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Spatiotemporal data, and more specifically origin-destination matrices, are critical inputs to mobility studies for transportation planning and urban management purposes. Traditionally, high-cost and hard-to-update household travel surveys are used to produce large-scale origin-destination flow information of individuals' whereabouts. In this paper, we propose a methodology to estimate origin-destination (O-D) matrices based on passively-collected cellular network signalling data of millions of anonymous mobile phone users in the Rhone-Alpes region, France. Unlike Call Detail Record (CDR) data which rely only on phone usage, signalling data include all network-based records providing higher spatiotemporal granularity. The explored dataset, which consists of time-stamped traces from 2G and 3G cellular networks with users' unique identifier and cell tower locations, is used to first analyse the cell phone activity degree indicators of each user in order to qualify the mobility information involved in these records. These indicators serve as filtering criteria to identify users whose device transactions are sufficiently distributed over the analysed period to allow studying their mobility. Trips are then extracted from the spatiotemporal traces of users for whom the home location could be detected. Trips have been derived based on a minimum stationary time assumption that enables to determine activity (stop) zones for each user. As a large, but still partial, fraction of the population is observed, scaling is required to obtain an O-D matrix for the full population. We propose a method to perform this scaling and we show that signalling data-based O-D matrix carries similar estimations as those that can be obtained via travel surveys.

The COVID-19 pandemic has affected our lives and how we use network infrastructures in an unprecedented way. While early studies have started shedding light on the link between COVID-19 containment measures and mobile network traffic, we presently lack a clear understanding of the implications of the virus outbreak, and of our reaction to it, on the usage of mobile apps. We contribute to closing this gap, by investigating how the spatiotemporal usage of mobile services has evolved through different response measures enacted in France during a continued seven-month period in 2020 and 2021. Our work complements previous studies in several ways: (i) it delves into individual service dynamics, whereas previous studies have not gone beyond broad service categories; (ii) it encompasses different types of containment strategies, allowing to observe their diverse effects on mobile traffic; (iii) it covers both spatial and temporal behaviors, providing a comprehensive view on the phenomenon. These elements of novelty let us lay new insights on how the demands for hundreds of different mobile services are reacting to the new environment set forth by the pandemics. TRUE inpress

Reliable and timely information on socio-economic status and divides is critical to social and economic research and policing. Novel data sources from mobile communication platforms have enabled new cost-effective approaches and models to investigate social disparity, but their lack of interpretability, accuracy or scale has limited their relevance to date. We investigate the divide in digital mobile service usage with a large dataset of 3.7 billion time-stamped and geo-referenced mobile traffic records in a major European country, and find profound geographical unevenness in mobile service usage -especially on news, e-mail, social media consumption and audio/video streaming. We relate such diversity with income, educational attainment and inequality, and reveal how low-income or low-education areas are more likely to engage in video streaming or social media and less in news consumption, information searching, e-mail or audio streaming. The digital usage gap is so large that we can accurately infer the socio-economic status of a small area or even its Gini coefficient only from aggregated data traffic. Our results make the case for an inexpensive, privacy-preserving, real-time and scalable way to understand the digital usage divide and, in turn, poverty, unemployment or economic growth in our societies through mobile phone data. This work has been supported by the research project CANCAN (Content and Context based Adaptation in Mobile Networks), grant no. ANR-18-CE25-0011, funded by the French National Research Agency (ANR). The work of M.F. was partially supported by the Atracción de Talento Investigador grant no. 2019-T1/TIC-16037 NetSense, funded by Comunidad de Madrid. E.M. and I.U. acknowledge partial support by Ministerio de Economía, Industria y Competitividad, Gobierno de España, grant nos. FIS2016-78904-C3-3-P and PID2019-106811GB-C32.

Reliable and affordable access to electricity has become one of the basic needs for humans and is, as such, at the top of the development agenda. It contributes to socio-economic development by transforming the whole spectrum of people's lives - food, education, healthcare. It spurs new economic opportunities, thus improving livelihoods. Using a comprehensive dataset of pseudonymized mobile phone records, we analyse the impact of electrification on attractiveness for rural areas in Senegal. We extract communication and mobility flows from call detail records and show that electrification is positively and specifically correlated with centrality measures within the communication network and with the volume of incoming visitors. This increased influence is however circumscribed to a limited spatial extent, creating a complex competition with nearby areas. Nevertheless, we found that the volume of visitors between any two sites could be well predicted from the level of electrification at the destination and the living standard at the origin. In view of these results, we discuss how to obtain the best outcomes from a rural electrification planning strategy. We determine that electrifying clusters of rural sites is a better solution than centralizing electricity supplies to maximize the development of specifically targeted sites., Royal Society Open Science, 8 (10), ISSN:2054-5703

Call detail records (CDR) collected by mobile phone network providers have been largely used to model and analyze human-centric mobility. Despite their potential, they are limited in terms of both spatial and temporal accuracy thus being unable to capture detailed human mobility information. Network Signaling Data (NSD) represent a much richer source of spatio-temporal information currently collected by network providers, but mostly unexploited for fine-grained reconstruction of human-centric trajectories. In this paper, we present TRANSIT, TRAjectory inference from Network SIgnaling daTa, a novel framework capable of processing NSD to accurately distinguish mobility phases from stationary activities for individual mobile devices, and reconstruct, at scale, fine-grained human mobility trajectories, by exploiting, with a DBSCAN-based clustering approach, the inherent recurrence of human mobility and the higher sampling rate of NSD. The validation on a ground-truth dataset of GPS trajectories showcases the superior performance of TRANSIT (80% precision and 96% recall) with respect to state-of-the-art solutions in the identification of movement periods, as well as an average 190 m spatial accuracy in the estimation of the trajectories. We also leverage TRANSIT to process a unique large-scale NSD dataset of more than 10 millions of individuals and perform an exploratory analysis of city-wide transport mode shares, recurrent commuting paths, urban attractivity and analysis of mobility flows. TRUE pub

