“Restorative-Repressive” perception on post-industrial parks based on artificial and natural scenarios: Difference and mediating effect

Abstract

The development of post-industrial landscapes at industrial sites plays an important role to fill urban green spaces. However, current research on the use and redevelopment of post-industrial sites has mainly focused on ecological restoration, and studies combined with objective and subjective data to quantify public preferences remain poorly understood. In this study, deep learning was used to semantically segment the post-industrial landscape, and a multiple stepwise regression model was used to analyze the non-linear correlation between quantitative indicators and public “restorative-repressive” perception, and structural equation model (SEM) between quantitative indicators and public perception data were established. We investigated and found (1) Semantic segmentation models for machine learning combined with principal component analysis (PCA) and non-metric multidimensional scaling (NMDS) analysis can categorize post-industrial parks into two groups dominated by artificial elements and natural elements. (2) Public perceptions varied more in the natural element-dominated group and less in the industrial element-dominated group. In addition, waterbody in the post-industrial landscape existed as a destabilizing factor. (3) There was a difference in the correlation between quantitative indicators and subjective perceptions in the two categories of parks. (4) Height of industrial building (HIB), function of industrial building(FIB), vegetation succession(VS) were significantly influenced public satisfaction. These findings informed that public satisfaction with post-industrial landscapes can be enhanced by taking full account of the different uses of natural and artificial elements and enabling researchers to analyze the redevelopment of post-industrial landscapes from a new perspective of evidence-based design.