Art & Political Entrenchment

I recently visited the Phillips Collection gallery here in DC and saw the work of one of my favorite artists: Camille Pissarro. In one of his paintings, The Seine Valley at Les Damps, he uses an impasto technique in the clouds with bold, hatch brushstrokes. I wanted to try re-create this hatch effect in a viz.

This viz shows how every state voted in the Presidential election since 1964. Each mark is a state where the angle is the degree to which they voted democrat (left) or republican (right). The sharper the angle the more heavily they voted for one party. The thickness of the mark is how many people voted and the color is which party won the state. I didn't quite achieve the effect I wanted but am happy with the result nonetheless. See findings below.

As you can see party shifts were much more common in the past. In 1964, 45 states voted for Johnson (Democrat) and in 1972, 49 states voted for Nixon (Republican). However, since 2000 party shifts have been increasingly less likely. In the past five elections only six states have voted with either party more than once: Colorado, Florida, Iowa, Nevada, Ohio, and Virginia. Seemingly, political division and entrenchment are up.

Fan Gauge

The second biggest disaster on election night might have been the New York Time's jittery gauge for their live presidential forecast. Some people took issue with the random jitter effect used to display uncertainty even calling it "irresponsible". Gregor Aisch, who works at the NYT and co-created the viz, explained their rationale. I appreciate their desire to explain uncertainty and generally love the work of the NYT Graphics Department but agree the randomness effect was confusing.

Displaying uncertainty is tricky but sometimes very important to a data visualization. Below is my proposed alternative: a fan gauge. This approach is like a typical gauge in that it displays a single point relative to a range of points or targets. The addition is the uncertainty displayed by the "fan" around the single point. The angle of the fan is the degree of uncertainty. The fan is like the jitter effect but static and easier to interpret. 




Update: Here is another version in a bullet, non-gauge format. This has a better data-to-ink ratio but still display a point relative to a range of targets as well as the degree of uncertainty. The point is the circle and the length of the "pill" is the degree of uncertainty. Let me know what you think?

Tableau Conference - Jedi Charts Presentation

I recently presented with Chris DeMartini at the Tableau Conference in Austin on how to create custom chart types in Tableau. I covered gauges and sunbursts while Chris covered jump plots and hive plots. You can view the video of the slides here. Or you can view and download the presentation in Tableau Public here. If you have any questions please leave a comment below.



Data16 Deleted Slides - Icicle Charts

I am super excited for the Tableau Conference in Austin this week. It will be nice to meet some Tableau internet friends in person. I assume 90% of them are real people. And I can't wait for my presentation on Jedi Charts with Chris DeMartini on Wednesday at 1:45 pm in Ballroom A of the Austin Convention Center.

Our presentation covers how to create few different custom chart types in Tableau. But we won't be able to get to every chart type on Wednesday. So I figured I would share this "deleted slide" from our presentation: creating Icicle Charts in Tableau. I will share "How To" details next week when I post our presentation slides. 

Custom Parallel Coordinate

I saw this cool parallel coordinate chart in the Guardian on the heptathlon in the Rio Olympics. Displaying multi-variate data is tricky. A traditional parallel coordinate chart is some times difficult to read with the high number of overlapping lines. This new approach, which I have re--created with 2017 Madden ratings data in Tableau, allows you to see every point on parallel axes and then view the relative position of each point for a selected player. The correlation between variables is not as immediately apparent but it is easier to see a specific data point across variables.