Elevate your mapping skills with 3D visualization

A 3D extrusion map is a form of data visualization where 3D bars or columns are positioned on a map based on their geographic coordinates. The peak of every bar represents a numerical value, comparable to population or temperature, related to that specific location. Here’s an example showing urban population density on the Hawaiian Islands:

Maps of this kind are presented with a “tilted” perspective in order that the peak of the bars is obvious. By combining the geographical information provided by the map with the vertical dimension represented by the bars, a 3D extrusion map can convey information and patterns in an interesting spatial context. Relative relationships are sometimes more vital than absolute values.

On this Quick Success Data Science project, we’ll use Python and the pydeck library to simply create 3D extrusion maps for population distribution in the US and Australia. After ending this short tutorial, you’ll don’t have any problem creating stunning visualizations of your personal geospatial datasets.

On this project, we’ll plot population data for the US and Australia. For the US, we’ll use the free Basic United States Cities Database at simplemaps.com [1].

This dataset comprises information on 30,844 towns and cities that make up the majority of the US population as of January 31, 2023. It’s provided under a Creative Commons Attribution 4.0 license and might be redistributed and used commercially. For convenience, I’ve already downloaded the information and stored it in a Gist.

For Australia, we’ll use a 2020 Kaggle dataset derived from the simplemaps.com World Cities Database [2]. It includes 1,035 outstanding cities in Australia that contain most of its population. It’s released without cost under an MIT license and Creative Commons Attribution 4.0 license. For convenience, this dataset has also been stored in a Gist.