write a research paper that contains the following

8 Annotation

Annotation is the third layer of the visualisation design anatomy and is concerned with the simple need to explain things: what is the right amount and type of help your viewers will need when consuming the visualisation?

Annotation is unquestionably the most often neglected layer of the visualisation anatomy. Maybe this is because it involves the least amount of pure design thinking relative to the other matters requiring attention, like interactivity and colour. More likely, it is because effective annotation requires visualisers truly to understand their intended audience. This can be a hard frame of mind to adopt, especially when your potential viewers are likely to have a diverse knowledge, range of interests and capability.

In contrast to the greater theoretical and technical concerns around data representation, colour and interactivity, I find thinking about annotation relatively refreshing. It is not only uncomplicated and based on a huge dose of common sense, but also hugely influential, especially in directly facilitating understanding.

Annotation choices often conform to the Goldilocks principle: too much and the display becomes cluttered, overwhelming, and potentially unnecessarily patronising; too little and the viewers may be inappropriately faced with the prospect of having to find their own way around a visualisation and form their own understanding about what it is showing.

Later in this chapter we will look at the factors that will influence your decision making but to begin with here is a profile of some of the key features of annotated design that exist across two main groups:

Project annotations: helping viewers understand what the project is about and how to use it. Chart annotations: helping viewers perceive the charts and optimise their potential interpretations.

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8.1 Features of Annotation: Project Annotation

This collection of annotation options is related to decisions about how much and what type of help you might need to offer your audiences in their understanding of the background, function and purpose of your project.

Headings: The titles and subtitles occupy such prime real estate within your project’s layout, yet more often than not visualisers fail to exploit these to best effect. There are no universal practices for what a heading should do or say; this will vary considerably between subject areas and industries, but should prove fundamentally useful.

Figure 8.1 A Sample of Project Titles

The primary aim of a title (and often subtitle combination) is to inform viewers about the immediate topic or display, giving them a fair idea about what they are about to see. You might choose to articulate the essence of the curiosity that has driven the project by framing it around a question or maybe a key finding you unearthed following the work. Subheadings, section headings and chart titles will tend to be more functional in their role, making clear to the viewer the contents or focus of attention associated with each component of the display. Your judgement surrounds the level of detail and the type of language

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you use in each case to fit cohesively with the overall tone of the work. Introductions: Essentially working in conjunction with titles, introductions typically exist as short paragraphs that explain, more explicitly than a title can, what the project is about. The content of this introduction might usefully explain in clear language terms some of the components you considered during the editorial thinking activity, such as:

details of the reason for the project (source curiosity); an explanation of the relevance of this analysis; a description of the analysis (angle, framing) that is presented; expression of the main message or finding that the work is about to reveal (possibly focus).

Some introductions will extend beyond a basic description of the project to include thorough details of where the data comes from and how it has been prepared and treated in advance of its analysis (including any assumptions, modifications or potential shortcomings). There may also be further links to ‘read more’ detail or related articles about the subject.

Figure 8.2 Excerpt from ‘The Color of Debt’

Introductions may be presented as fixed text located near the top (or start) of a project (usually underneath a title) as in Figure 8.2 or, through interactivity, may be hidden from view and brought up in a separate window or pop-up to provide the details upon request. User guides: As you have seen, some projects can incorporate many different features of interactivity. While they may not necessarily be overly technical – and therefore not that hard to learn how to use

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them – the full repertoire of features may be worth walking through, as in Figure 8.3. This is important to consider so that, as a visualiser, you can be sure your users are acquainted with the entire array of options they have to explore, interrogate and control their experience. You should want people to fully utilise all the different features you have carefully curated and created, so it is in everyone’s interest to think about including these types of user guides.

Figure 8.3 Excerpt from ‘Kindred Britain’

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Multimedia: There is increasing potential and usage of broader media assets in visualisation design work beyond charts, such as video and imagery. In visualisation this is perhaps a relatively contemporary trend (infographics have incorporated such media but visualisations generally have done so far less) and, in some ways, reflects the ongoing blurring of boundaries between this and other related fields. Incorporating good-quality and sympathetically styled assets like illustrations or photo-imagery can be a valuable complementary device alongside your data representation elements. In the ‘Color of Debt’ (Figure 8.4) project, different neighbourhoods of Chicago that have been hardest hit by debt are profiled using accompanying imagery to show more graphic context of the communities affected, including a detailed reference map of the area and an animated panel displaying a sequence of street view images. Imagery, in particular, will be an interesting option to consider when it adds value to help exhibit the subject matter in tangible form, offering an appealing visual hook to draw people in or simply to aid immediate recognition of the topic. In Bloomberg’s billionaires project (Figure 8.5), each billionaire is represented by a pen-and-ink caricature. This is elegant in choice and also dodges the likely flaws of having to compose the work around individual headshot photographs that would have been hard to frame and colour consistently.

Figure 8.4 Excerpt from ‘The Color of Debt’

It was worth Bloomberg investing in the time/cost involved in commissioning these illustrations, given that the project was not a one-off but something that would be an ongoing, updated daily

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resource. Problems with the integration of such media within a visualisation project will occur when unsuitable attempts are made to combine imagery within the framework of a chart. Often the lack of cohesion creates a significant hindrance whereby the data representations are obscured or generally made harder to read, as the inherent form and colour clashes undermine the functional harmony. Researching, curating, capturing or creating assets of imagery requires skill and a professional approach, otherwise the resulting effect will look amateurish. Incorporating these media into a data visualisation is not about quickly conducting some Google Image fishing exercise. Determining what imagery you will be able to use involves careful considerations around image suitability, quality and, critically, usage rights. Beware the client or colleague who thinks otherwise.

‘Although all our projects are very much data driven, visualisation is only part of the products and solutions we create. This day and age provides us with amazing opportunities to combine video, animation, visualisation, sound and interactivity. Why not make full use of this? ... Judging whether to include something or not is all about editing: asking “is it really necessary?”. There is always an aspect of “gut feel” or “instinct” mixed with continuous doubt that drives me in these cases.’ Thomas Clever, Co-founder CLEVER°FRANKE, a data driven experiences studio

A frequent simple example of incorporated imagery is when you have to include logos according to the needs of the organisation for whom your work is being created. Remember to consider this early so you at least know in advance that you will have to assign some space to accommodate this component elegantly.

Footnotes: Often the final visible feature of your display, footnotes provide a convenient place to share important details that further substantiate the explanation of your work. Sometimes this information might be stored within the introduction component (especially if that is interactively hidden/revealed to allow it more room to accommodate detail):

Data sources should be provided, ideally in close proximity to the relevant charts.

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Credits will list the authors and main contributors of the work, often including the provision of contact details.

Figure 8.5 Excerpt from ‘Bloomberg Billionaires’

Attribution is also important if you wish to recognise the influence of other people’s work in shaping your ideas or to acknowledge the benefits of using an open source application or free typeface, for example. Usage information might explain the circumstances in which the work can be viewed or reused, whether there are any confidentialities or copyrights involved. Time/date stamps are often forgotten but they will give an indication to viewers of the moment of production and from that they might be able to ascertain the work’s current accuracy and contextualise their interpretations accordingly.

Figure 8.6 Excerpt from ‘Gender Pay Gap US’

8.2 Features of Annotation: Chart Annotation

This second group of annotated features concerns the ways you provide viewers with specific assistance for perceiving and interpreting the charts. Think of these as being the features that refer directly to your charts or exist directly within or in immediate proximity to each chart.

Reading guides: These are written or visual instructions that provide 427

viewers with a guide for how to read the chart or graphic and offer greater detailed assistance than a legend (see later). The idea of learnability in visualisation is an important consideration. It is a two- way commitment requiring will and effort from the viewer and sufficient assistance from the visualiser. This is something to be discussed in Chapter 11 under ‘Visualisation literacy’.

Recognising that their readership may not necessarily understand connected scatter plots, Bloomberg’s visual data team offer a ‘How to Read this Graphic’ guide immediately as you land on the project shown in Figure 8.7. This can be closed but a permanent ‘How to’ button remains for those who may need to refer to it again. The connected scatter plot was the right choice for this angle of analysis, so rather than use a different ‘safer’ representation approach (and therefore alter what analysis was shown) it is to their credit that they respected the capacity of their viewers to be willing to learn how to read this unfamiliar graphical form.

Figure 8.7 Excerpt from ‘Holdouts Find Cheapest Super Bowl Tickets Late in the Game’

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Figure 8.8 Excerpt from ‘The Life Cycle of Ideas’

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The second example shown (Figure 8.8) is from the ‘How to Read’ guide taken from the ‘Life Cycle of Ideas’ graphic created by Accurate, a studio renowned for innovative and expressive visualisation work. Given the relative complexity of the encodings used in this piece, it is necessary to equip the viewer with as much guidance as possible to ensure its potential is fully realised.

Chart apparatus: Options for chart apparatus relate to the structural components found in different chart types. Every visualisation displayed in this book has different elements of chart apparatus (Figure 8.9), specifically visible axis lines, tick marks or gridlines to help viewers orient their judgements of size and position. There is no right or wrong for including or excluding these features, it tends to be informed by your tonal definitions based on how much precision in the perceiving of values you wish to facilitate. I will discuss the range of different structures underlying each chart type (such as Cartesian, Radial or Spatial) in Chapter 10 on composition, as these have more

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to do with issues of shape and dimension.

Labels: There are three main labelling devices you will need to think about using within your chart: axis titles, axis labels and value labels:

Axis titles describe what values are being referenced by each axis. This might be a single word or a short sentence depending on what best fits the needs of your viewers. Often the role of an axis is already explained (or implied) by project annotations elsewhere, such as titles or sub-headings, but do not always assume this will be instantly clear to your viewers. Axis labels provide value references along each axis to help identify the categorical value or the date/quantitative value associated with that scale position. For categorical axes (as seen in bar charts and heat maps, for example) one of the main judgments relates to the orientation of the label: you will need to find sufficient room to fit the label but also preserve its readability. For non-categorical data the main judgement will be what scale intervals to use. This has to be a combination of what is most useful for referencing values by your viewer, what is the most relevant interval based on the nature of the data (e.g. maybe a year-level label is more relevant than marking each month), and also what feels like it achieves the best-looking visual rhythm along the chart’s edge. This will be another matter that is discussed more in the composition chapter.

Figure 8.9 Mizzou’s Racial Gap Is Typical On College Campuses

‘Labelling is the black magic of data visualization.’ Gregor Aisch, Graphics Editor, The New York Times

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Value labels will appear in proximity to specific mark encodings inside the chart. Typically, these labels will be used to reveal a quantity, such as showing the percentage sizes of the sectors in a pie chart or the height of bars. Judging whether to include such annotations will refer back to your definition of the appropriate tone: will viewers need to read off exact values or will their perceived estimates of size and/or relationship be sufficient? The need to include categorical labels will be a concern for maps (whether to label locations?) or charts like the scatter plot seen in Figure 8.9, where you may wish to draw focus to a select sample of the categories plotted across the display.

As you have seen, one way of providing detailed value labels is 432

through interaction, maybe offering a pop-up/tooltip annotation that is triggered by a hover or click event on different mark encodings. Having the option for interactivity here is especially useful as it enables you to reduce clutter from your display that can develop as more annotated detail is added.

Legend: A legend is an annotated feature within or alongside your chart that presents one or several keys to help viewers understand the categorical or quantitative meaning of different attributes.

Figure 8.10 Excerpt from ‘The Infographic History of the World’

For quantitative data the main role for a legend will be if the attribute of area size has been used to encode values, as found on the bubble plot chart type. The keys displayed there will provide a reference for the different size scales. Which selection of sizes to show needs careful thought: what is the most useful guide to help your viewers make their perceptual judgements from a chart? This might not entail showing only even interval sizes (50, 100, 150 etc.); rather, you might offer viewers a indicative spread of sizes to best represent the distribution of your data values. The example in Figure 8.10 shows logical interval sizes to reflect the range of values in the data and also helpfully includes reference to the maximum value size to explain that no shape will be any larger than this. For categorical data you also see a key showing the meaning of different colours and shapes

Redundancy in labelling occurs when you include value labelling of quantities for all marks whilst also including axis-scale labelling. You are effectively unnecessarily doubling the assistance being offered and so, ideally, you should choose to include one or the other.

