Related Work on Better Data Visualizations

Am I the only one who thinks that critiquing and improving data visualizations is a great way to spend an evening? No! In fact, it’s rather an old tradition.  In Edward Tufte’s The Visual Display of Quantitative Information, perhaps the preeminent work on data visualizations, dedicates Chapter 4 (and subsequent areas) to maximizing the “data-ink” of a graphic.  Graphic creators should only use ink to add additional meaning.  This results in graphics that minimize the amount of ink and are relatively clear.  Everything else is “chart junk.”  For example, most strikingly, he proposes a much simplistic version of the boxplot, which most individuals would already describe as a simple graphic to begin with:

10 Traditional Boxplots

Those are pretty simple boxplots, aren’t they? Most people would be hard pressed to find a way to simplify them beyond.  Tufte did it fairly easily, as seen in the following:

10 Simplified Boxplots

Here he leverages white space to add value.  Tufte argues that since we know that whiskers of a boxplot begin at the first and third quartiles, we can actually remove them.

Tufte’s approach is focused on making sure that graph creators use only a minimal amount of ink, which allows the data tell its story.  Up close, each bit of ink relates to a specific data point, while at a 40,000-foot level, we can pull out trends and bigger picture narratives.  Everything else is “chart-junk.”

Tufte is a bit extreme in his work that he only wants the data to tell the story.  He fails to recognize that there is a place and some value for “chart-junk.”  Bateman, et al, argue that some chart junk is actually useful.  We create graphs and charts because we want to easily share the information.  We want the information to be easily understood.  By eliminating chart junk as Tufte suggests, we make it easier to understand.  Tufte is only thinking about the current moment.  In his research, Bateman focuses on retention and recall of information.  He finds that chart junk can actually increase the likelihood someone would be able to recall the chart’s information.

This leads us to the age-old adage that moderation is likely the best course of action.  While Tufte argues for a stark minimalist view of the world, Bateman recognizes that there is some value in Tufte’s junk.

With that in mind, a modern take on improving data graphics can be seen everyday on the internet.  One website, Viz WTF highlights graphics that fall short of their objective or are clearly manipulated.  In highlighting it, the sites provides a short summary as to the issues these images.  The end result in these images the same — the graphic does not accurately portray the data.  The first is a design problem, while the second is an integrity issue that Tufte also cautions creators to avoid.  A google search of “bad visualizations” will yield articles and listicles from sites like Gizmodo and BusinessInsider.  Plenty of people have noted that data visualizations can be improved.  Those failed visualizations will be an excellent source material for this blog.

VizFix is not the first to attempt to regularly rehabilitate data visualizations.  As part of #MakeoverMonday, Eva Murray’s Tri My Data, and Andy Kriebel’s VizWiz, they both reinvision a troubled graphic on their site.  As part of the analysis, they each highlight what they think is good about the original graphic, the issues they see with it, and what they are trying to do in their reimagination.

In one recent example, they took a map of bike thefts in London:

Original Map of London Stolen Bikes

Which Murray rethought as:

Tri My Data’s Bike Thefts Recreation

While Kriebel broke down into several different components:

Their work is spectacular, and they do it with a joy only a data enthusiast could.  For VizFix, I aim to have that same enthusaism.  Since I do not have the graphic creating experience or second sense either of them possess (both are Tableau gurus), I’ll create a rubric that brings up many of the issues that graphics often face.  Then, my recreations will seek to alleviate many of those issues.

Please share in the comments any additional thoughts about relevant work in the word of data visualization rehabilitation.

References:

  1. https://www.edwardtufte.com/tufte/books_vdqi
  2. http://hci.usask.ca/uploads/173-pap0297-bateman.pdf
  3. http://viz.wtf/
  4. https://www.google.com/search?q=bad+visualizations
  5. https://gizmodo.com/8-horrible-data-visualizations-that-make-no-sense-1228022038
  6. http://www.businessinsider.com/the-27-worst-charts-of-all-time-2013-6
  7. http://www.makeovermonday.co.uk/
  8. https://trimydata.com/
  9. http://www.vizwiz.com/
  10. https://trimydata.com/2017/09/11/mm-week37/
  11. http://www.vizwiz.com/2017/09/stolen-bikes.html
  12. https://www.tableau.com/

Featured image by Lauren Manning.  Used under Creative Commons 2.0 License.