Characteristics of crowd disaster
Database construction and pattern identification
Bibliographic Data
| ID | 22026541 |
|---|---|
| Authors | Xiangmin Yang (0009-0004-6618-1331, Imperial College London, corresponding author), Yuelin Liu (0000-0002-1051-7236, Imperial College London), Arnab Majumdar (0000-0002-6332-7858, Imperial College London), Emilia Grass (0000-0001-8460-8395, Karlsruhe Institute of Technology), Washington Ochieng (Imperial College London) |
| Year | 2024 |
| Volume | 111 |
| Pages | 104653 |
| Publication date | 2024-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Disaster Risk Reduction (JOURNAL) |
| Journal identifiers | ISSN: 2212-4209 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijdrr.2024.104653 |
| OpenAlex | W4400387976 |
| Language | EN |
| References cited | 33 |
Crowd disasters pose a serious societal threat with frequent occurrence and high fatality. Despite thorough research having been conducted on the basic mechanisms behind crowd disasters, further qualitative study is required to explain patterns of crowd movement under different environmental conditions. This paper describes the methodology behind the creation of a novel crowd disaster database, incorporating unprecedentedly strict inclusion criteria and well-defined characteristics in order to facilitate better qualitative understanding. The data characteristics are retrieved from previous literature reviews and a 2014 Shanghai crowd disaster case study. Online news database Factiva is used as the main data source, combining with supplemental Internet sources. The completed database contains 293 crowd disasters from 1989 to 2021 and is subject to a data quality assessment procedure. Through the cluster analysis algorithm K-Modes, the entire database has been categorized into nine clusters and distinct features within each cluster are discussed. Each cluster is thus considered as a unique type of crowd disaster, enabling event organizers to link combination of characteristics with likelihood of crowd disaster. Consequently, a more explicit crowd management strategy can be developed to target most common causal factors
Computer security · Data mining · Data science · Database · Emergency management · Computer Science · Disaster Management and Resilience · Evacuation and Crowd Dynamics · Flood Risk Assessment and Management
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Simulating dynamical features of escape panic
Social force model for pedestrian dynamics
The Measurement of Observer Agreement for Categorical Data
Silhouettes
Interrater reliability
Human stampede causative factors and cluster risk
Extreme Environmental Events
Crowd Detection in Mass Gatherings Based on Social Media Data
Crowd disasters
Collective behavior in a simulated panic situation
| Highly cited | No |
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