Between Surprise Maps, Dear Data, and election graphics, 2016 was an exciting year. There have also been some gaps, however, notably around storytelling. How far have we come, and where is visualization going?
Elections always bring large numbers of visualizations with them, and last year’s U.S. Presidential election was no exception. There is a lot to be said about the various visualizations that were used (I’m no fan of choropleths). But one in particular stood out: the New York Times’ jittery gauges.
A lot of scorn and ridicule has been heaped on this design, but the situation is more complicated than it is often depicted. The idea of motion to show distributions and uncertainty has been studied in a paper by Jessica Hullman and others (this blog post nicely summarizes the ideas) and found to work well. The design was certainly well considered, but it’s always easy to criticize these things after the fact.
However, the severity of the situation, the high confidence before election night, and the unfamiliar way of showing the data all ended up causing a lot of stress in a lot of people. I think it’s important to note that this wasn’t just a problem of the visual representation, but also of the expectations that had been set by the forecasts. Things wouldn’t have been nearly as bad if we hadn’t been led to believe that this was pretty much a done deal for Clinton.
Communicating uncertainty is extremely difficult, even when your audience understands the concept. Doing this for a general audience is close to impossible. Many things have been tried, some have turned out better than others. Uncertainty is everywhere and needs to be shown in forecasts, estimates like the unemployment rate, etc. Too many of these numbers are still presented as certain, precise facts, when they’re not.
That this is a problem is not a secret. However, most existing ways of showing uncertainty are too difficult to understand for people without a statistics background, or don’t work well for things like timelines. A lot of work is clearly needed here. This is not an easy area to work in when doing academic research, but there’s clearly enormous potential to do good work.
Interestingly, this is a topic that has been recognized by both the data journalism and the intelligence (read: three-letter agencies) communities. I’m aware of efforts in both to bring together ideas and work on new solutions. At least the ideas developed in the former should show up for the next election, if not sooner.
I mentioned Dear Data in last year’s State of InfoVis, because I thought it was a great project – but I didn’t think it would explode into the huge success that it has become (including a very nice book, the postcards being acquired by MoMA, etc.). It has also inspired a number of other projects, like the knock-off Dear Data 2, the somewhat more thoughtful Data Sketches, and I’m sure it inspired RJ Andrews’ Profiling the Parks. Sketching has also been explored in visualization research a little bit in the past.
Drawing data with colored pencils or crayons is a good gateway drug to getting people interested in visualization. And it’s a good exercise to get your brain juices flowing, even – or maybe especially – if you’ve been doing visualization for a while. Give people a few numbers and 20 minutes of uninterrupted time, and it’s amazing how many different things they come up with.
Apart from the sketching, the other interesting aspect of Dear Data is that the underlying data is all collected by hand, and rather unusual (like the number times you’ve smiled at somebody, the kinds of smells you’ve experienced, etc.). It’s this sort of personally relevant data that we often ignore in visualization, because it’s hard to get to and difficult to handle. It is important, however, for people who might not be interested if all they see are boring and irrelevant standard datasets.
