Spatial co-locations mining is an important research domain in spatial data mining. Spatial co-locations represent the subsets of spatial features which are frequently located together in geographic space. Up to present, all the existing co-location mining algorithms only focus on discovering ordinary co-location patterns or co-location rules. However, in real-world applications, the data in a database do not usually remain a stable condition, making efficient incremental mining for co-locations very indispensable and interesting. The evolutionary analysis of co-locations can discover the development rules of co-locations, and predict the particular event happened in future. However, no results have yet been reported from these researches. This paper studies the incremental mining for co-locations and the evolutionary analysis of co-locations. Firstly, an efficient basic algorithm and a prune algorithm for incremental mining are proposed. Secondly, evolutionary co-locations are discovered based on several real datasets. Thirdly, both the basic algorithm and prune algorithm are proved correct and complete. Fourth, extensive experiments are performed to verify the performance and effectiveness of the basic algorithm and prune algorithm. At last, the basic algorithm and prune algorithm for incremental mining in conjunction with the evolutionary co-locations mining algorithm are applied to the Three Parallel Rivers of Yunnan protected Areas plant database to predict the development rules of co-locations, and dynamically track and protect the rare plants of this area.