Data Visualisation: A Handbook for Data Driven Design

by Andy Kirk

These titles work well for exploratory visualisation experiences.

Characteristically, descriptive headings (Figure 8.3) tend to be aligned with exhibitory visualisations.

Introductions are commonly provided in close proximity to a heading in the form of short paragraphs that concisely explain in further detail what a project is about, why it exists and what it is for.

Furthermore, the viewer might encounter the work on a repeated basis, but will only need to read an introduction on the first occasion.

For some projects, the introduction may be used to provide a more extensive description of the data, where data has come from, how it has been transformed, and comments about any assumptions or potential shortcomings.

It can be a mistake to assume that every user will be sufficiently sophisticated to understand immediately the workings of the functionality you are offering.

Without offering a detailed user guide, many users may miss out on some of the skilfully crafted opportunities to interrogate the data.

If you have used an unfamiliar and/or particularly complicated chart type, with many attributes that need to be decoded and synthesised, you might need to provide instructions to assist viewers who need it.

In Figure 8.11 you will see how colour associations are integrated into the introductory text, with certain words highlighted through shading or in the colour of their font.

The legend takes the form of a bar chart that acts as both a colour key, explaining the association with the different languages, and a method of showing a quantitative summary of the total number of tweets for each language.

Your choices will generally be informed by the degree of emphasis you place on viewers efficiently judging values with precision and also be influenced by your desired presentation style.

Chart references can be usefully included as visual overlays to provide context of scale, to clarify the expected and unexpected, and to separate the normal from the exceptional.

Depending on your editorial framing definitions, you might also expand your quantitative range outside the observed value range in order to use empty chart space to support a point of narrative.

Unless you are highlighting important values for key items, if you have clear axis labels you should not need to double up your value reading assistance. Choose one or the other.

Below the main chart there is a ‘What This Data Reveals’ section which presents some of the main findings.

If you are providing 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’ phase 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 ‘interpreting’ phase).

‘Exploratory’ experiences are likely to need instructive guides, ensuring that viewers (or specifically, in this case, users) have as much understanding as possible about the functional controls available.

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.

These typefaces are commonly used for shorter sections of text, such as axis or value labels or titles, and for screen displays.

ask others to proofread if you are too ‘close’ to the work to see it rationally.

When a colour looks like it conveys meaning, the viewer will think about that and spend time establishing what the meaning

Your objective is to establish meaning first and worry about decoration last.

The ‘hex’ values take the form of #RRGGBB using two-digit codes for each component ranging from 00 to FF.

If you are creating something for print you will 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.

CMYK communicates from your software to a printer, telling it what colours to print as an output. RGB does the same but communicates the colour messages to a screen display.

One of the most accessible colour models for considering the application of colour in data visualisation is known as HSL (Hue, Saturation, Lightness),

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.

The term legibility places an emphasis on making sure the differences between and associations of any colours used are readable and meaningful.

These two examples both employ a converging colour scale, moving through discrete variations in the lightness of a single hue to represent small through to large quantities. Sometimes the shape and range of your data may warrant a diverging colour scale. This is when you want to show how quantities are changing in two directions either side of a specified breakpoint.

Are you plotting observed data or observable data?

What is the distribution of your data?

Sometimes, you will have legitimate outliers that, if included, will stretch your colour scales far beyond the meaningful concentration of where most values reside.

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.

Variation in hue is typically the colour dimension to consider using for differentiating categories. From a stylistic perspective, you might choose to vary the saturation across all hues, but you should not consider using variations in the lightness dimension.

The ability to preserve clear differentiation becomes harder as the unique colours available diminish. A useful guide to follow is once you exceed 12 categories, there are no longer sufficiently different hues available to assign to categories 13+.

There are three ways of handling excessive numbers of distinct categories:

When using colour to classify different ordinal categories you are striving not only to create visible distinction between each distinct category, but also to portray the hierarchical relationship that exists between them.

There is an extension in the potential application of ordinal colouring which becomes relevant when you might wish to apply the notion of hierarchical emphasis to draw out significant categorical features of your data that would otherwise merit nominal colouring practices.

Additionally, once you commit a colour to mean something you should not use the same colour to mean something different, at least not in the same view or page. Exclusivity in a colour’s association is important to preserve for as long as possible so the viewer does not have to relearn its meaning.

‘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 cannot organise, what it cannot understand.’ Jill Morton, Colour Expert and Researcher

For interactive projects, every control needs colour in order to be visible, but it must also demonstrate ‘affordance’: properties that indicate what functional events are possible and, where relevant, what events have been activated.

if your work is generally intended for consumption in a light environment, lighter backgrounds with saturated foreground colours tend to be more fitting;

if your audience are large and undefined you may need to consider colour-blind-friendly alternatives. Some options are presented in Figure 9.29.

Composition is the final part of your design anatomy. It concerns the management of space.

Wireframing involves creating low-fidelity sketches of the potential layout of all your design elements within a single page, like an infographic or an interactive where all functions apply within the same page or view.

Storyboarding is used to establish the overall structure of your work when it will entail multiple distinct views (e.g. a report or presentation, wide-ranging interactive).

Arranging concerns the ordering and direction of your data content as it is displayed within a chart.

The technique of ‘small multiples’ is commonly used to replicate distinct chart displays for multiple categories or points in time and arrange them usually in a grid layout.

‘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.’

When a chart encodes quantitative values using size, the viewer needs to see the full, representative size of the mark, otherwise it will be a distortion of the truth.