Major updates to Gerry-Suite

Since the beginning of the year, we’ve made several major updates to our main suite of redistricting analysis tools.


Our GerryChain library has several new features that help catch the public releases up to the code we’ve been using in Lab projects. In particular, users can now run what we call “region-aware” chains (e.g., prioritizing county intactness) and “exploratory” chains that heuristically optimize a particular metric or score.

To accompany these new features, we have also made a concerted effort to overhaul the package’s documentation. This includes adding tutorials, re-working the old tutorials to make sure that they are up to date with current best practices, and adding a complete, searchable package reference.

Region-Aware Chains

When redistricting a specific locality, it is often a stated preference that smaller regions (such as counties or cities or neighborhoods) are preserved un-split as much as possible. The newest version of GerryChain allows the user to select a parameter interpolating from no region-awareness to a maximal level of priority on region preservation.

For those curious about the math: this is accomplished by using a shifted Kruskal’s algorithm. The splitting step in a recombination chain is done by drawing a random spanning tree and deleting an edge of that tree so that the halves remain balanced. One fast algorithm for randomizing spanning trees is MST, which selects the minimum spanning tree, given a set of edge weights. Instead of drawing all weights from the same distribution (as in standard Kruskal), we “surcharge” the weights on edges between different regions. This makes us more likely to choose a spanning tree that restricts to one tree within each region; when the tree is split, the region is then split at most once. To learn more, please see our new documentation.

Exploratory Chains

Version 0.3.2 of GerryChain came with the addition of two optimizer classes:

  1. SingleMetricOptimizer This is a general-purpose class that is designed to seek higher-scoring plans with respect to a single metric.
  2. Gingleator This is a subclass of the SingleMetricOptimizer class, specifically designed to search for plans with increased numbers of majority-minority districts. The class is named for the Supreme Court case Thornburg v. Gingles which set a requirement to find these when bringing VRA cases.

Each of these optimization classes has three different modes:

  1. Tilted Runs: In a tilted run, we always accept a proposed plan with a better score, and we accept a worse-scoring plan with a user-chosen probability p.
  2. Short Bursts: The short bursts heuristic—originally proposed for redistricting by Zach Schutzman and then studied in the paper “Voting Rights, Markov Chains, and Optimization by Short Bursts” by MGGG collaborators (Cannon, Goldbloom-Helzner, Gupta, Matthews, and Suwal)—starts from some plan and then performs b steps of an unbiased random walk (the user sets the number b, known as the burst length). A new burst of length b is then run starting from the most extreme configuration found in the previous burst, and we iterate.
  3. Simulated Annealing: This is an optimization technique borrowed from statistical physics. The basic idea is to start a chain “hot,” meaning that every proposed new plan is accepted. As time passes, the chain “cools,” making it less and less likely for lower scores to be accepted. The probability is governed by a value called beta, which varies on a user-chosen temperature schedule, creating a heating and cooling cycle that repeats over the life of the chain.


In addition to the major updates to GerryChain, we have been working hard to help improve the functionality of its companion library GerryTools, which contains auxiliary functions meant to facilitate analysis and visualization. GerryTools will continue to evolve throughout Summer 2024, but has two exciting new features in this release.

A new compression scheme called Binary Ensemble (BEN)

When it comes to ensemble analysis work, reproducibility and auditability are vital. Ideally, we can preserve and publish the exact data that was used in any given analytical project. However, when the data itself is tens or hundreds of millions of districting plans, each consisting of an assignment of potentially millions of geographical units, this creates massive data files that are costly to store and hard to manipulate.

In previous years, MGGG made substantial progress towards solving this issue with the PCompress package due to Lab alum Max Fan, which successfully reduced the storage burden for large ensembles where the differences from one plan to the next are small relative to the size of the plan, as in common MCMC methods. But for chains run on the census-block level or for ensembles of less correlated plans, the resulting files can still be quite unwieldy.

The new BEN file format tackles both of these issues. The BEN module in GerryTools comes with a suite of tools that allows users to compress ensembles of districting plans down by 1-2 orders of magnitude (depending on the type of plan and how conveniently the nodes are indexed). There’s also an extreme compression tool and file format known as XBEN, letting users see up to a 4000-fold improvement in the compression of their files. This means that many ensembles of plans can now be compressed small enough to fit into an email, and several hundred can now fit on a thumb drive.

The documentation contains an example here where we reduce a 27Gb file containing 100,000 block-level Colorado plans down to a slim 6Mb, small enough for GitHub.

Wrappers for diverse sampling methods

The main Markov chain technique implemented in GerryChain is called ReCom, or recombination, first introduced by DeFord, Duchin, Najt, and Solomon. Lab alum Parker Rule created a blazingly fast implementation of both regular ReCom and of a reversible variant called RevReCom in the high-performance programming language Rust. The Rust implementation lets users do bigger and more complicated runs than are possible in Python alone, for instance enabling block-level runs on large states in a matter of hours. In the lab, we use the Rust implementation for our resesarch and consulting work. This new release of GerryTools includes a Python wrapper for the Rust code, so that Python users of GerryChain can now access the power of the faster implementation without needing any knowledge of Rust syntax.

MGGG is one group in a larger ecosystem of researchers building mathematical and computational approaches to redistricting. For example, teams led by Jonathan Mattingly at Duke and by Kosuke Imai at Harvard have built highly-cited tools for sampling districting plans, called Forest ReCom and Sequential Monte Carlo (or SMC), respectively. Forest ReCom is a Metropolis Markov chain implemented in Julia and SMC is a re-weighted recursive splitting technique implemented in R/C++. Both are based on the spanning tree partitioning idea first introduced in ReCom.

The differences in programming language and file format can make it challenging for users to benchmark differences in the samplers, so we have also added Python wrappers to GerryTools that let you run Forest ReCom and SMC without setting up the original development pipelines. (We also have some supporting documentation to help users understand the methods.) This is available in the MGRP module of GerryTools.

The contact person and main developer for all of these innovations and improvements is Peter Rock (, who would love to hear from you!