Spatial Preference Heterogeneity in Policies for Improving Urban Green Spaces

Abstract

Urban green spaces enhance the quality of urban life. The South Korean government has implemented polices to quantitatively increase and restore urban green spaces damaged by rapid urbanization. However, increasing the supply of such green spaces without considering the demands of urban residents may limit the effective implementation of such policies. This study identifies and analyzes the heterogeneity in urban residents’ preferences for urban green space improvement policies and, investigates the spatial preference heterogeneity for such policies in the planned city of Seongnam, South Korea. Online surveys were conducted in 2015, and 414 valid responses were collected. A mixed logit model was applied to analyze the preference for green space improvement policies, and to capture the heterogeneous preferences. Getis-ord Gi* was computed to identify the spatial heterogeneity of the estimated preference coefficient and the marginal willingness to pay (MWTP). The results indicate that heterogeneity exists in the preference for urban green space improvement policies but its degree by attribute fluctuated. Urban residents showed a high preference for policies that improve qualitative aspects, such as enhancement of biodiversity and expansion of convenience facilities. Spatial preference heterogeneity was identified by clustering hot spots of the estimated preference coefficient and the MWTP. The degree of heterogeneity in preference and spatial heterogeneity showed different trends. With small volatility in preference heterogeneity, hot spots or cold spots, may not be detected with large volatility, can be formed. Furthermore, the estimated preference coefficient and the MWTP showed different patterns of spatial distribution. Therefore, spatial analysis is required to utilize preferences in policy. These findings will contribute to ensuring the satisfaction of all urban residents by identifying their preferences for urban green space policies. It will further help prioritize cost-effective implementation of regions for policy by identifying the spatial preference heterogeneity.