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and their associated values. Figure 8.11 Twitter NYC: A Multilingual Social City

A nice approach to getting more out of your legends is shown in Figure 8.11. Here you will see a key explaining the colour associations combined with a bar chart to display the distribution of quantities for each language grouping from this analysis of tweets posted around New York City.

Captions: These exist typically as small passages of written analysis that bring to the surface some of the main insights and conclusions from the work. These might be presented close to related values inside the chart or in separate panels to provide commentary outside the chart.

In ‘Gun Deaths’ (Figure 8.12), there is a nice solution that combines annotated captions with interactive data adjustments. Below the main chart there is a ‘What This Data Reveals’ section which some of the main findings from the analysis of the gun death data. The captions double up as clickable shortcuts so that you can quickly apply the relevant framing filters and update the main display to see what the captions are referring to.

Figure 8.12 Excerpt from ‘US Gun Deaths’

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As creative tools become more ubiquitous the possibility for incorporating non-visual data in you work increases. As an alternative to the written caption there is greater scope to consider using audio as a means of verbally narrating a subject and explaining key messages. Over the past few years one of the standout projects using this feature was the video profiling ‘Wealth Inequality in America’ (Figure 8.13), as introduced in Chapter 3, where the voiceover provides a very compelling and cohesive narrative against the backdrop of the animated visuals that present the data being described.

Figure 8.13 Image taken from ‘Wealth Inequality in America’

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8.3 Typography

As you have seen, many features of annotation utilise text. This means your choices will be concerned not just with what text to include, but also with how it will look. This naturally merits a brief discussion about how typography will have a significant role in the presentation of your work.

Firstly, some clarity about language. A typeface is a designed collection of glyphs representing individual letters, numbers and other symbols of language based on a cohesive style. A font is the variation across several physical dimensions of the typeface, such as weight, size, condensation and italicisation. A typeface can have one or many different fonts in its family. Type effectively represents the collective appearance formed by the choice of typeface and the font.

Tahoma and Century Gothic are different typefaces. This font and this font both belong to the Georgia typeface family but display variations in size, weight and italicisation.

I discussed earlier the distinction between definitions of data visualisation and other related fields. I mentioned how the person creating their design is not necessarily conscious or concerned about what label is attached to their work, they are simply doing their work regardless. The same could be applied to people’s interchangeable use of and meaning of the terms typeface and font, the clarity of which has been irreparably confused by Microsoft’s desktop tools in particular.

Serif typefaces add an extra little flourish in the form of a small line at the end of the stroke in a letter or symbol. Garamond is an example of a serif font. Serif typefaces are generally considered to be easier to read for long sequences of text (such as the full body text) and are especially used in print displays.

Sans-serif typefaces have no extra line extending the stroke for each character. Verdana is an example of a sans-serif typeface. These typefaces are commonly used for shorter sections of text, such as axis or value labels or titles, and for screen displays.

In making choices about which type to use, there are echoes with the thinking you are about to face on using colour. As you will see, colour

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decisions concern legibility and meaning first, decoration last. With typeface choices you are not dressing up your text, you are optimising its readability and meaning across your display. The desired style of typeface only comes into your thinking after legibility and meaning.

In terms of legibility, you need to choose a typeface and font combination that will be suitable for the role of each element of text you are using. Viewers need to be able to read the words and numbers on display without difficulty. Quite obvious, really. Some typefaces (and specifically fonts) are more easily read than others. Some work better to make numbers as clearly readable as possible, others work better for words. There are plenty of typefaces that might look cool and contemporary but if they make text indecipherable then that is plain wrong.

Just as variation in colour implies meaning, so does variation in typeface and font. If you make some text capitalised, large and bold-weight this will suggest it carries greater significance and portrays a higher prominence across the object hierarchy than any text presented in lower case, with a smaller size and thinner weight. So you should seek to limit the variation in font where possible.

Text-based annotations should be considered part of the supporting cast and the way you consider typeface and font choices should reflect this role. Typography in visualisation should be seen but really not heard. Deciding on the most suitable type is something that can ultimately come down to experience and influence through exposure to other work. Every individual has their own relied-upon preferences. In practice, I find there is a good chunk of trial and error as well as viewer testing that goes into resolving the final selection. Across the spectrum of data visualisation work being produced there are no significant trends to be informed by largely because judging the most suitable typography choices will be unique to the circumstances influencing each project.

Typography is just another of the many individual ingredients relevant to data visualisation that exists as a significant subject in its own right. It is

Typeface decisions will often be taken out of your hands by the visual identity guidelines of organisations and publications, as well as by technical reasons relating to browser type, software compatibility and availability.

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somewhat inadequate to allocate barely two pages of this book to discussing its role in visualisation, but these will at least offer you a bite- sized window into the topic.

8.4 Influencing Factors and Considerations

Having become familiar with the principal options for annotating, you now have to decide which features to incorporate into your work and how you might deploy these.

Formulating Your Brief

‘Never choose Times New Roman or Arial, as those fonts are favored only by the apathetic and sloppy. Not by typographers. Not by you.’ Matthew Butterick, Typographer, Lawyer and Writer

‘Think of the reader – a specific reader, like a friend who’s curious but a novice to the subject and to data-viz – when designing the graphic. That helps. And I rely pretty heavily on that introductory text that runs with each graphic – about 100 words, usually, that should give the new-to- the-subject reader enough background to understand why this graphic is worth engaging with, and sets them up to understand and contextualize the takeaway. And annotate the graphic itself. If there’s a particular point you want the reader to understand, make it! Explicitly! I often run a few captions typeset right on the viz, with lines that connect them to key elements in the design.’ Katie Peek, Data Visualisation Designer and Science Journalist, on making complex and/or complicated subject matter accessible and interesting to her audience

Audience: Given that most annotations serve the purpose of viewer assistance, your approach will inevitably be influenced by the characteristics of your intended audience. Having an appreciation of and empathy towards the knowledge and capabilities of the different cohorts of viewers is especially important with this layer of design. How much help will they need to understand the project and also the data being portrayed? You will need to consider the following:

Subject: how well acquainted will they be with this subject matter? Will they understand the terminology, acronyms,

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abbreviations? Will they recognise the relevance of this particular angle of analysis about this subject? Interactive functions: how sophisticated are they likely to be in terms of being able to understand and utilise the different features of interactivity made possible through your design? Perceiving: how well equipped are they to work with this visualisation? Is it likely that the chart type(s) will be familiar or unfamiliar; if the latter, will they need support to guide them through the process of perceiving?

Interpreting: will they have the knowledge required to form legitimate interpretations of this work? Will they know how to understand what is good or bad, what big and small mean, what is important, or not? Alternatively, will you need to provide some level of assistance to address this potential gap?

Purpose map: The defined intentions for the tone and experience of your work will influence the type and extent of annotation features required. If you are working towards a solution that leans more towards the ‘reading’ tone you are placing an emphasis on the perceptibility of the data values. It therefore makes sense that you should aim to provide as much assistance as possible (especially through extensive chart annotations) to maximise the efficiency and precision of this process. If it is more about a ‘feeling’ tone then you may be able to justify the absence of the same annotations. Your intent may be to provide more of a general sense – a ‘gist’ – of the order of magnitude of values.

If you are seeking to provide an ‘explanatory’ experience it would be logical to employ as many devices as possible that will help inform your viewers about how to read the charts (assisting with the ‘perceiving’ stage of understanding) and also bring some of the key insights to the surface, making clear the meaning of the quantities and relationships displayed (thus assisting with the stage of ‘interpreting’). The use of captions and visual overlays will be particularly helpful in achieving this, as will the potential for audio accompaniments if you are seeking to push the explanatory experience a step further.

‘Exploratory’ experiences are less likely to include layers of insight assistance, instead the focus will be more towards project-level annotation, ensuring that viewers (and particularly here, users) have as much understanding as possible about how to use the project for their exploratory benefit. You might find, however, that devices like

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‘How to read this graphic’ are still relevant irrespective of the definition of your intended experience. Characteristically, ‘exhibitory’ work demonstrates far less annotated assistance because, by intention, it is more about providing a visual display of the data rather than offering an explanatory presentation or the means for exploratory interrogation. The assumptions here are that audiences will have sufficient domain and project knowledge not to require extensive additional assistance. Common chart annotations like value labels and legends, and project annotations like titles and introductions, are still likely to be necessary, but these might reflect the extent required.

Establishing Your Editorial Thinking

Focus: During your editorial thinking you considered focus and its particular role in supporting explanatory thinking. Are there specific value labels that you wish to display over others? Rather than labelling all values, for example, have you determined that only certain marks and attributes will merit labelling? As you saw earlier in the example scatter plot about the under-representation of black students in US colleges, only certain points were labelled, not all. These would have been judged to have been the most relevant and interesting elements to emphasise through annotation.

Trustworthy Design

Transparency: Annotation is one of the most important aids to ensure that you secure and sustain trust from your viewers by demonstrating integrity and openness:

Explain what the project is and is not going to show. Detail where the data came from and what framing criteria were used during the process of acquisition, and also make what has been ultimately included in the chart(s). Outline any data transformation treatments, assumptions and calculations. Are there any limitations that viewers need to be aware of? Highlight and contextualise any findings to ensure accuracy in interpretation. With digital projects in particular, provide access to coding

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repositories to lay open all routines and programmatic solutions.

Accessible Design

Understandable: If you recall, in the section profiling circumstances you considered what the characteristics were of the setting or situation in which your audience might consume your visualisation. Well-judged project and chart annotations are entirely concerned with providing a sufficient level of assistance to achieve understanding. The key word there is ‘sufficient’ because there is a balance: too much assistance makes the annotations included feel overburdening; too little and there is far more room for wrong assumptions and misconceptions to prosper. A setting that is consistent with the need to deliver immediate insights will need suitable annotations to fulfil this. There will be no time or patience for long introductions or explanations in that setting. Conversely, a visualisation about a subject matter that is inherently complex may warrant such assistance.

Elegant Design

Minimise the clutter: A key concern about annotations is judging the merits of including structural or textual assistance against the potential disruption and obstruction caused by these to the view of the data. Any annotation device added to your display has a spatial and visual consequence that needs to be accommodated. Of course, as mentioned, with the benefit of interactivity it is possible to show and hide layers of detail. Overall, you will have to find the most elegant solution for presenting your annotations to ensure you do not inadvertently undermine the help you are trying to provide.

Summary: Annotation Project annotations help viewers understand what the project is about and

how to use it, and may include the following features:

Headings: titles, sub-titles and section headings. Introductions: providing background and aims of the project.

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User guides: advice or instruction for how to use any interactive features. Multimedia: the potential to enhance your project using appropriate imagery, videos or illustrations.

Footnotes: potentially includes data sources, credits, usage information, and time/date stamps.

Chart annotations help viewers perceive the charts and optimise their potential interpretations and may include the following features:

Chart apparatus: axis lines, gridlines, tick marks. Labels: axis titles, axis labels, value labels. Legend: providing detailed keys for colour or size associations. Reading guides: detailed instructions advising readers how to perceive and interpret the chart. Captions: drawing out key findings and commentaries.

Typography Most of the annotation features you include are based on text and so you will need to consider carefully the legibility of the typeface you choose and the logic behind the font-size hierarchy you display.

Influencing Factors and Considerations

Formulating the brief: consider the characteristics and needs of the audience. Certain chart choices and subjects may require more explanation. From the ‘purpose map’ what type of tone and experience are you trying to create and what role might annotation play?

Establishing your editorial thinking: what things do you want to emphasise or direct the eye towards (focus)? Trustworthy design: maximise the information viewers have to ensure all your data work is transparent and clearly explained.

Accessible design: what is the right amount and type of annotation suitable to the setting and complexity of your subject? Elegant design: minimise the clutter.

Tips and Tactics

Attention to detail is imperative: all instructions, project information, captions and value labels need to be accurate. Always spell-check

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digitally and manually, and ask others to proofread if you are too ‘close’ to see. Do not forget to check on permission to use any annotated asset, such as imagery, photos, videos, quotations, etc.

