Development of a process model for urban growth simulation using cohort component method, cellular automata model and GIS/RS

This study developed a process model which includes three steps for urban growth simulation. A preprocessing step is required to classify three satellite images acquired in 1990, 2000, and 2009 into six land use types using remote sensing (RS) and geographic information system (GIS). The first step of the process model is to project the population using cohort component method for 2014. The second step is to quantify the corresponding amount of urban land demands based on a regression model between population and urban land use. The third step is to optimize the weighting values for six criteria using the weighted scenario method (WSM), cellular automata (CA) model, and GIS in order to make a grid-based optimal potential map. Two accuracy assessment methods were adopted to evaluate the accuracy of urban growth simulation, pixel by pixel comparison, and zonal statistics method, the latter of which was used to calculate the number of grid cells per zone of time distance. The comparison results show a good agreement between observed and simulated urban land use maps. Finally, the urban land use map for 2014 was simulated. This process model can be used to evaluate the spatial expansion phenomena and simulate urban growth.