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<p>Network science metrics such as <i>centrality</i> play a crucial role in solving facility placement problems, which in turn have important applications in guiding decision processes for municipalities or regions. Decision makers and planners are increasingly interested in addressing situations where demographics and socioeconomic status are distributed in a non-uniform way over a region. In those cases, optimizing centrality alone may result in disparate access. In this paper, as an illustrative case study, we use cluster analysis and publicly available demographic data to characterize census tracts in Spokane County, WA, USA across many socioeconomic variables. We then compare placement of existing facilities in Spokane using a novel fairness metric and network centrality, first separately and then in combination, to determine optimal facility placement. Our analysis found that undesirable facilities are not fairly placed, but some key desirable facilities such as hospitals and parks are ideally located based on both fairness and centrality. Our approach can easily be applied to any region where similar geographic and demographic data are available, and analyses are performed to inform facility siting, upgrades, or relocation.</p>

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Holistic fairness considerations in facility placement decisions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • James Crabb,
  • Stephanie L. Kane,
  • Assefaw H. Gebremedhin

摘要

Network science metrics such as centrality play a crucial role in solving facility placement problems, which in turn have important applications in guiding decision processes for municipalities or regions. Decision makers and planners are increasingly interested in addressing situations where demographics and socioeconomic status are distributed in a non-uniform way over a region. In those cases, optimizing centrality alone may result in disparate access. In this paper, as an illustrative case study, we use cluster analysis and publicly available demographic data to characterize census tracts in Spokane County, WA, USA across many socioeconomic variables. We then compare placement of existing facilities in Spokane using a novel fairness metric and network centrality, first separately and then in combination, to determine optimal facility placement. Our analysis found that undesirable facilities are not fairly placed, but some key desirable facilities such as hospitals and parks are ideally located based on both fairness and centrality. Our approach can easily be applied to any region where similar geographic and demographic data are available, and analyses are performed to inform facility siting, upgrades, or relocation.