  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.
