Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Learning and inferring transportation routines
Artificial Intelligence
Cellular Census: Explorations in Urban Data Collection
IEEE Pervasive Computing
Inferring land use from mobile phone activity
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Adaptive non-parametric identification of dense areas using cell phone records for urban analysis
Engineering Applications of Artificial Intelligence
Quantifying the potential of ride-sharing using call description records
Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
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Pervasive large-scale infrastructures generate datasets that contain human behavior info rmation. In this context, cell phones and cell phone networks, due to its pervasiveness, can be considered sensors of human behavior and one of the main elements that define our digital footprint. In this paper we present a technique for the automatic identification and classification of land uses from the information generated by a cell-phone network infrastructure. Our approach first computes the aggregated calling patterns of the antennas of the network and, after that, finds the optimum cluster distribution to automatically identify how citizens use the different geographic regions within a city. We present and validate our results using cell phone records collected for the city of Madrid.