Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/97636
| Title: | Exploring metro vibrancy and its relationship with built environment : a cross-city comparison using multi-source urban data | Authors: | Tu, W Zhu, T Zhong, C Zhang, X Xu, Y Li, Q |
Issue Date: | 2022 | Source: | Geo-Spatial Information Science, 2022, v. 25, no. 2, p. 182-196 | Abstract: | Recent urban transformations have led to critical reflections on the blighted urban infrastructures and called for re-stimulating vital urban places. Especially, the metro has been recognized as the backbone infrastructure for urban mobility and the associated economy agglomeration. To date, limited research has been devoted to investigating the relationship between metro vitality and built environment in mega-cities empirically. This paper presents a multisource urban data-driven approach to quantify the metro vibrancy and its association with the underlying built environment. Massive smart card data is processed to extract metro ridership, which denotes the vibrancy around the metro station in physical space. Social media check-ins are crawled to measure the vitality of metros in virtual spaces. Both physical and virtual vibrancy are integrated into a holistic metro vibrancy metric using an entropy-based weighting method. Certain built environment characteristics, including land use, transportation and buildings are modeled as independent variables. The significant influences of built environmental factors on the metro vibrancy are unraveled using the ordinary least square regression and the spatial lag model. With experiments conducted in Shenzhen, Singapore and London, this study comes up with a conclusion that spatial distributions of metro vibrancy metrics in three cities are spatially autocorrelated. The regression analysis suggests that in all the three cities, more affluent urban areas tend to have higher metro virbrancy, while the road density, land use and buildings tend to impact metro vibrancy in only one or two cities. These results demonstrate the relationship between the metro vibrancy and built environment is affected by complex urban contexts. These findings help us to understand metro vibrancy thus make proper policy to re-stimulate the important metro infrastructure in the future. | Keywords: | Smart card data Social media Spatial lag model Spatial-autocorrelation Urban vibrancy |
Publisher: | Taylor & Francis Asia Pacific (Singapore) | Journal: | Geo-spatial information science (地球空间信息科学学报) | ISSN: | 1009-5020 | EISSN: | 1993-5153 | DOI: | 10.1080/10095020.2021.1996212 | Rights: | © 2021 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The following publication Yijing Li, Qunshan Zhao, Chen Zhong. (2022) GIS and urban data science. Annals of GIS 28:2, pages 89-92. is available at https://doi.org/10.1080/10095020.2021.1996212. |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Tu_Exploring_Metro_Vibrancy.pdf | 7.24 MB | Adobe PDF | View/Open |
Page views
146
Last Week
0
0
Last month
Citations as of Nov 9, 2025
Downloads
108
Citations as of Nov 9, 2025
SCOPUSTM
Citations
50
Citations as of Dec 19, 2025
WEB OF SCIENCETM
Citations
48
Citations as of Dec 18, 2025
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.



