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This paper describes a design and implementation of a distributed high-performance partial spatial replica location service. Our replica location service identifies the set of partial replicas that intersect with a region of interest, an important component of partial spatial replica selection. We find that using an R-Tree data structure is superior to relying on a relational database alone when handling spatial data queries. We have also added a collection of optimizations that together improve performance. In particular, database Query Aggregation and using a Morton curve during R-tree construction produce significant performance gains. Experimental results show that the proposed partial spatial replica location service scales well for multi-client and distributed large spatial queries, queries that return more than 10,000 replicas. Individual servers with one million pieces of replica metadata in the backend database can support up to 100 clients concurrently when handling large spatial queries. Our previous work solved the same problem using an unmodified Globus Toolkit, but the work described here modifies and extends existing Globus Toolkit code to handle spatial metadata operations.