Assessing the impact of day and night urban outdoor environments on women's physiological and psychological states using pedestrian-centric street view images

Chongxian Chen, Yongqi Hou, Xinrui Xiong, Yuru Hua, Guangsi Lin, Mengyun Chen and Jingyi Liu

Social Science & Medicine, 2025, vol. 383, issue C

Abstract: The urban environment significantly influences women's health and overall quality of life. However, less attention has been given to how the diurnal differences in these environments affect women's physiological and psychological responses during their experiences within them. This study employed field experiments, questionnaires, pedestrian-centric street view images, and deep learning methods to assess how residential, commercial, and leisure environments impact women's physiological and psychological states during both daytime and nighttime. Spatial autocorrelation analysis and Multiscale Geographically Weighted Regression (MGWR) were utilized to examine spatial patterns and identify key influential environmental factors. The results indicate that women's physiological and psychological states exhibit geographical clustering and are influenced by various outdoor environments during both day and night. Leisure environments during the day are associated with the lowest physiological arousal and the highest positive emotions, while residential environments at night correlate with the highest arousal and the lowest positive emotions. Factors such as "brightness," "openness," "colorfulness," and "greenness" affect women's states throughout the day and night. Additionally, "color saturation" significantly influences arousal in residential areas during the day, while "color temperature" enhances positive emotions in nighttime commercial environments. This study contributes to advancing our understanding of gendered experiences in contemporary urban spaces and supports the development of women-friendly cities by integrating a multidisciplinary perspective.

Keywords: Urban outdoor environment; Women well-being; Physiological and psychological states; Deep learning; Day and night (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0277953625007646
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:socmed:v:383:y:2025:i:c:s0277953625007646

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
http://www.elsevier. ... _01_ooc_1&version=01

DOI: 10.1016/j.socscimed.2025.118433

Access Statistics for this article

Social Science & Medicine is currently edited by Ichiro (I.) Kawachi and S.V. (S.V.) Subramanian

More articles in Social Science & Medicine from Elsevier
Bibliographic data for series maintained by Catherine Liu ().