基于偏最小二乘地理元胞模型的城市生长模拟

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.

Modelling Urban Growth Using Geographical Cellular Automata Based on Partial Least Squares Regression

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 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 difficult in remove the harmful effects of correlation. Using partial least squares regression (PLS) integrated with Geo-CA and 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 simulate 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.