License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.83
URN: urn:nbn:de:0030-drops-189781
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18978/
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Werner, Christian ; Loidl, Martin

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

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LIPIcs-GIScience-2023-83.pdf (0.8 MB)

Abstract

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.

BibTeX - Entry

@InProceedings{werner_et_al:LIPIcs.GIScience.2023.83,
  author =	{Werner, Christian and Loidl, Martin},
  title =	{{Betweenness Centrality in Spatial Networks: A Spatially Normalised Approach}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{83:1--83:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18978},
  URN =		{urn:nbn:de:0030-drops-189781},
  doi =		{10.4230/LIPIcs.GIScience.2023.83},
  annote =	{Keywords: spatial network analysis, edge betweenness centrality, flow estimation, SIBC, spatial interaction, spatial centrality, urban analytics}
}

Keywords: spatial network analysis, edge betweenness centrality, flow estimation, SIBC, spatial interaction, spatial centrality, urban analytics
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023
Supplementary Material: Other (Code and examples): https://doi.org/10.5281/zenodo.8125632

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