An extended cellular automaton using case-based reasoning for simulating urban development in a large complex region

Rule-based cellular automata (CA) have been increasingly applied to the simulation of geographical phenomena, such as urban evolution and land-use changes. However, these models have difficulties and uncertainties in soliciting transition rules for a large complex region. This paper presents an extended cellular automaton in which transition rules are represented by using case-based reasoning (CBR) techniques. The common κ-NN algorithm of CBR has been modified to incorporate the location factor to reflect the spatial variation of transition rules. Multitemporal remote-sensing images are used to obtain the adaptation knowledge in the temporal dimension. This model has been applied to the simulation of urban development in the Pearl River Delta which has a hierarchy of cities. Comparison indicates that this model can produce more plausible results than rule-based CA in simulating this large complex region in 1988-2002.