2 research outputs found
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
Understanding the impact of data sparsity and duration for location prediction applications
As mobile devices capable of sensing location have become pervasive, the collection and transmission of location data has become commonplace, enabling the creation of models of behaviour that support location prediction. With such devices often heavily resource-constrained, the nature of data used in location prediction must be understood in order to optimise storage and processing requirements. This paper specifically explores data sparsity and collection duration. The results presented provide insight which suggest: (i) a relationship of diminishing returns in predictive accuracy when collecting user location data at increased rates over a fixed period, and (ii) the duration over which a fixed size sample of location data is collected has a greater impact on predicative accuracy than data sparsity