This paper proposes a framework to extract dynamic trip flows and travel demand patterns from large-scale 2 G and 3 G cellular signaling data. Novel data pre-processing techniques based on cell phone activity metrics are presented. The trip extraction method relies on the detection of stationary activities to form trip sequences related to resident users. A probabilistic solution is introduced to estimate the trip starting time, allowing to aggregate trips by time of the day and reconstruct hourly travel flows. To better characterize these flows, a spatial clustering process combined with land-use data is proposed based on the temporal demand profile of each zone. Empirical comparisons have been performed showing that the resulting dynamic travel demand patterns are consistent with those obtained from travel survey data with high correlation coefficients of about 0.9. The results prove the potential of signaling data to generate low-cost valuable information for large-scale travel demand modeling.

Proceeding of: 2020 Mediterranean Communication and Computer Networking Conference (MedComNet), Aroa, Italy, 17-19 June 2020 In this paper, we investigate the existence and prevalence of comparable dynamics in the temporal fluctuations for the traffic demands generated by mobile applications.To this end, we hinge upon a spectral analysis framework, by computing Discrete Fourier Transforms of the typical demands for tens of popular mobile services observed in an operational metropolitan-scale network. We filter, cluster, and analyse hundreds of frequency components, and identify a substantial set of regular patterns that are common across most service demands. We also unveil how several mobile services defy classification, and have instead highly distinguishing temporal dynamics. The work of Orange Labs was supported by ANR through the CANCAN project (ANR-18-CE25-0011). The work of UC3M was supported by the H2020 5G-TOURS project (grant agreement no. 856950).

High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a popular proxy to evaluate the density, activity and social characteristics of a population. They offer additional advantages: they are updated in real-time, include mobility information and record visitors' activity. However, we show with the example of Senegal that the direct correlation between the average phone activity and both the population density and the nighttime lights intensity may be insufficiently high to provide an accurate representation of the situation. There are reasons to expect this, such as the heterogeneity of the market share or the particular granularity of the distribution of cell towers. In contrast, we present a method based on the daily, weekly and yearly phone activity curves and on the network characteristics of the mobile phone data, that allows to estimate more accurately such information without compromising people's privacy. This information can be vital for development and infrastructure planning. In particular, this method could help to reduce significantly the logistic costs of data collection in the particularly budget-constrained context of developing countries.

Estimating migration flows and forecasting future trends is important, both to understand the causes and effects of migration and to implement policies directed at supplying particular services. Over the years, less research has been done on modeling migration flows than the efforts allocated to modeling other flow types, for instance, commute. Limited data availability has been one of the major impediments for empirical analyses and for theoretical advances in the modeling of migration flows. As a migration trip takes place much less frequent compared to the commute, it requires a longitudinal set of data for the analysis. This study makes use a massive mobile phone network data to infer migration trips and their distribution. Insightful characteristics of the inferred migration trips are revealed, such as intra/inter-district migration flows, migration distance distribution, and origin-destination (O-D) movements. For migration trip distribution modelling, log-linear model, traditional gravity model, and recently introduced radiation model were examined with different approaches taken in defining parameters for each model. As the result, the gravity and log-linear models with a direct distance (displacement) used as its travel cost and district centroids used as the reference points perform best among the other alternative models. A radiation model that considers district population performs best among the radiation models, but worse than that of the gravity and log-linear models.

In this work, we discuss how an existing algorithm to extract long-distance trips from mobile phone data (Janzen et al., 2016 a,b) can be supplemented with man-made heuristics to arrive at plausible domestic tourism trips. In total, we detect 18,380 domestic tourism trips from mobile phone data of 69,000 users sampled in 32 cities in France. By analysing temporal, spatial and social characteristics of the domestic tourism trips, we explore several possible directions for the use of mobile phone data when studying domestic tourism on a large scale. We show how temporal patterns of mobile phone use differ from baseline behaviour, how destinations of trips can algorithmically be derived, and how future research can integrate information on the activation of the social network. Our main contribution is that we describe current barriers and sketch direction for future research. Nous discutons ici comment un algorithme d’extraction des trajets longue distance à partir de données de téléphonie mobile (Janzen et al., 2016 a,b) peut être complété par des heuristiques spécifiques afin de détecter des déplacements touristiques. Au total, nous avons détecté 18 380 voyages de tourisme interne à partir de données de 69 000 utilisateurs du mobile échantillonnés dans 32 villes françaises. En analysant les caractéristiques temporelles, spatiales et sociales de ces voyages, nous montrons comment les usages du téléphone mobile lors de ces déplacements diffèrent du comportement ordinaire, comment les destinations de voyages peuvent être dérivées algorithmiquement et comment l’information sur l’activation du réseau social pourrait être intégrée à la recherche future. L’objectif principal de ce travail est d’esquisser quelques directions possibles de l’utilisation des données collectées automatiquement par les opérateurs de télécommunication dans l’étude du tourisme interne à grande échelle.

