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Maritime accidents and corresponding consequences vary substantially acrossspatial dimensions as affected by various factors. Understanding the effects ofkey factors on maritime accident consequence would be of great benefit toprevent the occurrence or reduce the consequences of maritime accidents. Basedon unique maritime accident data with geographical information covering fifteenyears in the East China Sea, a multi-scale geographically weighted regression(MGWR) model considering the multi-scale spatial variation is employed toquantify the influences of different factors as well as the spatialheterogeneity in the effects of key factors on maritime accident consequence.The performances of MGWR are compared with multiple linear regression (MLR) andgeographically weighted regression (GWR). Especially, MGWR outperforms theother two models in terms of modeling fitness and clearly capturing theunobserved spatial heterogeneity in effects of factors. Results reveal notablydistinct and even inverse influences of some factors in different water areason maritime accident consequences. For instance, approximately 50% of theaccident locations present positive coefficients of good visibility while otherlocations are negative, which are ignored by MLR. The outcomes provide insightsfor making appropriate safety countermeasures and policies customized fordifferent geographic areas.

Deciphering Spatial and Multi-scale Variations in the Effects of Key Factors of Maritime Safety: A Multi-scale Geographically Weighted Approach