Prediction of multinomial probability of land use change using a bisection decomposition and logistic regression

Land use change is an important research area in landscape ecology and urban development. Prediction of land use change (urban development) provides critical information for making the right policies and management plans in order to maintain and improve ecosystem and city functions. Logistic regression is a widely used method to predict binomial probabilities of land use change when just two responses (change and no-change) are considered. However, in practice, more than two types of change are encountered and multinomial probabilities are therefore needed. The existing methods for predicting multinomial probabilities have limits in building multinomial probability models and are often based on improper assumptions. This is due to the lack of proper methodology and inadequate software. In this study, a procedure has been developed for building models to predict the multinomial probabilities of land use change and urban development. The foundation of this procedure consists of a special bisection decomposition system for the decomposition of multiple-class systems to bi-class systems, conditional probability inference, and logistic regression for binomial probability models. A case study of urban development has been conducted to evaluate this procedure. The evaluation results demonstrated that different samples and bisection decomposition systems led to very similar quality and performance in the developed multinomial probability models, which indicates the high stability of the proposed procedure for this case study.