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.
Highlights
Big Data, the masses of digital breadcrumbs produced by the information technologies that humans use in their daily activities, allow us to scrutinize individual and collective behavior at an unprecedented scale, detail, and speed
We show that the diversity of human mobility significantly adds a predictive power in both regression and classification models, substantially more than the diversity of social contacts and demographic measures such as population density, a factor that is known to be correlated with the intensity of human activities [42,62]
It is surprising that widely accepted measures of human mobility have not been used so far. We overcome these issues by providing an analytical framework as support for official statistics, which allows for a systematic evaluation of the relations between relevant aspects of human behavior and the development of a territory
Summary
Big Data, the masses of digital breadcrumbs produced by the information technologies that humans use in their daily activities, allow us to scrutinize individual and collective behavior at an unprecedented scale, detail, and speed Building on this opportunity we have the potential capability of creating a digital nervous system of our society, enabling the measurement, monitoring and prediction of relevant aspects of the socio-economic structure in quasi real time [23]. Int J Data Sci Anal (2016) 2:75–92 of the inhabitants of a municipality is positively associated with a socio-economic indicator of poverty, independently surveyed by the official statistics institutes [17] This result suggests that social behavior, to some extent, is a proxy for the economic status of a given territory. We apply our analytical framework on large-scale mobile phone data—20 million users and 5.7 billions calls— and quantify the relations between human mobility, social interactions and economic development in France using municipality-level official statistics as external comparison measurements.
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