Data VisualisationAn introduction to the art & science…

Ben Logan @VisualVolumes

Definition?• Still, after almost a decade of being popularised, under

discussion…

• Here is a definition that is broadly agreed upon within the community;

• Based on (non-visual) data.

• Produce an image.

• The result must be readable and recognisable.

• https://eagereyes.org/criticism/definition-of-visualization

Aim?• Allow the user to draw meaning from large, unwieldy,

data sets.

• Speed - rapid interpretation of the data.

• Depth - interpretation at many levels.

• Insight - genuine discovery of information.

• Recall - images are easier to remember.

• Engagement - encourage interaction and discovery.

– David McCandless

“In an endless jungle of websites with text-based content, a beautiful image with a lot of space and colour can be like walking into a

clearing. It's a relief.”

– Edward Tufte

“The minimum we should hope for with any display technology is that it should do no

harm.”

In the field of Data Visualisation there is a growing idea that you need to fall into one of these two camps;

• Scientific and evidence based - Tufte • Fun, bold and colourful - McCandless

You don’t - you need to be in both…

Bad Examples

Good Examples

Why?

• Why is it so important?

• “Big Data”

• Impatience - people just don’t look at your data!

• let’s go through a quick example…

Before• Tell me about the distribution of earthquakes across the

globe in the first month of 2015?

• The USGS ATOM data file for the last 24 hrs is over 200 lines long;

After• Now take a look at the visualisation and see if you feel

more able to answer the question?

• http://fathom.info/quakes/

How? In Theory• Use traditional story telling techniques - a

beginning, middle and end. It works!

• Photography. Proven techniques, e.g. blur.

• Standard patterns!

• Elements from the natural world (blue is sea, not land!).

How? In Practice• Excel

• Tableau

• D3. Really? Is it interactive?

• Static - Photoshop

• Does it matter?

Deep Dive• Let’s take a look at an example in a bit more

detail…

• “Male vs Female membership of the UK Parliament”

• You kind of already know the answer before I show you?

• This is a common weakness in many visualisations - they aren’t showing you anything new!

What do you notice?• It was definitely a leading question. The author had a

clear agenda - to highlight discrimination against women.

• What about the context? How many women actually stood for election?

• We are implying that people aren’t voting for women, but we aren’t backing that up with evidence.

• This is only part of the picture. Don’t leave your users with more questions than answers!

• In the context of UK Politics there is heavy colour bias, so you need to consider that in your design.

• Detail - I expect to see the differences between political parties.

• If we are showing scale accurately, I would have matched either the X or Y.

• You can read more about this case study on Visual Volumes, where it is fully deconstructed;

• https://visualvolumes.wordpress.com/2015/05/21/houses-of-parliament/

Key Lessons• What’s the story? What are you really trying to tell people,

or hope that they discover themselves?

• Visualisations can and should be powerful - they should prompt a debate and discussion.

• They should also form a level playing field for that debate, with no obvious bias.

• Don’t take sides and don’t choose a leading question - let the user explore the data and draw their own conclusions.

• Make it as easy as possible to reach those conclusions.

• Try to paint a complete picture. This is not always easy, but at least be honest about the gaps in your data.

• Don’t leave them wanting more, or more confused than they were to begin with.

• Always disclose your data source.

• Be careful to not distort or miss-represent the data (e.g. uneven scales).

• Focus on giving the user the ability to digest and interpret the numbers, not the medium you use to visualise the numbers.

• User experience and good design is essential.

• Be wary of information overload.

• Remember your goal and the original story and try to tie your user back to that - stay focussed.

– Bill Gates

“Content is King.”

– Maya Angelou

“Content is of great importance, but we must not underestimate the value of style.”

Ben Logan

• CV and visualisation portfolio online…

• http://www.benlogan.co.uk

• @VisualVolumes

• https://visualvolumes.wordpress.com