Pervasive infrastructures, such as cell phone networks, enable to capture large amounts of human behavioral data but also provide information about the structure of cities and their dynamical properties. In this article, we focus on these last aspects by studying phone data recorded during 55 days in 31 Spanish cities. We first define an urban dilatation index which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure. We then focus on hotspots, the most crowded places in the city. We propose a parameter free method to detect them and to test the robustness of our results. The number of these hotspots scales sublinearly with the population size, a result in agreement with previous theoretical arguments and measures on employment datasets. We study the lifetime of these hotspots and show in particular that the hierarchy of permanent ones, which constitute the ‘heart' of the city, is very stable whatever the size of the city. The spatial structure of these hotspots is also of interest and allows us to distinguish different categories of cities, from monocentric and “segregated” where the spatial distribution is very dependent on land use, to polycentric where the spatial mixing between land uses is much more important. These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data.

The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has relevance for many applications. An important example is seen in origin-destination matrices, which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method, which extracts a coarse-grained signature of mobility networks, under the form of a 2 Â 2 matrix that separates the flows into four categories. We apply this method to origindestination matrices extracted from mobile phone data recorded in 31 Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally, the method allows the determination of categories of networks, and in the mobility case, the classification of cities according to their commuting structure.

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Dark matter annihilation signals coming from Galactic subhaloes may account for a small fraction of unassociated point sources detected in the second Fermi-LAT Catalogue (2FGL). To investigate this possibility, we present SIBYL, a Random Forest classifier that offers predictions on class memberships for unassociated Fermi-LAT sources at high Galactic latitudes using gamma-ray features extracted from the 2FGL. SIBYL generates a large ensemble of classification trees that are trained to vote on whether a particular object is an active galactic nucleus (AGN) or a pulsar. After training on a list of 908 identified/associated 2FGL sources, SIBYL reaches individual accuracy rates of up to 97.7 per cent for AGNs and 96.5 per cent for pulsars. Predictions for the 269 unassociated 2FGL sources at |b| ≥ 10 • suggest that 216 are potential AGNs and 16 are potential pulsars (with majority votes greater than 70 per cent). The remaining 37 objects are inconclusive, but none is an extreme outlier. These results could guide future quests for dark matter Galactic subhaloes.

Abstract-The recent adoption of ubiquitous computing technologies has enabled capturing large amounts of human behavioral data. The digital footprints computed from these datasets provide information for the study of social and human dynamics, including social networks and mobility patterns, key elements for the effective modeling of virus spreading. Traditional epidemiologic models do not consider individual information and hence have limited ability to capture the inherent complexity of the disease spreading process. To overcome this limitation, agent-based models have recently been proposed as an effective approach to model virus spreading. However, most agent-based approaches to date have not included real-life data to characterize the agents' behavior. In this paper we propose an agent-based system that uses social interactions and individual mobility patterns extracted from call detail records to accurately model virus spreading. The proposed approach is applied to study the 2009 H1N1 outbreak in Mexico and to evaluate the impact that government mandates had on the spreading of the virus. Our simulations indicate that the restricted mobility due the government mandates reduced by 10% the peak number of individuals infected by the virus and postponed the peak of the pandemic by two days.