Abstract
Reliable and timely information on socio-economic status and divides is critical to social and economic research and policing. Novel data sources from mobile communication platforms have enabled new cost-effective approaches and models to investigate social disparity, but their lack of interpretability, accuracy or scale has limited their relevance to date. We investigate the divide in digital mobile service usage with a large dataset of 3.7 billion time-stamped and geo-referenced mobile traffic records in a major European country, and find profound geographical unevenness in mobile service usage—especially on news, e-mail, social media consumption and audio/video streaming. We relate such diversity with income, educational attainment and inequality, and reveal how low-income or low-education areas are more likely to engage in video streaming or social media and less in news consumption, information searching, e-mail or audio streaming. The digital usage gap is so large that we can accurately infer the socio-economic status of a small area or even its Gini coefficient only from aggregated data traffic. Our results make the case for an inexpensive, privacy-preserving, real-time and scalable way to understand the digital usage divide and, in turn, poverty, unemployment or economic growth in our societies through mobile phone data.
Highlights
Inequality is a central societal problem, especially within rapidly expanding urban areas
Since our traffic data are collected by base station (BS), they include app usage by residents of that area and users from other areas that visit that BS throughout the day
To link traffic data to the residents of a particular statistical area, we implemented a temporal consolidation of our data in which we only consider the mobile service usage recorded during the hours in which we can safely consider people to be at home, i.e. between 20.00 and 7.00 during weekdays
Summary
Inequality is a central societal problem, especially within rapidly expanding urban areas. While it is a crucial driver for economic growth [1], the progressive clusterization of workers, industries, companies and services in cities has a tremendous cost in terms of segregation and discrimination. This cost is economic: in the same city, different areas can have a 10- to 15-year imbalance in life expectancy and highly divergent education levels, with little chances of social mobility [2]. The traditional ways of understanding cities tend to explain what happened 5 years earlier rather than nowcasting or even predicting urban transformations
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