How spatial patterns affect urban green space equity at different equity levels: A Bayesian quantile regression approach

Abstract

In many cities, urban green space (UGS) is not evenly distributed, and marginalized communities often lack UGS due to legacies of disinvestment. Existing research mainly focused on the effect of socioeconomic factors to understand the disparity in access to UGS. However, little is known on how spatial patterns affect UGS equality at the regional and subregional levels. Moreover, the potential quantile effects in the UGS pattern and equality relationship remain unclear. Here, we explored how UGS equality varied among UGS spatial patterns and socioeconomic gradients in seven counties (1,227 census tracts) in Southeast Michigan. First, we quantified UGS equality of each tract with a spatially explicit Gini coefficient and UGS spatial patterns via landscape metrics. Then, we applied a Bayesian quantile regression model to investigate the nonlinear relationship between spatial pattern and UGS equality in the whole study area and its three subregions of different population density. Our results showed that spatial patterns had quantile effects on UGS equality. At the regional scale, patch density and the largest patch index had significant positive effects on UGS equality at all equality levels. The mean patch shape index positively correlated with UGS equality in areas with higher equality level (Gini coefficient: 0.52–0.92). At the subregional level, patch density most efficiently predicted UGS equality in densely populated areas, while the largest patch index also affected UGS equality in areas with low population density. We suggest policymakers should focus on areas with extremely unequal access to UGS to increase the number rather than the cumulative area of UGS. Besides, more linear and irregular UGS patches with longer perimeters have potential to serve more communities.