2026-07-22T06:49:16Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18978 2024-03-06T11:03:07Z ddc:004 open_access

Betweenness Centrality in Spatial Networks: A Spatially Normalised Approach (Short Paper) Werner, Christian Loidl, Martin spatial network analysis edge betweenness centrality flow estimation SIBC spatial interaction spatial centrality urban analytics 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Christian Werner and Martin Loidl 2023 Is Part Of LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2023.83 urn:nbn:de:0030-drops-189781 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.83 eng https://creativecommons.org/licenses/by/4.0/legalcode