Assessing the association between overcrowding and human physiological stress response in different urban contexts: a case study in Salzburg, Austria

Zhaoxi Zhang*, Kristýna Měchurová, Bernd Resch, Prince Amegbor, Clive E. Sabel

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

3 Citations (Scopus)

Abstract

Overcrowding in densely populated urban areas is increasingly becoming an issue for mental health disorders. Yet, only few studies have examined the association between overcrowding in cities and physiological stress responses. Thus, this study employed wearable sensors (a wearable camera, an Empatica E4 wristband and a smartphone-based GPS) to assess the association between overcrowding and human physiological stress response in four types of urban contexts (green space, transit space, commercial space, and blue space). A case study with 26 participants was conducted in Salzburg, Austria. We used Mask R-CNN to detect elements related to overcrowding such as human crowds, sitting facilities, vehicles and bikes from first-person video data collected by wearable cameras, and calculated a change score (CS) to assess human physiological stress response based on galvanic skin response (GSR) and skin temperature from the physiological data collected by the wristband, then this study used statistical and spatial analysis to assess the association between the change score and the above elements. The results demonstrate the feasibility of using sensor-based measurement and quantitative analysis to investigate the relationship between human stress and overcrowding in relation to different urban elements. The findings of this study indicate the importance of considering human crowds, sitting facilities, vehicles and bikes to assess the impact of overcrowding on human stress at street level.

Original languageEnglish
Article number15
JournalInternational Journal of Health Geographics
Volume22
Issue1
Number of pages12
ISSN1476-072X
DOIs
Publication statusPublished - Jun 2023

Keywords

  • Individual-level measurement
  • Machine learning
  • Public open space
  • Wearable device

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