16: Visualization

Austin Mason
Thu, 26 Feb 2015 18:59:34 +0000
Assignments, Week 8: Visualization, dataviz, Gapminder, infographics, Many Eyes, Minard, Motion Charts, Tableau Public, Tufte, visualization

Today we are exploring some dos and don’ts of data visualization as you prepare the final versions of your projects. Below is a a provisional list of links to explore during class.

The Minard Map

It may well be the best statistical graphic ever drawn.

—Edward Tufte, The Visual Display of Quantitative Information (1983)

The Classic Discussed

Michael Sandberg’s deep dive on the DataViz History blog

Another discussion of the classic by John Corbett

Static variants (Do they add anything?)

Kraak’s variants from his 2013 book Mapping Time

Interactive variants (Does it help to be able to manipulate the map?)

A custom coded instance at http://www.masswerk.at/minard/

An instance using the Neatline plugin for Omeka

And the developer’s discussion of his process on the Scholar’s Lab blog

Questions

Why is this considered such a landmark visualization, if not the best ever?

What are the key features that make it stand out?

How would you improve on it, if you were to take a stab?

Keeping it Honest: How Not to Lie with Pictures

We’ve already discussed how not to lie with maps, but it’s easy to do with visualizations as well. One of the biggest issues that Tufte stresses in his seminal work is how to stay honest with infographics. One of the easiest errors to make, for instance, is to scale the radius of circles, or one axis of two dimensional shapes, which results in massively larger areas than your data actually warrants.

Explore this gallery of images illustrating “The Lie Factor”

Think about how the literal measurements of the images contradict the implicit argument the graphic is trying to make.

Explore some more of Michael Friendly’s gallery of the Best and the Worst of Statistical Graphics

What mistakes did you not think of before that you might want to avoid?

What examples might you like to emulate for your own projects?

Why?

Tools

Google Motion Charts (Gapminder)

One of the most impressive data visualization breakthroughs of recent years was Hans Rosling’s invention of Gapminder: an application that really unleashed the “fourth dimension” of time and allowed data to be animated in an immediately understandable and powerful way. His TED talk below illustrating global health data with the tool is legendary.

Google bought the technology and made it available for all to use as Motion Charts.

We’ve already explored some visualization environments, but here are two more to check out:

Tableau Public

Many Eyes

Choose one and check it out to see what people are creating. Download or join a service and see if you can create something.

Upload some of your own data, or download data sets from around the web

One interesting source is JSTOR for Research, which aggregates all the scholarly literature housed by JStor