maup: the geospatial toolkit for redistricting data

Anyone who has done research or data science about redistricting can tell you that one of the most difficult (and important!) steps in the process is collecting, cleaning, and processing the geospatial data that underpins everything: shapefiles of precincts and census blocks.

maup is our attempt to make working with redistricting data easier for everyone. It is a Python package that streamlines the basic workflows that arise when working with blocks, precincts, and districts, such as:

• Assigning precincts to districts,
• Aggregating block data to precincts,
• Disaggregating data from precincts down to blocks, and
• Prorating data when units do not nest neatly.

The project’s priorities are to be efficient by using spatial indices whenever possible and to integrate well with the existing ecosystem around pandas, geopandas and shapely. The package is distributed under the MIT License.

The name of the package comes from the modifiable areal unit problem (MAUP), the fact that the same spatial data will look different depending on how you divide up the space. Since maup is all about changing the way your data is aggregated and partitioned, we have named it after the MAUP to encourage users to use the toolkit thoughtfully and responsibly.

For more information on the package, including examples of how to use it on real data, check out the project’s GitHub repo.