A new method of attribute-oriented spatial generalization

Extraction of interesting and general knowledge from large spatial databases is a crucial task in the development of geographical information system. We develop a new method of attribute-oriented spatial generalization. The method is incremental, allowing changes of the spatial database to be reflected in the generalization results without rereading input data. The method also allows fast re-generalization to both higher and lower levels of generality without rereading input. A new data structure, equivalence partition tree, is introduced in the algorithm design of the method. It essentially assures that the algorithm for new method has characters of high efficiency, increment and fast re-generalization. We implemented our new algorithm and ran empirical tests, and we found the method efficient. In addition, its runtime increases only linearly as input size increases.