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.)
Get instant access to this tutorial and over a hundred more, plus courses, guides, and additional resources.
You'll get unlimited access to hundreds of hours worth of 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.
The tutorials are very helpful to move from "Oooo, cool!" to how to actually DO the cool.
Members also recieve a weekly newsletter, The Process. Keep up-to-date on visualization tools, the rules, and the guidelines and how they all work together in practice.
See samples of everything you gain access to:
More Tutorials See All →
How to Make an Animated Pyramid Chart with D3.js
Compare distributions side-by-side with a pyramid chart. Observe the change over the years by animating it.
How to Make Horizon Graphs in R
The relatively new and lesser known time series visualization can be useful if you know what you’re looking at, and they take up a lot less space.
Transitioning Map, Part 2: Refining the Format and Layout
How to make a more readable and more visually accurate map, before you dive into the big transitions.