Integrating spatial relations into case-based reasoning to solve geographic problems

Case-based reasoning (CBR) method has been widely used to study environmental and spatial problems since the 1990s. Spatial relations among geographic cases and between case and environment (hereinafter to be referred as spatial relations) were not well considered in most of the previous studies. However, these relations are extremely important in geographic problems solving as spatially closer geographic phenomena are more likely to be similar than those are disperse in space. This paper presents a generic application paradigm based on CBR to solve geographic problems. To better consider the spatial relations, a new component of “Geographic Environment” was added into the standard CBR case representation model. A rough set-based algorithm was used to prune essential spatial relations, which were then used to extract key decision rules and retrieve similar past cases for the new problem. Standard CBR directly accepts the solution that was derived from the past similar cases to solve the new problem. In this study, however, solution was not accepted unless it also satisfied the key decision rules. An illustrating example was used to demonstrate how the general framework and algorithm could be applied to solve geographic problems. The algorithm was then evaluated by examining two datasets, the 2003 land use in the Pearl River Estuary and land use change from 1995 to 2000 in Zhuhai city of China. These two datasets were also examined by the standard CBR and Bayesian Networks (BNs) methods. Comparison of the stratified 10-fold cross-validation results indicated that the algorithm proposed in this study yielded statistically significant higher overall validation accuracy than the standard CBR and BNs methods.