A new method for discovering behavior patterns among animal movements. Issue 5 (3rd May 2016) - Wang, Yuwei; Luo, Ze; Takekawa, John; Prosser, Diann; Xiong, Yan; Newman, Scott; Xiao, Xiangming; Batbayar, Nyambayar; Spragens, Kyle; Balachandran, Sivananinthaperumal; Yan, Baoping
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.