Sensitivity Analysis of a Gis-based Cellular Automata Model

Urban growth is dynamic and complex spatial process that has severe environmental and social impacts. High population growth results in the transformations of forested areas or high quality agricultural lands into urban land-use. The study and modeling of the urban growth and land-use change processes are complex due to the difficulties to represent the interactions between the physical and human environments. One of the models increasingly applied to urban research is based on cellular automata (CA) theory. Since models are approximations of the real world, they contain inherent errors due to the digital data input and are sensitive on model parameters and model misspecification thereby generating uncertainties in the results. The objective of this study is to explore these uncertainties through the sensitivity analysis (SA) of a GIS-based CA urban growth model. The impacts of changing CA neighbourhood size and type on the model outcome were addressed. The cross-classification, KAPPA statistic and spatial metrics were used as measures of sensitivity analysis in order to understand the CA model behavior and its limitations. The results from this research can provide better insights for improving the capabilities of current CA models to create more realistic output scenarios. * Corresponding author.