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Article: Urban landscape as a spatial representation of land rent: A quantitative analysis

TitleUrban landscape as a spatial representation of land rent: A quantitative analysis
Authors
KeywordsLand rent
Urban landscape
Association rules analysis
Big data
China
Issue Date2019
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/ceus
Citation
Computers, Environment and Urban Systems, 2019, v. 74, p. 62-73 How to Cite?
AbstractDue to the emergence of geographical ‘big data,’ the field of urban studies is enjoying many new research opportunities. By using several sources of geographical ‘big data’, an analysis framework was structured to measure the urban landscape based on three aspects: city plan, pattern of building form, and urban land use. An association rule analysis was used to explore the relationship between land rent and the urban landscape, and the results indicate that the urban landscape differs across urban areas. The blocks classified as being located in main centers were associated with more convenient public transportation, denser road networks, more vertical street space, more diverse block patterns, more flexible architecture arrangements, mixed uses, high density, and more services. By contrast, non-center areas usually comprised blocks that were larger, tabular, single-purpose, and more regular. Non-center areas often cannot provide high-quality public goods, and they contain scattered large industrial enterprises. The urban landscapes of sub-center blocks fell in between these two urban areas. To our knowledge, this is the first paper that attempts to explain the relationship between land rent and urban landscapes based on ‘big data,’ and our study may provide meaningful insights into urban design for government officials and academics.
Persistent Identifierhttp://hdl.handle.net/10722/278834
ISSN
2020 Impact Factor: 5.324
2020 SCImago Journal Rankings: 1.549
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, J-
dc.contributor.authorWang, S-
dc.contributor.authorZhang, Y-
dc.contributor.authorZHANG, A-
dc.contributor.authorXIA, C-
dc.date.accessioned2019-10-21T02:14:53Z-
dc.date.available2019-10-21T02:14:53Z-
dc.date.issued2019-
dc.identifier.citationComputers, Environment and Urban Systems, 2019, v. 74, p. 62-73-
dc.identifier.issn0198-9715-
dc.identifier.urihttp://hdl.handle.net/10722/278834-
dc.description.abstractDue to the emergence of geographical ‘big data,’ the field of urban studies is enjoying many new research opportunities. By using several sources of geographical ‘big data’, an analysis framework was structured to measure the urban landscape based on three aspects: city plan, pattern of building form, and urban land use. An association rule analysis was used to explore the relationship between land rent and the urban landscape, and the results indicate that the urban landscape differs across urban areas. The blocks classified as being located in main centers were associated with more convenient public transportation, denser road networks, more vertical street space, more diverse block patterns, more flexible architecture arrangements, mixed uses, high density, and more services. By contrast, non-center areas usually comprised blocks that were larger, tabular, single-purpose, and more regular. Non-center areas often cannot provide high-quality public goods, and they contain scattered large industrial enterprises. The urban landscapes of sub-center blocks fell in between these two urban areas. To our knowledge, this is the first paper that attempts to explain the relationship between land rent and urban landscapes based on ‘big data,’ and our study may provide meaningful insights into urban design for government officials and academics.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/ceus-
dc.relation.ispartofComputers, Environment and Urban Systems-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectLand rent-
dc.subjectUrban landscape-
dc.subjectAssociation rules analysis-
dc.subjectBig data-
dc.subjectChina-
dc.titleUrban landscape as a spatial representation of land rent: A quantitative analysis-
dc.typeArticle-
dc.identifier.emailZHANG, A: anqi.xc@pku.edu.cn-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.compenvurbsys.2018.12.004-
dc.identifier.scopuseid_2-s2.0-85058176267-
dc.identifier.hkuros307854-
dc.identifier.volume74-
dc.identifier.spage62-
dc.identifier.epage73-
dc.identifier.isiWOS:000458227000006-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0198-9715-

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