@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}
}
