Driving factors and spatio-temporal evolution of tourist accident clusters in Chinese outbound tourism

Author Identifier

Songshan Huang: https://orcid.org/0000-0003-4990-2788

Document Type

Journal Article

Publication Title

Current Issues in Tourism

Publisher

Taylor & Francis

School

School of Business and Law

RAS ID

76768

Funders

National Natural Science Foundation of China (41971182 , 42271243)

Comments

Xie, C., Zhang, K., Zhang, J., & Huang, S. (2025). Driving factors and spatio-temporal evolution of tourist accident clusters in Chinese outbound tourism. Current Issues in Tourism. Advance online publication. https://doi.org/10.1080/13683500.2025.2456080

Abstract

The clustering of tourist accidents is a critical yet underexplored area of research. This study employs cluster theory and safety system theory to develop a comprehensive measurement tool for analysing tourist accident clusters, examining their spatiotemporal evolution patterns, and identifying the driving mechanisms from a holistic destination perspective. By analysing accident records of Chinese outbound tourists across 48 countries from 2011 to 2019, the findings reveal a reduction in disparities among tourist accident clusters (α-convergence effect) and a fluctuating trend over time. Spatial autocorrelation analysis identifies four distinct spatial distribution patterns: low-low (L-L), high-low (H-L), high-high (H-H), and low-high (L-H). Furthermore, Boosted Regression Tree (BRT) analysis uncovers a nonlinear relationship between accident clusters and four primary factors: human, machine, environment, and management. Within this framework, indicators such as tourist volume pressure, traffic risk, and employee training levels emerge as significant contributors. Additionally, geographic detector analysis reveals interactions among the driving mechanisms, offering deeper insights into their interrelated effects. This study provides practical implications for destinations, tourists, and tourism practitioners, emphasising strategies to prevent and mitigate the impact of accident clusters.

DOI

10.1080/13683500.2025.2456080

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