automated land use identification using cell-phone records
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.
land use classification clustering call detail records