think about which will be on top and which will be below. There is no significant difference between a landscape or portrait layout, which will depend on the subject matter ‘fit’ and the space within which you have to work. Try to ensure that the sorting of the categorical dimensions is as logical and meaningful as possible.

VAR IATION S & ALTE R N ATIVE S

The concept of a Sankey diagram showing composition and flow can also be mapped onto a geographical projection as one of the variations of the ‘flow map’. You could use a ‘chord diagram’ as an alternative to show how larger networks are composed proportionally and in their connections. Showing how component parts have changed over time could just be displayed using a ‘stacked area chart’. A ‘funnel chart’ is a much simplified display to show how a single value changes (usually diminishing) across states, for topics like sales conversion. This often is based on a funnel-like shape formed by a wide bar at the top (those entering the system) and then gradually narrower bars, stage by stage towards the end state.

C harts Trends

Line chart

ALSO KN OW N AS Fever chart, stock chart

R E PR E SE N TATION D E SC R IPTION

A line chart shows how quantitative values for different categories have changed over time. They are typically structured around a temporal x-axis with equal intervals from the earliest to latest point in time. Quantitative values are plotted using joined-up lines that effectively connect consecutive points positioned along a y-axis. The resulting slopes formed between the two ends of each line provide an indication of the local trends between points in time. As this sequence is extended to plot all values across the time frame it forms an overall line representative of the quantitative change over time story for a single categorical value. Multiple categories can be displayed in the same view, each represented by a unique line. Sometimes a point (circle/dot) is also used to substantiate the visibility of individual values. The lines used in a line chart will generally be straight. However, sometimes curved line interpolation may be used as a method of estimating values between known data points. This approach can be useful to help emphasise a general trend. While this might slightly compromise the visual accuracy of discrete values if you already have approximations, this will have less impact.

E X AM PLE Showing changes in percentage income growth for the Top 1% and Bottom 90% of earners in the USA between 1917 and 2012.

Fi g u re 6.38 The Fall and Rise of U.S. Inequality, in 2 Graphs

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HOW TO R E AD IT & W HAT TO LOOK FOR

Firstly, learn about the axes: what is the time period range presented on the x-axis (and in what order) and what is the range of quantitative values shown on the y-axis, paying particular attention to the origin value (which may not be zero)? Inside the chart, determine what categories each line represents: for single lines this will usually be clear from the chart title, for multiple lines you might have direct labelling or a legend to learn colour associations. Think about what high and low values mean: is it ‘good’ to be large/small, increasing or decreasing? Glance at the general patterns (especially if there are many) looking for observations such as any trends (short or long term), any sudden moments of a rise or fall (V- or W -shapes, or inverted), any sense of seasonal or cyclical patterns, any points of interest where lines cross each other or key thresholds that are reached/exceeded. Can you mentally extrapolate from the values shown any sense of a forecasted trend? Avoid jumping to spurious interpretations if you see two line series following a similar pattern; this does not necessarily mean that one thing has caused the other, it might just be coincidence. Then look more closely at categories of interest and at patterns around specific moments in time, and pick out the peak, low, earliest and latest values for each line. Where available, compare the changing quantities against annotated references such as targets, forecast, previous time periods, range bands, etc.

PR E SE N TATION TIPS

IN TE R AC TIVITY : Interactivity may be especially helpful if you have many categories and wish to enable the user to isolate (in focus terms) a certain line category of interest.

AN N OTATION : Chart apparatus devices like tick marks and gridlines in particular can be helpful to increase the accuracy of the reading of the quantitative values. If you have axis labels you should not need direct labels on each value point – this will be label overload. You might choose to annotate specific values of interest (highest, lowest, specific milestones). Think carefully about what is the most useful and meaningful interval for your time axis labelling. When several categories are being shown, if possible, try directly to label the categories shown by each line, maybe at the start or end position.

C OLOUR : When many categories are shown it may be that only certain emphasised lines of interest possess a colour and a label – the rest are left in greyscale for context.

