An intriguing open question is whether measurements made on Big Data
recording human activities can yield us 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
