skip to main content
article

Extracting places from traces of locations

Published:01 July 2005Publication History
Skip Abstract Section

Abstract

Location-aware systems are proliferating on a variety of platforms from laptops to cell phones. Though these systems offer two principal representations in which to work with location (coordinates and landmarks) they do not offer a means for working with the user-level notion of "place". A place is a locale that is important to a user and which carries a particular semantic meaning such as "my place of work", "the place we live", or "My favorite lunch spot". Mobile devices can make more intelligent decisions about how to behave when they are equipped with this higher-level information. For example, a cell phone can switch to a silent mode when its owner enters a place where a ringer is inappropriate (e.g., a movie theater, a lecture hall, a place for personal reflection.) In this paper, we describe an algorithm for extracting significant places from a trace of coordinates. Furthermore, we experimentally evaluate the algorithm with real, long-term data collected from three participants using a Place Lab client [15], a software client that computes location coordinates by listening for RF-emissions from known radio beacons in the environment (e.g. 802.11 access points, GSM cell towers).

References

  1. Daniel Ashbrook, Thad Starner. Using GPS to learn significant locations and predict movement across multiple users. In Personal and Ubiquitous Computing, Volume 7, Number 5, October 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Jeffrey D. Banfield, Adrian E. Raftery. Modelbased Gaussian and Non-Gaussian Clustering. Biometrics 49, September 1993.]]Google ScholarGoogle Scholar
  3. Gaetano Borriello, et. al., Reminding about Tagged Objects using Passive RFIDs. In Proc. of Ubicomp 2004, Nottingham, England, September 2004.]]Google ScholarGoogle Scholar
  4. Richard O. Duda, Peter E. Hart. Pattern Classification and Scene Analysis. John Wiley & Sons, 1973.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Ramaswamy Hariharan, Kentaro Toyama. Project Lachesis: Parsing and Modeling Location Histories. In the 3rd International Conference on Geographic Information Science, October 2004.]]Google ScholarGoogle Scholar
  6. John Krumm, Ken Hinckley. The NearMe Wireless Proximity Server. In Proc. of Ubicomp 2004, Nottingham, England, September 2004.]]Google ScholarGoogle Scholar
  7. Kari Laasonen, et. al., Adaptive On-Device Location Recognition. In Proc. of Pervasive 2004, Vienna, Austria, April 2004.]]Google ScholarGoogle Scholar
  8. Anthony LaMarca., et. al., Place Lab: Device Positioning Using Radio Beacons in the Wild. In Proc. of Pervasive 2005, Munich, Germany, May 2005.]]Google ScholarGoogle Scholar
  9. Lin Liao, et. al., Learning and Inferring Transportation Routines. In Proc. of AAAI-04, 2004.]]Google ScholarGoogle Scholar
  10. Natalia Marmasse, Chris Schmandt. Location-Aware Information Delivery with Com-Motion. In Proc. HUC 2000, Bristol, UK, September 2000.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Donald J. Patterson, et. al., The Activity Compass. In Proc. of UbiCog 2002, September 2002.]]Google ScholarGoogle Scholar
  12. Donald J. Patterson, et. al., Inferring High-Level Behavior from Low-Level Sensors. In Proc. of Ubicomp 2003, Seattle, WA, October 2003.]]Google ScholarGoogle Scholar
  13. Donald J. Patterson, et. al., Opportunity Knocks: a System to Provide Cognitive Assistance with Transportation Services. In Proc. Ubicomp 2004, Nottinghan, England, September 2004.]]Google ScholarGoogle ScholarCross RefCross Ref
  14. Dau Pelleg, Andrew Moore. X-means: Extending K-means with Efficient Estimation of the Number of Clusters, In Proc. of the 17th International Conference on Machine Learning, 2000.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Place Lab. http://www.place.org]]Google ScholarGoogle Scholar
  16. Bill Schilit, et. al. Challenge: Ubiquitous Location-Aware Computing and the Place Lab Initiative. In Proc. of WMASH 2003, San Diego, CA, September 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Extracting places from traces of locations

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM SIGMOBILE Mobile Computing and Communications Review
          ACM SIGMOBILE Mobile Computing and Communications Review  Volume 9, Issue 3
          July 2005
          85 pages
          ISSN:1559-1662
          EISSN:1931-1222
          DOI:10.1145/1094549
          Issue’s Table of Contents

          Copyright © 2005 Authors

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 1 July 2005

          Check for updates

          Qualifiers

          • article

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader