Olle Järv (p), Ate Poorthuis, Håvard Aagesen, Tuomas Väisänen, Michaela Söderholm

Matteo Berzi

One of the main objectives for the EU is to enhance integration and cohesion within internal border regions by strengthening political, economic, and social ties through cross-border cooperation and governance. While institutional and economic ties across borders are well examined and understood, the perspective of people are less known. Besides potential access to cross borders, actual interactions of people and their cross-border practices are challenging to capture. Yet, these social interactions contribute to the (re)production of functional cross-border regions.

To narrow this research gap, we stem from the conceptual framework of mobility as a tool to understand society and propose to use mobile big data sources to capture mobilities of people. Mobility reveals cross-border interactions of local people, which can characterize how a border region functions across the border.

Here, we examine all internal border regions in Europe using social media data as an example to empirically examine flows of cross-border mobilities. By separating flows from both side of a border, we show how the proportion of directionality, temporal rhythms and spatial extent of cross-border flows enable to capture the interaction patterns of cross-border regions. The multilayered analysis of given interactions reveals various clusters that characterize integration structure and level of cross-border regions – e.g. higher integration levels with mutual interactions, average integration level with mixed interactions and lower integration level with one-way interactions. We conclude by addressing how digital sources, in general, and mobile big data specifically can benefit research and practice for policy and planning of cross-border regions in the European Union and beyond.

Tobias Chilla, Elias Günther (p), Hannah Paul, Stefan Hippe, Dominik Bertram

Järv Olle

Territorial analyses of border regions are hampered by data gaps – almost per definition. Despite all efforts for data harmonisation across borders, the challenges remain relevant. The alternatives to official statistical data are threefold, namely a) new/big data sources, b) empirical (primary) data, and c) ‘derived data’. Derived data are those information that combine heterogeneous sources of existing data with meaningful data treatments, as in particular interpolation, regionalisation, and expert calibration.
In our presentation, we explore the potentials and limitations of these approaches for border regions. We illustrate our arguments with examples from ongoing projects either at a pan-European level or for specific border regions. The examples include cross-border accessibility information, tourism and mobility patterns, cross-border commuting at a fine scale and economic interlinkages in different sectors across borders. Ensuring data quality is an important concern, in particular when private data, web scraping information, and expert interviews come into play. In these cases, data combinations and calibrations can help. Against this background, deriving meaningful data in ‘difficult’ themes is a challenging, but promising path. In times of datafication, there is an obvious need to exploit the involved potentials. This is in particular true for border-regions as prominent ‘data-gaps’ where harmonized/fine-scaled data is often lacking.

Marie-France Gaunard-Anderson (p)

Benedikt Herrmann

The case of the Greater Region is complex because it gathers together five regions belonging to four countries (Belgium, France, Germany, Luxembourg), but it has a long experience in cross-border cooperation. It aims to develop a coherent space, to build a common cross-border living area and reduce the border barrier role. The EU encourages the development of cross-border regions as they are part of the European integration process; they contribute to economic and social cohesion and stability beyond borders. For the past few decades, cross-border cooperation has been getting stronger in different fields, such as health. Many cross-border projects have been carried out in the field of health, above all since the pandemic of Covid19 in 2020. People can cross the border to get treatment; nurses and doctors can apply for a job on the other side of the border. The mobility of patients is increasing, but that of health professionals as well. In the Greater Region, this health mobility is due above all to the attractiveness of Luxembourg, due to better infrastructures, medical equipment and higher wages for medical staff. How can we measure health mobility? It needs data to analyze the growth of cross-border crossings in the field of health. What kind of data can we find and where? It took time to collect and analyze data in the field of health, because there are many data sources scattered between the five regions of the Greater Region. If gathering data and analyzing it is fundamental to guiding policy development strategies, the question is also how policymakers can get them and use them? They are aware of the need to have reliable data to target their actions and a solution could be the creation of a cross-border health observatory, which will be necessary in the Greater Region.