CAJ | 학술논문

提出了一种基于偏最小二乘回归(PLS)方法的地理元胞(cellular automata,CA)模型PLS - CA,并用来模拟城市生长和扩展.CA模型的定义涉及存在严重相关性的众多空间变量,而传统的多准则判别技术(MCE)和主成分分析(PCA)不能够彻底地解决变量相关性问题.利用偏最小二乘回归从空间变量中提取线性无关的主成分,从而获取地理元胞自动机(CA)的转换规则,在地理信息系统(GIS)环境下建立PLS - CA模型,可以优化城市生长和扩展的模拟.利用提出的PLS - CA模型,模拟了上海市嘉定区1989年与2006年城市生长和扩展情况.
제출료일충기우편최소이승회귀(PLS)방법적지리원포(cellular automata,CA)모형PLS - CA,병용래모의성시생장화확전.CA모형적정의섭급존재엄중상관성적음다공간변량,이전통적다준칙판별기술(MCE)화주성분분석(PCA)불능구철저지해결변량상관성문제.이용편최소이승회귀종공간변량중제취선성무관적주성분,종이획취지리원포자동궤(CA)적전환규칙,재지리신식계통(GIS)배경하건립PLS - CA모형,가이우화성시생장화확전적모의.이용제출적PLS - CA모형,모의료상해시가정구1989년여2006년성시생장화확전정황.
Based on partial least squares regression,a novel geographical cellular automata model (PLS - CA) is proposed for simulating urban growth and expansion.In definition of cellular automata(CA) transition rules,numerous highly correlated independent spatial variables are utilized for obtaining more actual simulation results.Conventional methods,such as multi-criteria evaluation(MCE) and principal component analysis (PCA),have difficulties in removing the harmful effects of correlation.Using partial least squares regression (PLS) integrated with CA and geographical information system(GIS),a new CA model is created for optimizing the simulation of urban growth and expansion. The PLS - CA model has been successfully applied to simulating urban growth of Jiading district,Shanghai from 1989 to 2006. And the simulation results show that the accuracy of PLS - CA is higher than that of conventional CA models.