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Predicting multiple land use transitions under rapid urbanization and implications for land management and urban planning: The case of Zhanggong District in central China
Wang, Lingzhi1,2,3; Pijanowski, Bryan3; Yang, Weishi4,5; Zhai, Ruixue6; Omrani, Hichem7; Li, Ke8
2018-12-01
Source PublicationHABITAT INTERNATIONAL
ISSN0197-3975
Volume82Pages:48-61
Corresponding AuthorWang, Lingzhi(wanglingz@jlu.edu.cn)
AbstractNumerous machine learning-based land change models have been presented by researchers over the last two decades. To date, however, far less have simulated multiple land use classes and specific land use subclasses at the same time. In some areas of the world, it is important to simulate both of these dynamics to understand fully the drivers and consequences of land change. One important example is the process of urbanization in China, as urban policies have been developed that guide urban expansion, rural protections, and urban subclass development. This paper presents a new model integrating geographic information systems (GIS) with artificial neural networks (ANNs) to predict multiple transitions among land use types and urban subclasses in the Zhanggong District of Ganzhou city in China. We show that the model produces satisfactory goodness of fit values-based on location, quantity and spatial configuration-between simulated and observed land use maps for 2015. Our simulated future maps produced by the model for 2020 and 2025 demonstrate that transitions from farmland and forest to urban will remain the main pathway of urbanization although we predict that the rate will slow after 2025. The goals of urban planning should be aligned with land use planning according to "Combining multiple laws and regulations" in China. Taking into account the current and future land use transitions will enhance the accuracy and timeliness of land use policy making and urban land planning. For the sustainable land use in Zhanggong District, we argue for a strengthened regulation of the land market by government and believe that planning officials should guide the spatial distribution of land supply actively. Furthermore, improving the production, living and ecological functions of land resources are the key points to optimize urban land use functions and the allocation of land resources. We discuss how our model can be adapted to other areas to benefit land use management and urban planning in China.
KeywordMultiple land use transitions Urbanization Artificial neural networks Land use management Urban planning China
DOI10.1016/j.habitatint.2018.08.007
WOS KeywordCELLULAR-AUTOMATA ; LOGISTIC-REGRESSION ; LONG-TERM ; EXPANSION ; GROWTH ; MODEL ; VALIDATION ; SPRAWL ; MAPS ; AREA
Indexed BySCI
Language英语
Funding ProjectJilin Province Science and Technology Development Plan Project[20180418111FG] ; Jilin Provincial Department of Education 13th Five-Year Science and Technology Project[JJKH20180163KJ] ; Key Program of National Natural Science Foundation of China[41731286] ; Major Science and Technology Program for Water Pollution Control and Treatment[2010ZX07320-003-004] ; Jilin Science Foundation for Excellent Young Scholars[20180520169JH]
Funding OrganizationJilin Province Science and Technology Development Plan Project ; Jilin Provincial Department of Education 13th Five-Year Science and Technology Project ; Key Program of National Natural Science Foundation of China ; Major Science and Technology Program for Water Pollution Control and Treatment ; Jilin Science Foundation for Excellent Young Scholars
WOS Research AreaEnvironmental Sciences & Ecology ; Public Administration ; Urban Studies
WOS SubjectEnvironmental Studies ; Planning & Development ; Urban Studies
WOS IDWOS:000452565000005
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/51372
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Lingzhi
Affiliation1.Jilin Univ, Coll Earth Sci, Changchun 130061, Jilin, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Purdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47906 USA
4.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
6.Zhejiang Huanke Environm Consultancy Co Ltd, Hangzhou 311100, Zhejiang, Peoples R China
7.LISER, Urban Dev & Mobil Dept, Luxembourg, Luxembourg
8.Jilin Jianzhu Univ, Minist Educ, Key Lab Songliao Aquat Environm, Changchun, Jilin, Peoples R China
Recommended Citation
GB/T 7714
Wang, Lingzhi,Pijanowski, Bryan,Yang, Weishi,et al. Predicting multiple land use transitions under rapid urbanization and implications for land management and urban planning: The case of Zhanggong District in central China[J]. HABITAT INTERNATIONAL,2018,82:48-61.
APA Wang, Lingzhi,Pijanowski, Bryan,Yang, Weishi,Zhai, Ruixue,Omrani, Hichem,&Li, Ke.(2018).Predicting multiple land use transitions under rapid urbanization and implications for land management and urban planning: The case of Zhanggong District in central China.HABITAT INTERNATIONAL,82,48-61.
MLA Wang, Lingzhi,et al."Predicting multiple land use transitions under rapid urbanization and implications for land management and urban planning: The case of Zhanggong District in central China".HABITAT INTERNATIONAL 82(2018):48-61.
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