Cluster-based trajectory segmentation with local noise

TL;DR

This paper introduces a novel trajectory segmentation framework based on spatial density and temporal criteria, effectively distinguishing between local noise and transitions, with applications in animal movement analysis and pattern discovery.

Contribution

The paper proposes a new noise model differentiating local noise from transition noise, and provides a comprehensive solution for temporally ordered trajectory segmentation.

Findings

Effective segmentation of trajectories into meaningful clusters

Successful validation against ground truth animal movement data

Facilitates discovery of periodic movement patterns

Abstract

We present a framework for the partitioning of a spatial trajectory in a sequence of segments based on spatial density and temporal criteria. The result is a set of temporally separated clusters interleaved by sub-sequences of unclustered points. A major novelty is the proposal of an outlier or noise model based on the distinction between intra-cluster (local noise) and inter-cluster noise (transition): the local noise models the temporary absence from a residence while the transition the definitive departure towards a next residence. We analyze in detail the properties of the model and present a comprehensive solution for the extraction of temporally ordered clusters. The effectiveness of the solution is evaluated first qualitatively and next quantitatively by contrasting the segmentation with ground truth. The ground truth consists of a set of trajectories of labeled points simulating animal movement. Moreover, we show that the approach can streamline the discovery of additional derived patterns, by presenting a novel technique for the analysis of periodic movement. From a methodological perspective, a valuable aspect of this research is that it combines the theoretical investigation with the application and external validation of the segmentation framework. This paves the way to an effective deployment of the solution in broad and challenging fields such as e-science.