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9 Colour

Having established which charts you will use, the potential interactive functions that might be required and the annotation features that will be especially useful, you have effectively determined all the visible elements that will be included in your project. The final two layers of design concern not what elements will be included or excluded, but how they will appear. After this chapter you will look at issues on composition, but before that the rather weighty matter of colour.

As one of the most powerful sensory cues, colour is a highly influential visual property. It is arguably the design decision that has the most immediate impact on the eye of the viewer. All the design features of your visualisation display hold some attribute of colour, otherwise they are invisible:

Every mark and item of apparatus in your charts will be coloured; indeed colour in itself may be an attribute that represents your data values. Interactive features do not always have an associated visible property (some are indeed invisible and left as intuitively discoverable). However, those features that involve buttons, menus, navigation tabs and value sliders will always have a colour.

Annotation properties such as titles, captions and value labels will all be coloured. Composition design mainly concerns the arrangement of all the above features, though you might use colour to help achieve a certain design layout. As you will see, emptiness is a useful organising device – leaving something blank is a colour choice.

Thankfully, there is a route through all of this potential complexity relying on just a little bit of science mixed in with lots of common sense. By replacing any arbitrary judgements that might have been previously based on taste, and through increasing the sensitivity of your choices, colour becomes one of the layers of visualisation design that can be most quickly and significantly improved.

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‘Colors are perhaps the visual property that people most often misuse in visualization without being aware of it.’ Robert Kosara, Senior Research Scientist at Tableau Software

The key factor in thinking about colour is to ensure you establish meaning first and decoration last. That is not to rule out the value of certain decorative benefits of colour, but to advise that these should be your last concern. Besides, in dealing with meaningful applications of colour you will already have gone a long way towards establishing the ‘decorative’ qualities of your project’s aesthetic appearance.

This chapter begins with a look at some of the key components of colour science, offering a foundation for your understanding about this topic. After that you will learn about the ways and places in which colour could be used. Finally, you will consider the main factors that influence colour decisions.

COLOUR thinking begins from inside the chart(s), working outwards across the rest of the visualisation anatomy:

Data legibility. Editorial salience. Functional harmony.

9.1 Overview of Colour Theory

COLOUR in visualisation is something of a minefield. As with many of these design layer chapters, an introduction to colour involves judging the right amount of science and the right amount of practical application. What does justice to the essence of the subject and gives you the most relevant content to work with is a delicate balance.

When you lift the lid on the science behind colour you open up a world of brain-ache. When this chapter is finalised I will have spent a great deal of time agonising over how to explain this subject and what to leave in or leave out because there is so much going on with colour. And it is tricky. Why? Because you almost come face to face with philosophical questions like ‘what is white?’ and the sort of mathematical formulae that you really rather hoped had been left behind at school. You learn how the colours you

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specify in your designs as X might be perceived by some people as Y and others as Z. You discover that you are not just selecting colours from a neat linear palette but rather from a multi-dimensioned colour space occupying a cubic, cylindrical or spherical conceptual shape, depending on different definitions.

The basis of this topic is the science of optics – the branch of physics concerned with the behaviour and properties of light – as well as colorimetry – the science and technology used to quantify and describe human colour perception. Two sciences, lots of maths, loads of variables, endless potential for optical illusions and impairment: that is why colour is tricky and why you need to begin this stage of thinking with an appreciation of some colour theory.

The most relevant starting point is to recognise that when dealing with issues of colour in data visualisation you will almost always be creating work on some kind of computer. Unless you are creating something by hand using paints or colouring pencils, you will be using software viewed through an electronic display.

This is important because a discussion about colour theory needs to be framed around the RGB (Red, Blue, Green) colour model. This is used to define the combination of light that forms the colours you see on a screen, conceptually laid out in a cubic space based on variations across these three attributes.

The output format of your work will vary between screen display and print display. If you are creating something for print you will have to shift your colour output settings to CMYK (Cyan, Magenta, Yellow and Black). This is the model used to define the proportions of inks that make up a printed colour. This is known as a subtractive model, which means that combining all four inks produces black, whereas RGB is additive as the three screen colours combine to produce white.

While CMYK communicates from your software to a printer, telling it 446

When you are creating work to be consumed on the Web through screen displays, you will often program using HEX (Hexadecimal) codes to specify the mix of red, green and blue light (in the form #RRGGBB using codes 00 to FF).

what colours to print as an output, it does not really offer a logical model to think about the input decisions you will make about colour. Neither, for that matter, does RGB: it just is not realistic to think in those terms when considering what choices are needed in a visualisation design. There are different levers to adjust and different effects being sought that require an alternative model of thinking.

Figure 9.1 HSL Colour Cylinder

Figure 9.2 Colour Hue Spectrum

I share the belief with many in the field that the most accessible colour model – in terms of considering the application of colour in data visualisation – is HSL (Hue, Saturation, Lightness), devised by Albert Munsell in the 1980s. These three dimensions combine to make up what is known as a cylindrical-coordinate colour representation of the RGB colour model (I did warn you about the cylinders).

Hue is considered the true colour. With hue there are no shades (adding black), tints (adding whites) or tones (adding grey) – a

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consideration of these attributes follows next. When you are describing or labelling colours you are most commonly referring to their hue: think of the colours of the rainbow ranging through various mixtures of red, orange, yellow, green, blue, indigo and violet. Hue is considered a qualitative colour attribute because it is defined by difference and not by scale. Saturation defines the purity or colourfulness of a hue. This does have a scale from intense pure colour (high saturation) through increasing tones (adding grey) to the no-colour state of grey (low saturation). In language terms think vivid through to muted.

Figure 9.3 Colour Saturation Spectrum

Lightness defines the contrast of a single hue from dark to light. It is not a measure of brightness – there are other models that define that – rather a scale of light tints (adding white) through to dark shades (adding black). In language terms I actually think of lightness more as degrees of darkness, but that is just a personal mindset.

Figure 9.4 Colour Lightness Spectrum

Technically speaking, black, white and grey are not considered colours.

I have deliberately described these dimensions separately because, as you will see when looking at the applications of colour in visualisation, your decisions will often be defined by how you might employ these distinct dimensions of colour to form your visual display. The main choices tend to fall between employing difference in hue and variation in lightness, with the different levels of saturation often being a by-product of the definitions made for the other two dimensions.

Alternative models exist offering variations on a similar theme, such as HSV (Hue, Saturation, Value), HSI (Hue, Saturation, Intensity), HSB (Hue, Saturation, Brightness) and HCL (Hue, Chroma, Luminance).

These are all primarily representations of the RGB model space but involve differences in the mathematical translation into/from RGB and

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offer subtle differences in the meaning of the same terms (local definitions of hue and saturation vary). The biggest difference relates to their emphasis as a means of specifying either a colour quality (in an input, created sense) or a colour perception (in how a colour is ultimately experienced).

The argument against using the HSL model for defining colour is that, while it is fine for colour setting (i.e. an intuitive way to think about and specify the colours you want to set in your visualisation work), the resulting colours will not be uniformly perceived the same, from one device to the next. This is because there are many variables at play in the projection of light to display colour and the light conditions present in the moment of perception. That means the same perceptual experience will not be guaranteed. It is argued that more rigorous models (such as CIELAB) offer an absolute (as opposed to a relative) definition of colour for both input and output. My view is that they are just a little bit too hard to easily translate into visualisation design thinking. Furthermore, trying to control for all the subtleties of variation in consumption conditions is an extra burden you should ideally avoid.

At this stage, it is important to be pragmatic about colour as much as possible. The vast majority of your colour manipulating and perceptual needs should be nicely covered by the HSL model. As and when you develop a deeper, purist interest in colour you should then seek to learn more about the nuances in the differences between the definitions of these models and their application.

9.2 Features of Colour: Data Legibility

Data legibility concerns the use of the attribute of colour to encode data values in charts. The objective here is to make the data being represented by differences in colour as clearly readable and as meaningful as possible.

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Pantone is another colour space that you might recognise. It offers a proprietary colour-matching, identifying and communicating service for print, essentially giving ‘names’ to colours based on the CMYK process.

intend to use, you still need to take think carefully – and separately – about how you will specifically employ colour. To do this we first need to revisit the classification of data types and consider how best to use colour for representing each different type.

Nominal (Qualitative)

With nominal data colour is used to classify different categorical values. The primary motive for the choice of colour is to create a visible distinction between each unique categorical association, helping the eye to discern the different categories as efficiently and accurately as possible.

Creating contrast is the main aim of representing nominal data. What you are not seeking to show or even imply is any sense of an order of magnitude. You want to help differentiate one category from the next – and make it easily identifiable – but to do so in a way that preserves the sense of equity among the colours deployed.

Figure 9.5 Excerpt from ‘Executive Pay by the Numbers’

Variation in hue is typically the colour dimension to consider using for 450

differentiating categories. Additionally, you might explore different tones (variations in saturation across the hues). You should not, though, consider using variations in the lightness dimension. That is because the result is insufficiently discernible. As you can see demonstrated in Figure 9.5, the lightness variation of a blue tone makes it quite hard to connect the colour scale presented in the key at the top with the colours displayed in the stacked bars underneath. With the shading in the column header and the 2011 grey bar also contributing similar tones to the overall aesthetic of the table our visual processing system has to work much harder to determine the associations than it should need to do.

Often the categories you will be differentiating with colour will be relatively few in number, maybe two or three, such as in the separation between political parties or plotting different values for gender, as seen in Figure 9.6.

Figure 9.6 How Nations Fare in PhDs by Sex

Figure 9.7 How Long Will We Live – And How Well?

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Beyond these small numbers, you still typically might only need to contend with assigning colours to around four to six categories, perhaps in analysis that needs to visually distinguish values for different continents of the world, as seen in the scatter plot in Figure 9.7.

As the range of different categories grows, the ability to preserve clear differentiation becomes harder. In expanding your required palette, the colours used become decreasingly unique. The general rule of thumb is that once you have more than 12 categories it will not be possible to find a sufficiently different colour to assign to categories from 13 upwards. Additionally, you are really increasing the demands of learning and recognition for viewers. This then becomes quite a cognitive burden and delays the process of understanding.

Figure 9.8 Charting the Beatles: Song Structure

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Two approaches for dealing with this. Firstly, consider offering interactive filters to modify what categories are displayed in a visualisation – thus potentially reducing the impact of so many being available. Secondly, think about transforming your data by excluding or combining categories in to a reduced number of aggregate groupings.

Depending on the subject of your data, sometimes you can look to supplement the use of colour with texture or pattern to create further visible distinctions. In Figure 9.8 you can see two patterns being used occasionally as additive properties to show the structure of tracks on The Beatles’ album.

Ordinal (Qualitative)

With ordinal data you are still dealing with categories but now they have a natural hierarchy or ordering that can be exploited. The primary motive for using colour in this case is not only to create a visible distinction between each unique category association but also to imply some sense of an order of magnitude through the colour variation. The colour dimensions used to achieve this tend to employ variations of either the saturation or the lightness (or a combination of both). You might also introduce different hues when dealing with diverging (dual-direction) scales rather than simply converging (single-direction) ones.

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Figure 9.9 displays a simple example of colour used to display a converging ordinal variable. This is the teacup that I use in my office. On the inside you can see it has a colour guide to help ascertain how much milk you might need to add: going through Milky, Classic British, Builder’s Brew, and finally Just Tea (zero milk).

Figure 9.9 Photograph of MyCuppa Mug

A typical example of a diverging ordinal scale might be seen in the stacked bar chart showing the results of a survey question (Figure 9.10). The answers are based on the strength of feeling: strongly agree, agree, neutral, disagree, strongly disagree. By colouring the agreement in red (‘hot’ sometimes used to represent ‘good’) and the disagreement in blue (‘cold’

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to mean ‘bad’) means a viewer can quickly perceive the general balance of feelings being expressed.

Figure 9.10 Example of a Stacked Bar Chart Based on Ordinal Data

Another example of ordinal data might be to represent the notion of recency. In Figure 9.11 you see a display plotting the 2013 Yosemite National Park fire. Colour is used to display the recorded day-by-day progress of the fire’s spread. The colour scale is based on a recency scale with darker = recent, lighter = furthest away (think faded memory).

