Mapping clusters of road traffic accident fatalities and injuries: A spatiotemporal study in Isfahan Province, Iran

OBJECTIVE: Road traffic accidents (RTAs) pose a significant challenge to global public health. In Iran, Isfahan Province experiences a high rate of RTAs, leading to many injuries and fatalities. Despite this, no comprehensive spatiotemporal analysis of RTA outcomes has been conducted in this region. To address this research gap, this study presents the first disaggregate-level spatiotemporal analysis of RTA injuries and fatalities in Isfahan Province by applying advanced spatial statistical techniques to identify critical patterns in RTA-related outcomes. METHODS: The research uses RTA data for the years 2017-2019 to investigate the spatiotemporal patterns of fatalities and injuries caused by RTAs. The analysis focuses on major suburban roadways in Isfahan Province, Iran. To measure overall spatial autocorrelation, Global Moran's I was employed, while localized hotspots were identified using the Getis-Ord Gi* statistic. Analyses were conducted on the whole dataset as well as separately for each year and each season to examine temporal variations. RESULTS: Moran's I test showed a strong spatial clustering pattern for RTA fatalities during the winter season. The spatial patterns during the other seasons did not reveal clustering, indicating randomness. Spatial clustering for the injury cases was consistently present across most temporal scales except during the autumn and winter seasons, where patterns approached randomness. Furthermore, Getis-Ord Gi* hotspot analysis detected obvious geographic hotspots and coldspots for both fatalities and injuries at different temporal scales, providing overall insights into high-risk areas. CONCLUSION: This study contributes to the field of traffic safety by providing the first spatiotemporal analysis of RTA injuries and fatalities in Isfahan Province, addressing a significant research gap. The application of spatial statistical methods at seasonal and annual scales unveiled distinct patterns of high-risk areas and differences in the spatial distributions of injuries and fatalities. The findings will help shift from broad policies to targeted, data-driven strategies that can guide enforcement, optimal placement of emergency stations, and resource allocation tailored to identified high-risk areas to enhance road safety and reduce RTA-related injuries and fatalities in the region.