a review of data-driven approaches to EU cross-border regions

Knowing where, how and for what purposes people frequently cross borders has significant policy implications. However, limited data availability often hinders effective decision-making. The lack of granular, reliable and comparable data frequently leads to an underestimation of the true scale of cross-border movements within the EU. Additionally, the assessment of the socioeconomic potential of border regions does not consider the hypothetical elimination of legal, administrative, infrastructural or linguistic obstacles. This contribution explores whether methodological innovations can (i) improve quantification of existing and potential cross-border interaction and (ii) support the delineation of functional cross-border areas. We first introduce the concept of cross-border areas as ‘living spaces’. We then examine conventional data sources such as administrative records, surveys and transport that try to capture existing cross-border flows. Following this, we showcase non-conventional data sources and methods, including digital traces, mobile big data and geospatial artificial intelligence to overcome gaps in data coverage, granularity and comparability. These enriched datasets allow us to present data-driven methods to define cross-border functional areas that integrate cross-border accessibility, interaction intensity and socio-economic performances. We finally discuss the current limits and opportunities towards an integrated, efficient and trustworthy data production for EU cross-border regions and worldwide.