L’article porte sur le potentiel des donnees passives de la telephonie mobile pour produire des matrices origine-destination de deplacements. L’utilisation des donnees Orange (contenant appels, SMS ainsi que les donnees de signalisation) collectees du 31 mars au 11 avril 2009 en Ile-de-France permet grâce aux estampilles spatiotemporelles, de construire une approximation de la trajectoire spatiotemporelle des utilisateurs. Cependant, une trajectoire ne suffit pas pour definir un deplacement, ce qui nous conduit a proposer une methode pour deriver des deplacements. L’objectif de cet article etant centre sur la « validation » de la matrice de deplacements issue des donnees de la telephonie mobile, nous l’avons compare a celle produite a partir de l’Enquete Globale Transport sur l’Ile-de-France. Les resultats sont tres encourageants avec des matrices relativement proches tant en structure qu’en volume. Pour conclure, nous abordons les limites de l’analyse et proposons des perspectives pour ameliorer la methodologie de production des matrices et de la comparaison.

A longitudinal mobile phone data that include both location and communication logs is analyzed to infer social influence in terms of ego-network effect in the commute mode choice. The results show that person’s strong ties are more important to determine if driving is the person’s transport mode choice, whereas weak ties are more important to determine if public transit is the person’s choice. It is also evident from the results that social ties that are geographically closer are more influential for the commute mode choice than the ones who are farther away. For public transit, access distance is also one of the influential factors. The portion of transit users decreases as the access distance becomes larger. Moreover, social network is shown to influence the commute mode choice, as the likelihood of choosing a particular mode choice rises with the portion of social ties choosing that specific mode.

For billing purposes, telecom operators collect communication logs of our mobile phone usage activities. These communication logs or so called CDR has emerged as a valuable data source for human behavioral studies. This work builds on the transportation modeling literature by introducing a new approach of crowdsource-based route choice behavior data collection. We make use of CDR data to infer individual route choice for commuting trips. Based on one calendar year of CDR data collected from mobile users in Portugal, we proposed and examined methods for inferring the route choice. Our main methods are based on interpolation of route waypoints, shortest distance between a route choice and mobile usage locations, and Voronoi cells that assign a route choice into coverage zones. In addition, we further examined these methods coupled with a noise filtering using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and commuting radius. We believe that our proposed methods and their results are useful for transportation modeling as it provides a new, feasible, and inexpensive way for gathering route choice data, compared to costly and time-consuming traditional travel surveys. It also adds to the literature where a route choice inference based on CDR data at this detailed level&mdash, i.e., street level&mdash, has rarely been explored.

Call Detail Records provide information on the origin and destination of voice calls at the level of the base stations in a cellular network. The low spatial resolution and sparsity of these data constitutes challenges in using them for mobility characterization. In this paper we analyze the impact on the detection of commuting patterns of four parameters: density of base stations per square kilometer, average number of calls made and received per day per user, regularity of these calls, and the number of active days per user. In this study, we use CDRs collected from Portugal over a period of fourteen months. Based on the result of our study, we are able to infer the commuting patterns of 10.42% of the users in our data set by considering users with at least 7.5 calls per day. Accounting users with over 7.5 calls per day, on average, does not result in a significant improvement on the result. Concerning the inference of routes in the home-to-work direction and vice versa, we examined users who connect to the cellular network, on average, every 17 days to everyday, which results in a 0.27% to 11.1% of trips detected, respectively. Finally, we found that with 208 days of data we are able to infer 5.67% of commuting trips and this percentage does not improve significantly by considering more data.

Commuting is recurring travel between home and workplace, which accounts for most trips made daily. Understanding commuting patterns and flows is therefore essential for city and transport system design and planning. Traditionally, commuting flow information was collected using surveys and interviews, which are expensive and time-consuming. This paper introduces a way to extract commuting flow and route choice information from analyzing mobile phone communication logs. We present two new methods for inferring individual commuting route choice, which collectively constitutes city-level commuting flows. Both morning and evening flows are inferred and visualized. We believe that our methods and results are useful and contributing to both theory and practice, especially in the interdisciplinary field of urban computing and city science.

Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment and mitigation processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a hypothetical infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individuals’ spatial behaviour and its relationship with the risk of infectious diseases’ contagion. In particular, we show that CDRs-based indicators of individuals’ spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing mitigation strategies to support decision-making during country-level epidemics.

Here we propose a correction of the Mobility Entropy indicator (ME) used to describe the diversity of mobility from passively observed movement patterns as captured by, e.g., mobile phone data, online social networks or location based services. We argue a correction of ME to be necessary as standard calculations show a structural dependency on the geographical density of observation points, rendering results biased and comparisons between regions with differing densities incorrect. As a solution we propose the Corrected Mobility Entropy (CME). We apply our solution to a French mobile phone dataset calculating CME values for ~18.5 million users. Results show CME to be less correlated to cell-tower density (Pearson correlation coefficient is -0.17 instead of -0.59 for ME) and to render a different nation-wide pattern of mobility diversity. Specifically, we find CME values to be significantly higher in sub-urban regions compared to their related urban centres, while both decrease considerably with lowering urban centre sizes. By means of regression models we explore interpretations of the observed pattern. We find that CME values at nation-scale relate to factors like income, employment in the municipality of residence, active population, and the distance to large cities. Interestingly, correcting mobility entropy for the density of observation points also unveils the role of car use in relation to land use which was not recognized as an important factor when using standard ME values. Ultimately, we believe our solution allows more correctly describing, understanding, and delineating regions with respect to individual mobility. We believe our findings to be relevant for planning and policy, especially from the perspective of urban development, and to have clear applications in official statistics, urban planning, and mobility research. ispartof: Journal of Urban Technology vol:25 issue:2 pages:27-61 status: published

