Integrating GIS and machine learning for maritime safety governance: evidence from China’s coastal shipping

Coastal shipping systems play a vital role in sustaining global trade but face growing maritime safety challenges under increasing traffic density and complex operating environments. This study develops an integrated Geographic Information System (GIS) and machine learning (ML) framework to map coastal accident risks and support differentiated governance. Using China’s coastal waters as a representative case, 648 reported accidents (2013–2024) were analyzed. Spatial clustering identified 13 high-risk zones, while interpretable ML models highlighted crew allocation, voyage planning, and improper operation as dominant factors associated with collision severity. Interactive risk maps visualize accident types and spatial heterogeneity, enabling intuitive exploration of risk hotspots. Beyond technical contributions, the framework offers practical insights for maritime policy and management by linking spatial analytics with differentiated governance. It enables regulators and port authorities to prioritize inspections, allocate resources efficiently, and tailor safety interventions to local risk contexts. Overall, the study demonstrates how data-driven and interpretable methods can strengthen adaptive maritime governance and contribute to safer, more resilient coastal shipping systems.