Spatial patterns and characteristics of global piracy analyzed using a geographic information system

Pirate attacks are among the most important factors affecting maritime safety in recent years. In this study, big data were analyzed to extract the patterns and characteristics of piracy incidents and to reveal risk control options and recommendations for anti-piracy measures. Global and regional spatial patterns and characteristics were identified with the support of a geographic information system. In total, 3675 maritime piracy incidents from 2010 to 2022 were collected from the Piracy and Armed Robbery module of the Global Integrated Shipping Information System to profile the characteristics of global piracy. To visualize and analyze the features of maritime piracy, we used three geospatial techniques: kernel density estimation (KDE), k-means clustering, and buffer analysis. These geospatial techniques were used to create a KDE map for identifying piracy-prone areas. We subdivided the incidents into several classes and analyzed the features within each class to calculate the incidents that occurred in coastal areas. The following results were obtained from the geospatial analysis. First, the results of the KDE method revealed specific regions that were particularly prone to piracy, namely the Gulf of Guinea in West Africa; the seas around East Africa and the Arabian Sea; the Malacca Strait and parts of Southeast Asia and the Bay of Bengal; and the seas around South America. Second, the distributions of maritime piracy incidents by different temporal criteria, location, and ship type were compared within different classes. In particular, we found that 79.97% of minor incidents, 43.4% of less serious incidents, 50% of serious incidents, and 30.9% of very serious incidents occurred within 40 km of the coastline. The results obtained from the study contributed to an understanding of the spatial patterns of maritime piracy and provided information useful to maritime safety organizations for enhancing shipping safety.