Modeling Urban Green Access: Combining Zone-Based Proximity and Demand-Weighted Metrics in a Medium-Sized U.S. City

Urban green space (UGS) accessibility is a cornerstone of equitable and sustainable city planning. However, existing studies focus on large metropolitan areas and rely on limited spatial models that overlook the complexity of urban morphology and socio-demographic diversity. This study shifts the focus to East Lansing, a medium-sized U.S. city that exhibits neither the spatial concentration of major metropolises nor the uniformity of small towns, thereby offering a distinctive context to examine urban green space equity. To this end, we develop a composite accessibility index by integrating four complementary spatial models: Euclidean distance, gravity-based access, two-step floating catchment area (2SFCA), and zone-based analysis. Utilizing high-resolution spatial, demographic, and environmental datasets, the study applies both Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) to uncover global patterns and local variations in accessibility determinants. The results reveal pronounced neighborhood-level disparities, with variables such as green coverage, park provision, and commercial density emerging as significant but spatially uneven predictors. The composite index yields a more robust and equitable representation of UGS accessibility than any individual model. This multi-model, spatially explicit framework contributes to methodological advances in accessibility assessment and offers actionable insights for place-based urban greening strategies.