Mapping High-Risk Traffic Zones in Jega, Nigeria: An Integrated Geospatial Framework for Road Safety Planning
Abubakar M.
Salmanu A.
Abstract
Traffic accidents remain a critical public safety challenge in rapidly urbanizing regions, particularly in sub-Saharan Africa, where heterogeneous road infrastructure and high population density exacerbate risk (WHO, 2023; Peden et al., 2004). This study applies Regression Kriging (RK) to model and predict spatial patterns of traffic accident counts across Jega Local Government Area (LGA), Kebbi State, Nigeria, using fifty georeferenced primary data points collected through Global Positioning System (GPS) surveys and manual traffic counts. The regression component, based on spatial coordinates, captures large-scale accident trends, while the kriging of residuals models localized spatial dependencies. Results reveal a significant negative easting coefficient (−0.00012, p<0.001) and a positive northing coefficient (0.00008, p<0.001), indicating accident counts decrease eastward but increase northward, consistent with high-risk urban centers such as BLB Junction. The spherical variogram model (range = 330.12 m; nugget/sill ratio = 24.1%) indicates 75.9% of variance is spatially structured, validating the kriging approach. Model performance metrics (R² = 0.682; RMSE = 3.214; MAE = 2.453) confirm strong predictive capacity, with location-specific validation showing ≤6% error at key sites. The resulting accident risk surface identifies high-risk corridors for targeted interventions, offering a robust geostatistical framework for micro-scale road safety planning in Nigerian cities and extending prior work on accident mapping in Kebbi State.
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