An Optimization Approach to Improve Equitable Access to Local Parks
Anisa Young, Emily L. Tucker, Mariela Fernandez, David White, Robert, Brookover, Brandon Harris

TL;DR
This paper introduces a mixed-integer programming model to optimize the placement and improvement of local parks, aiming to reduce inequities in access for marginalized communities using a data-driven, multi-criteria approach.
Contribution
It develops a novel optimization framework that incorporates budget, infrastructure, and equity considerations for park placement decisions, addressing limitations of existing platforms.
Findings
Model applied to Asheville, NC case study
Policy analysis shows impact of budget and priorities
Framework is adaptable to other cities
Abstract
Local parks are public resources that promote human and environmental welfare. Unfortunately, park inequities are commonplace as historically marginalized groups may have insufficient access. Platforms exist to identify the geographical areas that would benefit from future park improvements. However, these platforms do not optimize decisions nor include key features, such as budget and infrastructure, that are relevant to park location decisions. To support recreational and government agencies in addressing inequities in the distribution and quality of parks, we propose a mixed-integer program that minimizes insufficient access, defined as weighted deviations across multiple categories (distance, capacity, and environmental features). We consider an equity-focused min-max objective and an overall objective to minimize total weighted deviations. We apply the model to a case study of…
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Taxonomy
TopicsEconomic and Environmental Valuation · Urban Transport and Accessibility · Smart Parking Systems Research
