TITLE:
Spatiotemporal Distribution of Road Traffic Crashes and Crash Hotspots in Bamako, Mali: A Retrospective Analysis of Police Registry Data, 2019-2022
AUTHORS:
Fatou Diawara, Fadjiné Soumaïla Diarra, Siaba Doumbia, Ibrahim Terera, Hassan Ibrahim, Fatoumata Sidibé, Youssouf Ogodana Dolo, Brahima Diarra, Mamadou Sanogo, Mamadou Diepkilé, Ahmadou Sidi Takiou, Faty Haïdara, Sogoba Jacqueline Dakouo, Akory Ag Iknane
KEYWORDS:
Road Traffic Crashes, Spatial Analysis, Spatiotemporal Analysis, Hotspots, Mali, Police Registry
JOURNAL NAME:
Open Journal of Epidemiology,
Vol.16 No.4,
November
6,
2026
ABSTRACT: Introduction: Road traffic crashes (RTCs) are a leading cause of preventable mortality in low- and middle-income countries. In Bamako, their spatial and temporal distribution remains poorly documented. The objective was to describe the spatiotemporal distribution of reported RTCs and identify areas of high crash concentration using data from the 2nd arrondissement Police registry (2019-2022). Methodology: Retrospective descriptive study based on secondary analysis of a police registry. Annual, seasonal, weekly and hourly distributions were compared with uniformity using chi-square tests. The spatial distribution was analysed using retrospectively geocoded coordinates (WGS84): kernel density estimation (KDE), spatial autocorrelation (Moran I) and hotspots (Getis-Ord Gi* on a grid), with analyses performed in Python at a significance level of 0.05. Results: A total of 3075 crashes were recorded with complete date, time, and location information and were geocoded from registry descriptions. Monthly crash counts declined by approximately 6% per year after accounting for the number of days in each month (RR = 0.94 per year; p Conclusion: Reported crashes concentrate in a few stable spatial foci and follow marked seasonal and hourly rhythms, offering actionable spatial and temporal targets for prevention, and systematic geocoding would strengthen spatial surveillance.