Résumé
The conservation of wild fisheries resources in the face of an ever-increasing world demand for seafood requires the use of a number of management tools, including no-take zones, and gear, species, and temporal restrictions. One way of enforcing some of these regulations is through the use of Vessel Monitoring System (VMS) data that provides enforcement officers with the position of fishing vessels in the management area. The increasing volume of movement data collected using VMS calls for new methods that could help analysts extract useful knowledge from these large data sets. Various approaches have been proposed for visualizing and exploring movement data and detecting patterns within these data, but those approaches have generally not been tested in a real-world context or compared together, making their actual usability and utility unclear. This paper describes, compares, and assesses three such approaches in the context of fisheries enforcement: an existing system used for fisheries enforcement operations in Canada (VUE), a novel Hybrid Spatio-temporal Filtering (HSF) system developed by the authors, and an automated Behavioural Change Point Analysis (BCPA) system. A field trial was conducted with experienced fisheries enforcement officers to compare and contrast the benefits and drawbacks of the three approaches. While all three presented advantages and disadvantages, the interactivity of VUE and HSF were identified as desirable features, as they provide analysts with more control over the data, while allowing flexible data exploration. BCPA, while providing an automated approach to the data analysis, was pointed out as being too much of a "black box", causing unease among the experts who require a level of transparency similar to that of legally admissible evidence. In the end, the experts suggested that the best approach would be to merge the analytical power of their existing VUE system with the exploratory power of the HSF system. This study provides insight into the value of using interactive mapping and filtering approaches in support of data analysis in the context of fisheries enforcement.