Risk management by spatial data mining Kamal Nematpoor Tehran University, Iran Email: knematpoor@yahoo.com Spatial data mining is a new and rapidly developing technique for analyzing geospatial data. In this paper, the usability of the technique is examined for the improvement of an existing Geo spatial model regarding rescue operation the main focus of spatial data mining is set on the discovery of interesting patterns of information embedded in large spatial data base. Due to its ability to operate without a previously formulated hypothesis, spatial data mining is becoming a popular tool for spatial data analyzes. After a short explanation of the best known Spatial data mining techniques this paper, concentrates on association rule mining. Discovered spatial association rules may detect useful relationships among spatially distributed objects. Once the relations are identified, the existing spatial model can be extended By the variables with strongest relations to the modeled phenomenon. The behavior of association rule mining is studied by applying it on sample data Representing incident locations within the Tehran city center. The core data is provided by the Fire and Rescue department in Tehran. Due to the fast development of geo-information technologies, a variety of new opportunities arise. Therefore, more accurate analyzes can be performed on spatial data. In this paper the possible use of spatial data mining methods is investigated for identifying factors that may influence occurrences of incidents within the Tehran city center.