Modeling spatial point patterns on and around spatial linear networks presents unique mathematical and computational challenges, from distance-metric distortions to edge effects. This session delves into recent methodological advances in crime modeling, accounting for the road network. The session will further provide an outline of the importance of space in understanding crime patterns. A brief overview will be provided on the history of spatial crime research and the importance of spatial units of analysis for crime prevention and reduction. Statistical topics will include the development of methodologies for crime mapping, which is essential for effective resource allocation and public safety, the integration of covariates like street-level geometry, and alternatives to planar hotspots. Attendees will gain insights into the rigorous statistical frameworks that drive modern quantitative criminology, and the session will showcase how spatial linear network modelling transforms raw data into actionable intelligence. Through real-world case studies, we will examine how analysing crime along actual street networks improves predictive accuracy, informs policing policy, and provides a truer reflection of neighbourhood risk.