Figure 9.11 The Extent of Fire in the Sierra Nevada Range and Yosemite National Park, 2013

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Interval and Ratio (Quantitative)

With quantitative data (ratio and interval) your motive, as it is with ordinal data, is to demonstrate the difference between and of a set of values. In the choropleth map in Figure 9.12, showing the variation in electricity prices across Switzerland, the darker shades of blue indicate the higher values, the lighter tints the lower prices. This approach makes the viewer’s perception of the map’s values immediate – it is quite intuitive to recognise the implication of the general patterns of light and dark shades.

Figure 9.12 What are the Current Electricity Prices in Switzerland [Translated]

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Typically, using colour to represent quantitative data will involve breaking up your data values into discrete classifications or ‘bins’. This makes the task of reading value ranges from their associated colour shade or tone a little easier than when using a continuous gradient scale. While our capacity to judge exact variations in colour is relatively low (even with a colour key for reference), we are very capable of detecting local variations of colour through differences in tint, shade or tone. Assessing the relative contrast between two colours is generally how we construct a quantitative hierarchy.

Look at the fascinating local patterns that emerge in the next map (Figure 9.13), comparing increases in the percentage of people gaining health insurance in the USA (during 2013–14). The data is broken down to county level detail with a colour scale showing a darker red for the higher percentage increases.

Some of the most relevant colour practices for data visualisation come from the field of cartography (as do many of the most passionate colour

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purists). Just consider the amount of quantitative and categorical detail shown in a reference map that relies on colour to differentiate types of land, indicate the depth of water or the altitude of high ground, present route features of road and rail networks, etc. The best maps pack an incredible amount of detail into a single display and yet somehow they never feel disproportionately overwhelming.

Figure 9.13 Excerpt from ‘Obama’s Health Law: Who Was Helped Most’

Aside from the big-picture observations of the darker shades in the west and the noticeably lighter tints to the east and parts of the mid-west, take a closer look at some of the interesting differences at a more local level. For example, notice the stark contrast across state lines between the dark regions of southern Kentucky (to the left of the annotated caption) and the light regions in the neighbouring counties of northern Tennessee. Despite their spatial proximity there are clearly strong differences in enrolment on the programme amongst residents of these regions.

Both of these previous examples use a convergent colour scale, moving through discrete variations in colour lightness to represent an increasing

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scale of quantitative values, from zero or small through to large. As illustrated with the stacked bar chart example shown earlier, portraying the range of feelings from an ordinal dataset, sometimes you may need to employ a divergent colour scale. This is when you want to show how values are changing in two directions either side of a set breakpoint.

Figure 9.14 Daily Indego Bike Share Station Usage

Figure 9.14 shows a cropped view of a larger graphic comparing the relative peaks and troughs of usage across all bike share stations in Philadelphia over a 24-hour period. The divergent colour scale uses two hues and variations in lightness to show the increasingly busy and increasingly slow periods of station activity either side of a breakpoint, represented by a very light grey to indicate the average point. The darkest red means the station is full, the darkest blue means the station is empty.

Regardless of whether you are plotting a converging or diverging scale, judging how you might divide up your colour scales into discrete value bins needs careful thought. The most effective colour scales help viewers perceive not just the relative order of magnitude – higher or lower – but also a sense of the absolute magnitude – how different a value might be compared to another value.

There is no universal rule about the number of value bins. Indeed, it is not uncommon to see entirely continuous colour scales. However, a general rule of thumb I use is that somewhere between between four and nine meaningful – and readable – value intervals should suffice. There are two key factors to consider when judging your scales:

Are you plotting observed data or observable data? You might only 459

have collected data for a narrow range of quantities (e.g. 15 to 35) so will your colour classifications be based on this observed range or on the potentially observable data range i.e. the values you know would/could exist with a wider sample size or on a different collection occasion (e.g. 0 to 50)?

What are the range and distribution of your data? Does it make sense to create equal intervals in your colour classifications or are there more meaningful intervals that better reflect the shape of your data and the nature of your subject? Sometimes, you will have legitimate outliers that, if included, will stretch your colour scales far beyond the meaningful concentration of most of your data values.

Figure 9.15 Battling Infectious Diseases in the 20th Century: The Impact of Vaccines

You can see this effect in Figure 9.15, showing the incidence of Hepatitis A per 100,000 population. There are only three values that exceed 100 (you can see them on the top line for Alaska in the late 1970s). To accommodate these outliers the colour scale becomes somewhat stretched- out, with a wide range of potential values being represented by a dark yellow to red colour. With 99.9% of the values being under 100 there is little discernibly in the blue/green shades used for the lower values. If outliers are your focus, it makes sense to include these and colour

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accordingly to emphasise their exceptional quality. Otherwise if they risk compromising the discrete detail of the lower values you might look to create a broad classification that uses a single colour for any value beyond a threshold of maybe 75, with even value intervals of maybe 15 below that help to show the patterns of smaller values.

For diverging scales, the respective quantitative shades either side of a breakpoint need to imply parity in both directions. For example, a shade of colour that means +10% one side of the breakpoint should have an equal shade intensity in a different hue on the other side to indicate the same interval, i.e. −10%. Additionally, the darkest shades of hues at the extreme ends of a diverging scale must still be discernible. Sometimes the darkest shades will be so close to black that you will no longer be able to distinguish the differences in their underlying hues when plotted in a chart or map.

As well as considering the most appropriate discrete bins for your values, for diverging scales one must also pay careful attention to the role of the breakpoint. This is commonly set to separate values visually above or below zero or those either side of a meaningful threshold, such as target, average or median.

One of the most common mistakes in using colour to represent quantitative data comes with use of the much-derided rainbow scale. Look at Figure 9.16, showing the highest temperatures across Australia during the first couple of weeks in 2013. Consider the colour key to the right of the map and ask yourself if this feels like a sufficiently intuitive scale. If the key was not provided, would you be able to perceive the order of magnitude relationship between the colours on the map? If you saw a purple colour next to a blue colour, which would you expect to mean hotter and which colder?

Figure 9.16 Highest Max Temperatures in Australia

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While the general implication of blue = ‘colder’ through to red = ‘hotter’ is included within sections of this temperature colour scale, it is the presence of many other hues that obstructs the accessibility and creates inconsistency in logic. For instance, do the colours used to show 24°C (light blue) jumping to 26°C (dark green) make sense as a means for showing an increasing temperature? How about 18°C (grey) to 20°C (dark blue), or the choice of the mid-brown used for 46°C which interrupts the increasingly dark red sequence? If you saw on the map a region with the pink tone as used for 16°C would you be confident that you could easily distinguish this from the lighter pink used to represent 38°C? Unless there are meaningful thresholds within your quantitative data – justifiable breakpoints – you should only vary your colour scales through the lightness dimension, not the hue dimension.

One of the interesting recurring challenges faced by visualisers is how to represent nothing. For example, if a zero quantity or no category is a meaningful state to show, you still need to represent this visually somehow, even though it might possess no size, no position and no area. How do you distinguish between no data and a zero value?

Figure 9.17 State of the Polar Bear 462

Typically, using colour is one of the best ways to portray this. Figure 9.17 shows one solution to making ‘no data’ a visible value. This map displays the population trends of the polar bear. Notice those significant areas of grey representing ‘data deficient’. A subtle but quite effective political point is being made here by including this status indicator. As I mentioned before, sometimes the absence of data can be the message itself.

Figure 9.18 Excerpt from ‘Geography of a Recession’

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When considering colour choices for quantitative classifications, you will need to think especially carefully about the lowest value grouping: is it to be representative of zero, an interval starting from zero up to a low value, or an interval starting only from the minimum value and never including zero? In this choropleth map (Figure 9.18) looking at the unemployment rate across the counties of the USA, no value is as low as zero. There might be value that are close, but nowhere is the unemployment rate at 0%. As you can see, the lowest tint used in this colour key is not white, rather a light shade of orange, so as not to imply zero. Whilst not relevant to this example, if you wanted to create a further distinction between the lowest value interval and the ‘null’ or ‘no data’ state you could achieve this by using a pure white/blank.

9.3 Features of Colour: Editorial Salience

Having considered options for the application of colour in facilitating data legibility, the next concern is colour used for editorial salience. Whereas data legibility was concerned with helping to represent data, using colour for editorial salience is about drawing the viewer’s attention to the

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significant or meaningful features of your display. Colour offers such a potent visual stimulus and an influential means for drawing out key aspects of your data and project that you might feel are sufficiently relevant to make prominent.

Consider again the idea of photography and the effect of taking a photograph of a landscape. You will find the foreground objects are darker and more prominent than the faded view of the background in the distance as light and colour diminish. Using colour to achieve editorial salience involves creating a similar effect of depth across your visualisation’s contents: if everything is shouting, nothing is heard.

The goal of using colour to facilitate editorial salience is a suitable contrast. For things to stand out, you are in turn determining which other things will not.

The degree of contrast you might seek to create will vary. Often you will be seeking to draw a significant contrast, maximising the emphasis of a value or subset of values so the viewer can quickly home in on what you have elevated for their attention relative to everything else.

For this reason, grey will prove to be one of your strongest allies in data visualisation. When contrasted with reasonably saturated hues, grey helps to create depth. Elements coloured in greyscale will sit quietly at the back of the view, helping to provide a deliberately subdued context that enables the more emphasised coloured properties to stand proudly in the foreground.

In Figure 9.19, the angle of analysis shows a summary of the most prevalent men’s names featuring among the CEOs of the S&P 1500 companies. As you can see there are more guys named ‘John’ or ‘David’ than the percentage of all the women CEOs combined. With the emphasis of the analysis on this startling statement of inequality the bar for ‘All women’ is emphasised in a burgundy colour, contrasting with the grey bars of all the men’s names. Notice also that the respective axis and bar value labels are both presented using a bold font, which further accentuates this emphasis. It is also editorially consistent with the overriding enquiry of the article. As discussed in Chapter 3, bringing to the surface key insights from data displays in this way contributes towards facilitating an ‘explanatory’ experience.

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Figure 9.19 Fewer Women Run Big Companies Than Men Named John

Figure 9.20 NYPD, Council Spar Over More Officers

Sometimes, only noticeable contrast – not shouting, just being slightly more distinguishable – may be appropriate. Compared with the previous bar chart example, Figure 9.20 creates a more subtle distinction between the slightly darker shade of green (and emboldened text) emphasising the New York figures compared to the other listed departments in a slightly lighter green. As with the CEOs’ example, the object of our attention is the

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subject of focus in the analysis, in this case regarding a drive for more NYPD officers. This does not need to be any more contrasting; it is just as sufficiently noticeable as the visualiser wishes it to be.

Sometime you will seek to create several levels of visual ‘urgency’ in the relative contrast of your display. The colour choices in Figure 9.21 gives foreground prominence to the yellow coloured markers and values (the dots are also larger) and then mid-ground/secondary prominence to the slightly muted red markers. In perceiving the values of the yellow markers, the viewer is encouraged to concentrate on primarily comparing these with the red markers. The subtle grey markers are far less visible – closer in shade to the background than the foreground – and deliberately relegated to a tertiary level so they do not clutter up the display and cause unwarranted attention. They provide further context for the distribution of the values but do not need to be any more prominent in their relationship with the foreground and mid-ground colours.

Figure 9.21 Excerpt from a Football Player Dashboard

I touched on the use of encoded overlays earlier where coloured areas or bandings can be used to help separate different regions of a display in order to facilitate faster interpretation of the meaning of values. In the bubble plot in Figure 9.22, you can see the circle markers are colour coded

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to help viewers quickly ascertain the significance of each location on the chart according to the quadrants in which they fall. Notice how in the background the diagonal shading further emphasises the distinction between above the line ‘improvement’ and below the line ‘worsening’, a very effective approach.

Figure 9.22 Elections Performance Index

9.4 Features of Colour: Functional Harmony

After achieving data legibility and editorial salience through astute colour choices, functional harmony is concerned with ensuring that any remaining colour choices will aid, and not hinder, the functional effectiveness and elegance of the overall visualisation.

