 Bridges across borders: A clustering approach to support EU regional policyAris Christodoulou and Panayotis Christidis Journal of Transport Geography, 2020, vol. 83, issue C 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. Keywords: Border regions; Accessibility; Network efficiency; Clustering; Machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:83:y:2020:i:c:s0966692319300997 DOI: 10.1016/j.jtrangeo.2020.102666 Access Statistics for this article Journal of Transport Geography is currently edited by Frank Witlox More articles in Journal of Transport Geography from Elsevier |
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