The Predictive Context Tree: Predicting Contexts and Interactions
  With a large proportion of people carrying location-aware smartphones, we
have an unprecedented platform from which to understand individuals and predict
their future actions. This work builds upon the Context Tree data structure
that summarises the historical contexts of individuals from augmented
geospatial trajectories, and constructs a predictive model for their likely
future contexts. The Predictive Context Tree (PCT) is constructed as a
hierarchical classifier, capable of predicting both the future locations that a
user will visit and the contexts that a user will be immersed within. The PCT
is evaluated over real-world geospatial trajectories, and compared against
existing location extraction and prediction techniques, as well as a proposed
hybrid approach that uses identified land usage elements in combination with
machine learning to predict future interactions. Our results demonstrate that
higher predictive accuracies can be achieved using this hybrid approach over
traditional extracted location datasets, and the PCT itself matches the
performance of the hybrid approach at predicting future interactions, while
adding utility in the form of context predictions. Such a prediction system is
capable of understanding not only where a user will visit, but also their
context, in terms of what they are likely to be doing.
