Abstract We present a methodology to analyse high resolution population and transport data in order to assess cross-border connectivity within the European Union. Transport infrastructure can strongly influence cross-border interactions as well as regional, urban or local development. The analysis is carried out using a policy perspective, with network efficiency as the main indicator of accessibility. The aim is to allow the quantification of the quality of cross-border road connections and the identification of areas where infrastructure improvements can lead to higher benefits. We propose a machine learning approach that combines cell level route assignment and k-means clustering at a fine −1 square km- population grid. The outputs cover all internal EU land borders and consist of sets of spatial clusters that meet user-defined policy criteria. The results can be used as input for investment decisions and can be easily combined with other policy support tools for tailored multi-criteria analysis.
Graphical abstract Display Omitted
Highlights Internal EU borders are still a barrier to cross-border collaboration. We apply an analytical approach to measure road network efficiency at highly detailed spatial level. Potential areas for policy intervention are clustered together based on user-defined criteria. Tangible, physical infrastructure may still play a role as a facilitator of cross-border collaborationt. Border regions can benefit from the spill-over effects of improved infrastructure in the neighbouring country.
Bridges across borders: A clustering approach to support EU regional policy
2020-02-11
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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