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C OM POSITION : Composition choices are mostly concerned with the chart’s dimensions: its aspect ratio, how high and wide to make it. The sequencing of values tends to be left to right for the sequence of the time-based x-axis and low rising to high values on the y-axis; you will need a good (and clearly annotated) reason to break this convention. Line charts do not always need the y-axis to start at zero, as we are not judging the size of a bar, rather the position along an axis. You should expect to see a zero baseline if zero has some critical significance in the interpretation of the trends. If your y-axis origin is not going to be zero, you might include a small gap between the x-axis and the minimum so that it is not implied. Be aware that the upward and downward trends on a line chart can seem more significant if the chart width is narrow and less significant if it is more stretched out. There is no single rule to follow here but a useful notion involves ‘banking to 45°’ whereby the average slope angle across your chart heads towards 45°. While it is impractical to actually measure this, judging by eye tends to be more than sufficient.

VAR IATION S & ALTE R N ATIVE S

Variations of the line chart may include the ‘cumulative line chart’ or ‘step chart’. ‘Spark lines’ are mini line charts that aim to occupy almost only a word’s length amount of space. Often seen in dashboards where space is at a premium and there is a desire to optimise the density of the display. ‘Bar charts’ can also be used to show how values look over time when there is perhaps greater volatility in the quantitative values across the time period and when the focus is on the absolute values at each point in time, more so than trends. Sometimes a line chart can show quantitative trends over continuous space rather than time. For showing ranking over time, consider the ‘bump chart’, and for before and after comparisons, the ‘slope graph’.

C harts Trends

B um p chart

ALSO KN OW N AS

R E PR E SE N TATION D E SC R IPTION

A bump chart shows how quantitative rankings for categories have changed over time. They are typically structured around a temporal x-axis with equal intervals from the earliest to latest point in time. Quantitative rankings are plotted using joined-up lines that effectively connect consecutive points positioned along a y- axis (typically top = first). The resulting slopes formed between the two ends of each line provide an indication of the local ranking trends between points in time. As this sequence is extended to plot all values across the time frame it forms an overall line representative of the ranking story for a single categorical value. Multiple categories are often displayed in the same view, showing how rankings have collectively changed over time. Sometimes a point (circle/dot) mark is also used to substantiate the connected visibility of category lines, as is colour (for the lines and/or the points).

E X AM PLE Showing changes in rank of the most populated US cities at each census between 1790 and 1890.

Fi g u re 6.39 Census Bump: Rank of the Most Populous Cities at Each Census, 1790—1890

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HOW TO R E AD IT & W HAT TO LOOK FOR

Firstly, you need to learn about the axes. What is the time period range presented on the x-axis (and in what order)? What are the range of quantitative rankings shown on the y-axis (check that the ranks start at 1 from the top downwards)? Inside the chart, determine what categories each line represents: this might be explained through direct labelling, a colour legend, interactivity or through differentiating point marker attributes of colour/shape/pattern. Think about what high and low ranks mean: is it ‘good’ to be high up the rankings and is it better to be moving up or down? Consider the general patterns to look for observations such as consistent trends (largely parallel lines) or completely non-relational patterns (lines moving in all directions). Are there any prominent stories of categories that have had a sudden rise or fall (V- or W- shapes, or inverted)? Is there any evidence of seasonal or cyclical patterns, any key points of interest where lines cross each other or key thresholds that are reached/exceeded? Next, look more closely at categories of interest and at patterns around specific moments in time, and pick out the peak, low, earliest and latest values for each line.

PR E SE N TATION TIPS

IN TE R AC TIVITY : Interactivity is usually necessary with bump charts, especially if you have many categories and wish to enable the user to isolate (in focus terms) a certain line category of interest.

AN N OTATION : The ranking labels can be derived from the vertical position along the scale so direct labelling is usually unnecessary. You might choose to annotate specific values of interest (highest, lowest, specific milestones). Think carefully about what is the most useful and meaningful interval for your time axis labelling.

