标题:Simulating urban expansion by coupling a stochastic cellular automata model and socioeconomic indicators
作者:Daqian Wu;Jian Liu;Shujun Wang;Renqing Wang
作者机构:[Wu, D] Institute of Ecology and Biodiversity, School of Life Sciences, Shandong University, Ji'nan, China;[ Liu, J] Institute of Environment Research 更多
通讯作者:Wang, R
通讯作者地址:[Wang, RQ]Shandong Univ, Sch Life Sci, Inst Ecol & Biodivers, Jinan, Peoples R China.
来源:Stochastic environmental research and risk assessment
出版年:2010
卷:24
期:2
页码:235-245
DOI:10.1007/s00477-009-0313-3
关键词:artificial neural network;cellular automata;dongying;socioeconomic indicator;urban expansion
摘要:Urbanization is one of the most important anthropogenic activities that create extensive environmental implications at both local and global scales. Dynamic urban expansion models are useful tools to understand the urbanization process, project its spatio-temporal dynamics and provide useful information for assessing the environmental implications of urbanization. A hybrid urban expansion model (NNSCA model) was proposed to simulate rapid urban growth in a typical industrial city, Dongying, China, by coupling a artificial-neural-network-based stochastic cellular automata model and several socioeconomic indictors, i.e., the per capita income of the rural population, the per capita income of the urban population, population and gross domestic products of the city. Good conformity between simulated and actual urban patterns suggested that the NNSCA model was able to effectively simulate historic urban growth and to generate realistic urban patterns. A series of scenario analyses suggested that the expanding urban would threaten the ecosystem health of coastal wetlands in the city unless environmental protection actions are taken in the future. The NNSCA model provides abilities to assess future urban growth under various planning and management scenarios, and can be integrated into ecological or environmental process models to evaluate urbanization\'s environmental implications.
收录类别:EI;SCOPUS;SCIE;SSCI
WOS核心被引频次:23
Scopus被引频次:21
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955049408&doi=10.1007%2fs00477-009-0313-3&partnerID=40&md5=73034371f63781ca91ada88f699280f4
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