摘要
提出一种基于费歇尔(Fisher)判别和离散选择模型相结合来自动获取地理元胞自动机转换规则的方法。CA的核心是如何定义转换规则,但目前主要是采用启发式的方法来定义转换规则,受主观因素影响较大。本模型结合离散选择模型,通过对Fisher判别方法进行改进,可以成功搜索最佳分隔单元发展和不发展的变量组合,自动确定模型参数值。与常用的Logistic回归模型进行对比分析,结果表明,所提出的方法具有更高的模拟精度,转换规则有着清晰的物理意义。此外,本模型在模拟多类复杂的土地利用变化时可能更具有优势。
This paper has put forward a new method for automatically getting transition rule of geographic Cellular Automaton （CA）, basing on the combination of Fisher discriminant and discrete selection model. The core of CA is how to define conversion rule. However, at present heuristic methods are mainly adopted to define transition rule, greatly influenced by subjective factors. Having combined discrete selection model and improved Fisher discrimination, this model can successfully search the best variable combination to separate developing and non-developing units, and automatically give the parameter value of model. The results of comparison with Logistic regression model indicate that, this method has higher precision and the transition rule has clear physical meaning. In addition, this model has predominance in simulating multi-class complex change of land-use. Key
出处
《测绘学报》
EI
CSCD
北大核心
2007年第1期112-118,共7页
Acta Geodaetica et Cartographica Sinica
基金
国家杰出青年基金资助项目(40525002)
国家自然科学基金资助项目(40471105)
教育部博士点基金资助项目(20040558023)
"985工程"GIS与遥感的地学应用科技创新平台资助项目(105203200400006)
