City Intelligence Data Design Guidelines

June 18th, 2019 by Mike Brondbjerg

Data visualisation is created by almost everyone in the City Intelligence unit at City Hall. It is used in everything from data analysis and internal reporting to public facing reports, mapping and interactive applications.

Effective communication of evidence and data through information design and data visualisation, is obviously important to help inform policy internally, but it is also just as important to help boroughs and individual Londoners better understand their city.

With this in mind, over the past year, we have been thinking more about how we can improve the clarity, consistency and accessibility of our data visualisation output.

City Intelligence Data Design Guidelines

A first step toward improvement was creating a set of guidelines to help us all understand the basic principles of data visualisation, provide some examples of good practice, working processes and links to tools we can all use.

Download the guidelines PDF

Download the Data Design Guidelines PDF

Chart styling & layout from the Data Design Guildelines PDF.

The guidelines are a “work in progress”, that have evolved through documenting what processes and tools have worked well so far in our work, and we hope will expand further in the future.

As we begin to create more interactive data visualisation applications, built on the expanding Datastore platform, these guidelines will be important to underpin the design of those applications.

Line Chart example from the Data Design Guildelines PDF.

They have been useful to us internally already, and hopefully the principles, guidance and links will be of use to people beyond the City Intelligence unit too.

If you have any feedback, comments or suggestions for future content, then please do get in touch with Mike Brondbjerg mike.brondbjerg@london.gov.uk.

About the Guidelines

The guidelines, which focus principally on chart design, cover the following areas:

Design Principles
Remove the noise, highlight the signal, and ensure that every visual element you introduce, actively helps to truthfully communicate your point.
Practical Steps
Specific steps you can take to create clearer more effective graphics.
Styling & Layout
Charts should feel part of the context they are displayed within. So, visual grammar should be consistent and text sizes should relate to their parent document’s text hierarchy.
Using Colour
Colour is tricky to get right, and not always as useful as we might hope. Balancing the requirements of accessibility, personal perception, cultural association and branding can limit our options when using colour.
Categorical Colour Palettes
While not ideal, we often have a need (particularly within mapping applications) to use many categorical colours. We worked hard to create and test a visually distinct set of categorical colour palettes derived from our core brand colours.
Find & Test Your Own Colours
The tools and processes we used to generate and test our colours, to help you create your own palettes and colour scales.
Introducing GGLAPlot
In an effort to make the layout, styling and colour palettes we have introduced in this styleguide easier to access, we have started the development of a new R package called “gglaplot”. As you can guess it’s a branch of the popular ggplot.
Chart Examples
A (far from exhaustive) set of the most common chart types, styled to our own style guidelines and highlighting the design decisions behind them.
Further Reading
Not exhaustive (at all), but hopefully useful collection of articles, podcasts, tools & resources from around the data viz world (see list below)

Map examples from the Data Design Guildelines PDF.

Thank You

To everyone who has helped review and contributed to the guidelines so far.
To Libby Rogers in City Intelligence who has single-handedly created GGLAPlot.
All the great data viz authors, and tool creators whose work we have leveraged and channelled into this guide. With particular thanks to Alan Smith, Andy Kirk, Lisa Charlotte Rost, Rob Fry and Alberto Cairo, whose writing has greatly shaped this guide.

We genuinely hope these guidelines, are useful to anyone working with data both within the GLA, and in the wider community, so please do get in touch if they have helped or if you have any suggestions for future versions.

Now we have established these base design principals and styles, we can move on to building better tools and interactive visualisation applications that will help explore and connect datasets held on the London Datastore and beyond!

City Intelligence Data Design Guidelines

City Intelligence Data Design Guidelines

Download the guidelines PDF

About the Guidelines

Thank You

Further Reading

Data Viz Reading & Listening:

Chart Choosers:

Guidance / How To:

Resource Lists & Tools:

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