Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata

Cellular automata (CA) have been increasingly used in simulating urban expansion and land-use dynamics. However, most urban CA models rely on empirical data for deriving transition rules, assuming that the historical trend will continue into the future. Such inertia CA models do not take into account possible external interventions, particularly planning policies, and thus have rarely been used in urban and land-use planning. This paper proposes to use artificial immune systems (AIS) as a technique for incorporating external interventions and generating alternatives in urban simulation. Inspired by biological immune systems, the primary process of AIS is the evolution of a set of 'antibodies' that are capable of learning through interactions with a set of sample 'antigens'. These 'antibodies' finally get 'matured' and can be used to identify/classify other 'antigens'. An AIS-based CA model incorporates planning policies by altering the evolution mechanism of the 'antibodies'. Such a model is capable of generating different scenarios of urban development under different land-use policies, with which the planners will be able to answer 'what if' questions and to evaluate different options. We applied an AIS-based CA model to the simulation of urban agglomeration development in the Pearl River Delta in southern China. Our experiments demonstrate that the proposed model can be very useful in exploring various planning scenarios of urban development.