A co-location pattern indicates a group of spatial features whose instances are frequently located together in proximate geographic area. Spatial co-location pattern mining (SCPM) is valuable for many practical applications. Numerous previous SCPM studies emphasize the equal participation per feature. As a result, the interesting co-locations with rare features cannot be captured. In this paper, we propose a novel interest measure, i.e., the weighted participation index (WPI), to identify co-locations with or without rare features. The WPI measure possesses a conditional anti-monotone property which can be utilized to prune the search space. In addition, a fast row instance identification mechanism based on the ordered NR-tree is proposed to enhance efficiency. Subsequently, the ordered NR-tree-based algorithm is developed. To further improve efficiency and process massive spatial data, we break the ordered NR-tree into multiple independent subtrees, and parallelize the ordered NR-tree-based algorithm on MapReduce framework. Extensive experiments are conducted on both real and synthetic datasets to verify the effectiveness, efficiency and scalability of our techniques.

共置模式指示一组空间特征，其实例经常在附近的地理区域中一起定位。空间共置模式挖掘（SCPM）对于许多实际应用而言都很有价值。先前的许多SCPM研究都强调每个功能均等参与。结果，无法捕获到具有稀少特征的有趣的共处一地。在本文中，我们提出了一种新颖的兴趣量度，即加权参与指数（WPI），以识别具有或不具有稀有特征的共址。WPI度量具有条件反单调属性，可用于修剪搜索空间。另外，提出了一种基于有序NR树的快速行实例识别机制，以提高效率。随后，开发了基于有序NR树的有序算法。为了进一步提高效率并处理大量空间数据，我们将有序NR树分成多个独立的子树，并在MapReduce框架上并行化基于有序NR树的算法。我们在真实和综合数据集上进行了广泛的实验，以验证我们技术的有效性，效率和可扩展性。