Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning

Abstract

Urban greening plays a crucial role in maintaining environmental sustainability and enhancing people's well-being. However, limited by the shortcomings of traditional methods, studying the heterogeneity and nonlinearity between environmental factors and green view index (GVI) still faces many challenges. To address the concerns of nonlinearity, spatial heterogeneity, and interpretability, an interpretable spatial machine learning framework incorporating the Geographically Weighted Random Forest (GWRF) model and the SHapley Additive exPlanation (Shap) model is proposed in this paper. In this paper, we combine multi-source big data, such as Google Street View data and remote sensing images, and utilize semantic segmentation models and geographic data processing techniques to study the global and local interpretation of the Beijing region with GVI as the key indicator. Our research results show that: (1) Within the Sixth Ring Road of Beijing, GVI shows significant spatial clustering phenomenon and positive correlation linkage, and at the same time exhibits significant spatial differences; (2) Among many environmental variables, the increase of vegetation coverage has the most significant positive effect on GVI, while the increase of building density shows a strong negative correlation with GVI; (3) Whether it is the vegetation cover rate, urban built environment or socio-economic factors, their influence on GVI shows non-linear characteristics and a certain threshold effect; (4) The performance of the GWRF model in simulating and predicting GVI is excellent and far exceeds that of existing models. Based on these findings, this study can provide an important reference for urban planners to enhance urban greening.