‘When something is not harmonious, it’s either boring or chaotic. At one extreme is a visual experience that is so bland that the viewer is not engaged. The human brain will reject under-stimulating information. At the other extreme is a visual experience that is so overdone, so chaotic, that the viewer can’t stand to look at it. The human brain rejects what it can not organise, what it cannot understand.’ Jill Morton, Colour

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Expert and Researcher

You must judge the overall balance of and suitability of your collective colour choices and not just see these as isolated selections. This is again primarily a judgement about contrast – what needs to be prominent and what needs to be less so. Such an apparent calming quality about a well- judged and cohesive colour palette is demonstrated by Stefanie Posavec’s choices in visualising the structure of Walter Benjamin’s essay ‘Art in the age of mechanical reproduction’ (Figure 9.23). There is effortless harmony here between the colour choices extending across the entire anatomy of design: the petals, branches, labels, titles, legend, and background.

A reminder that any and every design feature you incorporate into your display will have a property of colour otherwise they will be invisible. In looking at data legibility and editorial salience you have considered your colour choices for representing data. A desire to achieve functional harmony means considering further colour decisions that will help establish visual relationships across and between the rest of your visualisation’s anatomy: its interactive features, annotations and composition.

Figure 9.23 Art in the Age of Mechanical Reproduction: Walter Benjamin

Interactive features: Visible interactive features will include controls such as dropdown menus, navigation buttons, time sliders and parameter selectors. The colour of every control used will need to be harmonious with the rest of the project but also, critically, must be functionally clear. How you use colour to help the user discern what

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is selected and what is not will need to be carefully judged. To illustrate this, Figure 9.24 shows an interactive project that examines the connected stories of the casualties and fatalities from the Iraqi and Afghan conflicts. Here you can see that there are several interactive features, all of which are astutely coloured in a way that feels both consistent with the overall tone of the project but also makes it functionality evident what each control’s selected status or defined setting is. This is achieved through very subtle but effective combinations of dark and light greys that help create intuitive clarity about which values the user has selected or highlighted. When a button has a toggle setting (on/off, something/something else), such as the ‘Afghanistan’ or ‘Iraq’ tabs at the top, the selected tab is highlighted in bright grey and the unselected tab in a more subdued grey. Filters can either frame (include/exclude) or focus (highlight/relegate) the data. The same approach to using brighter greys for the selected parameter values makes it very clear what you have chosen, but also what you have excluded (while making evident the other currently-unselected values from which you can potentially choose).

Figure 9.24 Casualties

Annotations: Chart annotations such as gridlines, axis lines and value labels all need colouring in a way that will be sympathetic to the colour choices already made for the data representation and, possibly, editorial contrasting. As mentioned in the last chapter, many

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annotation devices exist in the form of text and so the relative font colour choices will need to be carefully considered. For any annotation device the key guiding decision is to find the level at which these are suitably prominent. Not loud, not hidden, just at the right level. This will generally take a fair amount of trial and error to get right but once again, depending on your context, your first thought should be to consider the merits offered by different shades of grey.

You might be starting to suspect I’m a lobbyist for the colour grey. Nobody wants to live in a world of only grey. The point is more about how its presence enables other colours to come alive. The great Bill Shankly once said ‘Football is like a piano, you need 8 men to carry it and 3 who can play the damn thing’. In data visualisation, grey does the heavy lifting so the more vibrant colours can bring the energy and vibrancy to your design.

Figure 9.25 First Fatal Accident in Spain on a High-speed Line [Translated]

Another example of the role of greyscale is demonstrated by Figure 9.25, illustrating key aspects of the tragic rail crash in Spain in 2013. The sense of foreground and background is clearly achieved by the prominence of the scarlet-coloured annotations and visual cues offset against the

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backdrop of an otherwise greyscale palette. Figure 9.26 Lunge Feeding

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There are other features of annotation that will have an impact on functional harmony through their colouring. Multimedia assets like photos, embedded videos, images and illustrations need to be consistent in tone according to their relative role on the display. If they are to dominate the page then unleash the vibrancy of their colours to achieve this; if they are playing more of a secondary or supporting role then relegate their constituent colours to allow other primary features due prominence.

Figure 9.26 includes small illustrations of a whale, showing how it goes through the stages of lunge feeding. The elegance of the colours used in these illustrations is entirely harmonious with the look and feel of the overall piece. They are entirely at one with the rest of the graphic.

Composition: The clarity in layout of a project will often be achieved by the use of background colour to create logical organisation. In the ‘Lunge Feeding’ graphic the shading of the blue sea getting darker as it moves down is not attempting to offer a precise representation of the sea, but it gives a sense of depth and draws maximum attention to that panel. It is also naturally congruent with the subject matter.

Figure 9.27 Examples of Common Background Colour Tones

In general, there are no fixed rules on the benefits of any particular colour for background shading. Your choices will depend mostly on the circumstances and conditions in which your viewers are

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consuming the work. Usually, when there is no associated congruence for a certain background colour, your options will tend to come from one of the selection of neutral and/or non-colours (Figure 9.27). This is because they particularly help to aid accentuation in combination with foreground colours.

Typically, though, a white background (at least for your chart area) gives viewers the best chance of being able to accurately perceive the different colour attributes used in your data representation and the contrasting nature of your editorial contrast.

White – or more specifically emptiness – is one of your most important options for creating functional meaning for nothingness, something I touched on earlier. The emptiness of uncoloured space can be used very effectively to direct the eye’s attention. It organises the relationship between space on a page without the need for visible apparatus, as seen in the left hand column of the lunge feeding graphic. It can also be used to represent or emphasise values that might have the state of ‘null’ or ‘zero’ to maximise contrast.

9.5 Influencing Factors and Considerations

Having mapped out the ways and places where colour could be used, you will now need to consider the factors that will influence your decisions about how colour should be used.

Formulating Your Brief

Format: This is a simple concern but always worth pointing out: if you are producing something for screen display you will need to set your colour output to RGB; if it is for print you will need CMYK. Additionally, when you are preparing work for print, running off plenty of proofs before finalising a design is imperative. What you are preparing digitally is a step away from the form of its intended

output. What looks like a perfect colour palette on screen may not ultimately look the same when printed.

‘The single most overlooked element in visual design is emptiness. Space must look deliberately used.’ Alex White, Author, The Elements of Graphic Design

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Print quality and consistency is also a factor. Graphics editors who create work for print newspapers or magazines will often consider using colours as close in tone as possible to pure CMYK, especially if their work is quite intricate in detail. This is because the colour plates used in printing presses will not always be 100% aligned and thus mixtures of colours may be slightly compromised.

As black and white printing is still commonplace, you need to be aware of how your work might look if printed without colour. If you are creating a visualisation that might possibly be printed by certain users in black and white, the only colour property that you can feasibly utilise will be the lightness dimension. Sometimes, as a designer or author, you will be unaware of this intent and the colourful design that you worked carefully towards will end up not being remotely readable.

Furthermore, there is an important difference in how colours appear when published in colour and how they appear when published in black and white. Hues inherently possess different levels of brightness: the purest blue is darker than the purest yellow. If these were printed in black and white, blue would therefore appear a darker, more prominent shade of grey. If your printed work will need to be compatible for both colour and black and white output, before finalising your decisions check that the legibility and intended meaning of your colour choices are being maintained across both forms.

Setting: For digital displays, the conditions in which the work will be consumed will have some influence over the choice between light and dark backgrounds. The main factor is the relative contrast and the stresses this can place on the eye to adjust against the surroundings. If your work is intended for consumption in a light environment, lighter backgrounds tend to be more fitting; likewise darker backgrounds will work best for consuming in darker settings. For tablets/smartphones, the bordering colour of the devices can also influence the most suitable choice of background tone to most sympathetically contrast with the surroundings.

Colour rules and identities: In some organisations there are style 475

We all refer to black and white printing, but technically printers do not actually print using white ink, it is just less black or no black.

guidelines or branding identities that require the strict use of only certain colour options. Similar guidelines may exist if you are creating work for publication in a journal, magazine, or on certain websites. Guidelines like these are well intended, driven by a desire to create conformity and consistency in style and appearance. However, in my experience, the basis of such colour guides rarely incorporates consideration for the subtleties of data visualisation. This means that the resulting palettes are often a bad fit for ideal visualisation colour needs, providing limited scope for the variation and salience you might seek to portray.

Your first task should always be to find out if there is any compromise – any chance of not having these colour restrictions imposed. If there is no flexibility, then you will just have to accept this and begin acquainting yourself with the colours you do have to work with. Taking a more positive view, achieving consistency in the use of colour for visualisation within an organisation does have merits if the defined palettes offer suitably rich variety. Developing a recognisable ‘brand’ and not having to think from scratch about what colours to use every time you face a new project is something that can be very helpful, especially across a team. Purpose map: Does it need to be utilitarian or decorative? Should it be functional or appealingly seductive? Does it lend itself to being vivid and varied in colour or more muted and distinguished? Colour is the first thing we notice as viewers when looking at a visualisation, so your choices will play a huge part in setting the visible tone of voice. How you define your thinking across the vertical dimension of your purpose map will therefore have an influence on your colour thinking. Along the horizontal dimension, the main influencing consideration will be a desire to offer an ‘explanatory’ experience. As mentioned, some of the tactics for incorporating editorial salience will be of specific value if you are seeking to emphasise immediately apparent, curated insights. Ideas and inspiration: In the process of sketching out your ideas and capturing thoughts about possible sources of influence, maybe there were already certain colours you had identified as being consistent with your thinking about this subject? Additionally, you might have already identified some colours you wish to avoid using.

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Working With Data

Data examination: The characteristics of your data will naturally have a huge impact, on the decisions you make around data legibility. Firstly, the type of data you are displaying (primarily nominal vs all other types) will require a different colour treatment, as explained. Secondly, the range of categorical colour associations (limits on discernible hues) and the range and distribution of quantitative values (numbers of divisions and definition of the intervals across your classification scale) will be directly shaped by the work you did in the examination stage. In Figure 9.28 you can see a census of the prevalence and species of trees found around the boroughs of New York City. This initial big- picture view creates a beautiful tapestry made up of tree populations across the region (notice the big void where JFK Airport is located).

Figure 9.28 Excerpt from ‘NYC Street Trees by Species’

To observe patterns for individual tree types is harder: with 52 different tree species there are simply too many classifications to be able to allocate sufficiently unique colours to each. To overcome this, the project features a useful pop-up filter list which then allows you to adjust the data on view to reveal the species you wish to explore. It is often the case when thinking about colour classifications that you may need to revisit the data transformation actions to find new ways of grouping your data to create better-fit quantitative value classifications or to look at ways of grouping your categories. For the latter, actions such as combining less important categories in an

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‘other’ bin to reduce the variability or eliminating certain values from your analysis may be necessary.

‘If using colour to identify certain data, be careful to not accidentally apply the same identity to a nearby part of the graphic. Don’t allow colour to confuse just for the sake of aesthetics. I also like to use colour to highlight. A single colour highlight on a palette of muted colours can be a strong way to draw attention to key information.’ Simon Scarr, Deputy Head of Graphics, ThomsonReuters

Establishing Your Editorial Thinking

Focus: When considering the perspective of ‘focus’ in the editorial thinking stage, you were defining which, if any, elements of content would merit being emphasised. Are there features of your analysis that you might wish to accentuate? How might colour be used to accentuate key insights in the foreground and push other (less important) features into the background? What are the characteristics of your data that you might want to emphasise through changes in colour? For example, are there certain threshold values that will need to be visually amplified if exceeded? Your decisions here will directly influence your thinking about using colour to facilitate editorial salience.

Data Representation

Chart type choice: Specifically in relation to data legibility, depending on which chart type you selected to portray your data, this may have attributes requiring decisions about colour. The heat map and choropleth map are just two examples that use variation in colour to encode quantitative value. Almost every chart has the potential to use colour for categorical differentiation.

Trustworthy Design

Data classification: The decisions you make about how to encode data through colour have a great bearing on the legibility and accuracy of your design, especially with quantitative data. You will

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need to ensure the classifications present a true reflection of the shape and characteristics of your data and do not suppress any significant interpretations.

Meaningful: Eliminating arbitrary decisions is not just about increasing the sophistication of your design thinking, it is also an essential part of delivering a trustworthy design. If something looks visually significant in its data or editorial colouring it will be read as such, so make sure it is significant, otherwise remove it. You especially want to avoid any connotation of significant meaning across your functional or decorative colour choices. This will be confusing at best, or will appear deceptive at worst.

