A new method for discovering behavior patterns among animal movements

Abstract

Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation of popular migration routes and underlying sequential behaviors during migration. Each stage in the pattern is characterized in terms of space (i.e., the places traversed during movements) and time (i.e. the time spent in those places); that is, the behavioral state corresponding to a stage is inferred according to the spatiotemporal and sequential context. Hence, the pattern may be interpreted predictably. We develop a candidate generation and refinement framework to derive all continuous behavior patterns from raw trajectories. In the framework, we first define the representative spots to denote the underlying potential behavioral states that are extracted from individual trajectories according to the similarity of relaxed continuous locations in certain distinct time intervals. We determine the common behaviors of multiple individuals according to the spatiotemporal proximity of representative spots and apply a projection-based extension approach to generate candidate sequential behavior sequences as candidate patterns. Finally, the candidate generation procedure is combined with a refinement procedure to derive continuous behavior patterns. We apply an ordered processing strategy to accelerate candidate refinement. The proposed patterns and discovery framework are evaluated through conceptual experiments on both real GPS-tracking and large synthetic datasets.

A Dempster-Shafer based approach to the detection of trajectory stop points

“…Moreover, these specific activities should be defined beforehand by a domain-based ontology and associated to additional descriptive data (Allahviranloo & Recker, 2015;Bogorny, Renso, Aquino, Lucca Siqueira, & Alvares, 2014). When modeling a trajectory, specific stop points and movements related to a series of activities, human behavior, and activity-based patterns analysis can be developed (Do & Gatica-Perez, 2012;Shen & Cheng, 2016;Wang et al, 2016).…”

Approach to discovering companion patterns based on traffic data stream

“…Some researchers focus on discovering moving clusters [14–20]. Their goal is to find clusters of objects with similar moving patterns or behaviours.…”

“…Jensen et al discovered moving object clusters incrementally within a period of time [17]. Wang et al defined continuous behaviour patterns as a concise representation of popular migration routes and underlying sequential behaviours during migration [18]. They developed a candidate generation and refinement framework to discover such patterns.…”

A Hybrid Approach Combining the Multi-Temporal Scale Spatio-Temporal Network with the Continuous Triangular Model for Exploring Dynamic Interactions in Movement Data: A Case Study of Football

“…Benefiting from the large amount of tracked movement data, the analysis of movement data has become a state-of-the-art research theme in the community of geographical information science (GIScience). Currently, numerous methods to analyze movement data, such as movement pattern mining [6,14,15], movement visualization [16][17][18] and movement modelling [19][20][21], have been undertaken extensively. In addition, the study of interactions in movement data has become active recently, but is still in its infancy [22,23].…”