Smartphones and other mobile devices are today pervasive across the globe. As an interesting side effect of the surge in mobile communications, mobile network operators can now easily collect a wealth of high-resolution data on the habits of large user populations. The information extracted from mobile network traffic data is very relevant in the context of population mapping: it provides a tool for the automatic and live estimation of population densities, overcoming the limitations of traditional data sources such as censuses and surveys. In this paper, we propose a new approach to infer population densities at urban scales, based on aggregated mobile network traffic metadata. Our approach allows estimating both static and dynamic populations, achieves a significant improvement in terms of accuracy with respect to state-of-the-art solutions in the literature, and is validated on different city scenarios.
As far as population distribution estimation is concerned, mobile communication data was first proposed as a proxy for the density of inhabitants in @cite_2 . Early evidences of the existence of an actual correlation between the mobile network activity and the underlying population density were presented in @cite_10 : the authors showed that city population sizes and the number of mobile customers follow similar distributions.
@cite_10 @cite_2
2165598812 2085009188
We analyze the anonymous communication patterns of 2.5 million customers of a Belgian mobile phone operator. Grouping customers by billing address, we build a social network of cities that consists of communications between 571 cities in Belgium. We show that inter-city communication intensity is characterized by a gravity model: the communication intensity between two cities is proportional to the product of their sizes divided by the square of their distance. The technology for determining the geographic location of cell phones and other handheld devices is becoming increasingly available. It is opening the way to a wide range of applications, collectively referred to as location-based services (LBS), that are primarily aimed at individual users. However, if deployed to retrieve aggregated data in cities, LBS could become a powerful tool for urban analysis. In this paper we aim to review and introduce the potential of this technology to the urban planning community. In addition, we present the ‘Mobile Landscapes’ project: an application in the metropolitan area of Milan, Italy, based on the geographical mapping of cell phone usage at different times of the day. The results enable a graphic representation of the intensity of urban activities and their evolution through space and time. Finally, a number of future applications are discussed and their potential for urban studies and planning is assessed.