Evaluation method of urban land population carrying capacity based on GIS¡ªA case of Shanghai, China

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• 作者：Yishao Shi ; ^{shiyishao@tongji.edu.cn} ; Hefeng Wang ^{wanghefeng1980@163.com} ; Changying Yin ^{changying.yin@hotmail.com}
• 关键词：Population carrying capacity ; GIS spatial analysis ; Spatial classification evaluation ; Spatial grading evaluation ; Shanghai
• 刊名：Computers, Environment and Urban Systems
• 出版年：2013
• 出版时间：May, 2013
• 年：2013
• 卷：39
• 期：Complete
• 页码：27-38
• 全文大小：2826 K

文摘

Although research on population carrying capacity has made significant progress, research on urban carrying capacity still has a weak theoretical basis and uses, imperfect regulation mechanisms and estimation methods. This study proposes a new method for evaluating urban population carrying capacity based on spatial analysis with GIS, which utilizes spatial classification and spatial grading of land use. The results demonstrate that urban construction and industrial development subspaces have most of the population, accounting for about 86.4 % of the total population carrying capacity, across 40.7 % of the total land area. Therefore, urban construction and industrial development subspaces are the centers of the population concentration, industrial agglomeration and wealth concentration in the Shanghai metropolis. The agricultural production and ecological protection subspaces, as noncommercial and ecological conservation areas of the metropolis, should not carry too much industrial development or added-value activities. In addition, under the current conditions of socio-economic and technological development in China, the gross population carrying capacity of Shanghai is estimated to be about 27.1732-30.3308 million persons, based on 2009 data. The actual population of Shanghai was 22.1028 million persons in 2009; thus, the population can continue to grow before reaching the population carrying capacity. The estimation in this paper takes into account both the internal disparities in carrying capacity of heterogeneous land spaces and composite factors such as natural resources, the environment, economic resources and social resources. Consequently, this method not only addresses defects in the existing research and estimation methods but also improves the credibility of the estimate.