The recent promotion of sustainable urban planning combined with a growing need for public interventions to improve well-being and health have led to an increased collective interest for green spaces in and around cities. In particular, parks have proven a wide range of benefits in urban areas. This also means inequities in park accessibility may contribute to health inequities. In this work, we showcase the application of classic tools from Operations Research to assist decision-makers to improve parks' accessibility, distribution and design. Given the context of public decision-making, we are particularly concerned with equity and environmental justice, and are focused on an advanced assessment of users' behavior through a spatial interaction model. We present a two-stage fair facility location and design model, which serves as a template model to assist public decision-makers at the city-level for the planning of urban green spaces. The first-stage of the optimization model is about the optimal city-budget allocation to neighborhoods based on a data exposing inequality attributes. The second-stage seeks the optimal location and design of parks for each neighborhood, and the objective consists of maximizing the total expected probability of individuals visiting parks. We show how to reformulate the latter as a mixed-integer linear program. We further introduce a clustering method to reduce the size of the problem and determine a close to optimal solution within reasonable time. The model is tested using the case study of the city of Montreal and comparative results are discussed in detail to justify the performance of the model.

翻译：近年来，可持续城市规划的推广，加之公众对提升福祉与健康干预需求的日益增长，促使城市及周边绿地的集体关注度显著提升。实证表明，公园在城市区域具有广泛效益，而公园可达性的不平等可能加剧健康不平等。本研究展示如何应用运筹学经典工具，辅助决策者改善公园的可达性、分布与设计。鉴于公共决策背景，我们特别关注公平性与环境正义，并基于空间交互模型对用户行为进行高级评估。我们提出一个两阶段公平设施选址与设计模型，作为城市级绿地规划的模板模型。优化模型的第一阶段基于揭示不平等属性的数据，实现城市预算向各街区的优化分配；第二阶段则针对每个街区寻求公园的最优选址与设计，目标为最大化个体访问公园的总期望概率。我们展示了如何将该问题重构为混合整数线性规划，并引入聚类方法降低问题规模，在合理时间内求得接近最优解。以蒙特利尔市为案例进行模型验证，通过详细对比结果论证模型的性能。