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Data Visualisation - An Introduction

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An introduction to the art & science of Data Visualisation. A whistle-stop tour, with some bad examples and some good examples. Key lessons and a case study (deep dive).

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Data Visualisation - An Introduction

  1. 1. Data Visualisation An introduction to the art & science… Ben Logan @VisualVolumes
  2. 2. 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
  3. 3. 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.
  4. 4. – 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.”
  5. 5. – Edward Tufte “The minimum we should hope for with any display technology is that it should do no harm.”
  6. 6. 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…
  7. 7. Bad Examples
  8. 8. Good Examples
  9. 9. Why? • Why is it so important? • “Big Data” • Impatience - people just don’t look at your data! • let’s go through a quick example…
  10. 10. 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;
  11. 11. After • Now take a look at the visualisation and see if you feel more able to answer the question? • http://fathom.info/quakes/
  12. 12. 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!).
  13. 13. How? In Practice • Excel • Tableau • D3. Really? Is it interactive? • Static - Photoshop • Does it matter?
  14. 14. 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!
  15. 15. 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!
  16. 16. • 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/
  17. 17. 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.
  18. 18. • 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).
  19. 19. • 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.
  20. 20. – Bill Gates “Content is King.”
  21. 21. – Maya Angelou “Content is of great importance, but we must not underestimate the value of style.”
  22. 22. Ben Logan • CV and visualisation portfolio online… • http://www.benlogan.co.uk • @VisualVolumes • https://visualvolumes.wordpress.com

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