Spatial clustering is a hot issue in the field of spatial data mining. For a spatial object, the spatial location and the thematic attributes of spatial data are the inherent characteristics. However, the existing approaches mostly regard only the distance of spatial location as the similarity metric of spatial clustering, ignoring the thematic attributes of spatial objects. The results of these spatial clustering methods are not reasonable. Thus, a new spatial clustering method, named Concept Lattices Based Spatial Cluster (CLBSC for short) is proposed in this paper. The method considers both the spatial distance and attribute distance, and it simplifies the computation via building multi-dimensional attribute lattices. Furthermore, many concepts about CLBSC are expounded and its algorithm is narrated in detain. Finally, two experiments demonstrate that CLBSC algorithm is able to find more outlier and improve the reliability of spatial clustering using the Same Lattices Number.