Do not try to make something look more interesting than it fundamentally is. Colour should not be used to decorate data. You might temporarily boost the apparent appeal of your work in the eye of the viewer but this will be short-lived and artificial. Illusions: The relationship between a foreground colour and a background one can create distorting illusions that modify the perceived judgement of a colour. You saw an effect of this earlier with the inverted area chart showing ‘Gun deaths in Florida’, whereby the rising white mountain was seen by some as the foreground data, when in fact it was the background emptiness framed by the red area of data and the axis line. Illusions can affect all dimensions of colour perception. There are simply too many to mention here and they are hard to legislate for entirely; it is really more about mentioning that you need to be aware of these as a consequence of your colour choices.

Accessible Design

Consistency: Consistency in the use of colours helps to avoid visual chaos and confusion and minimises cognitive effort. When you establish association through colour you need to maintain that meaning for as long as possible. Once a viewer has allocated time and effort to learn what colours represent, that association becomes

‘Start with black and white, and only introduce color when it has relevant meaning. In general, use color very sparingly.’ Nigel Holmes, Explanation Graphic Designer

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locked down in the eye and the mind. However, if you then allocate the same colour(s) to mean something different (within the same graphic or on a different page/screen view) this creates an additional cognitive burden. The viewer has almost to disregard the previous association and learn the new one. This demands effort that undermines the accessibility of your design.

Sometimes this can prove difficult, especially if you have a restricted colour palette. The main advice here is to try to maximise the ‘space’ between occasions of the same colour meaning different things. This space may be physical (different pages, interactive views), time (the simple duration of reading between the associations being changed) or editorial (new subject matter, new angle of analysis). Such space effectively helps to clean the palate (pun intended). Of course, at the point of any new assignment in your colour usage, clear explanations are mandatory.

Visual accessibility: Approximately 5% of the population have visual impairments that compromise their ability to discern particular colours and colour combinations. Deuteranopia is the most common form, often known as red–green colour blindness, and is a particular genetic issue associated with men. The traffic light scheme of green = ‘good’, red = ‘bad’ is a widespread approach for using colour as an indicator. It is a convenient and common metaphor and the reasons for its use are entirely understandable. However, as demonstrated in the pair of graphics in Figure 9.29, looking at some word-usage sentiment analysis, the reds and greens that most of us would easily discern (from the left graphic) are often not at all distinguishable for those with colour blindness (simulated on the right).

Figure 9.29 Demonstrating the Impact of Red-green Colour Blindness (deuteranopia)

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Of course, if you have a particularly known, finite and fixed audience then you can easily discover if any colour-blindness issues do in fact exist. However, if your audience is much larger and undefined you are going to alienate potentially 1 person in every 14, in which case the use of the default red–green colour combination is not acceptable. Be more sensitive to your viewers by considering other options:

Figure 9.30 Colour-blind Friendly Alternatives to Green and Red

If you are working on an interactive solution, you may consider having a toggle option to switch between different colour modes. For

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print outputs you might normally have reduced flexibility, but in certain circumstances the option of creating dual versions (second output for colour-impaired viewers) may be legitimate. Connotations and congruence: Whether it is in politics, sport, brands or in nature, there are many subjects that already have established colour associations you can possibly look to exploit. This association may sit directly with the data, such as the normal colour associations for political party categories, or more through the meaning of the data, such as perhaps through the use of green to present analysis about ecological topics.

In support of accessible design, exploiting pre-existing colour associations in your work can create more immediacy in subject recognition. You might also benefit from the colour learning experiences your viewers may already have gone through. This provides a shortcut to understanding through familiarity. However, while some colour connotations can be a good thing, in some cases they can be a bad thing and possibly should be avoided. You need to be considerate of and sensitive to any colour usage to ensure that you do not employ connotations that may have a negative implication and may evoke strong emotions and reactions from people.

Sometimes a colour is simply incongruent with a subject. You would not use bright, happy colours if you were portraying data about death or disease. Earlier, in the ‘Vision: Ideas’ section, I described a project context where I knew I wanted to avoid the use of blue colours in a particular project about psychotherapy treatment in the Arctic, because it would carry an unwelcome clichéd association given the subject matter. The use of ‘typical’ skin colours to represent ethnic groups in a visualisation is something that would be immediately clumsy (at best) and offensive (at worst).

Cultural sensitivities and inconsistencies are also important to consider. In China, for example, red is a lucky colour and so the use of red in their stock market displays, for example, indicates the rising values. A sea of red on the FTSE or Dow Jones implies the opposite. In Western society red is often the signal for a warning or danger. Occasionally established colour associations are out of sync with contemporary culture or society. For example, when you think about colour and the matter of gender, because it has been so endlessly utilised down the years, it is almost impossible not to think instinctively about the use of blue (boys) and pink (girls). My

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personal preference is to avoid this association entirely. I agree with so many commentators out there that the association of pink to signify the female gender, in particular, is clichéd, outdated and no longer fit for purpose. It is not too much to expect viewers to learn the association of – at most – two new colours for representing gender.

Elegant Design

Unity: As I alluded to in the discussion about using colours for editorial salience, colour choices are always about contrast. The effect of using one colour is not isolated to just that instance of colour: choosing one colour will automatically create a relationship with another. There is always a minimum of two colours in any visualisation – a foreground and background colour – but generally there are many more. We notice the impact of colour decisions more when they are done badly. Inconsistent and poorly integrated colour combinations create jarring and discordant results. If we do not consciously notice colour decisions this probably means they have been seamlessly blended into the fabric of the overall communication. Neutral colouring: Even if there is no relevance in the use of colour for quantitative or categorical classifications, you still have to give your chart some colour, otherwise it will be invisible. The decision you make will depend again on the relative harmony with other colour features but should also avoid unnecessarily ‘using up’ a useful colour. Suppose you colour your bars in blue but then elsewhere across your visualisation project blue would have been a useful colour to show something meaningful; you then have unnecessarily taken blue out of the reckoning. My default choice is to go with grey to begin with (Figure 9.31) and only use a colour if there is a suitable and available colour not used elsewhere or if it needs to be left as a back- or mid-ground artefact to preserve prominence elsewhere in the display.

Figure 9.31 Excerpt from ‘Pyschotherapy in The Arctic’

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Justified: Achieving elegant design is about eliminating the arbitrary. In thinking about colour usage I often get quite tough with myself. If I want to show any feature on my visualisation display I have to seek permission from myself to unlock access to the more vibrant colours by justifying why I should be allowed to use and apply that colour (I know what you’re thinking, ‘what a fun existence this guy leads’). Elegance in visualisation design is often about using only the colours you need to use and avoiding the temptation to inject unnecessary decoration. The Wind Map project (Figure 9.32) demonstrates unquestionable elegance and yet uses only a monochromatic palette. There is no colouring of the sea, no topographic detail, no emphasising of any extreme wind speed thresholds being reached. The resulting elegance is quite evident: the map has artistic and functional beauty.

To emphasise again, I am not advocating a need to pursue minimalism: while you can create incredibly elegant and detailed works from a limited palette of colours, justifying the use of colours is not the same as unnecessarily restricting the use of colour. Feels right: The last component of influence is yourself. Sometimes you will just find colours that feel right and look good when you apply them to your work. There is maybe no underlying science behind such choices, and as such you will simply need to back your own instinctive judgement as an astute visualiser and know when something looks good. Creating the right type of visual appeal, something that is pleasing to the eye and equally fit for purpose in all

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the functional ways I have outlined, is a hard balance to achieve, but you will find that weighing up all these different components of influence alongside your own flair for design judgement will give you the best chance of getting there.

Figure 9.32 Wind Map

Figure 9.32 Wind Map

Summary: Colour

Data legibility involves using colours to represent different types of data. The most appropriate colour association or scale decisions will depend on the data type: nominal (qualitative), ordinal (qualitative), interval and ratio (quantitative).

Editorial salience is about using colour to direct the eye. For which features and to what degree of emphasis do you want to create contrast?

Functional harmony concerns deciding about every other colour property as applied to all interactive features, annotations and aspects of your composition thinking.

Influencing Factors and Considerations

Formulating the brief: format, setting, colour rules and imposed guidelines all have a significant impact. Your definitions about both tone and and experience, on the purpose map, will lead to specific

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choices being more suitable than others. What initial ideas did you form? Have any sources of inspiration already implanted ideas inside your head about which colours you could use? Working with data: what type of data and what range of values/number of classifications have you got?

Establishing your editorial thinking: what things do you want to emphasise or direct the eye towards (focus)? Data representation: certain chart type choices will already include colour as an encoded attribute.

Trustworthy design: ensure that your colour choices are faithful to the shape of your data and the integrity of your insights. If something looks meaningful it should be, otherwise it will confuse or deceive. Accessible design: once you’ve committed colour to mean something preserve the consistency of association for as long as possible. Be aware of the sensitivities around visual accessibility and positive/negative colour connotations.

Elegant design: the perception of colours is relative so the unity of your choices needs to be upheld. Ensure that you can justify every dot of colour used and, ultimately, rely on your own judgment to determine when your final palette feels right.

Tips and Tactics

Use the squint test: shrink things down and/or half close your eyes to see what coloured properties are most prominent and visible – are these the right ones? Experimentation: trial and error is still often required in colour, despite the common sense and foundation of science attached to it. Developing a personal style guide for colour usage saves you the pain of having to think from scratch every time and will help your work become more immediately identifiable (which may or may not be an important factor).

Make life easier by ensuring your preferred (or imposed) colour palettes are loaded up into any tool you are using, even if it is just the tool you are using for analysis rather than for the final presentation of your work. If you are creating for print, make sure you do test print runs of the draft work to see how your colours are looking – do not wait for the first print when you (think you) have finished your process.

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10 Composition

Composition concerns making careful decisions about the physical attributes of, and relationships between, every visual property to ensure the optimum readability and meaning of the overall, cohesive project.

Composition is the final layer of your design anatomy, but this should not imply that it is the least important part of your design workflow. Far from it. It is simply that now is the most logical time to think about this, because only at this point will you have established clarity about what content to include in your work. As I explained, this final layer of design thinking, along with colour, is no longer about what elements will be included but how they will appear. Composition is a critical component of any design discipline. The care and attention afforded in the precision of your composition thinking will continue until the final dot or pixel has been considered.

Visual assets such as your chart(s), interactive controls and annotations all occupy space. In this chapter you will be judging what is the best way to use space in terms of the position, size and shape of every visible property. In many respects these individual dimensions of thought are inseparable and so, similar to the discussion about annotation, the division in thinking is separated between project- and chart-level composition options:

Project composition: defining the layout and hierarchy of the entire visualisation project. Chart composition: defining the shape, size and layout choices for all components within your charts.

10.1 Features of Composition: Project Composition

This first aspect of composition design concerns how you might lay out and size all the visual content in your project to establish a meaningful hierarchy and sequence. Content, in this case, means all of your charts, interactive operations and elements of annotation.

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Where will you put all of this, what size will it be and why? How will the hierarchy (across views) and sequencing (within a view) best fit the space you have to work in? How will you convey the relative importance and provide a connected narrative where necessary?

I will shortly run through all the key factors that will influence your decisions, but it is worth emphasising that so much about composition thinking is rooted in common sense and involves a process of iteration towards what feels like an optimum layout. Of course, there are certain established conventions, such as the positioning of titles first or at the top (usually left or centrally aligned). Introductions are inevitably useful to offer early, whereas footnotes detailing data sources and credits might be of least importance, relatively speaking. You might choose to show the main features first, exploiting the initial attention afforded by your audience, or you may wish to build up to this, starting off with contextual content before the big ‘reveal’.

Figure 10.1 City of Anarchy

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The hierarchy of content is not just a function of relative position through layout design, it can also be achieved through the relative variation in size of the contents. Just as variation in colour implies significance, so too does variation in size: a chart that is larger than another chart will imply that the analysis it is displaying carries greater importance.

