Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/97742
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorLi, Zen_US
dc.creatorJiao, Len_US
dc.creatorZhang, Ben_US
dc.creatorXu, Gen_US
dc.creatorLiu, Jen_US
dc.date.accessioned2023-03-09T07:43:13Z-
dc.date.available2023-03-09T07:43:13Z-
dc.identifier.issn1009-5020en_US
dc.identifier.urihttp://hdl.handle.net/10397/97742-
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 Li, Z., Jiao, L., Zhang, B., Xu, G., & Liu, J. (2021). Understanding the pattern and mechanism of spatial concentration of urban land use, population and economic activities: A case study in Wuhan, China. Geo-spatial Information Science, 24(4), 678-694 is available at https://doi.org/10.1080/10095020.2021.1978276.en_US
dc.subjectConcentration degree indexen_US
dc.subjectConcentration patternsen_US
dc.subjectInverse S-shape functionen_US
dc.subjectSpatial concentrationen_US
dc.subjectSpatial regression modelen_US
dc.titleUnderstanding the pattern and mechanism of spatial concentration of urban land use, population and economic activities : a case study in Wuhan, Chinaen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage678en_US
dc.identifier.epage694en_US
dc.identifier.volume24en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1080/10095020.2021.1978276en_US
dcterms.abstractQuantifying the aggregation patterns of urban population, economic activities, and land use are essential for understanding compact development, but little is known about the difference among the distribution characteristics and how the built environment influences urban aggregation. In this study, five elements are collected in Wuhan, China, namely population density, floor area ratio, business POIs, road network and built-up area as the representative of urban population, economic activities and land use. An inverse S-shape function is employed to fit the elements’ macro distribution. An aggregation degree index is proposed to measure the aggregation level of urban elements. The kernel density estimation is used to identify the aggregation patterns. The spatial regression model is used to identify the built environment factors influencing the spatial distribution of urban elements. Results indicates that all urban elements decay outward from the city center in an inverse S-shape manner. The business Point-of-Interest (POI) density and population density are highly aggregated; floor area ratio and road density are moderately aggregated, whereas the built-up density is poorly aggregated. Three types of spatial aggregation patterns are identified: a point-shaped pattern, an axial pattern and a planar pattern. The spatial regression modeling shows that the built environment is associated with the distribution of the urban population, economic activities and land use. Destination accessibility factors, transit accessibility factors and land use diversity factors shape the distribution of the business POI density, floor area ratio and road density. Design factors are positively associated with population density, floor area ratio and built-up density. Future planning should consider the varying spatial concentration of urban population, economic activities and land use as well as their relationships with built environment attributes. Results of this study will provide a systematic understanding of aggregation of urban land use, population, and economic activities in megacities as well as some suggestions for planning and compact development.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationGeo-Spatial Information Science, 2021, v. 24, no. 4, p. 678-694en_US
dcterms.isPartOfGeo-spatial information science (地球空间信息科学学报)en_US
dcterms.issued2021-
dc.identifier.isiWOS:000714806900001-
dc.identifier.scopus2-s2.0-85118552724-
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.fundingTextNational Natural Science Foundation of China, NSFC: 41971368en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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