Dynamical modelling of cellular tower data

Work with Nathan Eagle (Santa Fe Institute/MIT) and Aaron Clauset (Santa Fe Institute)

Using 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.

Paper in Pervasive 2009

Paper in AAAI Spring Symposium 2009 on Technosocial Predictive Analytics