Leveraging similarity analysis to understand variability in movement behavior

Abstract

The increasingly large volume of trajectories of moving entities obtained through GPS and cellphone tracking, telemetry, and other location‐aware technologies motivates researchers to understand the implicit patterns hidden in movement trajectories and understand how movement is influenced by the environmental context. Trajectory similarity serves as an important tool in computational movement analysis and as the foundation of revealing those patterns. However, there are various trajectory similarity measures, each of which has its own strengths and weaknesses. In this article, we present a hierarchical clustering framework that integrates five commonly used similarity measures, including Fréchet distance, dynamic time warping, Hausdorff distance, longest common subsequence, and normalized weighted edit distance, a special kind of edit distance for movement analysis. The framework aims at clustering similar patterns and identifying variability in movement. The optimal number of clusters are first obtained. Then, the clusters are characterized by environmental variables to explore the associations between variability in movement and the environmental conditions. We evaluate the proposed framework using 15 years of tracking data of turkey vultures, tracked at 1‐ to 3‐h sampling intervals, during their fall and spring migration seasons. The results suggest that, at 5% significance level, turkey vultures select their movement paths intentionally and those selections appear to be related to certain environmental context variables, including thermal uplift, vegetation state (observed indirectly through Normalized Difference Vegetation Index), temperature, precipitation, tailwind, and crosswind. And interestingly, there exist preferential differences among individuals. Although the preference of the same turkey vulture is not strictly consistent over different years, each individual tends to preserve a more similar preference over different years, compared with the preferences of other turkey vultures.

Measuring Trajectory Similarity Based on the Spatio-Temporal Properties of Moving Objects in Road Networks

“…On the other hand, the presence of noise causes the trajectory data to be incorrectly mapped on the network and the accuracy of the results obtained for similarity to be questioned, especially in methods based on a set theory, which depend on the results of mapping lines on the network space [20]. Regarding the outlier data, it should be mentioned that, if these data remain in the similarity calculation process, it will cause heterogeneous changes in the similarity results and will make errors in the distance metrics used to determine the similarity [21]. However, they also contribute to the computational complexity and processing time, underscoring the demand for more efficient methods that can alleviate these challenges [17].…”