Towards a sustainable city: Deciphering the determinants of restorative park and spatial patterns

Abstract

Urban parks have been found to provide mental health benefits. Some empirical studies have tested natural features and perceptual measures respectively, announcing their contribution to psychological restoration. However, inconsistent findings were occasionally reported, whereas few attempts have been made to combine both observed and perceptual factors for validation. Little is known about the variation of restorative drivers and their spatial patterns. To address these problems, this study combined public participation geographic information system (PPGIS) and deep learning method to capture visual qualities of landscape features along with several important perceptual measures. A typical urban park in Wuhan, China, was selected for a pilot study, and 1560 crowdsourced on-site images were collected, with thematic and geographic information being integrated. A series of statistical models, e.g., OLS, QRM, and MGWR, were employed successively for validation. The results showed that landscape preference, place attachment, greenery and water were validated as the global explanatory factors to estimate the conditional mean of psychological restoration. The variation of influential effects of these factors were detected at different restoration levels. There exist spatial heterogeneity for these influential factors on restorative effects. Findings provided new knowledge on a deeper understanding of the subtlety of restoration drivers and their spatial patterns. The findings offered useful insights and guidance for urban planners in creating high-quality green parks with restorative values.