Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/97636
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorTu, Wen_US
dc.creatorZhu, Ten_US
dc.creatorZhong, Cen_US
dc.creatorZhang, Xen_US
dc.creatorXu, Yen_US
dc.creatorLi, Qen_US
dc.date.accessioned2023-03-09T07:42:06Z-
dc.date.available2023-03-09T07:42:06Z-
dc.identifier.issn1009-5020en_US
dc.identifier.urihttp://hdl.handle.net/10397/97636-
dc.language.isoenen_US
dc.publisherTaylor & Francis Asia Pacific (Singapore)en_US
dc.rights© 2021 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.rightsThis 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.en_US
dc.rightsThe 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.en_US
dc.subjectSmart card dataen_US
dc.subjectSocial mediaen_US
dc.subjectSpatial lag modelen_US
dc.subjectSpatial-autocorrelationen_US
dc.subjectUrban vibrancyen_US
dc.titleExploring metro vibrancy and its relationship with built environment : a cross-city comparison using multi-source urban dataen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage182en_US
dc.identifier.epage196en_US
dc.identifier.volume25en_US
dc.identifier.issue2en_US
dc.identifier.doi10.1080/10095020.2021.1996212en_US
dcterms.abstractRecent 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.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationGeo-Spatial Information Science, 2022, v. 25, no. 2, p. 182-196en_US
dcterms.isPartOfGeo-spatial information science (地球空间信息科学学报)en_US
dcterms.issued2022-
dc.identifier.isiWOS:000725957600001-
dc.identifier.scopus2-s2.0-85121030652-
dc.identifier.eissn1993-5153en_US
dc.description.validate202303 bcwwen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOS-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was jointly supported by the National Natural Science Foundation of China [grant numbers 42071360 and 71961137003]; Natural Science Foundation of Guangdong Provinces [grant number 2019A1515011049]; the ESRC under JPI Urban Europe/NSFC [grant number ES/T000287/1]; the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme[grant number 949670]; the Basic Research Program of Shenzhen Science and Technology Innovation Committee[JCYJ20180305125113883].en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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