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

Abstract

Maritime accidents and corresponding consequences vary substantially across spatial dimensions as affected by various factors. Understanding the effects of key factors on maritime accident consequence would be of great benefit to prevent the occurrence or reduce the consequences of maritime accidents. Based on unique maritime accident data with geographical information covering fifteen years in the East China Sea, a multi-scale geographically weighted regression (MGWR) model considering the multi-scale spatial variation is employed to quantify the influences of different factors as well as the spatial heterogeneity in the effects of key factors on maritime accident consequence. The performances of MGWR are compared with multiple linear regression (MLR) and geographically weighted regression (GWR). Especially, MGWR outperforms the other two models in terms of modeling fitness and clearly capturing the unobserved spatial heterogeneity in effects of factors. Results reveal notably distinct and even inverse influences of some factors in different water areas on maritime accident consequences. For instance, approximately 50% of the accident locations present positive coefficients of good visibility while other locations are negative, which are ignored by MLR. The outcomes provide insights for making appropriate safety countermeasures and policies customized for different geographic areas.