Social norms and risks at mass gatherings: A systematic review

Alhajri, Waleed; Templeton, Anne; Moore, Adam D.

doi:10.1016/j.ijdrr.2023.103586

Cited by 3 publications

(1 citation statement)

References 209 publications

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“…This extended framework thus not only categorizes crucial aspects of crowd disasters but also interconnects these elements, offering a holistic view of each event. This comprehensive approach is vital for accurate risk assessment, effective crowd management, and reducing the incidence and severity of crowd-related disasters [28]. These elements are the basic information about crowd disaster incidents.…”

Section: A Frameworkmentioning

confidence: 99%

Development of a Risk Space Prediction Model Based on CCTV Images Using Deep Learning: Crowd Collapse

Chang,

Jung,

Park

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Crowd disasters are not limited to events but can occur at any time and place where large crowds are densely gathered. The most significant factor contributing to injuries in mass gatherings is pressure. The likelihood of injuries due to pressure significantly increases when the crowd density exceeds 5-6 individuals per square meter. Once a crowd disaster occurs, it becomes challenging for rescue and medical personnel to access the affected area, potentially exacerbating the situation. However, since there is no single clear solution to address crowd disasters, there is a need for a system that can detect and analyze them in advance or in real-time. This research aims to contribute to the proactive detection and analysis of various crowd disasters, focusing on improving strategies for disaster response. In this study, we utilized a dataset of 50 videos from 35 individual incidents to propose a framework that classifies the risk levels into Crowd Crush, Crowd Wave, and Crowd Collapse. Additionally, we analyzed the feasibility of real-time detection using P2PNet (Point to Point Network), Yolo (You Only Look Once) v8, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms, moving away from prior still image-based detection methods. Our research outcomes provide new evidence for the feasibility of real-time detection using DBSCAN in Crowd Disaster scenarios. Moreover, the findings from this study can serve as valuable reference material for upcoming research, particularly emphasizing the algorithmic analysis of crowd collapses.

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Section: A Frameworkmentioning

confidence: 99%

Development of a Risk Space Prediction Model Based on CCTV Images Using Deep Learning: Crowd Collapse

Chang,

Jung,

Park

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Continuing from the Sendai Framework midterm: Opportunities for urban digital twins in disaster risk management

Macatulad,

Biljecki

2024

International Journal of Disaster Risk Reduction

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Shared social identity and social norms shape risk‐taking at mass gatherings

Alhajri,

Moore,

Templeton

2024

British J Social Psychol

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Shared social identity and social norms are often un(der)recognized within mass gatherings health literature, yet they can increase infectious disease transmission by motivating people to engage in risk‐taking behaviours. Across three experiments (Ntotal = 1551), we investigated how shared social identities, perceived norms of resource‐sharing, and perceived riskiness of sharing interact to shape decisions that can lead to disease transmission. In Experiment 1 (N = 528), we examined how shared social identity and perceived descriptive norms affect the likelihood of crowd members sharing resources that may contribute to disease spread. We then replicated this in Experiment 2 (N = 511) using perceived injunctive norms. In Experiment 3 (N = 512), we explored how high shared social identity, perceived norms, and perceived health risks of resource‐sharing impact the likelihood of sharing that may, in turn, increase infectious disease transmission at mass gatherings. We found that high shared social identity interacts with perceived descriptive and injunctive norms, and low health risk perceptions, to increase the likelihood of accepting resources and giving resources at mass gatherings. We discuss the theoretical and practical implications for researchers, practitioners, and policymakers to build effective strategies to mitigate infectious disease transmission at mass gatherings.

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Social norms and risks at mass gatherings: A systematic review

Cited by 3 publications

References 209 publications

Development of a Risk Space Prediction Model Based on CCTV Images Using Deep Learning: Crowd Collapse

Development of a Risk Space Prediction Model Based on CCTV Images Using Deep Learning: Crowd Collapse

Continuing from the Sendai Framework midterm: Opportunities for urban digital twins in disaster risk management

Shared social identity and social norms shape risk‐taking at mass gatherings

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