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Share a personal connection that identifies specific knowledge and theories from this course.

 demonstrate a connection to your desired work environment. Data Visualisation

Sara Miller McCune founded SAGE Publishing in 1965 to support
the dissemination of usable knowledge and educate a global
community. SAGE publishes more than 1000 journals and over
800 new books each year, spanning a wide range of subject areas.
Our growing selection of library products includes archives, data,
case studies and video. SAGE remains majority owned by our
founder and after her lifetime will become owned by a charitable
trust that secures the company’s continued independence.

Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne

Data Visualisation
A Handbook for Data Driven Design

Andy Kirk

2nd Edition

SAGE Publications Ltd
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London EC1Y 1SP

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Cover design: Shaun Mercier
Typeset by: C&M Digitals (P) Ltd, Chennai, India
Printed in the UK

© Andy Kirk 2019

First edition published 2016. Reprinted four times in 2016, twice
in 2017, three times in 2018, and three times in 2019.

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: 2018964578

British Library Cataloguing in Publication data

A catalogue record for this book is available from
the British Library

ISBN 978-1-5264-6893-2
ISBN 978-1-5264-6892-5 (pbk)

At SAGE we take sustainability seriously. Most of our products are printed in the UK using responsibly sourced
papers and boards. When we print overseas we ensure sustainable papers are used as measured by the PREPS
grading system. We undertake an annual audit to monitor our sustainability.

Contents

Acknowledgements vii

About the Author ix

Discover Your Textbook’s Online Resources xi

Introduction 1

PART A FOUNDATIONS 13

1 Defining Data Visualisation 15

2 The Visualisation Design Process 31

PART B THE HIDDEN THINKING 59

3 Formulating Your Brief 61

4 Working With Data 95

5 Establishing Your Editorial Thinking 119

PART C DEVELOPING YOUR DESIGN SOLUTION 133

6 Data Representation 135

7 Interactivity 203

8 Annotation 231

9 Colour 249

10 Composition 277

Epilogue 295

References 301

Index 303

Acknowledgements

I could not have written this book without the unwavering support of my wonderful wife, Ellie,

and my family. The book is dedicated to my inspirational Dad who sadly passed away before

its publication. I want to acknowledge the contributions of the thousands of data visualisation

practitioners who have created such a wealth of exceptional design work and smart writing. I

have been devouring this for over a decade now and I am constantly inspired by the talents

and minds behind it all. I also want to express my gratitude to the people and organisations

who have granted me permission to reference and showcase their visualisation work in this

book. Sincere thanks to the many people at Sage who have played a role in making this book

grow from the first proposal and now to a second edition. Finally, to you the readers, I am

hugely thankful that you chose to invest in this book. I hope it helps you in your journey to

learning about this super subject.

About the Author

Andy Kirk is a freelance data visualisation specialist based in Yorkshire, UK. He is a visualisation

design consultant, training provider, teacher, author, speaker, researcher and editor of the

award-winning website visualisingdata.com.

After graduating from Lancaster University in 1999 with a BSc (hons) in Operational Research,

Andy’s working life began with a variety of business analysis and information management

roles at organisations including CIS Insurance, West Yorkshire Police and the University of

Leeds.

He discovered data visualisation in early 2007, when it was lurking somewhat on the fringes of

the Web. Fortunately, the timing of this discovery coincided with his shaping of his Master’s

(MA) degree research proposal, a self-directed research programme that gave him the opportu-

nity to unlock and secure his passion for the subject.

He launched visualisingdata.com to continue the process of discovery and to chart the course

of the increasing popularity of the subject. Over time, this award-winning site has grown to

become a popular reference for followers of the field, offering contemporary discourse, design

techniques and vast collections of visualisation examples and resources.

Andy became a freelance professional in 2011. Since then he has been fortunate to work with

a diverse range of clients across the world, including organisations such as Google, CERN,

Electronic Arts, the EU Council, Hershey and McKinsey. At the time of publication, he will have

delivered over 270 public and private training events in 25 different countries, reaching more

than 6000 delegates. Alongside his busy training schedule, Andy also provides design consul-

tancy, his primary client being the Arsenal FC Performance Team, since 2015.

In addition to his commercial activities, he maintains regular engagements in academia.

Between 2014 and 2015 he 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, inter alia, helped to shape an understanding of the human factors that affect visualisation

literacy and the effectiveness of design.

Andy joined the highly respected Maryland Institute College of Art (MICA) as a visiting lecturer

in 2013 teaching a module on the Information Visualisation Master’s Programme through to

2017. From January 2016, he taught a data visualisation module as part of the MSc in Business

Analytics at the Imperial College Business School in London through to 2018. As of May 2019,

Andy has started teaching at University College London (UCL).

