The previous spatial clustering methods calculate the distance value between two spatial objects using the Euclidean distance function, which cannot reflect the grid path, and their computational complexity is high in the presence of obstacles. Therefore, in this paper, we propose a novel spatial clustering algorithm called DBSCAN-MDO. It reflects the grid path in the real world using the Manhattan distance function and reduces the number of obstacles to be considered by grouping obstacles in accordance with MBR of each cluster and filtering obstacles that do not affect the similarity between spatial objects.