Spatial data warehouse provides efficient analysis environment for both spatialdata and non-spatial data, which can satisfy the urgent need for embedding spatial data into decision support system. The range aggregate query on both non-spatial dimensions and spatial dimensions is a very important operation to support spatial on-line analytical processing (OLAP). To optimize the operation, an indexing scheme named aCR-tree and its corresponding algorithms with asymptotical performance analysis are proposed based on aggregate cubetree and aR-tree. Using both synthetic and real enterprise data, experiments are conducted to demonstrate storage overhead and range aggregate query performance of the indexing scheme. The analytical and experimental results show that the costs of range aggregate queries and the storage space of aCR-tree are superior to that of the traditional storage structures.