Restorative perception of urban streets: Interpretation using deep learning and MGWR models

Frontiers in Public Health

Public Health

Xin Han¹  Taeyeol Jung¹  Lei Wang²  Jie He³ 

[1] Department of Landscape Architecture, Kyungpook National University, Daegu, Republic of Korea;School of Architecture, Tianjin University, Tianjin, China;School of Architecture, Tianjin University, Tianjin, China;School of Architecture, Harbin Institute of Technology, Shenzhen, Shenzhen, China;

关键词: deep learning;    street view;    semantic segmentation;    multiscale geographically weighted regression;    urban street environment;    restorative quality;   

DOI  :  10.3389/fpubh.2023.1141630

 received in 2023-01-10, accepted in 2023-02-16,  发布年份 2023

来源: Frontiers

【 摘 要 】

Restorative environments help people recover from mental fatigue and negative emotional and physical reactions to stress. Excellent restorative environments in urban streets help people focus and improve their daily behavioral performance, allowing them to regain efficient information processing skills and cognitive levels. High-density urban spaces create obstacles in resident interactions with the natural environment. For urban residents, the restorative function of the urban space is more important than that of the natural environment in the suburbs. An urban street is a spatial carrier used by residents on a daily basis; thus, the urban street has considerable practical value in terms of improving the urban environment to have effective restorative function. Thus, in this study, we explored a method to determine the perceived restorability of urban streets using street view data, deep learning models, and the Ordinary Least Squares (OLS), the multiscale geographically weighted regression (MGWR) model. We performed an empirical study in the Nanshan District of Shenzhen, China. Nanshan District is a typical high-density city area in China with a large population and limited urban resources. Using the street view images of the study area, a deep learning scoring model was developed, the SegNet algorithm was introduced to segment and classify the visual street elements, and a random forest algorithm based on the restorative factor scale was employed to evaluate the restorative perception of urban streets. In this study, spatial heterogeneity could be observed in the restorative perception data, and the MGWR models yielded higher R2 interpretation strength in terms of processing the urban street restorative data compared to the ordinary least squares and geographically weighted regression (GWR) models. The MGWR model is a regression model that uses different bandwidths for different visual street elements, thereby allowing additional detailed observation of the extent and relevance of the impact of different elements on restorative perception. Our research also supports the exploration of the size of areas where heterogeneity exists in space for each visual street element. We believe that our results can help develop informed design guidelines to enhance street restorative and help professionals develop targeted design improvement concepts based on the restorative nature of the urban street.

【 授权许可】

Unknown   
Copyright © 2023 Han, Wang, He and Jung.