Prevalent co-location pattern (PCP) mining is a crucial task in spatial data analysis, aimed at identifying sets of features that frequently appear together within a certain proximity. Existing methods often generate numerous redundant PCPs, complicating the interpretation and application of results. This paper presents DCPCPM, a novel miner designed to discover concise PCPs efficiently. Our approach integrates a post-mining framework named Mine-and-Concise, which introduces a new measure to quantify the distance between patterns, defining a covering relationship that reduces redundancy. Using this measure, DCPCPM can effectively prune nonessential patterns, ensuring the resulting set is both comprehensive and interpretable. A demonstration on an urban dataset is shown to prove the efficacy of our method in uncovering meaningful spatial relationships from spatial data.