A few days ago, my wife, a bunch of my kids, and I were huddled around a big wall map of the United States, joking about the relative unimportance of Rhode Island, the smallest state in the US. It’s one of the states I never ever think about: …and it’s just so small . Amid the joking, my wife came to Rhode Island’s defense by declaring that even though it’s so small, it has one of the highest proportions of coastline to land borders.

The students in my summer data visualization class are finishing up their final projects this week and I’ve been answering a bunch of questions on our class Slack. Often these are relatively standard reminders of how to tinker with specific ggplot layers (chaning the colors of a legend, adding line breaks in labels, etc.), but today one student had a fascinating and tricky question that led me down a realy fun dataviz rabbit hole.

As I explained in my previous blog post, in June 2023 I drove my family across the country in a 5,000-mile roundtrip odyssey from Atlanta, Georgia to just-south-of-Salt Lake City, Utah, and back again.

In a couple days, I’m going to drive across the country to Utah, my home state. I haven’t been out west with my whole family in four years—not since 2019 when we moved from Spanish Fork, Utah to Atlanta, Georgia. According to Google Maps, it’s a mere 1,900 miles (3,000 kilometers) through the middle of the United States and should take 28 hours, assuming no stops.

I’ve taught a course on data visualization with R since 2017, and it’s become one of my more popular classes, especially since it’s all available asynchronously online with hours of Creative Commons-licensed videos and materials.

Just because you have some geographical data doesn’t mean you should always map it but often the usage of data visualization with an interactive web map application can bring benefits to your project. Map visualization helps to reduce the degree of abstraction and supports communication and sense-making (a.k.a. data analysis) of your data.

@mikeal a little tedious. you can take OSM and then convert it to SHP and then http://github.com/maxogden/shp2geocouchless than a minute ago via web max ogden maxogden The tweet above inspired me to take a quick look at GeoCouch, a version of CouchDB that supports spatial queries. This is something I need if I'm going to start playing seriously with CouchDB.