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Article: Delineating early warning zones in rapidly growing metropolitan areas by integrating a multiscale urban growth model with biogeography-based optimization

TitleDelineating early warning zones in rapidly growing metropolitan areas by integrating a multiscale urban growth model with biogeography-based optimization
Authors
KeywordsEarly warning zones
Urban agglomerations
Planning scenarios
Illegal development
Cellular automata
Issue Date2020
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/landusepol
Citation
Land Use Policy, 2020, v. 90, p. article no. 104332 How to Cite?
AbstractUrban agglomerations have become the core areas of new urbanization in China, leading to major structural adjustments in regional development patterns and thus threatening the ecosystem. Predicting future urban growth and delineating early warning zones can provide scientific guidance for land use planning and ecological protection. However, limited research has targeted fast-urbanizing metropolitan areas, which can be particularly difficult compared with existing approaches conducted in a single city. Another concern is that the adjustable demand of urban land in the future can invalidate simple and rigid zoning schemes. This study proposed a framework of delineating hierarchical early warning zones to represent different probabilities of potential development in vulnerable areas. Specifically, a multiscale urban growth model and biogeography-based optimization (BBO) were integrated for calibrating transition rules and simulating historical urban evolution (2005–2015) in the rapidly growing Yangtze River middle reaches megalopolis (YRMRM) in China. Then, the future urban land demand in 2025 was predicted under different economic development strategies, and multiple urban growth scenarios were simulated by coupling urban demand and spatial planning policies. Finally, simulation results of different scenarios were overlaid in a GIS environment to provide three levels of zones for the early warning of illegal development in the protected areas. Results indicate that BBO performed better in calibrating parameters of the urban growth model than did particle swarm optimization and genetic algorithm. The prediction of future urban dynamics suggests that urbanization will continue in this region until 2025, and these early warning areas should be given attention for farmland protection and ecological security in the YRMRM.
Persistent Identifierhttp://hdl.handle.net/10722/289951
ISSN
2020 Impact Factor: 5.398
2020 SCImago Journal Rankings: 1.668
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXIA, C-
dc.contributor.authorZHANG, A-
dc.contributor.authorWang, H-
dc.contributor.authorLiu, J-
dc.date.accessioned2020-10-22T08:19:48Z-
dc.date.available2020-10-22T08:19:48Z-
dc.date.issued2020-
dc.identifier.citationLand Use Policy, 2020, v. 90, p. article no. 104332-
dc.identifier.issn0264-8377-
dc.identifier.urihttp://hdl.handle.net/10722/289951-
dc.description.abstractUrban agglomerations have become the core areas of new urbanization in China, leading to major structural adjustments in regional development patterns and thus threatening the ecosystem. Predicting future urban growth and delineating early warning zones can provide scientific guidance for land use planning and ecological protection. However, limited research has targeted fast-urbanizing metropolitan areas, which can be particularly difficult compared with existing approaches conducted in a single city. Another concern is that the adjustable demand of urban land in the future can invalidate simple and rigid zoning schemes. This study proposed a framework of delineating hierarchical early warning zones to represent different probabilities of potential development in vulnerable areas. Specifically, a multiscale urban growth model and biogeography-based optimization (BBO) were integrated for calibrating transition rules and simulating historical urban evolution (2005–2015) in the rapidly growing Yangtze River middle reaches megalopolis (YRMRM) in China. Then, the future urban land demand in 2025 was predicted under different economic development strategies, and multiple urban growth scenarios were simulated by coupling urban demand and spatial planning policies. Finally, simulation results of different scenarios were overlaid in a GIS environment to provide three levels of zones for the early warning of illegal development in the protected areas. Results indicate that BBO performed better in calibrating parameters of the urban growth model than did particle swarm optimization and genetic algorithm. The prediction of future urban dynamics suggests that urbanization will continue in this region until 2025, and these early warning areas should be given attention for farmland protection and ecological security in the YRMRM.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/landusepol-
dc.relation.ispartofLand Use Policy-
dc.subjectEarly warning zones-
dc.subjectUrban agglomerations-
dc.subjectPlanning scenarios-
dc.subjectIllegal development-
dc.subjectCellular automata-
dc.titleDelineating early warning zones in rapidly growing metropolitan areas by integrating a multiscale urban growth model with biogeography-based optimization-
dc.typeArticle-
dc.identifier.emailZHANG, A: anqi.xc@pku.edu.cn-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.landusepol.2019.104332-
dc.identifier.scopuseid_2-s2.0-85075450618-
dc.identifier.hkuros316232-
dc.identifier.volume90-
dc.identifier.spagearticle no. 104332-
dc.identifier.epagearticle no. 104332-
dc.identifier.isiWOS:000503093800042-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0264-8377-

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