Spatio-temporal distribution and aggregation analysis of road traffic fatalities in Shandong Province, China, 2012–2022
Bibliographic Data
| ID | 22085800 |
|---|---|
| Authors | Tao Wang (0000-0002-8367-8946, Shandong University), Zilong Lu (0000-0002-7432-7343, Shandong Center for Disease Control and Prevention), Zi-Long Lu, Jie Chu (0000-0002-4037-5558, Shandong Center for Disease Control and Prevention), Qi Li (0000-0003-3042-3584, Shandong University), Zhi-Ying Yao (Shandong University), Xiaolei Guo (0000-0002-8074-7205, Shandong Center for Disease Control and Prevention, corresponding author), Xiao-Lei Guo, Cun-Xian Jia (0000-0002-2651-147X, Shandong University, corresponding author) |
| Year | 2025 |
| Volume | 13 |
| Pages | 1711959-1711959 |
| Publication date | 2025-12-15 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2025.1711959 |
| PMID | 41473715 |
| OpenAlex | W4417312490 |
| Language | EN |
| References cited | 53 |
Objective 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. Methods 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. Results 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 ( χ 2 = 12.247, p = 0.007) and vacation ( χ 2 = 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. Conclusion 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
Legislature · Poison control · Road traffic · Road traffic safety · Traffic and Road Safety · Traffic Prediction and Management Techniques · Urban Transport and Accessibility
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| Highly cited | No |
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