<p>Regional co-location pattern mining is used to discover co-location patterns that are not prevalent globally but are prevalent in local regions. Although several regional co-location pattern mining methods have been developed, they have the following limitations: (1) they cannot capture the dynamic relationship between features; (2) their regional partitioning may disrupt the neighboring relationships between instances and is inefficient. To solve the above issues, the concept of Dynamic Region Co-location Patterns (DRCPs) is first defined to capture dynamic relationships between features in local regions. An interest measure of DRCPs is also presented, which comprehensively considers the effect of increasing or decreasing in the same feature instances. Second, a regional partitioning method based on generalized maximal row instances (GMRIs) is proposed. Since a GMRI typically merges multiple maximal row instances, it achieves more reasonable regional partitioning results. Additionally, due to the reduction in the number of maximal row instances, the partitioning efficiency is significantly improved. Third, a novel DRCP mining algorithm is designed, which fully utilizes the advantages of the GMRI and improves the mining efficiency. Finally, extensive experiments are conducted on both real and synthetic datasets to verify the effectiveness and efficiency of the proposed method.</p>