The paper presents a situational analysis model for maritime anomaly detection in which we study the role of context and the impact of the imperfection of information in detecting vessel’s deviation from destination. The focus is on the exploitation of non-kinematic information, contextual information in the form of previously extracted routes, and the predicted kinematic features as outputs of a vessel tracking algorithm. An uncertainty graphical model example is designed manually to represent expert knowledge and measurement uncertainty. The evaluation of the situation is performed in terms of availability and variability of contextual information, and in terms of reliability (i.e. observability and correctness) of non-kinematic information. The results of the analysis show benefits of using the position prediction algorithm, confirm the advantage of using routes as contextual information, and highlight the characteristics of AIS data in detecting the considered anomaly. These results facilitate the requirements and design specifications for the development of an efficient system for maritime anomaly detection.