A spatial co-location pattern corresponds to a subset of spatial features, whose instances are frequently located in spatial neighborhoods. Spatial co-location pattern mining is an important direction of spatial data mining. However, spatial database is changing continually, efficient spatial co-location pattern incremental mining is vital. This paper presented an incremental mining method of spatial colocation patterns based on changed participating instances, which directly searched for changed participating instances without performing time-consuming operations of generating change table instances. Furthermore, to speed up the search of changed participating instances, this paper designed an instance searching optimization strategy and a heuristic pattern pruning technique. On this basis, this paper introduced incremental mining method of spatial colocation patterns based on changed participating instances(IMCP-CPI), and comprehensively analyzed its complexity, correctness, and completeness. The extensive experiments on real and synthetic datasets validated the efficiency of IMCP-CPI algorithm. The results show that IMCP-CPI is much better than 5 known incremental mining algorithms of spatial co-location patterns, especially with a performance gain of several times or even orders of magnitude. In a new dataset where the proportion of changed data accounts for 5% of the original dataset, when the distance threshold d is very large or the participation threshold min_prev is very small, the performance of the IMCP-CPI is 2~3 times better than the current optimal algorithm for co-location pattern mining, CPM-Col, and its improved version CPM-iCol. Furthermore, when the proportion of changed data is less than or equal to 25% and 50% of the original dataset, respectively, IMCP-CPI outperforms both CPM-iCol and CPM-Col in terms of parameter variations and scalability. This provides valuable reference insights for method selection in practical applications.