GIS-based analysis on the spatial patterns of global maritime accidents

Geographical spatial analysis and risk prediction based on machine learning for maritime traffic accidents: A case study of Fujian sea area

“…After converting meteorological and environmental data and maritime accident data into spatial units, they clustered units with similar meteorological environmental conditions and compared maritime accident characteristics in each cluster. Wang et al (2022) determined the spatial patterns of maritime accidents in terms of accident frequency and severity using the global maritime accident data from 2010 to 2019 by means of density analysis and clustering analysis. Their study could guide the relevant maritime authorities to improve maritime traffic management.…”

“…Accident prediction is usually conducted by predicting future safety conditions of a system based on the past and present safety information of the system through a series of scientific methods (Guo et al, 2022). The spatial distribution characteristics of maritime accidents and accident prediction results can provide maritime authorities a more intuitive understanding of the traffic safety conditions of ships within their jurisdiction so that they can take targeted measures to reduce maritime accidents and ensure navigation safety (Wang et al, 2022).…”

Shipping Accident Analysis in Restricted Waters: Lesson from the Suez Canal Blockage in 2021

“…To conduct quantitative analysis of accidents, the database is often used as one of the most available sources to obtain the primary data, including the GISIS (Pristrom et al, 2016;Wang et al, 2022), automatic identification system (AIS) data (Zhang et al, 2019a), and the historical accident data collected from national/regional maritime administrations (Liu et al, 2021;Xu and Hu, 2019;Coraddu et al, 2020). However, such databases reveal different formats to present the results of accident analysis and have no uniform criteria to assess risks.…”

“…With more regional accident records, accident reports from regional maritime administration were utilised to generate vital risk factors influencing the severity of accidents (Wang and Yang, 2018). To analyse the spatial patterns of global maritime accidents, density analysis and clustering analysis were utilised to find that approximately 60% of serious and very serious accidents happened within 30 nm to the coastline (Wang et al, 2022). In addition, it was found that the small general cargo ships are the riskiest in the coastal waters of China through the analysis of public and national databases (Liu et al, 2021).…”

A spatial-temporal analysis approach to global maritime accidents with accident point density clustering

“…Huang 11 uses GIS spatial analysis tools and ArcGIS 10 software to carry out cluster analysis and buffer analysis, to determine the hot spots of maritime accidents, and to carry out buffer analysis of accidents in coastal areas. WANG et al 12 combined the traditional statistical analysis of data with the spatial analysis based on geographic information system (GIS), and analyzed the spatial pattern of maritime accidents according to the frequency and severity of accidents. The maritime accident data based on GIS plays a key role in the spatial analysis of maritime accidents.…”