The ‘City of anarchy’ infographic demonstrates a clear visual hierarchy across its design. There is a primary focal point of the main subject ‘cutaway’ illustration in the centre with a small thumbnail image above it for orientation. At the bottom there are small supplementary illustrations to provide further information. It is clear through their relative placement at the bottom of the page and their more diminutive stature that they are of somewhat incidental import compared with the main detail in the centre.

There are generally two approaches for shaping your ideas about this project-level composition activity, depending on your entry-point perspective: wireframing and storyboarding. I profiled these at the start of this part of the book, but it is worth reinforcing their role now you are focusing on this section of design thinking.

Wireframing involves sketching the potential layout and size of all the major contents of your design thinking across a single-page view. This might be the approach you take when working on an infographic or any digital project where all the interactive functions are contained within a single-screen view rather than navigating users elsewhere. Any interactive controls included would have a description within the wireframe sketch to explain the functions they would trigger.

Figure 10.2 is an early wireframe drawn by Giorgia Lupi when shaping up her early thoughts about the potential layout of a graphic exploring various characteristics of Nobel prizes and laureates between 1901 and 2012.

Figure 10.2 Wireframe Sketch

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Storyboarding is something you would undertake with wireframing if you have a project that will entail multiple pages or many different views and you want to establish a high-level feel for the overall architecture of content, its navigation and sequencing. This would be an approach relevant for linear outputs like discrete sequences in reports, presentation slides or video graphics, or for non-linear navigation around different pages of a multi-faceted interactive. The individual page views included as cells in this big-picture hierarchy will each merit more detailed wireframing versions to determine how their within-page content will be sized and arranged, and how the navigation between views would operate.

With both wireframing and storyboarding activities all you are working towards, at this stage, are low-fidelity sketched concepts. Whether this sketching is on paper or using a quick layout tool does not matter; it just needs to capture with moderate precision the essence of your early thinking about the spatial consequence of bringing all your design choices together. Gradually, through further iteration, the precision and finality of your solution will emerge.

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10.2 Features of Composition: Chart Composition

After establishing your thoughts about the overall layout, you will now need to go deeper in your composition thinking and contemplate the detailed spatial matters local to each chart, to optimise its legibility and meaning. There are many different components to consider.

Chart size: Do not be afraid to shrink your charts. The eye can still detect at quite small resolution and with great efficiency chart attributes such as variation in size, position, colour, shape and pattern. This supports the potential value of the small-multiples technique, an approach that tends to be universally loved in data visualisation. As I explained earlier, this technique offers an ideal solution for when you are trying to display the same analysis for multiple categories or multiple points in time. Providing all the information in a simultaneous view means that viewers can efficiently observe overall patterns as well as perform a more detailed inspection. Figure 10.3 provides a single view of a rugby team’s match patterns across the first 12 matches of a season. Each line chart panel portrays the cumulative scoring for the competing teams across the 80 minutes of a match. The 12 match panels are arranged in chronological order, from top left to bottom right, based on the date of the match.

Figure 10.3 Example of the Small Multiples Technique

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The main obstacle to shrinking chart displays is the impact on text. The eye will not cope too well with small fonts for value or category labels, so there has to be a trade-off, as always, between the amount of detail you show and the size you show it.

Chart scales: When considering your chart-scales try to think about how you might use these to tell the viewer something meaningful. This can be achieved through astute choices around the maximum value ranges and also in the choice of suitable intervals for labelling and gridline guides.

The maximum values that you assign to your chart scales, informed by decisions around editorial framing, can be quite impactful in surfacing key insights. You may recall the chart from earlier that looked at the disproportionality of women CEO’s amongst the S&P 1500 companies. Figure 10.4 is another graphic on a similar subject, which contextualises the relative progress in the rise of women CEOs amongst the Fortune 500 companies. By setting the maximum y-axis value range to reflect the level at which equality would exist, the resulting empty space emphasises the significant gap that still persists.

Figure 10.4 Reworking of ‘The Glass Ceiling Persists’

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Figure 10.5 Fast-food Purchasers Report More Demands on Their Time

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Figure 10.5 shows how the lack of careful thought about your scales can undermine the ease of readability. This chart shows how American adults spend their time on different activities. The analysis is broken down into minutes and so the maximum is set at 1440 minutes in a day. For some reason, the y-axis labels and the associated horizontal gridlines are displayed at intervals of 160 minutes. This is an entirely meaningless quantity of time so why divide the day up into nine intervals? To help viewers perceive the significance and size of the different stacked activities it would have been far more logical to use 60-minute time intervals as that is how we tend to think when dividing our daily schedule.

Chart orientation: Decisions about the orientation of your chart and its contents can sometimes help squeeze out an extra degree of readability and meaning from your display.

Figure 10.6 Illustrating the Effect of Chartorientation Decisions

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The primary concern about chart orientation is towards the legibility of labels along the axis. A vertical bar chart, with multiple categories along the x-axis, will present a challenge of making the labels legible and avoiding them overlapping. Ideally you would want to preserve label reading in line with the eye, but you might need to adjust their orientation to either 45° or 90°. My preference for handling this with bar charts is to switch the orientation of the chart and to then have

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much more compatible horizontal space to accommodate the labels. The meaning of your subject’s data may also influence your choice. While there may have been constraints on the dimension of space in its native setting, Figure 10.6, portraying the split of political parties in Germany, feels like a missed opportunity to display a political axis of the Left and the Right through using a landscape rather than portrait layout.

As you saw earlier, the graphic about ‘Iraq’s bloody toll’ (Figure 1.11) uses an inverted bar chart to create a potent display of data that effectively conveys the subject matter, but importantly does so without introducing any unnecessary obstacles in readability. In the previous section I presented a wireframe sketch of a graphic about Nobel prize winners. Figure 10.7 shows the final design. Notice how the original concept of the novel diagonal orientation was accomplished in the final composition, exploiting the greater room that this dimension of space offers within the page. It feels quite audacious to do this in a newspaper setting.

Figure 10.7 Nobels no Degrees

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Figure 10.8 Kasich Could Be The GOP’s Moderate Backstop

Figure 10.8, from FiveThirtyEight, rotates the scatter plot by 45° and then overlays a 2 × 2 grid which helps to guide the viewer’s interpretation by making it easier to observe which values are located in each quadrant. It is also used to emphasise the distinction between location in the top and bottom halves of the chart along the axis of popularity, essentially the primary focus of the analysis.

Although the LATCH and CHRTS acronyms share some similarities, the application of each concerns entirely different aspects of your design thinking. They are independent of one another. A bar chart, which belongs to the categorical (C) family of charts, could have its data potentially sorted by location, alphabet, time, category or hierarchy.

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Chart value sorting: Sorting content within a chart is important for helping viewers to find and compare quickly the most relevant content. One of the best ways to consider the options for value sorting comes from using the LATCH acronym, devised by Richard Saul Wurman, which stands for the five ways of organising displays of data: Location, Alphabet, Time, Category or Hierarchy.

Location sorting involves sequencing content according to the order of a spatial dimension. This does not refer to sorting data on a map locations are fixed, rather it could be sorting data by geographical spatial relationships (such as presenting data for all the stops along a subway route) or a non-geographical spatial relationship (like a sequence based on the position of major parts of the body from head to toe). You should order by location only when you believe it offers the most logical sequence for the readability of the display or if there is likely to be interest or significance in the comparison of neighbouring values. An example of location sorting is displayed in ‘On Broadway’ (Figure 10.9) on the following page, an interactive installation that stitches together a sequenced compilation of data and media related to 30 metre intervals of life along the 13 miles (21 km) of Broadway that stretches across the length of Manhattan. This continuous narrative offers compelling views of the fluctuating characteristics as you transport yourself down the spine of the city.

Figure 10.9 On Broadway

Alphabetical sorting is a cataloguing approach that facilitates efficient lookup and reference. Only on rare occasions, when you are

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especially keen to offer convenient ordering for looking up categorical values, will you find that alphabetical sorting alone offers the best sequence. In Figure 10.10, investigating different measures of waiting times in emergency rooms across the United States, the bar charts are presented based on the alphabetical sorting of each state. This is the default setting but users can also choose to reorder the table hierarchically based on the increasing/decreasing values across the four columns.

Alphabetical sorting might be seen as a suitably diplomatic option should you not wish to imply any ranking significance that would be displayed when sorting by any other dimension. Additionally, there is a lot of sense in employing alphabetical ordering for values listed in dropdown menus as this offers the most immediate way for viewers to quickly find the options they are interested in selecting.

Figure 10.10 ER Wait Watcher: Which Emergency Room Will See You the Fastest?

Data representation techniques that display overlapping connections, like Sankey diagrams, slope graphs and chord diagrams, also introduce the need to contemplate value sorting in the z-dimension: that is, which of these connections will be above and which will be below, and why.

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Time-based sorting is used when the data has a relevant chronological sequence and you wish to display and compare how changes have progressed over time. In Figure 10.11, you can see a snapshot of a graphic that portrays the rain patterns in Hong Kong since 1990. Each row of data represents a full year of 365/366 daily readings running from left to right. The subject matter and likely interest in the seasonality of patterns make chronological ordering a common-sense choice.

Figure 10.11 Rain Patterns

Categorical sorting can be usefully applied to a sequence of categories that have a logical hierarchy implied by their values or unique to the subject matter. For example, if you were presenting analysis about football players you might organise a chart based on the general order of their typical positions in a team (goalkeeper > defenders > midfielders > forwards) or use seniority levels as a way to present analysis about staff numbers. Alternatively, if you have ordinal data you can logically sort the values according to their inherent hierarchy. In Figure 10.12, that you saw earlier in the profile of ordinal colours, the columns are sequenced left to right in order from ‘major deterioration’ to ‘major improvement’, to help reveal the balance of treatment outcomes from a sample of psychotherapy clients.

Figure 10.12 Excerpt from ‘Pyschotherapy in The Arctic’

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Hierarchical sorting organises data by increasing or decreasing quantities so a viewer can efficiently perceive the size, distribution and underlying ranking of values. In Figure 10.13, showing the highest typical salaries for women in the US, based on analysis of data from the US Bureau of Labour Statistics, the sorting arrangement presents the values by descending quantity to reveal the highest rankings values.

Figure 10.13 Excerpt from ‘Gender Pay Gap US’

In Figure 10.12 the bubbles in each column do not need to be coloured as their position already provides a visual association with the ‘deterioration’ through to ‘improvement’ ordinal categories. The attribute of colour, specifically, can therefore be considered redundant encoding. However, you might still choose to include this redundancy if

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you believed it aided the immediacy of association and distinction. In this case, the chart was part of a larger graphic that employed the same colour associations across several different charts and therefore it made sense to preserve this association.

10.3 Influencing Factors and Considerations

You are now familiar with the array of various aspects of composition thinking. At this point you will need to weigh up your decisions on how you might employ these in your own work. Here are some of the specific factors to bear in mind.

Formulating Your Brief

Format: Naturally, as composition is about spatial arrangement, the nature and dimensions of the canvas you have to work with will have a fundamental bearing on the decisions you make. There are two concerns here: what will be the shape and size of the primary format and how transferable will your solution be across the different platforms on which it might be used or consumed?

Another factor surrounding format concerns the mobility of viewing the work. If the form of your output enables viewers to easily move a display or move around a display in a circular plane (such as looking at a printout or work on a tablet) this means that issues such as label orientation can be largely cast aside. If your output is going to be consumed in a relatively fixed setting (desktop/laptop or via a presentation) the flexibility of viewing positions will be restricted.

Working With Data

Data examination: Not surprisingly, the shape and size of your data will directly influence your chart composition decisions. When discussing physical properties in Chapter 4, I described the influence of quantitative values with legitimate outliers distorting ideal scale choices. One solution for dealing with this is to use a non-linear logarithmic (often just known as a ‘log’) scale. Essentially, each major interval along a log scale increases the value at that marked position by a factor of 10 (or by one order of magnitude) rather than

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by equal increments. In Figure 10.14, looking at ratings for thousands of different board games, the x-axis is presented on a log scale in order to accommodate the wide range of values for the ‘Number of ratings’ measure and to help fit the analysis into a square-chart layout. Had the x-axis remained as a linear scale, to preserve a square layout would have meant squashing values below 1000 into such a tightly packed space that you would hardly see the patterns. Alternatively, a wide rectangular chart would have been necessary but impractical given the limitations of the space this chart would occupy.