Discover Your Textbook’s
Online Resources

Want more support around understanding and creating data visualisations? Andy Kirk is here

to help, offline and on!

Hosted by the author and with resources organized by chapter, the supporting website for this

book has everything you need to explore, practice, and hone your data visualisation skills.

• Explore the field: expand your knowledge and reinforce your learning about working

with data through libraries of further reading, references, and tutorials.

• Try this yourself: revise, reflect, and refine your skill and understanding about the chal-

lenges of working with data through practical exercises.

• See data visualisation in action: get to grips with the nuances and intricacies of work-

ing with data in the real world by navigating instalments of the narrative case study and

seeing an additional extended example of data visualisation in practice. Follow along with

Andy’s video diary of the process and get direct insight into his thought processes, chal-

lenges, mistakes, and decisions along the way.

• Chartmaker directory: access crowd-sourced guidance that aims to answer the crucial

question ‘which tools make which charts?’ with this growing directory of examples and

technical solutions for chart building.

Ready to learn more? Go beyond the book and dive deeper into data visualisation via the rest

of Andy’s website (www.visualisingdata.com), which contains data visualisation tools

and software, links to additional influential further reading, and a blog with monthly

collections of the best data visualisation examples and resources each month.

Introduction

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 a big subject. There is no single

book to rule it all because there is no one book that can truly 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 books

in their own right.

The secondary challenge when writing a book about data visualisation is to decide how to

weave the content together. Data visualisation is not rocket science; it is not an especially

complicated discipline, though it can be when working on sophisticated topics and with

advanced applications. It is, however, a complex subject. There are lots of things to think about,

many things to do and, of course, things that will need making. Creative and journalistic

sensibilities need to blend harmoniously with analytical and scientific judgement. In one

moment, you might be checking the statistical rigour of an intricate calculation, in the next

deciding which shade of orange most strikingly contrasts with a vibrant blue. The complexity

of data visualisation manifests in how the myriad small ingredients interact, influence and

intersect to form a whole.

The decisions I have made when formulating this book’s content have been shaped by my own

process of learning. I have been researching, writing about and practising data visualisation for

over a decade. I believe you only truly learn about your own knowledge of a subject when you

have to explain it and teach it to others. To this extent I have been fortunate to have had

extensive experience designing and delivering commercial training as well as academic teaching.

I believe this book offers an effective and proven pedagogy that successfully translates the

complexities of this subject in a form that is fundamentally useful. I feel well placed to bridge

the gap between the everyday practitioners, who might identify themselves as beginners, and

the superstar talents expanding the potential of data visualisation. I am not going to claim to

belong to the latter cohort, but I have certainly been a novice, taking tentative early steps into

this world. Most of my working hours are spent helping others start their journey. I know what

I would have valued when I started out in this field and this helps inform how I now pass this

on to others in the same position I was several years ago.

There is a large and growing library of fantastic books offering different theoretical and

practical viewpoints on this subject. My aim is to add value to this existing collection by

approaching the subject through the perspective of process. I believe the path to mastering data

visualisation is achieved by making better decisions: namely, effective choices, efficiently made.

I will help you understand what decisions need to be made and give you the confidence to

make the right choices. Before moving on to discuss the book’s intended audience, here are its

key aims:

2 DATA VISUALISATION

• To challenge your existing approaches to creating and consuming visualisations. I will

challenge your beliefs about what you consider to be effective or ineffective visualisation. I

will encourage you to eliminate arbitrary choices from your thinking, rely less on taste and

instinct, and become more reasoned in your judgements.

• To enlighten you I will increase your awareness of the possible approaches to visualising

data. This book will broaden your visual vocabulary, giving you a wider and more sophisti-

cated understanding of the contemporary techniques used to express your data visually.

• To equip is to provide you with robust tactics for managing your way through the myriad

options that exist in data visualisation. To help you overcome the burden of choice, an

adaptable framework is offered to help you think for yourself, rather than relying on inflex-

ible rules and narrow instruction.

• To inspire is to open the door to a subject that will stimulate you to elevate your ambition

and broaden your confidence. Developing competency in data visualisation will take time

and will need more than just reading this book. It will require a commitment to embrace

the obstacles that each new data visualisation opportunity poses through practice. It will

require persistence to learn, apply, reflect and improve.

Who Is This Book Aimed At?
Anyone who has reason to use quantitative and qualitative methods in their professional or

academic duties will need to grasp the demands of data visualisation. Whether this is a large

part of your duties or just a small part, this book will support your needs.

