Journal cover Journal topic
Proceedings of the ICA
Journal topic
Volume 2
Proc. Int. Cartogr. Assoc., 2, 141, 2019
https://doi.org/10.5194/ica-proc-2-141-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Proc. Int. Cartogr. Assoc., 2, 141, 2019
https://doi.org/10.5194/ica-proc-2-141-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Jul 2019

10 Jul 2019

Building-network: Concept, generation method and centrality analysis

Xiao Wang and Dirk Burghardt Xiao Wang and Dirk Burghardt
  • Dresden University of Technology, Institute of Cartography, Germany

Keywords: building-network, network theory, centrality analysis

Abstract. Buildings are among the most important features of cities. In the suburban or rural regions, buildings are normally constructed along the roads, which forms the smooth and consistent patterns so that the building arrangements also can be described with network models. In previous studies, network theory has achieved good performance in cartography and GIS. In this paper, a study of a building-network is proposed, including the concepts, generation methods and centrality analysis. Firstly, with the constraint Delaunay triangulation and the refinement strategy by facing ratio, the building-network is generated by considering the buildings and the proximal segments as the nodes and segments of the network, respectively. Then, centrality analysis is applied on the building-network, aiming to reveal the crucial relationships among buildings, which is useful for understanding the structural properties of the complex network. Four different centrality measures, i.e. degree, closeness, betweenness, and eigenvector centrality, are calculated based on the building-networks. The buildings show different distribution effects and patterns under the four centrality measures. From the results, the degree centrality reveals the local centre of the region; closeness and eigenvector centrality have the ability to cluster buildings into different groups; while betweenness centrality can detect the linear patterns. Therefore, using network theory to analyse buildings can reveal some inner relationships of buildings and has great potential in the application of building pattern detection, classification, clustering and further generalization.

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