Optimization of the location and design of urban green spaces

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.