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Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning
An urban built environment is an important part of the daily lives of urban residents. Correspondingly, a poor design can lead to psychological stress, which can be harmful to their psychological and physical well-being. The relationship between the urban built environment and the perceived psycholo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131010/ https://www.ncbi.nlm.nih.gov/pubmed/35646775 http://dx.doi.org/10.3389/fpubh.2022.891736 |
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author | Han, Xin Wang, Lei Seo, Seong Hyeok He, Jie Jung, Taeyeol |
author_facet | Han, Xin Wang, Lei Seo, Seong Hyeok He, Jie Jung, Taeyeol |
author_sort | Han, Xin |
collection | PubMed |
description | An urban built environment is an important part of the daily lives of urban residents. Correspondingly, a poor design can lead to psychological stress, which can be harmful to their psychological and physical well-being. The relationship between the urban built environment and the perceived psychological stress of residents is a significant in many disciplines. Further research is needed to determine the stress level experienced by residents in the built environment on a large scale and identify the relationship between the visual components of the built environment and perceived psychological stress. Recent developments in big data and deep learning technology mean that the technical support required to measure the perceived psychological stress of residents has now become available. In this context, this study explored a method for a rapid and large-scale determination of the perceived psychological stress among urban residents through a deep learning approach. An empirical study was conducted in Gangnam District, Seoul, South Korea, and the SegNet deep learning algorithm was used to segment and classify the visual elements of street views. In addition, a human–machine adversarial model using random forest as a framework was employed to score the perception of the perceived psychological stress in the built environment. Consequently, we found a strong spatial autocorrelation in the perceived psychological stress in space, with more low-low clusters in the urban traffic arteries and riverine areas in Gangnam district and more high-high clusters in the commercial and residential areas. We also analyzed the street view images for three types of stress perception (i.e., low, medium and high) and obtained the percentage of each street view element combination under different stresses. Using multiple linear regression, we found that walls and buildings cause psychological stress, whereas sky, trees and roads relieve it. Our analytical study integrates street view big data with deep learning and proposes an innovative method for measuring the perceived psychological stress of residents in the built environment. The research methodology and results can be a reference for urban planning and design from a human centered perspective. |
format | Online Article Text |
id | pubmed-9131010 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91310102022-05-26 Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning Han, Xin Wang, Lei Seo, Seong Hyeok He, Jie Jung, Taeyeol Front Public Health Public Health An urban built environment is an important part of the daily lives of urban residents. Correspondingly, a poor design can lead to psychological stress, which can be harmful to their psychological and physical well-being. The relationship between the urban built environment and the perceived psychological stress of residents is a significant in many disciplines. Further research is needed to determine the stress level experienced by residents in the built environment on a large scale and identify the relationship between the visual components of the built environment and perceived psychological stress. Recent developments in big data and deep learning technology mean that the technical support required to measure the perceived psychological stress of residents has now become available. In this context, this study explored a method for a rapid and large-scale determination of the perceived psychological stress among urban residents through a deep learning approach. An empirical study was conducted in Gangnam District, Seoul, South Korea, and the SegNet deep learning algorithm was used to segment and classify the visual elements of street views. In addition, a human–machine adversarial model using random forest as a framework was employed to score the perception of the perceived psychological stress in the built environment. Consequently, we found a strong spatial autocorrelation in the perceived psychological stress in space, with more low-low clusters in the urban traffic arteries and riverine areas in Gangnam district and more high-high clusters in the commercial and residential areas. We also analyzed the street view images for three types of stress perception (i.e., low, medium and high) and obtained the percentage of each street view element combination under different stresses. Using multiple linear regression, we found that walls and buildings cause psychological stress, whereas sky, trees and roads relieve it. Our analytical study integrates street view big data with deep learning and proposes an innovative method for measuring the perceived psychological stress of residents in the built environment. The research methodology and results can be a reference for urban planning and design from a human centered perspective. Frontiers Media S.A. 2022-05-11 /pmc/articles/PMC9131010/ /pubmed/35646775 http://dx.doi.org/10.3389/fpubh.2022.891736 Text en Copyright © 2022 Han, Wang, Seo, 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 Seo, Seong Hyeok He, Jie Jung, Taeyeol Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning |
title | Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning |
title_full | Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning |
title_fullStr | Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning |
title_full_unstemmed | Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning |
title_short | Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning |
title_sort | measuring perceived psychological stress in urban built environments using google street view and deep learning |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131010/ https://www.ncbi.nlm.nih.gov/pubmed/35646775 http://dx.doi.org/10.3389/fpubh.2022.891736 |
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