Zonal co-location patterns represent subsets of feature-types that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-location patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However, discovering these patterns with dynamic parameters (i.e., repeated specification of zone and interest measure values according to user preferences) is computationally complex due to the repetitive mining process. Also, the set of candidate patterns is exponential in the number of feature types, and spatial datasets are huge. Previous studies have focused on discovering global spatial co-location patterns with a fixed interest measure threshold. In this paper, we propose an indexing structure for co-location patterns and propose algorithms (Zoloc-Miner) to discover zonal co-location patterns efficiently for dynamic parameters. Extensive experimental evaluation shows our proposed approaches are scalable, efficient, and outperform naive alternatives.