PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Pappalardo L., Vanhoof M., Gabrielli L., Smoreda Z., Pedreschi D., Giannotti F. An analytical framework to nowcast well-being using mobile phone data. In: International Journal of Data Science and Analytics, vol. 2 (1) pp. 75 - 92. Springer, 2016.
 
 
Abstract
(English)
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.
URL: http://link.springer.com/article/10.1007/s41060-016-0013-2
DOI: 10.1007/s41060-016-0013-2
Subject Big data
Complex systems
Forecasting
Human mobility
Social networks
Economic development
Economic data
Nowcasting
H.2.8 DATABASE MANAGEMENT. Database Applications. Data Mining


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional