Extracting meaningful user locations from temporally annotated geospatial data

The pervasive nature of location-aware devices has enabled the collection of geospatial data for the provision of personalised services. Despite this, the extraction of meaningful user locations from temporally annotated geospatial data remains an open problem. Meaningful location extraction is typically considered to be a 2-step process, consisting of visit extraction and clustering. This paper evaluates techniques for meaningful location extraction, with an emphasis on visit extraction. In particular, we propose an algorithm for the extraction of visits that does not impose a minimum bound on visit duration and makes no assumption of evenly spaced observation

Context Trees: Augmenting Geospatial Trajectories with Context

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

Identifying locations from geospatial trajectories

Harnessing the latent knowledge present in geospatial trajectories allows for the potential to revolutionise our understanding of behaviour. This paper discusses one component of such analysis, namely the extraction of significant locations. Specifically, we: (i) present the Gradient-based Visit Extractor (GVE) algorithm capable of extracting periods of low mobility from geospatial data, while maintaining resilience to noise, and addressing the drawbacks of existing techniques, (ii) provide a comprehensive analysis of the properties of these visits and consequent locations, extracted through clustering, and (iii) demonstrate the applicability of GVE to the problem of visit extraction with respect to representative use-cases