Spanning Trees and Redistricting: New Methods for Sampling and Validation

TL;DR

This paper introduces a new reversible recombination algorithm and advocates for a spanning tree distribution as a robust baseline for sampling and validating political districting plans, aiding in gerrymandering detection.

Contribution

It presents a novel reversible recombination algorithm and establishes a canonical spanning tree distribution for more effective redistricting analysis.

Findings

Proves fundamental properties of the new algorithm

Defines a principled spanning tree distribution for redistricting

Provides an efficient open-source implementation for large datasets

Abstract

Deciding whether a political districting plan was distorted by a hidden agenda, or whether it dilutes the voting power of some group, requires a neutral baseline for comparison. Remarkably, all nine U.S. Supreme Court justices have now signed on to decisions that find that computational methods can provide key evidence. Today, the leading approaches for benchmarking districting plans are based on the use of spanning trees for sampling graph partitions. We present a new *reversible recombination* algorithm and rigorously prove its fundamental properties. Furthermore, we argue for a canonical sampling distribution called the *spanning tree distribution* that is well adapted to redistricting and provides a principled foundation for comparing and validating methods. Together with a highly efficient (and open-source) implementation that can generate and handle large datasets, this work provides the most powerful null model to date for the gerrymandering problem, meeting an urgent democratic challenge with sound scientific methodology.