Analyzing long-distance travel demand has become increasingly relevant because the share of traffic induced by journeys related to remote activities which are not part of daily life is growing. In today’s mobile world, such journeys are responsible for almost 50 percent of all traffic. Traditionally, surveys have been used to gather data needed to analyze travel demand. Due to the high response burden and memory issues, respondents are known to underreport their number of long-distance journeys. The question of the actual number of long-distance journeys therefore remains unanswered without additional data sources. This paper is the first to quantify the underreporting of long-distance tour frequencies in travel diaries. We took a sample of mobile phone billing data covering five months and compared the observed long-distance travel with the results of a national travel survey covering the same period and the same country. The comparison shows that most of the estimates of the number of missing tours by researchers have thus been too low. Our work suggests that the actual number of long-distance journeys is twice as high as that reported in surveys. Two different causes of underreporting were identified. Firstly, soft refusers travelled long distances but reported no long-distance tours. Secondly, respondents underestimated their number of long-distance tours. Consequently, there is a need to use alternative data sources in order to gain better estimates of long-distance travel demand., Travel Behaviour and Society, 11, ISSN:2214-367X

Large-scale location based traces, such as mobile phone data, have been identified as a promising data source to complement or even enrich official statistics. In many cases, a prerequisite step to deploy the massively gathered data is the detection of home location from individual users. The problem is that little research exists on the validation (comparison with ground truth datasets) or the uncertainty estimation of home detection methods, not at individual user level, nor at nation-wide levels. In this paper, we present an extensive empirical analysis of home detection methods when performed on a nation-wide mobile phone dataset from France. We analyze the validity of 9 different Home Detection Algorithms (HDAs), and we assess different sources of uncertainty. Based on 225 different set-ups for the home detection of around 18 million users we discuss different measures for validation and investigate sensitivity to user choices such as HDA parameter choice and observation period restriction. Our findings show that nation-wide performance of home detection is moderate at best, with correlations to ground truth maximizing at 0.60 only. Additionally, we show that time and duration of observation have a clear effect on performance, and that the effect of HDA criteria and parameter choice are rather small compared to other uncertainties. Our findings and discussion offer welcoming insights to other practitioners who want to apply home detection on similar datasets, or who are in need of an assessment of the challenges and uncertainties related to mobilizing mobile phone data for official statistics., Comment: 18 pages, 8 figures, 1 table. Winner of the student paper award of the BigSurv18 conference

This paper proposes a methodology for using mobile telephone based sensor data for detecting spatial and temporal differences in everyday activities in cities. Mobile telephone based sensor data has great applicability in developing urban monitoring tools and smart city solutions. The paper outlines methods for delineating indicator points of temporal events referenced as ‘midnight’ ‘morning start’ ‘midday’ and ‘duration of day’ which represent the mobile telephone usage of residents (what we call social time) rather than solar or standard time. Density maps by time quartiles were also utilized to test the versatility of this methodology and to analyze the spatial differences in cities. The methodology was tested with data from cities of Harbin (China) Paris (France) and Tallinn (Estonia). Results show that the developed methods have potential for measuring the distribution of temporal activities in cities and monitoring urban changes with georeferenced mobile phone data.

Urban dynamics are accelerated by new techniques for storing goods and information, and for transporting them and human beings. With humans, these changes raise the question of the forms of mobility and encounter that are made relevant by the ways territories are equipped for different types of flow. Theoretical concerns and technological developments share a growing focus on questions of territories and mobilities, coordination, and communication. The chapter shows how it is possible to systematically analyze mobilities and communication practices on the basis of location-specific data. The methodology on which the experimental protocol is based consists of three parts. First, an empirical apparatus for collecting and aggregating data concerning the locations of users and their communication practices. Second, two series of interviews over ten days, in which they were shown these objective data and questioned about their interpretation. The third is, use of the previous interviews to qualify the data gathered over six months.

Mobile phone datasets allow for the analysis of human behavior on an unprecedented scale. The social network, temporal dynamics and mobile behavior of mobile phone users have often been analyzed independently from each other using mobile phone datasets. In this article, we explore the connections between various features of human behavior extracted from a large mobile phone dataset. Our observations are based on the analysis of communication data of 100000 anonymized and randomly chosen individuals in a dataset of communications in Portugal. We show that clustering and principal component analysis allow for a significant dimension reduction with limited loss of information. The most important features are related to geographical location. In particular, we observe that most people spend most of their time at only a few locations. With the help of clustering methods, we then robustly identify home and office locations and compare the results with official census data. Finally, we analyze the geographic spread of users' frequent locations and show that commuting distances can be reasonably well explained by a gravity model., Comment: 16 pages, 12 figures

International audience; Urban landscapes present a variety of socio-topological environments that are associated to diverse human activities. As the latter affect the way individuals connect with each other, a bound exists between the urban tissue and the mobile communication demand. In this paper, we investigate the heterogeneous patterns emerging in the mobile communication activity recorded within metropolitan regions. To that end, we introduce an original technique to identify classes of mobile traffic signatures that are distinctive of different urban fabrics. Our proposed technique outperforms previous approaches when confronted to ground-truth information, and allows characterizing the mobile demand in greater detail than that attained in the literature to date. We apply our technique to extensive real-world data collected by major mobile operators in ten cities. Results unveil the diversity of baseline communication activities across countries, but also evidence the existence of a number of mobile traffic signatures that are common to all studied areas and specific to particular land uses.

