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Article: Predicting vibrancy of metro station areas considering spatial relationships through graph convolutional neural networks: The case of Shenzhen, China

TitlePredicting vibrancy of metro station areas considering spatial relationships through graph convolutional neural networks: The case of Shenzhen, China
Authors
Xiao, LLo, SZhou, JLIU, JYang, L
Issue Date2020
Citation
Environment and Planning B: Urban Analytics and City Science, 2020, p. 239980832097786 How to Cite?
DOI: http://dx.doi.org/10.1177/2399808320977866
Persistent Identifierhttp://hdl.handle.net/10722/305143
ISI Accession Number ID
WOS:000627543900001

 

DC FieldValueLanguage
dc.contributor.authorXiao, L-
dc.contributor.authorLo, S-
dc.contributor.authorZhou, J-
dc.contributor.authorLIU, J-
dc.contributor.authorYang, L-
dc.date.accessioned2021-10-05T02:40:20Z-
dc.date.available2021-10-05T02:40:20Z-
dc.date.issued2020-
dc.identifier.citationEnvironment and Planning B: Urban Analytics and City Science, 2020, p. 239980832097786-
dc.identifier.urihttp://hdl.handle.net/10722/305143-
dc.languageeng-
dc.relation.ispartofEnvironment and Planning B: Urban Analytics and City Science-
dc.titlePredicting vibrancy of metro station areas considering spatial relationships through graph convolutional neural networks: The case of Shenzhen, China-
dc.typeArticle-
dc.identifier.emailZhou, J: zhoujp@hku.hk-
dc.identifier.authorityZhou, J=rp02236-
dc.identifier.doi10.1177/2399808320977866-
dc.identifier.scopuseid_2-s2.0-85097291665-
dc.identifier.hkuros326038-
dc.identifier.spage239980832097786-
dc.identifier.epage239980832097786-
dc.identifier.isiWOS:000627543900001-

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