An unsupervised collaborative approach to identifying home and work locations

Rong Liu, Swapna Buccapatnam, Wesley M. Gifford, Anshul Sheopuri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

There is a growing interest in leveraging geo-spatial data to provide location-aware services. With a large amount of collected geo-spatial data, a crucial step is to identify important "base" locations (e.g., home or work) and understand users' behavior at these locations. In this paper, we propose an unsupervised collaborative learning approach to identifying home and work locations of individuals from geo-spatial trajectory data. Our approach transforms user trajectory records into intuitive and insightful user-location signatures, clusters these signatures, and then identifies location types based on cluster characteristics. This clustering model can be used to identify base locations for new users. We validate this approach using Open Street Map and Foursquare location tags and obtain an accuracy of 80%.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 17th International Conference on Mobile Data Management, IEEE MDM 2016
EditorsChi-Yin Chow, Prem Jayaraman, Wei Wu
Pages310-317
Number of pages8
ISBN (Electronic)9781509008834
DOIs
StatePublished - 20 Jul 2016
Event17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016 - Porto, Portugal
Duration: 13 Jun 201616 Jun 2016

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2016-July
ISSN (Print)1551-6245

Conference

Conference17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016
Country/TerritoryPortugal
CityPorto
Period13/06/1616/06/16

Keywords

  • spatio-temporal analysis
  • user mobility behavior

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