Spatio-temporal distribution and aggregation analysis of road traffic fatalities in Shandong Province, China, 2012-2022.
This study aimed to analyze the temporal and spatial distribution, as well as spatio-temporal aggregation, of road traffic fatalities in Shandong Province, China, from 2012 to 2022, with the aim of establishing scientific foundation for crafting customized intervention strategies and preventive actions to mitigate road traffic fatalities.
Data were obtained from the Chinese Center for Disease Control and Prevention Population Death Information Registration Management System. Statistical analyses, including composition ratios, chi-square tests, spatial autocorrelation analyses, and spatio-temporal aggregation, were conducted. Software tools, such as Excel, Geoda, and SaTScan v10.1.2, were utilized for data analysis.
The study showed pedestrians were the most affected group (55.18%), followed by motorized drivers, non-motorized drivers, and passengers. The temporal distribution showed cyclical trends, with the largest number of deaths in autumn. Passengers had a higher number of deaths during leave in lieu (χ2 = 12.247, p = 0.007) and vacation (χ2 = 17.599, p = 0.001) than other subgroups. The spatial distribution identified varying hotspots and cold spots across different cities in Shandong Province. The spatial autocorrelation analysis indicated unique patterns for different groups of road traffic fatalities. Spatio-temporal cluster analysis indicated that a notable and novel finding was the emergence of non-motorized drivers as the newest spatio-temporal agglomeration in southwestern Shandong, while that of motorized drivers was distinctly located in the Jiaodong Peninsula.
Targeted measures in high-risk areas and peak periods have reduced road traffic fatalities. Legislative efforts and educational campaigns have improved road safety; however, challenges with e-bikes require focused interventions.
Data were obtained from the Chinese Center for Disease Control and Prevention Population Death Information Registration Management System. Statistical analyses, including composition ratios, chi-square tests, spatial autocorrelation analyses, and spatio-temporal aggregation, were conducted. Software tools, such as Excel, Geoda, and SaTScan v10.1.2, were utilized for data analysis.
The study showed pedestrians were the most affected group (55.18%), followed by motorized drivers, non-motorized drivers, and passengers. The temporal distribution showed cyclical trends, with the largest number of deaths in autumn. Passengers had a higher number of deaths during leave in lieu (χ2 = 12.247, p = 0.007) and vacation (χ2 = 17.599, p = 0.001) than other subgroups. The spatial distribution identified varying hotspots and cold spots across different cities in Shandong Province. The spatial autocorrelation analysis indicated unique patterns for different groups of road traffic fatalities. Spatio-temporal cluster analysis indicated that a notable and novel finding was the emergence of non-motorized drivers as the newest spatio-temporal agglomeration in southwestern Shandong, while that of motorized drivers was distinctly located in the Jiaodong Peninsula.
Targeted measures in high-risk areas and peak periods have reduced road traffic fatalities. Legislative efforts and educational campaigns have improved road safety; however, challenges with e-bikes require focused interventions.