C OLOUR : Often, with many categories to show in the same chart, the big challenge is to distinguish each line, especially as they likely criss-cross often with others. Using colour association can be useful for less than 10 categories, but for more than that you really need to offer the interactivity or maybe decide that only certain emphasised lines of interest will possess a colour and the rest are left in greyscale for context.

C OM POSITION : The sequencing of values tends to be left to right for the sequence of the time-based

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x-axis with high rankings (low number) on the y-axis moving downwards. You will therefore need a good (and clearly annotated) reason to break this convention.

VAR IATION S & ALTE R N ATIVE S

Alluvial diagrams (similar to Sankey diagrams) can show how rankings have changed over time while also incorporating a component of quantitative magnitude. This approach is effectively merging the ‘bump chart’ with the ‘stacked area chart’. Consider ‘line charts’ and ‘area charts’ if the ranking is of secondary interest to the absolute values.

C harts Trends

Slope graph chart

ALSO KN OW N AS Slope chart

R E PR E SE N TATION D E SC R IPTION

A slope graph shows a ‘before and after’ display of changes in quantities for different categories. The display is based on (typically) two parallel quantitative axes with a consistent scale range to cover all possible quantitative values. A line is plotted for each category connecting the two axes together with the vertical position on each axis representing the respective quantitative values. Sometime a dot is also used to further substantiate the visibility of the value positions. These connecting lines form slopes that indicate the upward, downward or stable trend between points in time. The resulting display incorporates absolute values, reveals rank and, of course, shows change between time. Colours are often used visually to distinguish different categorical lines, otherwise this can be used to surface visibly the major trend states (up, down, no change). A slope graph works less well when all values (or the majority) are going in the same direction; consider alternatives if this is the case.

E X AM PLE Showing changes in the share of power sources across all US states between 2004 and 2014.

Fi g u re 6.40 Coal, Gas, Nuclear, Hydro? How Your State Generates Power

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HOW TO R E AD IT & W HAT TO LOOK FOR

Firstly, learn about the axes: what are the two points in time being presented and what is the possible range of quantitative values shown on the y-axis, checking that the ranks start from the top down? Inside the chart, learn what each category line relates to and determine what categories each line represents: this might be explained through direct labelling, a colour legend, or through interactivity. Think about what upward, downward and stable trends mean: is it ‘good’ to be moving up or down? Is it more interesting to show no change? Look at the general patterns to observe such things as consistent trends (largely parallel lines in either direction) or completely non-relational patterns (lines moving in all directions). Colour may be used to accentuate the distinction between upward and downward trends. Are there any prominent stories of categories that have had a dramatic rise or fall? Even if no values have dramatically altered, that in itself can be an important finding, especially if change was expected. Next, look more closely at categories of interest and pick out the highest and lowest values on each side to learn about those stories. Look for the gaps where there are no values, and at outlier values too, to see if some sit outside the normal value clusters.

PR E SE N TATION TIPS

IN TE R AC TIVITY : Depending on the number of category values being presented, slope graphs can become quite busy, especially if there are bunches of similar values and slope transitions. This also causes a problem with accommodating multiple labels on the same value. On these occasions you might find interactive slope graphs to help filter/exclude certain values.

AN N OTATION : Labelling of each category will get busy, especially when there are shared values, so you might choose to annotate specific values of interest (highest, lowest, of editorial interest).

C OLOUR : Often when you have many categories to show in the same chart the big challenge is to

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distinguish each line, especially as they likely criss-cross often with others. Using colour association can be useful for less than 10 categories usually with direct labelling on the left and/or right of the chart.

C OM POSITION : The aspect ratio of the slope graph (height and width) will often be determined by the space you have to work with.

VAR IATION S & ALTE R N ATIVE S

Rather than showing a before and after story, some slope graphs are used to show the relationship between different quantitative measures for linked categories. In this case the connecting line is not indicative of a directional relationship, just the relationship itself. An alternative option would be the ‘connected dot plot’ which can also show before and after stories and is a better option when all values are moving in the same direction.

