Methodologies for Continuous Cellular Tower Data Analysis
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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- 4.Chen, M., Sohn, T., Chmelev, D., Haehnel, D., Hightower, J., Hughes, J., LaMarca, A., Potter, F., Smith, I., Varshavsky, A.: Practical metropolitan-scale positioning for gsm phones. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 225–242. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 6.Davis, M., King, S., Good, N., Sarvas, R.: From context to content: leveraging context to infer media metadata. In: Proceedings of the 12th annual ACM international conference on Multimedia, New York, NY, USA, October 10-16 (2004)Google Scholar
- 12.Liao, L., Patterson, D., Fox, D., Kautz, H.: Learning and inferring transportation routines. In: Proceedings of the Nineteenth National Conference on Artificial Intelligence, pp. 348–353 (January 2004)Google Scholar
- 14.Newman, M.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences (January 2006)Google Scholar
- 15.Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69 (January 2004)Google Scholar
- 16.Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Physical Review E 70(6) (December 2004)Google Scholar
- 17.Schwaighofer, A., Grigoras, M., Tresp, V., Hoffmann, C.: Gpps: A gaussian process positioning system for cellular networks. Advances in Neural Information Processing Systems 16 (January 2004)Google Scholar
- 18.Quinn, J., Williams, C., McIntosh, N.: Factorial switching linear dynamical systems applied to physiological condition monitoring. IEEE Transactions on Pattern Analysis and Machine Intelligence (January 2008)Google Scholar