Betweenness Centrality in Spatial Networks: A Spatially Normalised Approach (Short Paper)

Authors Christian Werner , Martin Loidl

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Author Details

Christian Werner
  • Department of Geoinformatics, University of Salzburg, Austria
Martin Loidl
  • Department of Geoinformatics, University of Salzburg, Austria

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Christian Werner and Martin Loidl. Betweenness Centrality in Spatial Networks: A Spatially Normalised Approach (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 83:1-83:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Centrality metrics are essential to network analysis. They reveal important morphological properties of networks, indicating e.g. node or edge importance. Applications are manifold, ranging from biology to transport planning. However, while being commonly applied in spatial contexts such as urban analytics, the implications of the spatial configuration of network elements on these metrics are widely neglected. As a consequence, a systematic bias is introduced into spatial network analyses. When applied to real-world problems, unintended side effects and wrong conclusions might be the result. In this paper, we assess the impact of node density on betweenness centrality. Furthermore, we propose a method for computing spatially normalised betweenness centrality. We apply it to a theoretical case as well as real-world transport networks. Results show that spatial normalisation mitigates the prevalent bias of node density.

Subject Classification

ACM Subject Classification
  • Theory of computation → Theory and algorithms for application domains
  • Mathematics of computing → Graph algorithms
  • Mathematics of computing → Network flows
  • Information systems → Geographic information systems
  • spatial network analysis
  • edge betweenness centrality
  • flow estimation
  • SIBC
  • spatial interaction
  • spatial centrality
  • urban analytics


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