Integration of principal components analysis and cellular automata for spatial decisionmaking and urban simulation  

作　　者：黎夏 叶嘉安 

出　　处：《Science China Earth Sciences》2002年第6期521-529,共9页中国科学（地球科学英文版）

基　　金：This project was supported by the National Natural Science Foundation of China (Grant No. 40071060) ; the Croucher Foundation of Hong Kong (Grant No. 21009619).

摘　　要：This paper discusses the issues about the correlation of spatial variables during spatial decisionmaking using multicriteria evaluation (MCE) and cellular automata (CA). The correlation of spatial variables can cause the malfunction of MCE. In urban simulation, spatial factors often exhibit a high degree of correlation which is considered as an undesirable property for MCE. This study uses principal components analysis (PCA) to remove data redundancy among a large set of spatial variables and determine 'ideal points' for land development. PCA is integrated with cellular automata and geographical information systems (GIS) for the simulation of idealized urban forms for planning purposes.This paper discusses the issues about the correlation of spatial variables during spatial decisionmaking using multicriteria evaluation (MCE) and cellular automata (CA). The correlation of spatial variables can cause the malfunction of MCE. In urban simulation, spatial factors often exhibit a high degree of correlation which is considered as an undesirable property for MCE. This study uses principal components analysis (PCA) to remove data redundancy among a large set of spatial variables and determine ‘ideal points’ for land development. PCA is integrated with cellular automata and geographical information systems (GIS) for the simulation of idealized urban forms for planning purposes.

关 键 词：principal components analysis  CELLULAR automata  GEOGRAPHICAL information systems  URBAN simulation. 

分 类 号：TU984[建筑科学—城市规划与设计]