The primary intended audiences are undergraduates, postgraduates and early-career researchers.

Although aimed at those in the social sciences, the content will be relevant to readers from

across the spectrum of arts and humanities right through to the natural sciences.

This book is intended to offer an accessible route for novices to start their data visualisation

learning journey and, for those already familiar with the basics, the content will hopefully

contribute to refining their capabilities. It is not aimed at experienced or established visualisation

practitioners, though there may be some new perspectives to enrich their thinking: some content

will reinforce existing knowledge, other content might challenge their convictions.

The people who are active in this field come from all backgrounds. Outside academia, data

visualisation has reached the mainstream consciousness in professional and commercial

contexts. An increasing number of professionals and organisations, across all industry types

and sizes, are embracing the importance of getting more value from their data and doing more

with it, 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 though not directly involved in the

creation of visualisation work, you might wish to improve the sophistication of the language

you coordinate or commission others who are. Everyone needs the lens and vocabulary to

evaluate work effectively.

INTrODUcTION 3

Data visualisation is a genuinely multidisciplinary discipline. Nobody arrives fully formed with

all constituent capabilities. The pre-existing knowledge, skills or experiences which, I think,

reflect the traits needed to get the most out of this book would include:

• 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 this 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. 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

an appetite to embrace some of the creative aspects presented in this book will heighten the

impact of your work. Time to unleash that suppressed imagination!

• If you are somebody fortunate to possess already a strong creative flair, 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.

• No particular technical skills are required to get value from this book, as I will explain

shortly. But you will ideally have some basic knowledge of spreadsheets and experience of

working with data irrespective of which particular tool.

This is a portable practice involving techniques that are subject-matter agnostic. Throughout

this book you will see a broad array of examples from different industries covering many

different topics. Do not be deterred by any example being about a subject different to your

own area of interest. Look beyond the subject and you will see analytical and design choices

that are just as applicable to you and your work: a line chart showing political forecasts

involves the same thought process as would a line chart showing stock prices changing or

average global temperatures rising. A line chart is a line chart, regardless of the subject

matter.

The type of data you are working with is the only legitimate restriction to the design methods

you might employ, not your subject and certainly not traditions in your subject. ‘Waterfall

charts are only for people in finance’, ‘maps are only for cartographers’, ‘Sankey diagrams are

only for engineers’. Enter this subject with an open mind, forget what you believe or have been

told is the normal approach, and your capabilities will be expanded.

Data visualisation is an entirely global community, not the preserve of any geographic region.

Although the English language dominates written discourse, the interest in the subject and

work created from studios through to graphics teams originates everywhere. There are cultural

influences and different flavours in design sensibility around the world which enrich the field

but, otherwise, it is a practice common and accessible to all.

4 DATA VISUALISATION

Finding the Balance
Handbook vs Manual

The description of this book as a ‘handbook’ positions it as distinct from a tutorial-based man-

ual. It aims to offer conceptual and practical guidance, rather than technical instruction. Think

of it more as a guidebook for a tourist visiting a city than an instruction manual for how to fix

a washing machine.

Apart from a small proportion of visualisation work that is created manually, the reliance on

technology to create visualisation work is an inseparable necessity. For many beginners in

visualisation there is an understandable appetite for step-by-step tutorials that help them

immediately to implement their newly acquired techniques.

However, writing about data visualisation through the lens of selected tools is hard, given the

diversity of technical options that exist in the context of such varied skills, access and needs.

The visualisation technology space is characterised by flux. New tools are constantly

emerging to supplement the many that already exist. Some are proprietary, others are open

source; some are easier to learn but do not offer much functionality; others do offer rich

potential but require a great deal of foundation understanding before you even accomplish

your first bar chart. Some tools evolve to keep up with current techniques; they are well

supported by vendors and have thriving user communities, others less so. Some will exist as

long-term options whereas others depreciate. Many have briefly burnt brightly but quickly

become obsolete or have been swallowed up by others higher up the food chain. Tools come

and go but the craft remains.

There is a role for all book types and a need for more than one to acquire true competency in

a subject. Different people want different sources of insight at different stages in their

development. If you are seeking a text that provides instructive tutorials, you will learn from

this how to accomplish technical developments in a given technology. However, if you only

read tutorial-based books, you will likely fall short in the fundamental critical thinking that will

be needed to harness data visualisation as a skill.

I believe a practical, rather than technical, text focusing on the underlying craft of data

visualisation through a tool-agnostic approach offers the most effective guide to help people

learn this subject.

