A 3D extrusion map is a type of data visualization where 3D bars or columns are positioned on a map based on their geographic coordinates. The height of each bar represents a numerical value, such as population or temperature, associated with that specific location. Here’s an example showing urban population density on the Hawaiian Islands:
Maps of this type are presented with a “tilted” perspective so that the height of the bars is apparent. 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 often more important than absolute values.
In this Quick Success Data Science project, we’ll use Python and the pydeck library to easily create 3D extrusion maps for population distribution in the United States and Australia. After finishing this short tutorial, you’ll have no problem creating stunning visualizations of your own geospatial datasets.
In this project, we’ll plot population data for the United States and Australia. For the US, we’ll use the free Basic United States Cities Database at simplemaps.com .
This dataset contains information on 30,844 towns and cities that make up the bulk of the US population as of January 31, 2023. It’s provided under a Creative Commons Attribution 4.0 license and can be redistributed and used commercially. For convenience, I’ve already downloaded the data and stored it in a Gist.
For Australia, we’ll use a 2020 Kaggle dataset derived from the simplemaps.com World Cities Database . It includes 1,035 prominent cities in Australia that contain most of its population. It’s released for free under an MIT license and Creative Commons Attribution 4.0 license. For convenience, this dataset has also been stored in a Gist.