Spatial data mining is the process of discovering interesting implicit knowledge in spatial databases that is an important task for understanding and use if spatial data-and knowledge-base and previously unknown, but potentially useful patterns from large spatial datasets; it is an important task for understanding and use the spatial data. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from conventional transaction based database due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation The purpose of in this paper is to do comparative study on spatial co-location rule mining and association rule mining of spatial data mining application based on classical papers, and refine some previous algorithms.