Identifying meaningful locations

Existing context-aware mobile applications often rely on location information. However, raw location data such as GPS coordinates or GSM cell identifiers are usually meaningless to the user and, as a consequence, researchers have proposed different methods for inferring so-called places from raw data. The places are locations that carry some meaning to user and to which the user can potentially attach some (meaningful) semantics. Examples of places include home, work and airport. A lack in existing work is that the labeling has been done in an ad hoc fashion and no motivation has been given for why places would be interesting to the user. As our first contribution we use social identity theory to motivate why some locations really are significant to the user. We also discuss what potential uses for location information social identity theory implies. Another flaw in the existing work is that most of the proposed methods are not suited to realistic mobile settings as they rely on the availability of GPS information. As our second contribution we consider a more realistic setting where the information consists of GSM cell transitions that are enriched with GPS information whenever a GPS device is available. We present four different algorithms for this problem and compare them using real data gathered throughout Europe. In addition, we analyze the suitability of our algorithms for mobile devices

[1]  Johan Koolwaaij,et al.  Context Watcher ─ Sharing context information in everyday life , 2006 .

[2]  Fumihiro Kato,et al.  Exploiting Multiple Radii to Learn Significant Locations , 2005, LoCA.

[3]  Chris Schmandt,et al.  A User-Centered Location Model , 2002, Personal and Ubiquitous Computing.

[4]  Kari Laasonen,et al.  Clustering and Prediction of Mobile User Routes from Cellular Data , 2005, PKDD.

[5]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[6]  Gaetano Borriello,et al.  Extracting places from traces of locations , 2004, MOCO.

[7]  Thad Starner,et al.  Using GPS to learn significant locations and predict movement across multiple users , 2003, Personal and Ubiquitous Computing.

[8]  Bill N. Schilit,et al.  Place Lab: Device Positioning Using Radio Beacons in the Wild , 2005, Pervasive.

[9]  Johan Koolwaaij,et al.  Watcher ─ Sharing context information in everyday life , 2006 .

[10]  P. Burke,et al.  Identity theory and social identity theory , 2000 .

[11]  R. Bharat Rao,et al.  Evolution of mobile location-based services , 2003, CACM.

[12]  Gerry White,et al.  The Past , 2000 .

[13]  Shashi Shekhar,et al.  Discovering personal gazetteers: an interactive clustering approach , 2004, GIS '04.

[14]  Marina Meila,et al.  A Comparison of Spectral Clustering Algorithms , 2003 .

[15]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[16]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[17]  Gregory D. Abowd,et al.  Cyberguide: A mobile context‐aware tour guide , 1997, Wirel. Networks.

[18]  P. Burke,et al.  The Past, Present, and Future of an Identity Theory* , 2000 .

[19]  Guanling Chen,et al.  A Survey of Context-Aware Mobile Computing Research , 2000 .

[20]  Mika Raento,et al.  Adaptive On-Device Location Recognition , 2004, Pervasive.

[21]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.