@article {961,
	title = {An analytical framework to nowcast well-being using mobile phone data},
	journal = {International Journal of Data Science and Analytics},
	volume = {2},
	number = {1-2},
	year = {2016},
	pages = {75{\textendash}92},
	abstract = {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{\textquoteright} 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 {\textquotedblleft}nowcast{\textquotedblright} the well-being and the socio-economic development of a territory.},
	doi = {10.1007/s41060-016-0013-2},
	author = {Luca Pappalardo and Maarten Vanhoof and Lorenzo Gabrielli and Zbigniew Smoreda and Dino Pedreschi and Fosca Giannotti}
}
@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}
}