Proceeding of: 13th International Conference on emerging Networking EXperiments and Technologies (CoNEXT '17) We investigate how individual mobile services are consumed at a national scale, by studying data collected in a 3G/4G mobile network deployed over a major European country. Through correlation and clustering analyses, our study unveils a strong heterogeneity in the demand for different mobile services, both in time and space. In particular, we show that: (i) somehow surprisingly, almost all considered services exhibit quite different temporal usage patterns; (ii) in contrast to such temporal behavior, spatial patterns are fairly uniform across all services; (iii) when looking at usage patterns at different locations, the average traffic volume per user is dependent on the urbanization level, yet its temporal dynamics are not. Our findings do not only have sociological implications, but are also relevant to the orchestration of network resources. This research work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA (Grant Agreement No. 671584).

The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. Along the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls - both, mobile and landline - and in either case uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylizes the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models. This increases our ability to understand, model and predict social activities and to plan the development of infrastructures across multiple scales., Deutscher Akademischer Austauschdienst (postdoctoral fellowship), European Commission. Future and Emerging Technologies-Open Project DATASIM (FP7-ICT-270833)

Extracting Ego-Centered Networks From Very Large Social Networks: This article presents a method at the intersection of macro and micro approaches in social networks. In examining ego-centered network, it proposes an approach to this type of study for very large networks. The calculation of several local indicators makes it possible to create aggregate statistics for each of the nodes of the network, which can then be seen as a corpus of ego-centered networks. An application is made on a network of telephone conversations between two million people, and a comparison is made with a classic ego-centered network study, based on an ethnographic research project. Social Networks, Ego-Centered Networks, Large Social Networks, Density, Communication Networks, Macro versus Micro, Qualitative Approaches (a corrected machine translation in English of this article is available on request from bms@reveus.org). Resume. Cet article presente une methode a l'intersection des approches « macro » et « micro » de l'analyse des reseaux sociaux. Prenant pour objectif l'etude de reseaux egocentres, il propose une demarche permettant ce type d'etude sur de tres grands reseaux. Le calcul de plusieurs indicateurs locaux peut en effet permettre de constituer des statistiques globales sur chacun des nœuds du reseau, qui sera alors vu comme un corpus de reseaux egocentres. Une mise en pratique est realisee sur un reseau d'echanges telephoniques entre deux millions d'individus et une comparaison est etablie avec une etude egocentree classique, s'appuyant sur une enquete ethnographique. Reseaux sociaux, Reseaux egocentres, Grands reseaux, Densite, Reseaux de communication, Macro vs. micro, Approches qualitatives. On oppose parfois deux axes dans l'analyse des reseaux 1 : l'un s'interessant a la structure d'un ensemble de liens, entre des individus possiblement tres nombreux

We report here on research aiming to reconstruct urban mobilities and communication practices through mobile phone base data. We have developed a software probe that can be implemented on a user's mobile phone, and which allows the joint recording and collection of the successive locations experienced by the user (through the identification of the cell in which the mobile phone is located) and all types of communicative acts performed through the mobile phone. This has been combined to indepth interviews with subjects over one week of their mobility and mobile communication behaviour. The method has been tested over a sample of 24 adults living in Paris, all in the 30–45 age range, half male and half female, with varying histories of mobility and professional flexibility constraints, in order to reconstruct their mobility and their communication-based activity spaces. We show how such a method enables the construction of a long time perspective on mobilities, and particularly on the articulation of displacements and mobile communication, which is an important issue in the ‘new mobilities paradigm’. We show how, over longer periods, mobility and communication practices combine into patterns marking social integration (or disintegration). We also show how our method allows us to construct new types of indicators, such as the propensity to communicate from a given type of place per unit of time, that reveal underlying patterns such as a higher propensity to call in mobile situations and transitory locations. This type of approach may be particularly relevant to the ongoing convergence of transport and communication studies, and to bridge the gap between communication research and mobility studies.

The common characteristic of Information and Communication Technology is that of being highly portable. Nevertheless, the dependence on the telecommunication operator's network and current communications infrastructures exert specific constraints on the mobile Internet user. We present the results of a study of the interactional complexities of a commuter reading WAP enabled devices. The WAP user is observed ethnographically and filmed while cell phone identification periodically gives information about the interviewee's physical location, indicating where and when the usability of a WAP device becomes difficult. In which way do communication and transport infrastructures intersect and how do people negotiate this intersection?

The rapid growth of mobile traffic and the emergence of advanced mobile services and infrastructures are shifting significant attention toward the cellular network back-hauling infrastructure. At this network segment, there is a growing interest in understanding spatio-temporal mobile traffic distributions at different network levels, in order to better define flexible networking solutions for forthcoming smart 5G infrastructures including, for instance, mobile edge computing features. In this work we study these aspects and characterize the load on cellular access networks using real-world anonymized subscriber data, from the Lyon metropolitan area in France, providing statistical distribution to the research community. We find that the traffic distribution at Node-B level is best fit by a Weibull distribution, and that at the radio network aggregation it is best fit by a hybrid Weibull-Pareto distribution.

An intriguing open question is whether measurements derived from Big Data recording human activities can yield high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory.

