GerryChain¶
GerryChain
Build and analyze ensembles of districting plans with Markov chain Monte Carlo.
1.0.0 Release
GerryChain 1.0.0 moves core graph computation to RustworkX and adds explicit, reproducible random number generation. Existing users should read the migration guide; the changelog lists the public API changes.
GerryChain is a library for using Markov Chain Monte Carlo methods to study the problem of political redistricting. Development of the library began during the 2018 Voting Rights Data Institute (VRDI).
The project is in active development in the mggg/GerryChain GitHub repository, where bug reports and feature requests, as well as contributions, are welcome.
Install¶
Most users can install GerryChain using pip:
pip install gerrychain
For more detailed installation instructions, including instructions for setting up virtual environments, please see the following section: Installation.
Where to next¶
Install the package and run your first chain on Pennsylvania’s VTDs.
Executable notebook guides with rendered outputs, from the anatomy of the chain through ReCom, real data, geometries, and optimization.
Reproducibility practices, companion tools, and how to contribute or report issues.
Every public class and function in gerrychain, organized by module.
We also highly recommend the resources prepared by Daryl R. DeFord of MGGG for the 2019 MIT IAP course Computational Approaches for Political Redistricting.