Spatial co-location patterns are subsets of spatial features usually located together in geographic space. Recent literature has provided different approaches to discover co-location patterns over point spatial data. However, most approaches consider the neighborhood relationship among spatial objects as binary and are mainly designed for point spatial features, thus are not appropriate for extended spatial features such as line strings and polygons, the neighborhood relationship among which is naturally continuous. This paper adopts a buffer-based model for measuring the spatial relationship of extended objects and mining co-location patterns. While the buffer-based model has several advantages for extended spatial features, it involves high computational complexity due to the expensive buffer-level overlay operation. To tackle this challenge, we introduce a coarse-level co-location mining framework, which follows a filter-and-refine paradigm. Within the framework, we develop a serious of rigorous upper bounds based on geometric property and progressively prune search space with these upper bounds. Moreover, we develop a join-less schema to further reduce computation cost of size-k($k>2$k>2) co-location patterns. Finally, we conduct experiments with large-scale spatial data to validate the efficiency of the developed algorithms against several state-of-art methods. All experimental results demonstrate the superiority of our methods.

中文翻译：

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使用扩展对象计算空间数据中的协同定位模式：一种基于可扩展缓冲区的方法

空间协同定位模式是空间特征的子集，通常位于地理空间中。最近的文献提供了不同的方法来发现点空间数据上的协同定位模式。然而，大多数方法将空间对象之间的邻域关系视为二元的，主要是针对点空间特征设计的，因此不适用于线串和多边形等扩展空间特征，它们之间的邻域关系是自然连续的。本文采用基于缓冲区的模型来测量扩展对象的空间关系并挖掘共置模式。虽然基于缓冲区的模型在扩展空间特征方面有几个优点，但由于昂贵的缓冲区级叠加操作，它涉及高计算复杂度。为了应对这一挑战，我们引入了一个粗略的协同定位挖掘框架，它遵循过滤和细化范式。在该框架内，我们基于几何属性开发了一系列严格的上限，并使用这些上限逐步修剪搜索空间。此外，我们开发了一种无连接模式以进一步降低 size-k($k>2$克>2) 共址模式。最后，我们使用大规模空间数据进行实验，以验证所开发算法针对几种最先进方法的效率。所有的实验结果都证明了我们方法的优越性。