An Urban Growth Model Based on a Cellular Automata Phenomenological Framework

In this paper we adopt a phenomenological approach in order to develop a suitable Cellular Automata (CA) model capable to satisfactory mimic city growth and urban sprawl. The use of CA in urban expansion modeling is well known since many years, but very rarely it has been related with a down-top approach which considers inhabitants' preferences as a driving tool to characterize the CA algorithm. In addition, we consider as a control mechanism of the cell conversion rate (i.e. the number of cells experiencing conversion into urban use) the logistic function. This function is tuned on the free space (cells) at disposal for the urban development. In this paper we present the basics of the model and we perform a simple simulation in the case of a generic geographic pattern.