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There have been a need for accessing spatial data from distributed and preexisting spatial database systems interconnected through a network. In a distributed environment, spatial joins for two spatial relations residing at geographically separated sites are expensive in terms of computation and transmission cost because of the large size and complexity of spatial data. Therefore, we propose a new spatial join method for distributed spatial databases. Previously, a distributed algorithm based on the spatial semijoin has accomplished performance improvements by eliminating objects before transmission to reduce both transmission and local processing costs. But with widespread high bandwidth data transmission, parallelism through data redistribution may improve the performance of spatial joins in spite of additional transmission costs. Hence, we propose a parallel spatial join processing that combines the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refinement strategy for spatial operation processing for distributed spatial databases. In experiments, we showed that the proposed method provides useful reductions in the cost of evaluating a join.