Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France

TL;DR

This study demonstrates that mobile app usage data can effectively predict socio-economic indicators across France, offering a cost-effective, up-to-date alternative to traditional data sources with promising implications for social research and policy.

Contribution

The paper introduces a large-scale analysis linking mobile app usage patterns to socio-economic indicators, achieving significant predictive accuracy and revealing disparities.

Findings

Best model attains R-squared score up to 0.66

Mobile app patterns can reveal socio-economic disparities

Potential for future interventions using network analysis

Abstract

Socio-economic indicators provide context for assessing a country's overall condition. These indicators contain information about education, gender, poverty, employment, and other factors. Therefore, reliable and accurate information is critical for social research and government policing. Most data sources available today, such as censuses, have sparse population coverage or are updated infrequently. Nonetheless, alternative data sources, such as call data records (CDR) and mobile app usage, can serve as cost-effective and up-to-date sources for identifying socio-economic indicators. This work investigates mobile app data to predict socio-economic features. We present a large-scale study using data that captures the traffic of thousands of mobile applications by approximately 30 million users distributed over 550,000 km square and served by over 25,000 base stations. The dataset covers the whole France territory and spans more than 2.5 months, starting from 16th March 2019 to 6th June 2019. Using the app usage patterns, our best model can estimate socio-economic indicators (attaining an R-squared score upto 0.66). Furthermore, using models' explainability, we discover that mobile app usage patterns have the potential to reveal socio-economic disparities in IRIS. Insights of this study provide several avenues for future interventions, including user temporal network analysis to understand evolving network patterns and exploration of alternative data sources.