Prevalent co-location pattern mining (PCPM) plays a critical role in spatial data mining, focusing on identifying subsets of spatial features that frequently co-occur within a given space. While existing PCPM methods have successfully identified co-location patterns, they often generate a significant amount of redundant patterns, which hinders both computational efficiency and interpretability. To address these challenges, we present MTRM (Multi-Threshold Redundancy Mitigation), a novel framework designed to mitigate redundancy through the introduction of multiple thresholds. MTRM allows for the flexible adjustment of significance levels, enabling the discovery of more concise and meaningful co-location patterns. Unlike traditional single-threshold methods, our approach dynamically filters out redundant patterns, retaining only those that exhibit strong spatial relationships across multiple criteria. MTRM is implemented as a web-based miner, providing users with a real-time, interactive platform to analyze spatial datasets. The flexibility of MTRM allows it to handle datasets of varying sizes and complexities, making it suitable for both real-world applications and large-scale synthetic data experiments. We benchmark the performance of MTRM against several state-of-the-art PCPM algorithms, demonstrating its ability to reduce redundancy while maintaining high accuracy in pattern discovery. Experimental results show that MTRM not only enhances scalability and efficiency but also improves the quality of discovered patterns by reducing noise and redundancy, leading to more actionable insights for decision-makers in fields such as urban planning, environmental monitoring, and epidemiology.