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http://hdl.handle.net/10397/93537
Title: | A multilayer mobility network approach to inferring urban structures using shared mobility and taxi data | Authors: | Zhang, J Liu, X Senousi, AM |
Issue Date: | Dec-2021 | Source: | Transactions in GIS, Dec. 2021, v. 25, no. 6, p. 2840-2865 | Abstract: | Developing data-driven approaches to understanding urban structures is important for urban planning. However, it is still challenging to combine different transport datasets into a unified framework and reveal the dynamics of urban structures with the emergence of shared mobility. In this study, we propose two empirical multilayer networks to infer and profile urban structures. First, a temporal network is constructed using traditional taxi data over years to reveal the urban structures. Second, a multimodal network is constructed using shared mobility and traditional taxi data over a year to reveal the urban structures. The proposed networks are tested in New York City using a large volume of shared bike, shared vehicle, and traditional taxi data. The multilayer network centralities and community detection enable us to profile the characteristics of the urban flows and urban structure. The analytical results allow us to acquire a better understanding of urban structures from a multilayer perspective, and also provide a geocomputation framework that is useful for urban and geographic researchers. | Publisher: | Wiley-Blackwell | Journal: | Transactions in GIS | ISSN: | 1361-1682 | EISSN: | 1467-9671 | DOI: | 10.1111/tgis.12817 | Rights: | © 2021 John Wiley & Sons Ltd. This is the peer reviewed version of the following article: Zhang, J., Liu, X., & Senousi, A. M. (2021). A multilayer mobility network approach to inferring urban structures using shared mobility and taxi data. Transactions in GIS, 25, 2840–2865, which has been published in final form at https://doi.org/10.1111/tgis.12817. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. |
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