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Spatial clustering is an important method for spatial data mining and knowledge discovery. According to the deficiency existing in density-based clustering algorithm DBSCAN, such as the I/O overhead, memory consumption etc. This paper improves the DBSCAN algorithm, which proposed directional density algorithm, the algorithm reduces lots of points which need to be queried. By taking Geographic Information System for the application background, we successfully applied to forecast the distribution of urban water points. Compared with the traditional DBSCAN algorithm, the results conformed to the actual situation, and efficiency increased by 20%.