Recently, I created an Edge Network Voronoi Diagram that partitions Chicago's walkable network into sets of street segments with a shorter walking distance to a particular 'L' station than any other. In other words, given a location along a street, alley, park pathway, etc. somewhere in Chicago, you can find the nearest 'L' station to that point by foot. Click here to view the final visualization over an interactive map of Chicago. Read the repository's README to learn more about the project, including issues and plans for future work. In this blog post, I will explain how I computed the Edge Network Voronoi Diagram using Python.

This post and any related projects to come are inspired by Ralph Straumann's blog post about creating a hexagonal cartogram to visualize the population of Swiss cantons and the Guardian's use of a hexagonal cartogram to display the 2017 U.K. General Election results. Both maps are aesthetically pleasing and a clever way of visualizing the underlying data, so naturally I wanted to come up with an easy way to create my own! In his blog post, Straumann describes the steps for prepping the geospatial data for the cartogram. His workflow relies partly on ArcGIS, so I wanted to see how much of it I could translate into a reusable workflow with Python. As a proof of concept, I created a hexagonal cartogram of the United States with the size of each state rescaled in proportion to the size of its congressional delegation