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Identifying Meaningful Places: The Non-parametric Way

  • Petteri Nurmi
  • Sourav Bhattacharya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5013)

Abstract

Gathering and analyzing location data is an important part of many ubiquitous computing applications. The most common way to represent location information is to use numerical coordinates, e.g., latitudes and longitudes. A problem with this approach is that numerical coordinates are usually meaningless to a user and they contrast with the way humans refer to locations in daily communication. Instead of using coordinates, humans tend to use descriptive statements about their location; for example, ”I’m home” or ”I’m at Starbucks.” Locations, to which a user can attach meaningful and descriptive semantics, are often called places. In this paper we focus on the automatic extraction of places from discontinuous GPS measurements. We describe and evaluate a non-parametric Bayesian approach for identifying places from this kind of data. The main novelty of our approach is that the algorithm is fully automated and does not require any parameter tuning. Another novel aspect of our algorithm is that it can accurately identify places without temporal information. We evaluate our approach using data that has been gathered from different users and different geographic areas. The traces that we use exhibit different characteristics and contain data from daily life as well as from traveling abroad. We also compare our algorithm against the popular k-means algorithm. The results indicate that our method can accurately identify meaningful places from a variety of location traces and that the algorithm is robust against noise.

Keywords

Markov Chain Monte Carlo Geographic Information System Gaussian Mixture Model Dirichlet Process Social Identity Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Petteri Nurmi
    • 1
  • Sourav Bhattacharya
    • 1
  1. 1.Helsinki Institute for Information Technology HIIT Department of Computer ScienceUniversity of HelsinkiFinland

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