Calibration of an urban cellular automaton model by using statistical techniques and a genetic algorithm. Application to a small urban settlement of NW Spain

Abstract

Cellular automata (CA) stand out among the most commonly used urban models for the simulation and analysis of urban growth because of their ability to reproduce complex dynamics, similar to those found in real cities, from simple rules. However, CA models still have to overcome some shortcomings related to their flexibility and difficult calibration. This study combines various techniques to calibrate an urban CA that is based on one of the most widely used urban CA models. First, the number of calibration parameters is reduced by using various statistical techniques, and, second, the calibration procedure is automated through a genetic algorithm. The resulting model has been assessed by simulating the urban growth of Ribadeo, a small village of NW Spain, characterized by low, slow urban growth, which makes the identification of urban dynamics and consequently the calibration of the model more difficult. Simulation results have shown that, by automating the calibration procedure, the model can be more easily applied and adapted to urban areas with different characteristics and dynamics. In addition, the simulations obtained with the proposed model show better values of cell-to-cell correspondence between simulated and real maps, and the values for most spatial metrics are closer to real ones.