Use Markov chain Monte Carlo to analyze districting plans and gerrymanders
GerryChain is a Python library for building ensembles of districting plans using Markov chain Monte Carlo. It is developed and maintained by the Metric Geometry and Gerrymandering Group and our network of volunteers. It is distributed under the 3-Clause BSD License.
The basic workflow is to start with the geometry of an initial plan and generate a large collection of sample plans for comparison. Usually, we will constrain these sampled plans in such a way that they perform at least as well as the initial plan according to traditional districting principles, such as population balance or compactness. Comparing the initial plan to the ensemble provides quantitative tools for measuring whether or not it is an outlier among the sampled plans.
See our Getting started guide for the basics of using GerryChain.
We also highly recommend the resources prepared by Daryl R. DeFord of MGGG for the 2019 MIT IAP course Computational Approaches for Political Redistricting.
To install GerryChain from PyPI, run pip install gerrychain from the command line.
If you plan on using GerryChain’s GIS functions, such as computing adjacencies or reading in shapefiles, then run pip install gerrychain[geo] from the command line.
This approach sometimes fails due to compatibility issues between our different Python GIS dependencies, like geopandas, pyproj, fiona, and shapely. For this reason, we recommend installing from conda-forge for users that encounter difficulty with PyPI.
conda install -c conda-forge gerrychain
For this command to work as intended, you will first need to activate the conda environment that you want to install GerryChain in. If the environment you want to activate is called vrdi (for example), then you can do this by running
conda activate vrdi
If this command causes problems, make sure conda is up-to-date by running
conda update conda conda init
For more information on using conda to install packages and manage dependencies, see Getting started with conda.
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