Resumen
Considering that ports are key nodes of the maritime transport network, it is of great importance to identify ships? arrivals and departures. Compared with partial proprietary data from a port authority or shipping company, approaches based on compulsory Automatic Identification System (AIS) data reported by ships can produce transparent datasets covering wider areas, which is necessary for researchers and policy makers. Detecting port calls based on trajectory data is a difficult problem due to the huge uncertainty inherent in information such as ships? ambiguous statuses and ports? irregular boundaries. However, we noticed that little attention has been paid to this fundamental problem of shipping network analysis, and considerable noise may have been introduced in previous work on maritime network assessment based on AIS data, which usually modeled each port as a circle with a fixed radius such as 1 or 2 km. In this paper, we propose a method for identifying port calls by uncertain reasoning with trajectory data, which represents each port with an arbitrary shape as a set of geographical grid cells belonging to berths inside this port. Based on this high-spatial-resolution representation, port calls were identified when a ship was in any of these cells. Our method was implemented with around 14 billion AIS messages worldwide over 8 months, and examples of the results are provided.