Methodologies for Continuous Cellular Tower Data Analysis

  • Nathan Eagle
  • John A. Quinn
  • Aaron Clauset
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5538)


This paper presents novel methodologies for the analysis of continuous cellular tower data from 215 randomly sampled subjects in a major urban city. We demonstrate the potential of existing community detection methodologies to identify salient locations based on the network generated by tower transitions. The tower groupings from these unsupervised clustering techniques are subsequently validated using data from Bluetooth beacons placed in the homes of the subjects. We then use these inferred locations as states within several dynamic Bayesian networks (DBNs) to predict dwell times within locations and each subject’s subsequent movements with over 90% accuracy. We also introduce the X-Factor model, a DBN with a latent variable corresponding to abnormal behavior. By calculating the entropy of the learned X-Factor model parameters, we find there are individuals across demographics who have a wide range of routine in their daily behavior. We conclude with a description of extensions for this model, such as incorporating contextual and temporal variables already being logged by the phones.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nathan Eagle
    • 1
    • 2
  • John A. Quinn
    • 3
  • Aaron Clauset
    • 2
  1. 1.Massachusetts Institute of TechnologyCambridge
  2. 2.The Santa Fe Institute
  3. 3.Makerere University, KampalaUganda

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