Static and Dynamic Social Network Models for the Analysis of Transshipment in Illegal Fishing

Illegal fishing is a global problem affecting society in numerous ways: from impacting the sustainability of artisanal and subsistence fishing communities to disturbing the balance of delicate oceanic ecosystems through the systematic overexploitation of fish stocks. On a global level, the success criminal organizations have had with hiding their activities is largely attributed to the practice of transshipment (where a fishing vessel offloads its catch to a transport vessel at sea), which makes tracing the origins of illegally caught fish extremely difficult. Extensive research has been conducted to leverage data from the Automatic Identification System (AIS) to identify vessels fishing illegally, but current methods often overlook interactions between vessels and have not been shown to affect the global extent of illegal fishing. In this paper, transshipment encounters are modelled as a social network graph where nodes represent vessels and edges represent transshipment encounters between them. A static analysis of the transshipment networks is conducted to identify key vessels enabling illegal fishing through transshipment. A dynamic visualization tool is then implemented to provide context on the formation and evolution of transshipment networks over time. Based on the dynamic analysis of the network, insights are gained about different ways in which transshipment encounters might be used to offload marine products. A dynamic analysis of criminal subnetworks is conducted, showcasing how network modelling can be used to enable a global approach in the combat against illegal fishing.