Adapting environments to the needs and preferences of their inhabitants is becoming increasingly important as the world population continues to grow. One way in which this can be achieved is through the provision of timely information, as well as through the personalisation of services. Providing personalisation in this way requires an understanding of both the historical and future actions of individuals. Using geospatial trajectories collected from personal location-aware hardware, e.g. smartphones, as a basis, this thesis explores the extent to which we can leverage the latent knowledge in such trajectories to understand the historic and future behaviours of individuals. In this thesis, several machine learning tools for the task are presented, including the development of a novel clustering algorithm that can identify locations where people spend their time while disregarding noise. The knowledge exposed by such a system is then enhanced with a procedure for identifying geographic features that the person was interacting with, providing information on what the user may have been doing at that time. Interactions with these features are subsequently used as a basis for understanding user actions through a new contextual clustering approach that identifies periods of time where the user may have been performing similar activities or have had similar goals. Combined, the presented techniques provide a basis for learning about the actions of individuals. To further enhance this knowledge, however, the research presented in this thesis concludes with the presentation of a new machine learning model capable of summarising and predicting the future context of individuals where only geospatial trajectories are required to be collected from the user. Throughout this work, the potential benefits offered by geospatial trajectories are explored, with thorough explorations and evaluations of the proposed techniques made alongside comparisons to existing approaches.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:723154 |
| Date | January 2017 |
| Creators | Thomason, Alasdair |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/91939/ |
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