ESCIP: An Expansion-Based Spatial Clustering Method for Inhomogeneous Point Processes
ESCIP: An Expansion-Based Spatial Clustering Method for Inhomogeneous Point Processes
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DOI：
10.1080/24694452.2019.1625747
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发表时间：
2019-07
影响因子：
3.9
通讯作者：
Ting Li;Yizhao Gao;Shaowen Wang
中科院分区：
文献类型：
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作者：
Ting Li;Yizhao Gao;Shaowen Wang
在异质点过程中检测不规则形状的空间簇是具有挑战性的，因为具有不同大小和形状的潜在簇的数量可能是巨大的。本研究开发了一种新方法，即基于扩展的非均质点过程空间聚类（ESCIP），用于在分析空间大数据的背景下检测异构点过程中任何形状的空间集群。统计测试是用来找到核心点，点与邻近地区有显着更多的情况下，比预期和扩展的方法来找到不规则形状的集群连接附近的核心点。这种方法不像空间扫描统计中那样对所有潜在的聚类进行强力搜索，而是只需要为每个潜在的核心点测试一个小的相邻区域。此外，利用空间索引来加速对附近点的搜索和聚类的扩展。所提出的方法实现泊松和伯努利模型和大型空间数据集的评估。实验结果表明，ESCIP可以从数以百万计的点检测不规则形状的空间集群与高效率。它还表明，该方法优于空间扫描统计的集群形状和计算性能的灵活性。此外，ESCIP确保检测到的聚类的每个子集在统计上是显著的和连续的。关键词：网络地理信息系统，空间算法，空间分析，空间聚类。
Detecting irregularly shaped spatial clusters within heterogeneous point processes is challenging because the number of potential clusters with different sizes and shapes can be enormous. This research develops a novel method, expansion-based spatial clustering for inhomogeneous point processes (ESCIP), for detecting spatial clusters of any shape within a heterogeneous point process in the context of analyzing spatial big data. Statistical testing is used to find core points—points with neighboring areas that have significantly more cases than the expectation—and an expansion approach is developed to find irregularly shaped clusters by connecting nearby core points. Instead of employing a brute-force search for all potential clusters, as done in the spatial scan statistics, this approach only requires testing a small neighboring area for each potential core point. Moreover, spatial indexing is leveraged to speed up the search for nearby points and the expansion of clusters. The proposed method is implemented with Poisson and Bernoulli models and evaluated for large spatial data sets. Experimental results show that ESCIP can detect irregularly shaped spatial clusters from millions of points with high efficiency. It is also demonstrated that the method outperforms the spatial scan statistics on the flexibility of cluster shapes and computational performance. Furthermore, ESCIP ensures that every subset of a detected cluster is statistically significant and contiguous. Key Words: cyberGIS, spatial algorithm, spatial analysis, spatial clustering.