Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91515
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.contributorResearch Institute for Sustainable Urban Development-
dc.creatorKan, Z-
dc.creatorKwan, MP-
dc.creatorHuang, J-
dc.creatorWong, MS-
dc.creatorLiu, D-
dc.date.accessioned2021-11-03T06:54:18Z-
dc.date.available2021-11-03T06:54:18Z-
dc.identifier.issn1361-1682-
dc.identifier.urihttp://hdl.handle.net/10397/91515-
dc.language.isoenen_US
dc.publisherWiley-Blackwellen_US
dc.rights© 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.rightsThe following publication Kan, Z., Kwan, M.-P., Huang, J., Sing Wong, M., & Liu, D. (2021). Comparing the space-time patterns of high-risk areas in different waves of COVID-19 in Hong Kong. Transactions in GIS, 25, 2982– 3001 is available at https://doi.org/10.1111/tg is.12800en_US
dc.titleComparing the space-time patterns of high-risk areas in different waves of COVID-19 in Hong Kongen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2982-
dc.identifier.epage3001-
dc.identifier.volume25-
dc.identifier.issue6-
dc.identifier.doi10.1111/tgis.12800-
dcterms.abstractThis study compares the space-time patterns and characteristics of high-risk areas of COVID-19 transmission in Hong Kong between January 23 and April 14 (the first and second waves) and between July 6 and Aug. 29 (the third wave). Using space-time scan statistics and the contact tracing data of individual confirmed cases, we detect the clusters of residences of, and places visited by, both imported and local cases. We also identify the built-environment and demographic characteristics of the high-risk areas during different waves of COVID-19. We find considerable differences in the space-time patterns and characteristics of high-risk residential areas between waves. However, venues and buildings visited by the confirmed cases in different waves have similar characteristics. The results can inform policymakers to target mitigation measures in high-risk areas and at vulnerable groups, and provide guidance to the public to avoid visiting and conducting activities at high-risk places.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransactions in GIS, Dec. 2021, v. 25, no. 6, p. 2982-3001-
dcterms.isPartOfTransactions in GIS-
dcterms.issued2021-12-
dc.identifier.scopus2-s2.0-85109763671-
dc.identifier.eissn1467-9671-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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