Visualising data II

Practical steps for good visualisation
In my previous post on Data Visualisation, I highlighted the four key principles for good visualisation:

• Design for your audience: Think about how to emphasise the key point(s) that you are trying to convey to this audience with this particular visualisation
• Accurately represent the data: The visualisation should show the underlying data without distortion, and avoid common pitfalls that obscure the real information.
• Organise the information: The visualisation should have a clear purpose - communication, exploration, tabulation or decoration.
• Keep it clear: The visualisation should focus on the message(s) for the audience, and all visual clutter kept to a minimum (except where useful to highlight key points).

What does this mean in practical terms? For each principle there are a number of basic steps that can be taken to improve your data visualisation. Some of these are straightforward to implement, for example ensuring that you are not using decorative effects that hide the data. Others require more work, for example testing your visualisation with key audiences.

Design for your audience   

• Test your visualisation with your key audience
• Know when to use dynamic tools, when to use charts, and when to use tables
• Limit the number of categories shown in a visualisation - be selective in what you present in order to emphasise the key message(s)

Accurately represent the data   

• Don’t distort the scale to give undue weight to statistically insignificant data 
• Keep the zero on the axis scale
• For bar-charts, set the base of the bars to zero (not the lowest value)
• Avoid varying the size/area of objects in graphs, except to convey difference in values
• Avoid using line charts where data is only available for a small number of data points

Organise the information

• Bar graphs are good for showing how data changes over time.
• Pie charts are visually simple and easily understood, but can be manipulated to give a false impression.
• Scatter graphs or line graphs are used to investigate the relationship between two variables, providing sufficient data points are available.
• Bubble charts or triangular graphs can be used to show how the relative dominance of one or more factors combined can influence direction of travel.
• Radar or kite charts are good for comparing multiple factors for different options.
• Choropleth/Isopleth maps show areas shaded according to a prearranged key.
• Treemaps display hierarchical (tree-structured) data as a set of nested rectangles.
• Sound and motion can be used to show changes over time, or changes based on dynamic variables.

Keep it clear   

• Avoid using purely decorative effects such as 3D that can hide the data
• When choosing a colour palette, limit the number of colours used and ensure that different colours can be distinguished from each other
• Where colour is needed, use solid blocks of colour and avoid complex fill patterns
• Avoid using strong or bold colours for the background in a visualisation

Visualising data I

Although information design has always been one of the many skills a designer is required to have, the discipline of structuring information is now recognised as a distinct skill set within creative work. Data visualisation, made popular through the Guardian Datablog, is now a central tool in helping people to navigate and explore an increasingly complex data landscape.

Good data visualisation can help researchers and other users to explore datasets and identify patterns, associations and trends, and also to communicate that understanding to others.

Good data visualisations provide an effective representation of the underlying data that illustrates the answer to a particular question. They can inform non-specialist audiences and help decision makers make robust decisions based on the data being presented. Presenting data in this way can support strategic planning, performance monitoring or delivery improvement.

Good data graphics should:

• Make large data sets coherent, and encourage the audience to make comparisons between different data sets
• Reveal the data at several levels of detail, from a broad overview to the fine detail.
• Avoid distorting what the data has to say
• Present many numbers in a small space - but also emphasise any significant numbers
• Help the audience think about the important message(s) from the data, rather than about methodology

Principles of a good visualisation
Good data visualisation is simply another way to communicate with your audience, and the same principles of good communication design applies to data visualisation as with other ways of communicating;

• Identify your key audience;
• Set out the data accurately;
• Identify the point(s) that you want to make; and
• Create the clearest visualisation that conveys that message, using that dataset to that audience.

Design for your audience   
Think about the message that you are trying to convey to your audience with your visualisation - and focus on emphasising this message. Try not to cram too many key points into the visualisation.

Different audiences may need different visualisations. An appropriate design for a visualisation to be used by researchers exploring how economic indicators vary over time and between places may not be appropriate when showing the same data to non-specialists in order to emphasise key economic trends.

Accurately represent the data   
The visualisation should show the underlying data without distortion. For example, axes should always show zero to avoid exaggerating the importance of differences between data values.

Clear, detailed and thorough labelling should be used. Write out explanations of the data on the graphic itself, and label important events in the data.

Don’t try to do everything with one visualisation.
Organise the information in order to emphasise what you are trying to say to the audience. Don’t bury the key messages in a mass of detail.

Set out key points on the graphic itself, and label any significant or anomalous data events - the graphic should speak for itself.

Keep it clear   
The graphic should focus on the message(s) for the audience, and all visual clutter kept to a minimum. But don’t cut out all visual elements - things that emphasise the key message are useful if they help get your points across to the audience

Reduce and refine. Keep asking yourself whether your visualisation suffers any loss of meaning or impact for the audience if an element is taken out.

The next post looks at some practical steps for good visualisations.

*Santa*™

Its a bit late for this Christmas, but I came back to find a link to the Santa Brand Book from Quietroom in my inbox.

Ho, ho ho!