The content of this book will be relevant to readers regardless of their technical knowledge and

experience. The focus will be to take your critical thinking towards a detailed, fully reasoned

design specification – a declaration of intent of what you want to develop. Think of the

distinction as similar to that between architecture (design specification) and engineering

(design execution).

There is a section in Chapter 3 that describes the influence technology has on your work and

the places it will shape your ambitions. Furthermore, among the digital resources offered online

are further profiles of applications, tools and libraries in common use in the field today and a

vast directory of resources offering instructive tutorials. These will help you to apply technically

the critical capabilities you acquire throughout this book.

INTrODUcTION 5

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 glossy ‘coffee table’ books as they offer great inspiration, but

often lack substance beyond the evident beauty. This book serves a different purpose to that.

I believe, for a beginner or relative beginner, the most valuable inspiration comes more from

understanding the thinking behind some of the amazing works encountered today, learning

about the decisions that led to their conceptual development.

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. It will be an elegantly presented and packaged book, but it should not

be something that invites you to look but not touch.

Pragmatic vs Theoretical

The content of this book has been formed through years of absorbing knowledge from as

many books as my shelves can hold, generations of academic work, endless web articles,

hundreds of conference talks, personal interactions with the great and the good of the

field, and lots and lots of practice. More accurately, lots and lots of mistakes. What I pres-

ent here is a pragmatic distillation of what I have learned and feel others will benefit from

learning too.

It is not a deeply academic or theoretical book. Experienced or especially curious practitioners

may have a desire for deeper theoretical discourse, but that is beyond the intent of this

particular text. You have to draw a line somewhere to determine the depth you can reasonably

explore about a given topic. Take the science of visual perception, for example, arguably the

subject’s foundation. There is no value in replicating or attempting to better what has already

been covered by other books in greater quality than I could achieve.

An important reason for giving greater weight to pragmatism is because of the inherent

imperfections of this subject. Although there is so much important empirical thinking in this

subject, the practical application can sometimes fail to translate beyond the somewhat artificial

context of a research study. Real-world circumstances and the strong influence of human

factors can easily distort the significance of otherwise robust concepts.

Critical thinking will be the watchword, equipping you with the independence of thought

to decide rationally for yourself which solutions best fit your context, your data,

your message and your audience. To accomplish this, you will need to develop 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).

6 DATA VISUALISATION

Contemporary vs Historical

I have huge respect for the ancestors of this field, the dominant 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 pioneers such as William

Playfair, W. E. B. Du Bois, Florence Nightingale and John Snow, to name but a few. To many

beginners in the field, the historical context of this subject is of huge interest. However, this

kind of content has already been covered by plenty of other book and article authors.

I do not want to bloat this book with the unnecessary reprising of topics that have been covered

at length elsewhere. 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. 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 time passes

there will be new techniques, new concepts and new, empirically evidenced rules. There will be

new thought-leaders, new sources of reference and new visualisers to draw insight from. Things

that prove a manual burden now may become seamlessly automated in the near future. That is

the nature of a fast-growing field.

Analysis vs Communication

A further distinction to make concerns the subtle but critical difference between visualisation

used for analysing data and visualisation used for communicating data.

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. In certain

contexts, this might only be achieved through exploratory data analysis. Here, the visualiser

and the viewer are the same person. Through visual exploration, interrogations of the data can

be conducted to learn about its qualities and to unearth confirmatory or enlightening

discoveries about what insights exist.

Visualisation for analysis is part of the journey towards creating visualisation for

communication, but 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 truly

to 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 audience. This influences many design decisions that do not exist alone with

visual analysis.

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 concerned more with techniques for

INTrODUcTION 7

exploratory analysis rather than visual communication, you will likely require a deeper

treatment of the topic than this book can reasonably offer.

Another matter to touch on here concerns the coverage of statistics, or lack thereof. For many

people, statistics can be a difficult topic to grasp. Even for those who are relatively numerate

and comfortable working with simple statistical methods, it is quite easy to become rusty

without frequent practice. The fear of making errors with intricate statistical calculations

depresses confidence and a vicious circle begins.

You cannot avoid the need to use some statistical techniques if you are going to work with data.

I will describe some of the most relevant statistical techniques in Chapter 4, at the point in your

thinking where they are most applicable. However, I do believe the range and level of statistical

techniques most people will need to employ on most of their visualisation tasks can be

overstated. I know there will be exceptions, and a significant minority will be exposed to

requiring advanced statistical thinking in their work.

It all depends, of course. In my experience, however, the majority of data visualisation

challenges will generally involve relatively straightforward univariate and bivariate statistical

techniques to describe …

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