International audience; Wi-Fi offloading is one of the most effective approaches to relieve the cellular radio access from part of the burgeoning mobile demand. To date, Wi-Fi offloading has been mainly leveraged in limited contexts, such as home, office or campus environments. In this paper, we investigate the scaling properties of Wi-Fi offloading, by studying how it would perform on a much larger scope than those considered today. To that end, we consider a real-world citywide scenario, built on data about actual infrastructure deployments and mobile traffic demand, and observe which amount of traffic could be accommodated by the existing pervasive Wi-Fi access infrastructure, were it opened to mobile users. We find that more than 80% of the mobile traffic demand in a large urban area may be easily served by Wi-Fi access points, under a wide range of system settings.

International audience; Wi-Fi technology has always been an attractive solution for catering the increasing data demand in mobile networks because of the availability of Wi-Fi networks, the high bit rates they provide, and the lower cost of ownership. However, the legacy WiFi technology lacks of seamless interworking between Wi-Fi and mobile cellular networks on the one hand, and between Wi-Fi hotspots on the other hand. Nowadays, the recently released Wi-Fi Certified Passpoint Program provides the necessary control-plane for these operations. Service providers can henceforth look to such Wi-Fi systems as a viable way to seamlessly offload mobile traffic and deliver added-value services, so that subscribers no longer face the frustration and aggravation of connecting to Wi-Fi hotspots. However, the technology being rather recent, we are not aware of public studies at the state of the art documenting the achievable gain in real mobile networks. In this paper, we evaluate the capacity and energy saving gain that one can get by offloading cellular data traffic over Passpoint hotspots as a function of different hotspot placement schemes and of access point selection policies (two enabled by the Passpoint control-plane and one independent of it). We compare the policies using real mobile data from the Orange network in Paris. We show that offloading using Passpoint control-plane information can grant up to 15% capacity gain and 13% energy saving gain with respect to Passpoint-agnostic ones based on signal quality information. As of placement strategy, installing Passpoint hotspots in the outer annulus of the macrocell coverage grants the maximum capacity gain.

Mobile communication technologies pervade our society and existing wireless networks are able to sense the movement of people, generating large volumes of data related to human activities, such as mobile phone call records. At the present, this kind of data is collected and stored by telecom operators infrastructures mainly for billing reasons, yet it represents a major source of information in the study of human mobility. In this paper, we propose an analytical process aimed at extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of population local densities. To accomplish this objective, we propose a process based on two analytical tools: (i) a method to estimate the presence of people in different geographical areas; and (ii) a method to extract time- and space-constrained sequential patterns capable to capture correlations among geographical areas in terms of significant co-variations of the estimated presence. The methods are presented and combined in order to deal with two real scenarios of different spatial scale: the Paris Region and the whole France. A new pattern aiming to discover connections between regions is presented.The C-pattern is based on spatiotemporal aggregations of presence.It overcomes typical completeness limitations of CDR data due to the sample rate.We have developed an algorithm to extract these patterns efficiently.We have presented three different case studies using two different granularities.

Communication mediated by various technologies (from ordinary mail to today's Information and Communication Technologies (ICT)) provides important evidence for the study of social networks. Given that networks generate the possibility of interpersonal communication, data on technology use can provide important information on sociability. However, it is also true that personal networks not only shape, but also are shaped by technological means for communication, since these entail the re-constituting of social ties and the re-drawing of social boundaries. We use material from empirical studies carried out over the last 3 years to develop our hypothesis of the way forms of relationship change with technology. In particular, we try to understand the relationship between social networks (a set of social ties possessing one or more relational dimensions), exchanges between actors (made up of a succession of embodied gestures and language acts) and the various technical means for communication available today, which enable an exchange to be completed. Each of these three poles poses constraints on interaction, and provides resources for it, and thus all three shape the form relational practices take. Empirical data show how technological means of communication allow people to re-negotiate the constraints of individual time rhythms, and of who one communicates with. They also illustrate how the relational economy (and power) is affected by the deployment of communication technologies. Tools of communication provide new resources to negotiate individual timetables and social exchanges, making it possible to adjust roles, hierarchies and forms of power in relational economies. We argue that the general change observed over the last 20 years is from established roles to mutual reachability. The traditional communication model, where tele-communication is used to connect people who are physically separated from each other, is gradually being supplanted with a new pattern of “connected presence”. In this new mode other people are telephoned, “SMSed”, seen and mailed in alternated way and small gestures or signs of attention are at least as important as the message content itself.

Big Data offer nowadays the potential capability of creating a digital nervous system of our society, enabling the measurement, monitoring and prediction of relevant aspects of socio-economic phenomena in quasi real time. This potential has fueled, in the last few years, a growing interest around the usage of Big Data to support official statistics in the measurement of individual and collective economic well-being. In this work we study the relations between human mobility patterns and socioeconomic development. Starting from nation-wide mobile phone data we extract a measure of mobility volume and a measure of mobility diversity for each individual. We then aggregate the mobility measures at municipality level and investigate the correlations with external socio-economic indicators independently surveyed by an official statistics institute. We find three main results. First, aggregated human mobility patterns are correlated with these socio-economic indicators. Second, the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits the strongest correlation with the external socio-economic indicators. Third, the volume of mobility and the diversity of mobility show opposite correlations with the socioeconomic indicators. Our results, validated against a null model, open an interesting perspective to study human behavior through Big Data by means of new statistical indicators that quantify and possibly "nowcast" the socio-economic development of our society.

International audience; Mobile phone operators produce enormous amounts of data. In this paper we present applications performed with a dataset (communication events + handover and Location Area Up-date) collected by the operator Orange from 31 March to 11 April 2009 for the whole Paris Region. Trips are deduced from the spatio-temporal trajectory of devices through a hypothesis of stationarity within a Location Area in order to define activities. Trips are then aggregated in an origin-destination matrix which is compared with traditional data (census data and household travel survey).

