Bayesian spatio-temporal modeling of Arctic maritime incidents with integrated nested Laplace approximation

Rapid environmental change and increased vessel activity have reshaped the risk landscape of Arctic maritime transportation. Understanding how different types of maritime incidents are distributed across space and evolve over time is essential for effective safety management in this highly heterogeneous environment. This study develops a Bayesian spatio-temporal framework to examine the relative propensity of Arctic maritime incident types from 2005 to 2017. Latent spatial and spatio-temporal dependence is represented through an INLA–SPDE approach, enabling efficient inference of temporally correlated spatial risk surfaces from sparse and irregularly distributed incident data. Model comparison indicates that incorporating spatio-temporal dependence improves interpretability and robustness. The preferred spatio-temporal AR(1) model reveals pronounced non-stationarity in incident-type composition, with shifting hotspots, regional sign reversals, and distinct temporal persistence. Results further indicate that vessel characteristics, route exposure, and seasonal conditions systematically influence incident-type composition after controlling for spatio-temporal effects. With mechanical failure as the reference incident type, collision-related incidents exhibit more diffuse, corridor-oriented patterns, whereas loss-of-control–related incidents display larger-amplitude spatio-temporal random effects, stronger localization and temporal sensitivity. This study advances Arctic maritime risk research toward explicit spatio-temporal mechanism inference and supports adaptive, region-specific safety management.