I have great sympathy for the challenges faced by designers like Zimbabwe-based Graham van de Ruit, when working on typesetting a book titled Millions, Billions, Trillions: Letters from Zimbabwe, 2005−2009 in 2014. The book was all text, apart from one or two tables. One of the tables of data supplied to Graham showed Zimbabwe’s historical monthly inflation rates, which, as you can see (Figure 10.15), included some incredibly diverse values. I love the subtle audacity of Graham’s solution. Even though it is presented in tabular form there is a strong visual impact created by allowing the sheer spatial consequence of the exceptional mid-2008 numbers to cause the awkward widening of the final column. I think this makes the point much more effectively than a chart might, in this case.

Figure 10.14 The Worst Board Games Ever Invented

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Figure 10.15 From Millions, Billions, Trillions: Letters from Zimbabwe, 2005−2009

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‘I thought that a graph might be more effective, but I quickly realised that the scale would be a big challenge... The whole point of graphing would have been to show the huge leap in 2008, something that I felt the log scale would detract from and was impractical with the space constraints. I also felt that a log scale might not be intuitive to the target audience.’ Graham van de Ruit, Editorial and Information Designer

Establishing Your Editorial Thinking

Angles: The greater the number of different angles of analysis you wish to cover in your work, the greater the challenge will be to seamlessly accommodate the resulting chart displays in one view. The more content you include increases the need to contemplate reductions in the size of charts or a non-simultaneous arrangement, perhaps through multi-page sequences with interactive navigation.

In defining your editorial perspectives, you will have likely established some sense of hierarchy that might inform which angles should be more prominent (regarding layout position and size) and which less so. There might also be some inherent narrative binding

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each slice of analysis that lends itself to being presented in a deliberate sequence.

Data Representation

Chart type choice: Different charts have different spatial consequences. A treemap generally occupies far more space than a pie chart simply because there are many more ‘parts’ being shown. A polar chart is circular in shape, whereas a waffle chart is squared. With each chart you include you will have a uniquely shaped piece that will form part of the overall jigsaw puzzle. Inevitably there will be some shuffling of content to find the right size and placement balance. The table in Figure 10.16 summarises the main chart structures and the typical shapes they occupy. This list is based only on the charts included in the Chapter 6 gallery but still offers a reasonable compilation of the main structures. These are ordered in descending frequency as per the distribution of the different structures of charts in the gallery.

Figure 10.16 List of chart structures

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Trustworthy Design

Chart-scale optimisation: Decisions about chart scales concern the maximum, minimum and interval choices that ensure integrity through the representation as well as optimise readability. Firstly, let’s look at decisions around minimum values used on the quantitative value axis, known as the origin, and the reasons why it is not OK for you to truncate the axis in methods like the bar chart. Any data representation where the attribute of size is used to encode a quantitative value needs to show the full, true size, nothing more and nothing less. The origin needs to be zero. When you truncate a bar chart’s quantitative value axis you distort the perceived length or height of the bar. Visualisers are often tempted to crop axis scales when values are large and the differences between categories are small. However, as you can see in Figure 10.17, the consequence is that it creates the impression of highly noticeable relative difference between values when the absolute values do not support this.

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Figure 10.17 Illustrating the Effect of Truncated Bar Axis Scales

The single instance in which it is remotely reasonable to truncate an axis would be if you had a main graphic which effectively offered a thumbnail view of the whole chart for orientation positioned alongside a separate associated chart (similar to that on the right). This separate chart might have a truncated axis that would provide a magnified view of the main chart, showing just the tips of the bar, to help viewers see the differences close up.

In contrast to the bar chart, a line chart does not necessarily need always to have a zero origin for the value axis (normally the y-axis). A line chart’s encoding involves a series of connected lines (marks) joining up continuous values based on their absolute position along a scale (attribute). It therefore does not encode quantitative values through size, like the bar chart does, so the truncation of a value axis will not unduly impact on perceiving the relative values against the scale and the general trajectory. For some data contexts the notion of a zero quantity might be impossible to achieve. In Figure 10.18, showing 100m sprint record times, no human is ever going to be able to run 100m in anywhere near zero seconds. Times have improved, of course, but there is a physical limit to what can be achieved. To show this analysis with the y-axis starting from zero would be unnecessary and even more so if you plotted similar analysis for longer distance races. However, if you were to plot the 100m results and the 400m results on the same chart, you would need to start from zero to enable orientation of the scale of comparable values. This sense of comparable scale is missing from the next chart, whereby including the full quantitative value range down to zero would be necessary to perceive the relative scale of attitudes towards same-sex marriage. The chart’s y-axis appears to start from an origin of 20 but as we are

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looking at part-to-whole analysis, the y-axis should really be displayed from an origin of zero. The maximum doesn’t need to go up to 100%, the highest observed value is fine in this case, but it could be interesting to set the maximum range to 100% in order to create a similar sense of the gap to be bridged before 100% of respondents are in agreement.

Figure 10.18 Excerpt from ‘Doping under the Microscope’

Figure 10.19 Record-high 60% of Americans Support Same-sex Marriage

Aspect ratios: The aspect ratio of a line chart, as derived from the height and width dimensions of the chart area, can have a large impact on the perceived trends presented. If the chart is too narrow,

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the steepness of connections will be embellished and look more significant; if the chart is stretched out too wide, the steepness of slopes will be much more dampened and key trends may be somewhat disguised. There is no absolutely right or wrong approach here but clearly there is a need for sensitivity to avoid the possibility of unintended deception. A general rule of thumb is to seek a chart area that enables the average slope to be presented at 45°, though this is not something that can be easily and practically applied, especially as there are many other variables at play, such as the range of quantitative and time values and the scales being used. My advice is just to make a pragmatic judgement by eye to find the ratio that you think is faithful to the significance of the trends in your data. Mapping projections: One of the most contentious matters in the visual representation of data relates to thematic mapping and specifically to the choice of map projection used. The Earth is not flat (hopefully no contention there, otherwise this discussion is rather academic), yet the dominant form through which maps are presented portrays the Earth as being just that. Features such as size, shape and distance can be measured accurately on Earth but when projected on a flat surface a compromise has to occur. Only some of these qualities can be preserved and represented accurately.

There are lots of exceptionally complicated calculations attached to the variety of spatial projections. The main things you need to know about projection mapping are that:

every type of map projection has some sort of distortion; the larger the area of the Earth portrayed as a flat map, the greater the distortion; there is no single right answer – it is often about choosing the least-worst case.

Thematic mapping (as opposed to mapping spatially for navigation or reference purposes) is generally best portrayed using mapping projections based on ‘equal-area’ calculations (so the sacrifice is more on the shape, not the size). This ensures that the phenomena per unit – the values you are

I qualify this with ‘dominant’ because, increasingly, advances in technology (such as WebGL) mean we can now interact with spherical portrayals of the Earth within a 2D space.

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typically plotting – are correctly represented by proportion of regional area. For choosing the best specific projection, in the absence of perfect, damage limitation is often the key: that is, which choice will distort the spatial truth the least given the level of mapping required. There are so many variables at play, however, based on the scope of view (world, continent, or country/sub-region), the potential distance from the equator of your region of focus and whether you are focusing on land, sea or sky (atmosphere), to name but a few. As with many other topics in this field, a discussion about mapping projections requires a dedicated text but let me at least offer a brief outline of five different projections to begin your acquaintance:

Many tools that offer rudimentary mapping options will tend to only come with a default (non-adjustable) projection, often the Mercator (or Web Mercator). The more advanced geospatial analysis tools will offer pre-loaded or add-in options to broaden and customise the range of projections. Hopefully, in time, an increasing range of the more pragmatic desktop tools will enhance projection customisations.

Figure 10.20 A Selection of Commonly Deployed Mapping Projections

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Accessible Design

Good design is unobtrusive: One of the main obstructions to facilitating understanding through a visualisation design is when viewers are required to rely on their memory to perform comparisons between non-simultaneous views.

When the composition layout requires viewers to flick between pages or interactively generated views, they have to try store one view in their mind and then mentally compare that against the live view that

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has arrived on the screen. This is too hard and too likely to fail given the relatively weak performance of the brain’s working memory. Content that warrants direct comparison should be enabled through proximity to and alignment with related items. I mentioned in the section on animation that if you want to compare different states over time, rather than see the connected system of change, you will need to have access to the ‘moment’ views simultaneously and without a reliance on memory.

Elegant Design

‘Using our eyes to switch between different views that are visible simultaneously has much lower cognitive load than consulting our memory to compare a current view with what was seen before.’ Tamara Munzner taken from Visualization Analysis and Design

‘I’m obsessed with alignments. Sloppy label placement on final files causes my confidence in the designer to flag. What other details haven’t been given full attention? Has the data been handled sloppily as well? ... On the flip side, clean, layered and logically built final files are a thing of beauty and my confidence in the designer, and their attention to detail, soars.’ Jen Christiansen, Graphics Editor at Scientific American

Unity: As I discussed with colour, composition decisions are always relative: an object’s place and its space occupied within a display immediately create a relationship with everything else in the display. Unity in composition provides a similar sense of harmony and balance between all objects on show as was sought with colour. The flow of content should feel logical and meaningful.

The enduring idea that elegance in design is most appreciated when it is absent is just as relevant with composition. Look around and open your eyes to composition that works and does not work, and recognise the solutions that felt effortless as you read them and those that felt punctured and confusing. This is again quite an elusive concept and one that only comes with a mixture of common-sense judgement, experience and exposure to inspiration from elsewhere. Thoroughness: Precision positioning is the demonstration of

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thoroughness and care that is so important in the pursuit of elegance. You should aim to achieve pixel-perfect accuracy in the position and size of every single property. Think of the importance of absolute positioning in the context of detailed architectural plans that outline the position of every fine detail down to power sockets, door handles and the arc of a window’s opening manoeuvre. A data visualiser has to commit to ultimate precision and consistency because any shortcomings will be immediately noticeable and will fundamentally impact on the function of the work. If you do not feel a warm glow from every emphatic snap-to-grid resize operation or upon seeing the results of a mass alignment of page objects, you are not doing it right. (Honestly, I am loads of fun to be around.

Summary: Composition Project composition defines the layout and hierarchy of the entire

visualisation project and may include the following features:

Visual hierarchy – layout: how to arrange the position of elements? Visual hierarchy – size: how to manage the hierarchy of element sizes? Absolute positioning: where specifically should certain elements be placed?

Chart composition defines the shape, size and layout choices for all components within your charts and may include the following features:

Chart size: don’t be afraid to shrink charts, so long as any labels are still readable, and especially embrace the power of small multiple. Chart scales: what are the most meaningful range of values given the nature of the data?

Chart orientation: which way is best? Chart value sorting: consider the most meaningful sorting arrangement for your data and editorial focus, based on the LATCH acronym.

Influencing Factors and Considerations

Formulating the brief: what space have you got to work within? 516

Working with data: what is the shape and size of your data and how might this affect your chart design architecture? Establishing your editorial thinking: how many different angles (charts) might you need to include? Is there any specific focus for these angles that might influence a sequence or hierarchy between them?

Data representation: any chart has a spatial consequence – different charts have different structures that will create different dimensions that will need to be accommodated. Trustworthy design: the integrity and meaning of your chart scale, chart dimensions, and (for mapping) your projection choices are paramount.

Accessible design: remember that good design is unobtrusive – if you want to facilitate comparisons between different chart displays these ideally need to be presented within a simultaneous view. Elegant design: unity of arrangement is another of the finger-tip sense judgments but will be something achieved by careful thinking about the relationships between all components of your work.

Tips and Tactics

You will find that as you reach the latter stages of your design process, the task of nudging things by fractions of a pixel and realigning features will dominate your attention. As energy and attention start to diminish you will need to maintain a commitment to thoroughness and a pride in precision right through to the end! Empty space is like punctuation in visual language: use it to break up content when it needs that momentary pause, just as how a comma or full stop is needed in a sentence. Do not be afraid to use empty space more extensively across larger regions as a device to create impact. Like the notes not played in jazz, effective visualisation design can also be about the relationship between something and nothing.

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Part D Developing Your Capabilities