How Urban Green Space Spatial Pattern Affects its Equity: a Bayesian Quantile Regression Approach

Abstract

Urban green space (UGS) is not evenly distributed in many urban areas. Marginalized communities often lack greenspace due to legacies of disinvestment. However, little is known regarding how the spatial pattern of UGS determine UGS equity at the regional level. Moreover, the potential nonlinearity and spatial heterogeneity in the USG pattern and equity relationship are obscure. Here, we explored how UGS equity varies among UGS spatial patterns and socioeconomic gradients in seven counties in Southeast Michigan. We quantified UGS equity by spatially explicit Gini coefficients and computed UGS spatial patterns by landscape metrics. A Bayesian quantile regression model was then applied to investigate the nonlinear relationship between landscape spatial patterns and UGS equity in the whole study area and three subregions with different population density. Our results showed that at the regional scale, patch density and the large patch index have significantly negative effect on UGS equity at all levels. The mean patch shape index is negatively correlated with UGS equity in areas with a moderate equity level (0.52-0.92). At the sub-regional level, patch density is the most efficient predictor of USG equity in densely populated areas, while in areas with low population density, the large patch index also affects UGS equity. Therefore, we recommend regions with extremely poor equity should increase the amount of UGS instead of increasing the total area of UGS blindly. To enhance UGS equity in comparatively fair regions, government should avoid the fragmentation of existing UGS and develop new UGS with a more complex shape and longer circumferences to serve more communities.