贝叶斯概率与元胞自动机的非线性转换规则

利用朴素贝叶斯分类器(NBC)、高斯径向基函数和离散模型获取城市CA模型的非线性转换规则,提出NBC—CA模型,并将该模型应用于深圳市1988-2010年城市演变的动态模拟中。研究结果表明,所提出的CA模型能反映复杂城市系统的非线性、不确定性特点,模型模拟的结果要比传统MCE方法模拟精度高。NBC—CA模型中的径向基参数可解释不同空间变量的作用下,城市发展的集中区域以及空间变量对城市发展的影响范围,能够反映城市发展的模式和特征。

Nonlinear Transition Rules of Urban Cellular Automata Based on a Bayesian Method

This paper presents a new method to simulate complex land use systems by integrating Naive Bayes Classification,cellular automata,and GIS.Traditional CA models simulate urban development with linear transition rules.Linear boundaries are often used to retrieve transition rules which define the probability of state conversion.However,many geographical phenomena are very complex and transition rules should be defined using nonlinear boundaries.In this study,a CA model based on Naive Bayes Classification is developed using Visual Basic and ArcObjects of GIS.The GIS provides both data and spatial analysis functions for constructing NBC-CA model.Training data are conveniently retrieved from remote sensing and GIS database for calibrating and testing the model.The NBC-CA model can be applied to the simulation of urban development.Complex global patterns can be generated from the local interactions with the NBC-CA model.This paper demonstrates that the proposed model can overcome some of the shortcomings of existing CA models in simulating complex urban systems by using Naive Bayes Classification.More over,the influence of different spatial variable to urban development can be obtained from the parameters of the model.The model has been successfully applied to the simulation of urban development in Shenzhen city of the Pearl River Delta.