C harts Trends

C onnected scatter plot

ALSO KN OW N AS Trail chart

R E PR E SE N TATION D E SC R IPTION

A connected scatter plot displays the relationship between two quantitative measures over time. The display is formed by plotting marks like a dot or circle for each point in time at the respective coordinates along two quantitative x- and y-axes. The collection of individual points is then connected (think of a dot-to-dot drawing puzzle) using lines joining each consecutive point in time to form a sequence of change. Generally there would only be a single connected line plotted on a chart to avoid the great visual complexity of overlaying several in one display. However, if multiple categories are to be included, colour is typically used to distinguish each series.

E X AM PLE Showing changes in the daily price and availability of Super Bowl tickets on the secondary market four weeks prior to the event across five Super Bowl finals.

Fi g u re 6.41 Holdouts Find Cheapest Super Bowl Tickets Late in the Game

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HOW TO R E AD IT & W HAT TO LOOK FOR

Learn what each quantitative axis relates to and make a note of the range of values in each case (min to max). Look at what each plotted value on the chart refers to in terms of its date label and determine the meaning of line direction. It usually helps to parse your thinking by considering what higher/lower values mean for each quantitative axis individually and then combining the joint meaning thereafter. Try to follow the chart from the start to the end, mapping out in your mind the sequence of a narrative as the values change in all directions and noting the extreme values in the outer edges of your line’s reach. Look at the overall pattern of the connected line: is it consistently moving in one direction? Does it ebb and flow in all directions? Does it create a spiral shape? Compare consecutive points for a more focused view of change between two points.

PR E SE N TATION TIPS

IN TE R AC TIVITY : The biggest challenge is making the connections and the sequence as visible as possible. This becomes much harder when values change very little and/or they loop back almost in spiral fashion, crossing back over themselves. It is especially hard to label the sequential time values elegantly. One option to overcome this is through interactivity and particularly through animated sequences which build up the display, connecting one line at a time and unveiling the date labels as time progresses. It is often the case that only one series will be plotted. However, interactive options may allow the user to overlay one or more for comparison, switching them on and off as required.

AN N OTATION : Connected scatter plots are generally seen as one of the most complex chart types for the unfamiliar reader to work out how to read, given the amount of different attributes working together in the display. It is therefore vital that as much help is given to the reader as possible with ‘how to read’ guides

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and illustrations of what the different directions of change mean.

C OLOUR : Colour is only generally used to accentuate certain sections of a sequence that might represent a particularly noteworthy stage of narrative.

C OM POSITION : As the encoding of the plotted point values is based on position along an axis, it is not necessary to start the axes from a zero baseline – just make the scale ranges as representative as possible of the range of values being plotted. Ideally a connected scatter plot will have a 1:1 aspect ratio (equally as tall as it is wide), creating a squared area to help patterns surface more evidently. If one quantitative variable (e.g. weight) is likely to be affected by the other variable (e.g. height), it is general practice to place the former on the y-axis and the latter on the x-axis.

VAR IATION S & ALTE R N ATIVE S

The ‘comet chart’ is to the connected scatter plot what the ‘slope graph’ is to the ‘line chart’ – a summarised view of the changing relationships across two quantitative values between just two points in time. Naturally a reduced variation of the connected scatter plot is simply the ‘scatter plot’ where there is no time dimension or elements of connectedness.

C harts Trends

Area chart

ALSO KN OW N AS

R E PR E SE N TATION D E SC R IPTION

A line chart shows how quantitative values for different categories have changed over time. They are typically structured around a temporal x-axis with equal intervals from the earliest to latest point in time. Quantitative values are plotted using joined-up lines that effectively connect consecutive points positioned along a y-axis. The resulting slopes formed between the two ends of each line provide an indication of the local trends between points in time. As this sequence is extended to plot all values across the time frame it forms an overall line representative of the quantitative change over time story for a single categorical value. To accentuate the magnitude of the quantitative values and the change through time the area beneath the line is filled with colour. The height of each coloured layer at each point in time reveals its quantity. Area charts can display values for several categories, using stacks, to show also the changing part-to-whole relationship.

