Deriving suitability factors for CA-Markov land use simulation model based on local historical data

Multiple Criteria Evaluation (MCE) is a multi-attributes decision making tool often used in land suitability analysis and land use simulation using Cellular Automata (CA)-Markov model. The goal of this research is to explore the feasibility of using historical data of a study area to select, score, and weight factors quantitatively in the MCE. We have developed logistic regression models fitted by the historical land use changes to select and score each potential factor, and used the Entropy method to determine weights for the selected factors. The MCE output is then used as the input of CA-Markov model to simulate land use changes from 2001 to 2011. The land use simulation result was compared against observed 2011 land use in order to examine the performance of the updated MCE method. The result shows that the use of MCE factors derived from historical data produces reasonable goodness of fit, based on current literature. The major advantage of the updated MCE method is that the factor selection, scores, and weights are all derived from local data reflecting the actual historical trend. This quantitative approach also allows one to efficiently calibrate CA-Markov model and develop different land use planning scenarios by adjusting scores and weights for different factors with the knowledge of historical change.