Individual and group fairness in geographical partitioning

TL;DR

This paper introduces a new fair partitioning method for heterogeneous populations, ensuring equitable group representation in geographical districts, with a novel algorithm and practical case study validation.

Contribution

It formulates a new class of fair geographical partitioning problems, proves the optimal solution as a generalized Voronoi diagram, and provides an efficient algorithm to compute it.

Findings

The optimal solution generalizes additively weighted Voronoi diagrams.

The proposed algorithm efficiently computes fair partitions.

Case study demonstrates practical applicability with multiple demographic groups.

Abstract

Socioeconomic segregation often arises in school districting and other contexts, causing some groups to be over- or under-represented within a particular district. This phenomenon is closely linked with disparities in opportunities and outcomes. We formulate a new class of geographical partitioning problems in which the population is heterogeneous, and it is necessary to ensure fair representation for each group at each facility. We prove that the optimal solution is a novel generalization of the additively weighted Voronoi diagram, and we propose a simple and efficient algorithm to compute it, thus resolving an open question dating back to Dvoretzky et al. (1951). The efficacy and potential for practical insight of the approach are demonstrated in a realistic case study involving seven demographic groups and $78$ district offices.