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.