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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.

In this paper, we present an algorithm for the analysis of opportunistically collected mobile phone location data to estimate a population's travel demand in terms of origins and destinations of individual trips. Aggregating the trips from millions individual mobile phone users in the Boston Metropolitan area, we show that the estimated Origin-Destination flows correlate well with the US Census estimates at both the county and census tract levels. Moreover, compared to traditional census survey data, our estimations allow capturing weekday and weekend patterns as well as seasonal variations. These features could make methods for Origin-Destination flow estimation based on opportunistically collected mobile phone location data a critical component for transportation management and emergency response.

Do regional boundaries defined by governments respect the more natural ways that people interact across space? This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions. Given a geographical area and some measure of the strength of links between its inhabitants, we show how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links. We tested our method on the largest non-Internet human network, inferred from a large telecommunications database in Great Britain. Our partitioning algorithm yields geographically cohesive regions that correspond remarkably well with administrative regions, while unveiling unexpected spatial structures that had previously only been hypothesized in the literature. We also quantify the effects of partitioning, showing for instance that the effects of a possible secession of Wales from Great Britain would be twice as disruptive for the human network than that of Scotland.

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The recent development of telecommunication networks is producing an unprecedented wealth of information and, as a consequence, an increasing interest in analyzing such data both from telecoms and from other stakeholders' points of view. In particular, mobile phone datasets offer access to insights into urban dynamics and human activities at an unprecedented scale and level of detail, representing a huge opportunity for research and real-world applications. This article surveys the new ideas and techniques related to the use of telecommunication data for urban sensing. We outline the data that can be collected from telecommunication networks as well as their strengths and weaknesses with a particular focus on urban sensing. We survey existing filtering and processing techniques to extract insights from this data and summarize them to provide recommendations on which datasets and techniques to use for specific urban sensing applications. Finally, we discuss a number of challenges and open research areas currently being faced in this field. We strongly believe the material and recommendations presented here will become increasingly important as mobile phone network datasets are becoming more accessible to the research community.