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.
Citywide population estimation from mobile traffic data has been addressed by a limited number of works in the literature. @cite_8 , LandScan , a tool for ambient population estimation was employed to explore the relationship between the voice call activity and the underlying inhabitant density, at a 1-km @math resolution. The authors found a weak correlation of 0.24, which improved to 0.45 by limiting the analysis to selected time intervals rather than considering the daily communication volume. @cite_14 , telecommunications data is mixed with a number of other sources, including information on Corine land use, OpenStreetMap infrastructure, satellite nightlights, and slope. This plethora of data is processed through a dasymetric modeling approach, resulting in high 0.92 correlation with census information. However, the correlation is a nationwide average, and the authors indicate that the accuracy is lower for the most densely populated areas, i.e., large cities. Indeed, in such areas, a normalized error of around 0.6 is measured in @cite_14 , whereas we obtain values below 0.06.
@cite_14 @cite_8
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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. 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. People spend most of their time at a few key locations, such as home and work. Being able to identify how the movements of people cluster around these "important places" is crucial for a range of technology and policy decisions in areas such as telecommunications and transportation infrastructure deployment. In this paper, we propose new techniques based on clustering and regression for analyzing anonymized cellular network data to identify generally important locations, and to discern semantically meaningful locations such as home and work. Starting with temporally sparse and spatially coarse location information, we propose a new algorithm to identify important locations. We test this algorithm on arbitrary cellphone users, including those with low call rates, and find that we are within 3 miles of ground truth for 88 of volunteer users. Further, after locating home and work, we achieve commute distance estimates that are within 1 mile of equivalent estimates derived from government census data. Finally, we perform carbon footprint analyses on hundreds of thousands of anonymous users as an example of how our data and algorithms can form an accurate and efficient underpinning for policy and infrastructure studies. Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.