Context Trees: Augmenting Geospatial Trajectories with Context

TL;DR

This paper introduces the context tree, a hierarchical data structure that combines geospatial trajectories with land usage data to better understand and predict individual and group behaviors.

Contribution

It presents a novel method for constructing and pruning context trees that integrate land usage data with trajectories, enhancing behavioral analysis.

Findings

Context trees effectively summarize user behavior.

Pruning methods retain key information while reducing size.

Evaluation shows improved understanding of movement patterns.

Abstract

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information.