Measurement of spatial heterogeneity in street restorative perceptions and street refinement design.

Restorative perception of streets is an essential metric for evaluating urban environments and serves as a key indicator of pedestrians' perspectives on street refinement design. However, restorative perception varies significantly across different streets, necessitating an analysis of these disparities. This study integrates street view data, deep learning algorithms, the MGWR model, and space syntax to analyze spatial heterogeneity in restorative perceptions and optimize street design strategies. Using the random forest (RF) algorithm and restorative component scales (RCS), we assessed residents' perceptions of street restoration in Shanghai's Huangpu District. Our analysis identified eight key visual elements influencing perceptions, such as sidewalks, buildings, and walls. Results revealed significant geographic variations, with high-perception areas concentrated around open parks, waterfronts, and well-designed buildings featuring thoughtful amenities. Visual elements like trees and plants were found to significantly enhance restorative perceptions. Moran's Ι statistics and multiple regression models further revealed spatial heterogeneity and clustering in perceptions, highlighting the importance of location-based planning. Among the regression models, the MGWR model achieved the highest R