Spatio-temporal simulation of future urban growth trends using an integrated CA-Markov model

Urban growth, a dynamic and demographic phenomenon, refers to the increased spatial value of urban areas, such as cities and towns, due to social and economic forces. Nowadays, urban lands are rapidly increasing, replacing non-urban lands such as agricultural, forest, water, rural, and open lands. In this study, a CA-Markov model was utilized to predict the growth of urban lands and their spatial trends in Seremban, Malaysia. The performance of the CA-Markov model was also assessed. The Markov chain model was applied to produce the quantitative values of transition probabilities for urban and non-urban lands. Subsequently, the CA model was used to predict the dynamic spatial trends of land changes. The change in urban and non-urban land use from 1984 to 2010 was modeled using the CA-Markov model for calibration purposes and to compute optimal CA transition rules, as well as to predict future urban growth. For accuracy assessment, the CA-Markov model was validated using a kappa coefficient. An 83% overall accuracy was observed for the kappa index statistics, which indicates the excellent performance of the proposed model. Finally, based on the CA transition rules and the transition area matrix produced from the Markov chain model-based calibration process, the future urban growth in Seremban for 2020 and 2030 was simulated.