Communication-enabled devices routinely carried by individuals have become pervasive, opening unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. Our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. More importantly, it enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. By leveraging substantial real-world mobile network metadata and ground-truth information, we demonstrate that the accuracy of our solution is superior to that granted by state-of-the-art methods in practical heterogeneous urban scenarios.
Spatial variations in the distribution and composition of populations inform urban development, health-risk analyses, disaster relief, and more. Despite the broad relevance and importance of such data, acquiring local census estimates in a timely and accurate manner is challenging because population counts can change rapidly, are often politically charged, and suffer from logistical and administrative challenges. These limitations necessitate the development of alternative or complementary approaches to population mapping. In this paper we develop an explicit connection between telecommunications data and the underlying population distribution of Milan, Italy. We go on to test the scale invariance of this connection and use telecommunications data in conjunction with high-resolution census data to create easily updated and potentially real time population estimates in time and space. Achieving accurate, real-time, and spatially fine-grained population estimation for a metropolitan city is extremely valuable for a variety of applications. Previous solutions look at data generated by human activities, such as night time lights and phone calls, for population estimation. However, these mechanisms cannot achieve both real-time and fine-grained population estimation because the data sampling rate is low and spatial granularity chosen is improper. We address these two problems by leveraging a key insight --- people frequently use data plan on cellphones and leave mobility signatures on cellular networks. Therefore, we are able to exploit these cellular signatures for real-time population estimation. Extracting population information from cellular data records is not easy because the number of users recorded by a cellular tower is not equal to the population covered by the tower, and mobile users' behavior is spatially and temporally different, where static estimating model does not work. We exploit context-aware city segmentation and dynamic population estimation model to address these challenges. We show that the population estimation error is reduced by 22.5 on a cellular dataset that includes 1 million users.