Identifying Rich Clubs in Spatiotemporal Interaction Networks

Jacob Kruse, Song Gao, Yuhan Ji, Keith Levin, Qunying Huang and Kenneth R. Mayer

Annals of the American Association of Geographers, 2025, vol. 115, issue 4, 899-922

Abstract: Spatial networks are widely used in various fields to represent and analyze interactions or relationships between locations or spatially distributed entities. Existing studies have proposed methods for hub identification and community detection in spatial networks, but relatively few have focused on quantifying the strength or density of connections shared within a community of hubs across space and time. Borrowing from network science, there is a relevant concept known as the rich club phenomenon, which describes the tendency of rich nodes to form densely interconnected subnetworks. Although there are established methods to quantify topological, weighted, and temporal rich clubs individually, there is limited research on measuring the rich club effect in spatially weighted temporal networks, which could be particularly useful for studying dynamic spatial interaction networks. To address this gap, we introduce the spatially weighted temporal rich club (WTRC), a metric that quantifies the strength and consistency of connections between rich nodes in a spatiotemporal network. Additionally, we present a unified rich club framework that distinguishes the WTRC effect from other rich club effects, providing a way to measure topological, weighted, and temporal rich club effects together. Through two case studies of human mobility networks at different spatial scales, we demonstrate how the WTRC is able to identify significant weighted temporal rich club effects, whereas the unweighted equivalent in the same network either fails to detect a rich club effect or inaccurately estimates its significance. In each case study, we explore the spatial layout and temporal variations revealed by the WTRC analysis, showcasing its value in studying spatiotemporal interaction networks. This research offers new insights into the study of spatiotemporal networks, with critical implications for applications such as transportation, redistricting, and epidemiology.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/24694452.2025.2464806 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:raagxx:v:115:y:2025:i:4:p:899-922

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/raag21

DOI: 10.1080/24694452.2025.2464806

Access Statistics for this article

Annals of the American Association of Geographers is currently edited by Jennifer Cassidento

More articles in Annals of the American Association of Geographers from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().