An analytical framework to nowcast well-being using mobile phone data

TL;DR

This paper presents a data-driven framework using mobile phone data to estimate and predict socio-economic development and well-being, highlighting mobility diversity as a key indicator.

Contribution

It introduces a novel analytical framework that leverages mobility and social measures from mobile data to nowcast socio-economic indicators.

Findings

Mobility diversity correlates strongly with socio-economic indicators.

Mobility entropy is the most important predictor in models.

The framework enables real-time monitoring of well-being.

Abstract

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.