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.

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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.