December 2023 Sequential Monte Carlo for sampling balanced and compact redistricting plans
Cory McCartan, Kosuke Imai
Author Affiliations +
Ann. Appl. Stat. 17(4): 3300-3323 (December 2023). DOI: 10.1214/23-AOAS1763


Random sampling of graph partitions under constraints has become a popular tool for evaluating legislative redistricting plans. Analysts detect partisan gerrymandering by comparing a proposed redistricting plan with an ensemble of sampled alternative plans. For successful application sampling methods must scale to maps with a moderate or large number of districts, incorporate realistic legal constraints, and accurately and efficiently sample from a selected target distribution. Unfortunately, most existing methods struggle in at least one of these areas. We present a new sequential Monte Carlo (SMC) algorithm that generates a sample of redistricting plans converging to a realistic target distribution. Because it draws many plans in parallel, the SMC algorithm can efficiently explore the relevant space of redistricting plans better than the existing Markov chain Monte Carlo (MCMC) algorithms that generate plans sequentially. Our algorithm can simultaneously incorporate several constraints commonly imposed in real-world redistricting problems, including equal population, compactness, and preservation of administrative boundaries. We validate the accuracy of the proposed algorithm by using a small map where all redistricting plans can be enumerated. We then apply the SMC algorithm to evaluate the partisan implications of several maps submitted by relevant parties in a recent high-profile redistricting case in the State of Pennsylvania. We find that the proposed algorithm converges faster and with fewer samples than a comparable MCMC algorithm. Open-source software is available for implementing the proposed methodology.


The authors would like to thank Moon Duchin, Ben Fifield, Greg Herschlag, Mike Higgins, Chris Kenny, Jonathan Mattingly, Justin Solomon, and Alex Tarr for helpful comments and conversations. Imai thanks Yunkyu Sohn for his contributions at an initial phase of this project. We also thank the Editor, Associate Editor, and anonymous reviewers for detailed and helpful comments.


Download Citation

Cory McCartan. Kosuke Imai. "Sequential Monte Carlo for sampling balanced and compact redistricting plans." Ann. Appl. Stat. 17 (4) 3300 - 3323, December 2023.


Received: 1 August 2021; Revised: 1 March 2023; Published: December 2023
First available in Project Euclid: 30 October 2023

MathSciNet: MR4661699
Digital Object Identifier: 10.1214/23-AOAS1763

Keywords: Gerrymandering , graph partition , sequential Monte Carlo , Spanning trees

Rights: Copyright © 2023 Institute of Mathematical Statistics


This article is only available to subscribers.
It is not available for individual sale.

Vol.17 • No. 4 • December 2023
Back to Top