Knowledge discovery for geographical cellular automata

Recently, cellular automata (CA) has been increas-ingly applied to the simulation of geographical phe-nomena, especially urban simulation[1,2]. The re-searches of using CA have been carried out in China with many publications nationally and internation-ally[3―7]. CA can be applied to the simulation of many geographical phenomena, such as diffusion of wild-fire[8], population fluctuation of animals[9], evolution of urban systems and land use[1,2], the formation of idealized urban forms[3,6],…

Knowledge discovery for geographical cellular automata

This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncer-tainties. Mathematical equations are used to represent transition rules implicitly and have limita-tions in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in de-fining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data.