Sensing labour mobility flows of cross-border urban regions using machine learning and geolocated social network data

Abstract

Cross-border commuting is central to economic integration but remains difficult to measure at fine spatial scales. This study introduces a fully data-driven machine-learning framework to detect and classify daily transnational commuters using geolocated social media data. Across the Greater Region of Luxembourg, the Basque Country and the Øresund Region, eighteen network and spatiotemporal features support models reaching up to 98% overall accuracy. Zero-shot transfer from Luxembourg to the other regions demonstrates the potential and limitations of using digital traces for scalable cross-border mobility analysis.

Citation

Mendoza-Hurtado, M., Järv, O., Malekzadeh, M., Karasov, O., & Ortiz-Boyer, D. (2026). Sensing labour mobility flows of cross-border urban regions using machine learning and geolocated social network data. EPJ Data Science, 15(1), Article 55. https://doi.org/10.1140/epjds/s13688-026-00662-1