Measuring hub locations in time-evolving spatial interaction networks based on explicit spatiotemporal coupling and group centrality. Issue 2 (1st February 2022)

• Record Type: Journal Article
• Authors: Kang, Chaogui; Jiang, Zhuojun; Liu, Yu
• Is Part Of: International journal of geographical information science. Volume 36:Issue 2(2022)
• Page Start: 360
• Page End: 381
• Publication Date: 2022-02-01
• Subjects: Spatial interaction -- temporal dynamics -- multilayer network -- spatiotemporal coupling -- group centrality; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1863411
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: As the essence of urban spatiotemporal interaction systems, hubs and centers empower cities to enhance socioeconomic prosperity and sustainability. However, a city manifests a time-evolving spatial interaction network with latent temporal interactions and irregular spatial partitions. This phenomenon is termed the spatiotemporal inconsistency problem. The aggregate, single-layer network model is defective for capturing the importance of locations in such time-evolving spatial interaction systems. This article therefore proposes a novel multilayer network model based on the nature of inherent spatial and temporal dependencies of urban interactions. First, the spatial agglomeration and the temporal correlation are explicitly modeled in multilayer networks for alleviating the spatiotemporal inconsistency problem. Secondly, generalized centrality metrics from a single-layered static network to the multi-layered dynamic network are acquired in order to discover grouped hub locations over time. Lastly, the capability of the proposed method is evaluated by an empirical analysis of the taxi mobility networks of Beijing, China, from 2012 to 2017. The empirical analysis indicates that the proposed method enables the identification of typical hub locations clustered in space and stable over time. This ability is essential to understand the centrality of locations informed by noisy and inconsistent data in their spatial and temporal dimensions.