Predicting context-sensitive urban green space quality to support urban green infrastructure planning

Abstract

Urban green spaces (UGSs) support human health and well-being in diverse ways. In addition to their availability and accessibility, also the quality of UGSs is relevant for understanding human-environment interactions between urban populations and their local UGS. However, data on UGS quality are rarely available with the geographic coverage required for spatial decision making and urban green infrastructure (UGI) planning and management.This study uses data from a large-scale public participation GIS (PPGIS) survey to predict perceived UGS quality across the city of Espoo, Finland. The respondents (n 3,132) mapped over 8,500 frequently visited sites situated in UGSs. Generalized linear mixed models were used to study associations between the perceived place quality of the respondent-mapped sites and diverse objectively measured UGS characteristics. The presence of blue elements, high forest biodiversity, level of UGS maintenance, and low daytime noise exposure contributed to positive perceptions of UGS quality, while daytime noise exposure and decreasing UGS size were associated with negative perceptions.The model was extrapolated spatially to predict perceived UGS quality across the entire city, revealing local differences in the accessibility of high-quality UGS. The results exemplify how both UGS quantity and quality are relevant for understanding the mechanisms leading to UGS visitation and the health and well-being benefits gained from UGS use and exposure. Moreover, the study demonstrates how UGS characteristics valued by the local population may be identified to support local UGI planning and management.