In this paper, we present our approach for mining spatiotemporal knowledge. The proposed method is based on the computation of neighborhood relationships between geographical objects during a time interval. This kind of information is non-explicitly stored in spatio-temporal database and is extracted by the means of special mining queries enriched by time management parameters. The general aim of our approach is to develop a method that utilizes the inherent structure of spatiotemporal information as well as its rich semantics to derive spatio-temporal association rules in order to improve the decision making process about land changes and resulting prohibited risks.
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