Computational Redistricting of Iowa's Congressional Districts

TL;DR

This paper improves a redistricting algorithm for complex states and applies it to Iowa, revealing partisan biases in the enacted map and analyzing recent election data for gerrymandering.

Contribution

It extends a previous redistricting algorithm to handle specific state constraints and applies it to Iowa, providing insights into partisan fairness and gerrymandering.

Findings

The initial Iowa map was rejected due to partisan bias.

The algorithm generates fairer district maps under complex constraints.

Analysis of 2024 election results indicates potential gerrymandering.

Abstract

This article expands on the redistricting algorithm proposed by Chen and Rodden (2015) for states with fewer than eight congressional districts, populations highly concentrated in urban areas, or state laws that require preservation of county lines. We used the updated algorithm to redistrict Iowa's four congressional districts. Non-partisan, randomly drawn maps were used to evaluate the fairness of the enacted congressional map. Notably, the evidence suggests that the first proposed map drawn by the Iowa Legislative Bureau was rejected due to partisan bias. This article also analyzes Iowa's 2024 election results for partisan gerrymandering.