EMLGNov 24, 2025

Individual and group fairness in geographical partitioning

arXiv:2511.19722v1
Originality Highly original
AI Analysis

This addresses fairness in resource allocation for diverse populations, with potential applications in public policy, though it is incremental in extending existing partitioning methods.

The paper tackles the problem of socioeconomic segregation in geographical partitioning, such as school districting, by formulating a new class of problems to ensure fair representation for each group at each facility, and resolves an open question from 1951 with a proof and efficient algorithm.

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.

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