Simulating urban growth through case-based reasoning

Abstract

Case-based reasoning (CBR) simplifies knowledge acquisition and is suitable for researching complex geographical problems. However, CBR analyses of land-use changes are difficult to apply in the study of urban growth due to shortcomings in the case structure and model algorithms. In response, this study proposes a three-step urban-growth simulation model based on CBR (UGSCBR). First, to adapt the CBR to an urban-growth simulation process, the characteristics of regional differentiation in geographical spaces are determined. Second, a comprehensive retrieval method is developed that improves upon traditional case-retrieval methods by giving full play to the comprehensive function of each component of the case. Third, a quantity demand constraint indirectly adds a time factor to solve the initial blurriness of the traditional CBR-inference cycle. Taking Jixi city as the research area, we test the accuracy of the proposed model. The total accuracy of simulation results is 95.4%, and the Kappa is 87.4%. The figure of merit and Mathews correlation coefficient are 0.151 and 0.23, respectively, indicating that the model can meet the application requirements. The results show that the UGSCBR model has strong flexibility and simplicity, and it provides an effective prediction method for urban growth.

Keywords

• Case-based reasoning
• urban growth simulation
• geographic information retrieval
• urbanization
• artificial intelligence
• jixi city