The goal of this paper is to infer features of urban ecology (i.e., social and economic activities, and social interaction) from spatiotemporal cell phone activity data. We present a novel approach that consists of (i) time series decomposition of the aggregate cell phone activity per unit area using spectral methods, (ii) clustering of areal units with similar activity patterns, and (ii) external validation using a ground truth data set we collected from municipal and online sources. The key

more »

... our approach is the spectral decomposition of the original cell phone activity series into seasonal communication series (SCS) and residual communication series (RCS). The former captures regular patterns of socio-economic activity within an area and can be used to segment a city into distinct clusters. RCS across areas enables the detection of regions that are subject to mutual social influence and of regions that are in direct communication contact. The RCS and SCS thus provide distinct probes into the structure and dynamics of the urban environment, both of which can be obtained from the same underlying data. We illustrate the effectiveness of our methodology by applying it to aggregate Call Description Records (CDRs) from the city of Milan.