Spatial preference heterogeneity in policies for improving urban green spaces

Abstract

Urban green spaces (UGSs) enhance the quality of urban dwellers’ lives. Thus, efficient policies for improving UGSs—which should reflect urban residents’ needs—are necessary. Additionally, it is crucial to manage the spatial distribution of residents’ preferences to find regions that can produce greater utility within limited budgets. Using online surveys conducted in 2015, which obtained 414 valid responses, this study analyzed urban residents’ preferences for UGS improvement policies and investigated spatial preference heterogeneity for such policies in the planned city of Seongnam, South Korea. A mixed logit model was applied to analyze policy preferences, and the preference heterogeneity level was assessed by coefficient of variation. Additionally, hot spot analysis was performed to examine spatial heterogeneity. The Getis-ord Gi* was computed to identify the spatial clusters of the estimated coefficients and the marginal willingness to pay (WTP). The results indicated a high preference for enhancing quality and connectivity. While the results showed that preference heterogeneity existed in each UGS improvement policy, heterogeneity levels differed per attribute. Statistically significant local spatial clusters of estimated coefficients and marginal WTP were observed for each UGS improvement policy despite global autocorrelation being insignificant. It was observed that estimate coefficients with low coefficient of variation can make extensive spatial clusters; otherwise, the opposite trend might occur. Additionally, marginal WTP hot spots did not change according to attribute but appeared in similar locations. Therefore, spatial heterogeneity analysis is necessary to manage UGSs with higher net utilities given limited budgets. These findings will ensure the satisfaction of all urban residents by identifying their preferences for UGS policies. They will also help prioritize cost-effective implementation of policy considering spatial preference heterogeneity.