Adaptive non-parametric identification of dense areas using cell phone records for urban analysis

Pervasive large-scale infrastructures (like GPS, WLAN networks or cell-phone networks) generate large datasets containing human behavior information. One of the applications that can benefit from this data is the study of urban environments. In this context, one of the main problems is the detection of dense areas, i.e., areas with a high density of individuals within a specific geographical region and time period. Nevertheless, the techniques used so far face an important limitation: the definition of dense area is not adaptive and as a result the areas identified are related to a threshold applied over the density of individuals, which usually implies that dense areas are mainly identified in downtowns. In this paper, we propose a novel technique, called AdaptiveDAD, to detect dense areas that adaptively define the concept of density using the infrastructure provided by a cell phone network. We evaluate and validate our approach with a real dataset containing the Call Detail Records (CDR) of fifteen million individuals.