International audience; The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

The appearance of large geolocated communication datasets has recently increased our understanding of how social networks relate to their physical space. However, many recurrently reported properties, such as the spatial clustering of network communities, have not yet been systematically tested at different scales. In this work we analyze the social network structure of over 25 million phone users from three countries at three different scales: country, provinces and cities. We consistently find that this last urban scenario presents significant differences to common knowledge about social networks. First, the emergence of a giant component in the network seems to be controlled by whether or not the network spans over the entire urban border, almost independently of the population or geographic extension of the city. Second, urban communities are much less geographically clustered than expected. These two findings shed new light on the widely-studied searchability in self-organized networks. By exhaustive simulation of decentralized search strategies we conclude that urban networks are searchable not through geographical proximity as their country-wide counterparts, but through an homophily-driven community structure., New England University Transportation Center (Year 23 Grant), King Abdulaziz City of Science and Technology (Saudia Arabia). Center for Complex Engineering Systems, MIT-Accenture Alliance, Orange Spain (France Telecom Group), Fundacion Caja Madrid (Spain), Spanish Ministry of Economy and Competitiveness (MINECO) (Grant MTM2012-39101)

International audience; Wi-Fi technology has always been an attractive solution for catering the increasing data demand in mobile networks because of the availability of Wi-Fi networks, the high bit rates they provide, and the lower cost of ownership. However, the legacy WiFi technology lacks of seamless interworking between Wi-Fi and mobile cellular networks on the one hand, and between Wi-Fi hotspots on the other hand. Nowadays, the recently released Wi-Fi Certified Passpoint Program provides the necessary control-plane for these operations. Service providers can henceforth look to such Wi-Fi systems as a viable way to seamlessly offload mobile traffic and deliver added-value services, so that subscribers no longer face the frustration and aggravation of connecting to Wi-Fi hotspots. However, the technology being rather recent, we are not aware of public studies at the state of the art documenting the achievable gain in real mobile networks. In this paper, we evaluate the capacity gain that one can get by offloading cellular data traffic over Wi-Fi Passpoint hotspots as a function of different hotspot placement schemes and of access point selection policies (two enabled by the Passpoint control-plane and one independent of it). We compare the policies using real mobile data from the Orange network in Paris. We show that offloading using Passpoint control-plane information can grant up to 15% gain with respect to Passpoint-agnostic ones based on signal quality information. As of placement strategy, installing Passpoint hotspots in the outer annulus of the macrocell coverage grants the maximum gains.

International audience; —A present issue in the evolution of mobile cellular networks is determining whether, how and where to deploy adaptive content and cloud distribution solutions at the base station and backhauling network level. Intuitively, an adaptive placement of content and computing resources in the most crowded regions can grant important traffic offloading, improve network efficiency and user quality of experience. In this paper we document the content consumption in the Orange cellular network for the Paris metropolitan area, from spatial and application-level extensive analysis of real data from a few million users, reporting the experimental distributions. In this scope, we propose a hotspot cell estimator computed over user's mobility metrics and based on linear regression. Evaluating our estimator on real data, it appears as an excellent hotspot detection solution of cellular and backhauling network management. We show that its error strictly decreases with the cell load, and it is negligible for reasonable hotspot cell load upper thresholds. We also show that our hotspot estimator is quite scalable against mobility data volume and against time variations.

This study draws on itemized telephone accounts to construct aggregated, "objective", representations of relations in which telephone calls are a concrete medium. The authors have narrowed their focus to the telephone and the length of calls. Their aim is to show and interpret diverse variations in the average length of telephone conversations, such as the increase in the length of telephone interaction with distant interlocutors, the growing role of the telephone in distant emotional relationships and, lastly, the effects of pressure and regulation in the domestic sphere on week-day evenings and the way this impacts on use of the telephone during that period. They also show how the frequency of meetings has a positive influence on the frequency and length of telephone calls. This shows the need to study interpersonal relations through all modalities of interaction between the actors., Ce travail repose au départ sur l'exploitation des factures téléphoniques détaillées pour construire des représentations agrégées, « objectives », des liens dont les échanges téléphoniques constituent le support concret. Nous avons travaillé de manière très ciblée sur le téléphone et les durées de conversation téléphonique. Nous tentons de montrer et interpréter diverses variations dans les durées moyennes de conversation téléphonique, comme l'accroissement de durée des interactions téléphoniques pour les correspondants distants et le rôle accru du téléphone dans la gestion des relations affectives distantes, ainsi que les effets de pression et de régulation dans la sphère domestique en soirée, les jours de semaine, et dans la manière dont ils se répercutent sur les usages du téléphone dans cette période. Nous montrons également comment le fait de se rencontrer plus ou moins fréquemment influe positivement sur la fréquence et la durée des échanges téléphoniques, ce qui appelle à un traitement des relations interpersonnelles qui suivent les acteurs au fil de toutes leurs modalités d'interaction., Licoppe Christian, Smoreda Zbigniew. Liens sociaux et régulations domestiques dans l'usage du téléphone. De l'analyse quantitative de la durée des conversations à l'examen des interactions. In: Réseaux, volume 18, n°103, 2000. Le sexe au téléphone. pp. 253-276.

