Research Article
Extracting Meaningful User Locations from Temporally Annotated Geospatial Data
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@INPROCEEDINGS{10.1007/978-3-319-19743-2_13, author={Alasdair Thomason and Nathan Griffiths and Matthew Leeke}, title={Extracting Meaningful User Locations from Temporally Annotated Geospatial Data}, proceedings={Internet of Things. IoT Infrastructures. First International Summit, IoT360 2014, Rome, Italy, October 27-28, 2014, Revised Selected Papers, Part II}, proceedings_a={IOT360}, year={2015}, month={7}, keywords={Clustering Extraction Geospatial Location Visits}, doi={10.1007/978-3-319-19743-2_13} }- Alasdair Thomason
Nathan Griffiths
Matthew Leeke
Year: 2015
Extracting Meaningful User Locations from Temporally Annotated Geospatial Data
IOT360
Springer
DOI: 10.1007/978-3-319-19743-2_13
Abstract
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.
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