Wednesday, July 2, 2014

The challenges of classification in choropleth maps

Building classes for choropleth maps is always tricky business. By grouping values together as intervals, you always put yourself at the risk of hiding important nuances in the data. There are reliable guidelines you can follow, but the process always requires a good dose of common sense. This excellent article by John Nelson (h/t Rob Simmon and Jorge Camões) explains this challenge really well.

The map below, published today by The New York Times —see it online,— is a good example. Notice that the last class corresponds to the values above 30%. The problem is that this class includes values as big as 89% —or even higher, I didn't check! Perhaps it makes sense to create a fifth class for the counties in which Evangelicals and Mormons are a majority of the population (51%)? Besides, I'm not sure that using equal intervals is the best choice here. But it may be just me. I haven't seen their dataset, after all.