Normalized closeness centrality of urban networks: impact of the location of the catchment area and evaluation based on an idealized network

Author:

Chen Hsiao-HuiORCID,Dietrich UdoORCID

Abstract

AbstractThe decision of where to locate the catchment area of an urban network exerts significant influence on the indicator values and in this research this influence is referred to as the placement effect. Placement effect has significant impact on the studies at the neighborhood scale focusing on the structural properties of the network models, the network analysis results and centrality measures, the inferred movement patterns and the accessibility to destination. Placement effect becomes even more significant when multiple catchment areas are sampled to be compared or classified. This research examines placement effect on one of the most affected indicators, closeness centrality, and proposes using an idealized network as a reference to be compared with the real network in order to find a solution to mitigate the placement effects. By comparing the normalized closeness centrality in the real network with that in the idealized network, we can (1) evaluate the placement effect on the closeness centrality and (2) find the threshold distance in order to mitigate the placement effect. The results show that the closeness centrality of the same node varies remarkably depending on its position and how central it is in the chosen catchment area. Specifically, in the selected areas in this research, if the center point of a catchment area is moved by more than 100 m away from the original center point, the closeness centrality of the same node starts to be significantly influenced by the placement effect. The threshold distance of 100 m offers a recommendation that a direct comparison of the closeness centrality between different nodes in the same catchment area should be drawn only if these nodes are less than 100 m away from each other. In other words, when comparing two nodes located further than the threshold distance from each other, it is advisable to create two separate catchment areas, where these nodes serve as the center points. It should be noted that the threshold distance of 100 m derived specifically from the current research should not be generalized to other cases. The threshold distance of different case studies remains open for further investigation in the future as it may vary among cities or areas.

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computer Networks and Communications,Multidisciplinary

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