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 district shapefiles.
One of the reasons that redistricting is hard to study is that the space of possibilities is so huge it’s unthinkable by humans. As a simplified warmup problem, we can ask: how many ways can you district a small grid?
Our open-source Python library for sampling ensembles of districting plans using MCMC. Developed by Mary Barker, Robert Dougherty-Bliss, Daryl DeFord, Max Hully, Anthony Pizzimenti and Preston Ward during the 2018 Voting Rights Data Institute.
Ecological inference (EI) is the main statistical technique used to establish racially polarized voting (RPV). A team at the 2018 Voting Rights Data Institute created R Shiny apps making it possible for anyone to run an ecological inference analysis.