This article analyses the results of a survey in 1996 on 312 households. Data on the length of telephone conversations between men and women are examined to show an original gendered effect consisting of a distinct influence of the gender of the person called on the length of the conversation. The authors' explanation is that during a telephone conversation (analysed as comparable to a visit), rules of courtesy, the distribution of caller-called roles, and the interlocutors' gender all influence the course of the interaction. Callers tend to adjust the style of interaction in relation to the gender of the person they are calling., Nous analysons dans cet article les données issues d'une enquête réalisée en 1996 auprès de 312 foyers. Les données sur la durée des conversations téléphoniques entre hommes et femmes sont examinées pour montrer un effet de sexe original qui consiste en une claire influence du sexe de la personne appelée sur la durée des échanges téléphoniques. Nous proposons une explication selon laquelle, pendant une conversation téléphonique (analysée comme comparable à une visite), les règles de politesse, la distribution des rôles appelant-appelé et les statuts de sexe des interlocuteurs influencent le déroulement de l'interaction à travers l'ajustement par appelant de son style d'interaction au sexe de l'appelé., Smoreda Zbigniew, Licoppe Christian. Identités sexuées et statuts interactionnels. De la gestion de la durée des conversations téléphoniques. In: Réseaux, volume 18, n°103, 2000. Le sexe au téléphone. pp. 119-141.

International audience; Réseaux was created under the auspices of the Centre National d’Études des Télécommunications (CNET) to bring closer together media communication research and the interpersonal exchanges that take place with telecommunication tools. This synthesis explores the journal’s production both on the question of telephone communication and on the digital sociabilities that appeared with the use of Internet as a platform for generalized interaction. The article examines four orientations in the articles published in Réseaux. The first two, the “continuist” hypothesis and the question of intertwined media, attest to the journal’s endeavour to introduce communication issues – until then considered mainly through the prism of mass media – into the sociology of everyday life. The other two, the strength of weak ties and the construction of collectives based on interactions, show how the question of digital sociabilities was introduced as one of the journal’s focuses and gave rise to the new research field of digital studies.; Né sous les auspices du Centre National d’Études des Télécommunications (CNET), Réseaux a eu pour projet de rapprocher la recherche sur la communication médiatique et les échanges interpersonnels se déployant à partir des outils de télécommunication. Cette synthèse explore la production de la revue portant à la fois sur la question des échanges téléphoniques et sur les sociabilités numériques apparues avec les usages d’Internet comme plate-forme d’échange généralisé. L’article propose de suivre quatre fils au sein des publications de Réseaux. Les deux premiers, l’hypothèse « continuiste » et la question de l’entrelacement des médias témoignent du travail accompli par la revue pour introduire les questions de communication, jusqu’alors principalement pensées à travers les médias de masse, dans la sociologie de la vie quotidienne. Les deux suivants, la force des liens faibles et la fabrication des collectifs sur la base des interactions, montrent comment la question des sociabilités numériques s’est introduite dans les préoccupations de la revue pour donner jour aux nouveaux champs de recherche des digital studies.

Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study., Author Summary The spatial dissemination of a directly transmitted infectious disease in a population is driven by population movements from one region to another allowing mixing and importation. Public health policy and planning may thus be more accurate if reliable descriptions of population movements can be considered in the epidemic evaluations. Next to census data, generally available in developed countries, alternative solutions can be found to describe population movements where official data is missing. These include mobility models, such as the radiation model, and the analysis of mobile phone activity records providing individual geo-temporal information. Here we explore to what extent mobility proxies, such as mobile phone data or mobility models, can effectively be used in epidemic models for influenza-like-illnesses and how they compare to official census data. By focusing on three European countries, we find that phone data matches the commuting patterns reported by census well but tends to overestimate the number of commuters, leading to a faster diffusion of simulated epidemics. The order of infection of newly infected locations is however well preserved, whereas the pattern of epidemic invasion is captured with higher accuracy by the radiation model for centrally seeded epidemics and by phone proxy for peripherally seeded epidemics.

Human mobility is differentiated by time scales. While the mechanism for long time scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analysing the temporal and spatial trajectories of thousands of persons as individual networks. Using the concept of motifs from network theory, we find only 17 unique networks are present in daily mobility and they follow simple rules. These networks, called here motifs, are sufficient to capture up to 90 per cent of the population in surveys and mobile phone datasets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, daily human mobility can be reproduced by an analytically tractable framework for Markov chains by modelling periods of high-frequency trips followed by periods of lower activity as the key ingredient., Volkswagen Foundation, NEC Corporation (Fund), Massachusetts Institute of Technology (Solomon Buchsbaum Research Fund), New England University Transportation Center (Year 23 grant)

Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method. Methodology/approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area. Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility. Originality/value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring.

In the past few years, mobile network data are considered as a useful complementary source of information for human mobility research. Mobile phone datasets contain massive amount of spatiotemporal localization of millions of users. The analyze of such huge amount of data for mobility studies reveals many issues such as time computation, users sampling, spatiotemporal heterogeneities, semantic incompleteness. In this chapter, two issues are addressed: (1) location sampling aiming at decreasing computation time without losing useful information on the one hand and to eliminate data considered as noise in the other hand and (2) users sampling whose goal is to select users having relevant information. For the first issue two measures allowing eliminating redundant information and ping-pong positions are proposed. The second issue requires the definition of a set of measures allowing estimating mobile phone data quality. New methods to qualify mobile phone data at local and global level are proposed. The methods are tested on one-day mobile phone data coming from technical mobile network probes.

Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions., National Science Foundation (U.S.), Singapore-MIT Alliance for Research and Technology