@conference {689,
	title = {Detecting and understanding big events in big cities},
	booktitle = {NetMob},
	year = {2015},
	month = {04/2015},
	address = {Boston},
	abstract = {Recent studies have shown the great potential of big data such as mobile phone location data to model human behavior. Big data allow to analyze people presence in a territory in a fast and effective way with respect to the classical surveys (diaries or questionnaires). One of the drawbacks of these collection systems is incompleteness of the users{\textquoteright} traces; people are localized only when they are using their phones. In this work we define a data mining method for identifying people presence and understanding the impact of big events in big cities. We exploit the ability of the Sociometer for classifying mobile phone users in mobility categories through their presence profile. The experiment in cooperation with Orange Telecom has been conduced in Paris during the event F^ete de la Musique using a
privacy preserving protocol.},
	url = {http://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf},
	author = {Barbara Furletti and Lorenzo Gabrielli and Roberto Trasarti and Zbigniew Smoreda and Maarten Vanhoof and Cezary Ziemlicki}
}
@article {Trasarti2014,
	title = {Discovering urban and country dynamics from mobile phone data with spatial correlation patterns},
	journal = {Telecommunications Policy},
	year = {2014},
	pages = {-},
	abstract = {Abstract 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.},
	keywords = {Urban dynamics},
	issn = {0308-5961},
	doi = {http://dx.doi.org/10.1016/j.telpol.2013.12.002},
	url = {http://www.sciencedirect.com/science/article/pii/S0308596113002012},
	author = {Roberto Trasarti and Ana-Maria Olteanu-Raimond and Mirco Nanni and Thomas Couronn{\'e} and Barbara Furletti and Fosca Giannotti and Zbigniew Smoreda and Cezary Ziemlicki}
}
