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基于PCA和PSR模型的城市土地集约利用动态分析——以南京市为例
引用本文:宋松,张建新,肖波,温丽娟.基于PCA和PSR模型的城市土地集约利用动态分析——以南京市为例[J].世界科技研究与发展,2009,31(4):744-746.
作者姓名:宋松  张建新  肖波  温丽娟
作者单位:南京大学地理与海洋科学学院,南京,210093
摘    要:人地关系对用地效益的影响广泛而深刻,“压力-状态-响应(Pressure-State—Response,PSR)”框架能很好地揭示出土地利用中人地相互作用的链式关系。据此,构建相应的评价指标体系,应用主成分分析的方法,以南京市2002~2006年土地利用数据为研究对象构建矩阵,对南京市近5年土地利用程度进行分析评价。研究结果表明:(1)近5年来,南京市土地利用程度呈不断加强,由最初的低度利用,到中度集约利用再到集约利用;(2)土地利用程度与经济发展水平呈正相关,相关系数达0.922;(3)南京市土地利用程度仍有提升空间。本研究为南京市城市土地资源管理提供理论依据和科学指导,并为发达地区城市土地集约利用评价与动态分析提供借鉴。

关 键 词:城市土地集约利用  PSR  PCA  南京市

Evaluation of Nanjing Urban Land Intensive Use——Based on PSR Model and the Principal Component Analysis
SONG Song,ZHANG Jianxin,XIAO Bo,WEN Lijuan.Evaluation of Nanjing Urban Land Intensive Use——Based on PSR Model and the Principal Component Analysis[J].World Sci-tech R & D,2009,31(4):744-746.
Authors:SONG Song  ZHANG Jianxin  XIAO Bo  WEN Lijuan
Institution:( School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093 )
Abstract:Man-land relationship influence the land-effectiveness profound and extensively,and “Pressure-Status-Response (PSR)” is a good framework to reveal interaction between land use and human function. Accordingly, we put forward the index system based on PSR model, and construct matrix with land use data of Nanjing from 2002 to 2006, and then method of principal component analysis is adopted to calculate the urban land intensive use of nearly 5 years. The results show that,in the past five years, the extent of land intensive use has been strengthened year by year,from extensive use to moderate use ,finally to intensive use;the extent of land intensive use and the level of economic development is positively correlated and relevant coefficient is 0. 922 ,while there is still room for the extent of land intensive use to promotion. The result provides science basis and support for Nanjing urban land resources administration,while the research technique can be used as reference for evaluation of urban land intensive use and dynamic analysis in developed areas.
Keywords:PSR  PCA
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