Process Modeling for Urban Growth Simulation with Cohort Component Method, Cellular Automata Model and GIS/RS: Case Study on Surrounding Area of Seoul, Korea

Abstract

This study developed a process model that 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 systems (GIS). The first step of the process model is to project the population using a cohort component method for 2014. The second step is to quantify the demand for urban land use 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 suitability map for urban growth. Two accuracy assessment methods, pixel-by-pixel comparison and calculation of zonal statistics, were adopted to evaluate the accuracy of simulation results. This study also showed that the process model can still be used according to population growth scenarios even if the population increases or decreases suddenly due to socioeconomic or political factors that cannot be projected using the cohort component method.