Abstract: Spatial query processing is widely applied for the location-based service and the facility selection. This paper proposes and solves a novel type of spatial queries named Topic-Relevant Region (T2R) queries, which can be used to spatial decision analysis and location selection. Given a topic T defined by a feature set R, a query window q, a T2R query retrieves k non-overlapping regions that have the highest relevance values computed by the number of feature objects and their importance. As an example, the finance topic is defined by stock exchanges, insurance companies and banks. On the topic, T2R query finds finance centers intensively distributed feature objects. We propose three efficient algorithms to process T2R, which are Baseline (BSL), Filtering-Refinement (FR) and Shrink (SHR) algorithm. SHR can obtain the best pruning performance by clustering regions with high relevance values and shrinking them compared with the other two algorithms. The proposed algorithms solve problem of sorting the regions according to their relevance values with the topic at an arbitrary location. We evaluate the efficiency of the proposed algorithms with extensive experiments on real and synthetic datasets under a wide range of parameter settings and verify the effectiveness of T2R query by the real topic query.