Spatial Autocorrelation and Political Redistricting: A Task for the Uniform Distribution

Abstract

An important quantitative geography application area derives from the periodically repeating apportionment process of redrawing legislative district boundaries to guarantee equal voter representation through identical or equivalent population counts, a situation confronting the U.S. Supreme Court with gerrymandering rulings on a decennial basis. This remains a small geographic sample size domain, with states having between one and fifty-two (i.e., California) congressional precincts in 2023. Texas constitutes an informative case study because its sample size of thirty-eight exceeds the frequently touted minimum of thirty, and its 2020 increase was two seats, more than any other state, some of which experienced decreases. Hence, although all fifty states also have undergone internal population redistribution, the Texas redrawn boundaries offer the greatest opportunity for dramatic borderline changes. This article appraises outcomes of this Texas gain within its legal constraints as they pertain to a novel uniform distribution explicitly embracing spatial autocorrelation, a fundamental georeferenced data property. Its name is sui-uniform to differentiate it from the prevalent auto- model convention; this article furnishes its first empirical application. After accounting for spatial autocorrelation, the inferential conclusion for Texas is as preferred: Superpopulation geotagged voting-age populations conform to a uniform distribution, whereas racial and ethnic subpopulations do not.