Assessing Emotional Responses to the Spatial Quality of Urban Green Spaces through Self-Report and Face Recognition Measures

Although creating a high-quality urban green space (UGS) is of considerable importance in public health, few studies have used individuals’ emotions to evaluate the UGS quality. This study aims to conduct a multidimensional emotional assessment method of UGS from the perspective of spatial quality....

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Main Authors: Lin Qiao, Jingwei Zhuang, Xuan Zhang, Yang Su, Yiping Xia
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:International Journal of Environmental Research and Public Health
Subjects:
Online Access:https://www.mdpi.com/1660-4601/18/16/8526
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spelling doaj-4e9025eda6054d69b43003c88158ed172021-08-26T13:49:24ZengMDPI AGInternational Journal of Environmental Research and Public Health1661-78271660-46012021-08-01188526852610.3390/ijerph18168526Assessing Emotional Responses to the Spatial Quality of Urban Green Spaces through Self-Report and Face Recognition MeasuresLin Qiao0Jingwei Zhuang1Xuan Zhang2Yang Su3Yiping Xia4Institute of Landscape Architecture, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, ChinaInstitute of Landscape Architecture, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, ChinaInstitute of Landscape Architecture, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, ChinaThe Architectural Design & Research Institute of Zhejiang University Co, Ltd., Hangzhou 310030, ChinaInstitute of Landscape Architecture, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, ChinaAlthough creating a high-quality urban green space (UGS) is of considerable importance in public health, few studies have used individuals’ emotions to evaluate the UGS quality. This study aims to conduct a multidimensional emotional assessment method of UGS from the perspective of spatial quality. Panoramic videos of 15 scenes in the West Lake Scenic Area were displayed to 34 participants. For each scene, 12 attributes regarding spatial quality were quantified, including perceived plant attributes, spatial structure attributes, and experiences of UGS. Then, the Self-Assessment-Manikin (SAM) scale and face recognition model were used to measure people’s valence-arousal emotion values. Among all the predictors, the percentages of water and plants were the most predictive indicators of emotional responses measured by SAM scale, while the interpretation rate of the model measured by face recognition was insufficiently high. Concerning gender differences, women experienced a significantly higher valence than men. Higher percentages of water and plants, larger sizes, approximate shape index, and lower canopy densities were often related to positive emotions. Hence, designers must consider all structural attributes of green spaces, as well as enrich visual perception and provide various activities while creating a UGS. In addition, we suggest combining both physiological and psychological methods to assess emotional responses in future studies. Because the face recognition model can provide objective measurement of emotional responses, and the self-report questionnaire is much easier to administer and can be used as a supplement.https://www.mdpi.com/1660-4601/18/16/8526urban green spacespatial qualityemotional responsesstructural attributesface recognition
collection DOAJ
language English
format Article
sources DOAJ
author Lin Qiao
Jingwei Zhuang
Xuan Zhang
Yang Su
Yiping Xia
spellingShingle Lin Qiao
Jingwei Zhuang
Xuan Zhang
Yang Su
Yiping Xia
Assessing Emotional Responses to the Spatial Quality of Urban Green Spaces through Self-Report and Face Recognition Measures
International Journal of Environmental Research and Public Health
urban green space
spatial quality
emotional responses
structural attributes
face recognition
author_facet Lin Qiao
Jingwei Zhuang
Xuan Zhang
Yang Su
Yiping Xia
author_sort Lin Qiao
title Assessing Emotional Responses to the Spatial Quality of Urban Green Spaces through Self-Report and Face Recognition Measures
title_short Assessing Emotional Responses to the Spatial Quality of Urban Green Spaces through Self-Report and Face Recognition Measures
title_full Assessing Emotional Responses to the Spatial Quality of Urban Green Spaces through Self-Report and Face Recognition Measures
title_fullStr Assessing Emotional Responses to the Spatial Quality of Urban Green Spaces through Self-Report and Face Recognition Measures
title_full_unstemmed Assessing Emotional Responses to the Spatial Quality of Urban Green Spaces through Self-Report and Face Recognition Measures
title_sort assessing emotional responses to the spatial quality of urban green spaces through self-report and face recognition measures
publisher MDPI AG
series International Journal of Environmental Research and Public Health
issn 1661-7827
1660-4601
publishDate 2021-08-01
description Although creating a high-quality urban green space (UGS) is of considerable importance in public health, few studies have used individuals’ emotions to evaluate the UGS quality. This study aims to conduct a multidimensional emotional assessment method of UGS from the perspective of spatial quality. Panoramic videos of 15 scenes in the West Lake Scenic Area were displayed to 34 participants. For each scene, 12 attributes regarding spatial quality were quantified, including perceived plant attributes, spatial structure attributes, and experiences of UGS. Then, the Self-Assessment-Manikin (SAM) scale and face recognition model were used to measure people’s valence-arousal emotion values. Among all the predictors, the percentages of water and plants were the most predictive indicators of emotional responses measured by SAM scale, while the interpretation rate of the model measured by face recognition was insufficiently high. Concerning gender differences, women experienced a significantly higher valence than men. Higher percentages of water and plants, larger sizes, approximate shape index, and lower canopy densities were often related to positive emotions. Hence, designers must consider all structural attributes of green spaces, as well as enrich visual perception and provide various activities while creating a UGS. In addition, we suggest combining both physiological and psychological methods to assess emotional responses in future studies. Because the face recognition model can provide objective measurement of emotional responses, and the self-report questionnaire is much easier to administer and can be used as a supplement.
topic urban green space
spatial quality
emotional responses
structural attributes
face recognition
url https://www.mdpi.com/1660-4601/18/16/8526
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