E X AM PLE Showing changes in the average monthly price ($ per barrel) of crude oil between 1985 and 2015.

Fi g u re 6.42 Crude Oil Prices (West Texas Intermediate), 1985—2015

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HOW TO R E AD IT & W HAT TO LOOK FOR

Firstly, learn about the axes: what is the time period range presented on the x-axis (and in what order) and what is the range of quantitative values shown on the y-axis, paying particular attention to whether it is a percentage or absolute based scale? Inside the chart, determine what categories each area layer represents: for single areas this will usually be clear from the chart title, for multiple areas you might have direct labelling or a nearby legend to learn colour associations. Think about what high and low values mean: is it ‘good’ to be large/small, increasing or decreasing? Glance at the general patterns (especially if there are many layers), looking at the visible ‘thickness’ of the coloured layers. At what points are the values highest or lowest? When are they growing or shrinking as the time axis moves along? If there are multiple categories, which ones take up the largest and smallest slices of the overall total? Are there any trends (short or long term), any sudden moments of a rise or fall, any sense of seasonal or cyclical patterns? If there are multiple categories, look more closely at individual layers of interest.

PR E SE N TATION TIPS

AN N OTATION : Direct labelling of quantitative values will get far too busy so you might choose to annotate specific values of interest (highest, lowest, specific milestones). Think about the most useful interval for your axis labelling. As ever there is no single rule, so adopt the Goldilocks principle of not too many, not too few. If you have a stacked area chart, try directly to label the category layers shown as closely as possible (if the heights allow it) or at least ensure any colour associations are easily identifiable through a nearby legend. Think carefully about what is the most useful and meaningful interval for your time axis labelling.

C OLOUR : If you are using a stacked area chart, ensure the categorical layers have sufficiently different colours so that their distinct reading can be efficiently performed.

C OM POSITION : Similar to the line chart, the area chart’s dimensions should ideally utilise an aspect ratio that optimises the readability through 45° banking (roughly judging the average slope angle). The sequencing of values tends to be left to right for the sequence of the time-based x-axis and low rising to high

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values on the y-axis; you will need a good (and clearly annotated) reason to break this convention. Unlike the line chart, the quantitative axis for area charts must start at zero as it is the height of the coloured areas under each line that helps readers to perceive the quantitative values. Do not have overlapping categories on the same chart because it makes it very difficult to see (imagine hills behind hills, peaking out and then hiding behind each other). Rather than stacking categories you might consider using small multiples, especially as this will present the respective displays from a common baseline (and make reading sizes a little easier).

VAR IATION S & ALTE R N ATIVE S

Like area charts, ‘alluvial diagrams’ display proportional stacked layers for multiple categories showing the absolute value change over time. However, they also show the evolving ranks, switching the relative ordering of each layer of values based on the current magnitude. Some deployments of the area chart are not plotted over time but over continuous dimensions of space, perhaps showing the changing nature of a given quantitative measure along a given route. When you have many concurrent layers to show and these layers start and stop at different times, a ‘slope graph’ is worth considering.

C harts Trends

Horizon chart

ALSO KN OW N AS

E X AM PLE Showing percentage changes in price for selected food items in the USA between 1990 and 2015.

R E PR E SE N TATION D E SC R IPTION

Horizon charts show how quantitative values for different categories have changed over time. They are valuable for showing changes over time for multiple categories within space-constrained formats (such as dashboards). They are structured around a series of rows each showing changes in quantitative values for a single category. The temporal x-axis has equal intervals from the earliest to latest point in time. Quantitative values are plotted using joined-up lines that connect consecutive points positioned along a value y-axis. The resulting slopes formed between the ends of each line provide an indication of the local trends between two points in time. As this sequence is extended to plot all values across the time frame it forms an overall line representative of the quantitative changes. To accentuate the magnitude of the quantitative values the area beneath the line is filled