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Restorative perception of urban streets: Interpretation using deep learning and MGWR models
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 ski...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101336/ https://www.ncbi.nlm.nih.gov/pubmed/37064708 http://dx.doi.org/10.3389/fpubh.2023.1141630 |
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author | Han, Xin Wang, Lei He, Jie Jung, Taeyeol |
author_facet | Han, Xin Wang, Lei He, Jie Jung, Taeyeol |
author_sort | Han, Xin |
collection | PubMed |
description | 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 R(2) 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. |
format | Online Article Text |
id | pubmed-10101336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101013362023-04-14 Restorative perception of urban streets: Interpretation using deep learning and MGWR models Han, Xin Wang, Lei He, Jie Jung, Taeyeol Front Public Health Public Health 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 R(2) 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. Frontiers Media S.A. 2023-03-30 /pmc/articles/PMC10101336/ /pubmed/37064708 http://dx.doi.org/10.3389/fpubh.2023.1141630 Text en Copyright © 2023 Han, Wang, He and Jung. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Han, Xin Wang, Lei He, Jie Jung, Taeyeol Restorative perception of urban streets: Interpretation using deep learning and MGWR models |
title | Restorative perception of urban streets: Interpretation using deep learning and MGWR models |
title_full | Restorative perception of urban streets: Interpretation using deep learning and MGWR models |
title_fullStr | Restorative perception of urban streets: Interpretation using deep learning and MGWR models |
title_full_unstemmed | Restorative perception of urban streets: Interpretation using deep learning and MGWR models |
title_short | Restorative perception of urban streets: Interpretation using deep learning and MGWR models |
title_sort | restorative perception of urban streets: interpretation using deep learning and mgwr models |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101336/ https://www.ncbi.nlm.nih.gov/pubmed/37064708 http://dx.doi.org/10.3389/fpubh.2023.1141630 |
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