Road safety is a critical issue that requires a comprehensive understanding of accident risk factors and the spatial distribution of hazardous road locations (HRLs). This thesis focuses on identifying HRLs in the Piedmont Region (Italy) using spatial-temporal analysis, particularly with Kernel Density Estimations (KDEs) and employing GIS-based modeling. Despite significant advancements in road safety across Italy, urban road networks with complex traffic patterns continue to face challenges, making it essential to identify crash-prone locations, so the road infrastructure administration can implement effective countermeasures by prioritizing safety interventions based on realistic analyses’ results. All the work is grounded in the understanding of Density-Based Methods functionality, more specifically with KDEs, and general insights into data spatial distribution.