Individual and group fairness in geographical partitioning
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908673339555840 |
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| author | Ryzhov, Ilya O. Carlsson, John Gunnar Zhu, Yinchu |
| author_facet | Ryzhov, Ilya O. Carlsson, John Gunnar Zhu, Yinchu |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_19722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Individual and group fairness in geographical partitioning Ryzhov, Ilya O. Carlsson, John Gunnar Zhu, Yinchu Econometrics Machine Learning 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. |
| title | Individual and group fairness in geographical partitioning |
| topic | Econometrics Machine Learning |
| url | https://arxiv.org/abs/2511.19722 |