Transitioning Map, Part 1: Mapping Irregular Data with Interpolation in R

Rarely do you have evenly-spaced data across an entire geographic space. Here is a way to fill in the gaps.

A lot of geographic data that you can download is aggregated by geographic boundaries. You get data by state. You get data by county. Unless you’re looking at weather data, you typically don’t get data across a continuous spectrum.

Because the data is binned, it’s often a good idea to map it in the same way. You don’t really know what the metrics really look like in between the estimated areas. However, sometimes interpolation can be useful in the same way it can be useful to fit a curve to a set of points over time. The smoothed data isn’t as exact, but sometimes it makes trends — over space or time — more obvious visually.

To access this full tutorial and download the source code you must be a member. (If you are already a member, log in here.)

Membership

Gain instant access to step-by-step visualization courses and tutorials for insight and presentation — all while supporting an independent site. Source code and data is included so that you can more easily apply what you learn in your own work.

Members also recieve a weekly newsletter, The Process, which looks more closely at the tools, the rules, and the guidelines and how they work in practice.

See samples of everything you gain access to:

About the Author

Nathan Yau is a statistician who works primarily with visualization. He earned his PhD in statistics from UCLA, is the author of two best-selling books — Data Points and Visualize This — and runs FlowingData. Introvert. Likes food. Likes beer.