Rail trespassing remains a persistent safety challenge at the system level in the United States. However, identifying hot spots proactively is difficult due to limited incident data and strong spatial dependencies within the built environment. This study creates a ZIP-code–level geospatial analytics framework to identify current and emerging trespassing hot spots across North Carolina by combining land-use composition, rail exposure metrics, and historical Federal Railroad Administration (FRA) trespassing records. Geospatial layers were integrated within a GIS workflow to derive predictors such as rail miles, grade crossings, crossings per mile, population density, and land-use types encoded as one-hot vectors. Exploratory spatial analysis showed significant clustering of trespassing incidents, with Global Moran’s I indicating positive spatial autocorrelation across multiple neighborhood sizes. Permutation z-scores confirmed non-random hotspot formation along major rail corridors. A k-means clustering method identified four structural risk environments, and a Cluster Risk Index (CRI) was developed from weighted, standardized exposure and land-use variables to quantify latent risk, independent of raw casualty counts. Results demonstrate that dense urban–industrial rail corridors have the highest CRI values and exhibit the strongest spatial autocorrelation. In contrast, rural ZIP codes with long rail lines show increased exposure-based risk despite fewer historical casualties. The resulting risk surfaces and hotspot classifications provide an interpretable and scalable framework for statewide safety planning, early hotspot detection, and targeted interventions by transportation agencies.