Data Visualisation - An Introduction
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Transcript of Data Visualisation - An Introduction
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