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	Detecting feature from spatial point processes using Collective Nearest Neighbor
	Pei T.
	2009
关键词	Classification entropy Noise Point pattern Feature Spatial scan method Shared nearest neighbor EM algorithm clusters foreshock algorithm sequence model earthquakes seismicity patterns clutter
英文摘要	In a spatial point set, clustering patterns (features) are difficult to locate due to the presence of noise. Previous methods, either using grid-based method or distance-based method to separate feature from noise, suffer from the parameter choice problem, which may produce different point patterns in terms of shape and area. This paper presents the Collective Nearest Neighbor method (CLNN) to identify features. CLNN assumes that in spatial data clustered points and noise can be viewed as two homogenous point processes. The one with the higher intensity is considered as a feature and the one with the lower intensity is treated as noise. As a result, they can be separated according to the difference in intensity between them. With CLNN, points are first classified into feature and noise based on the kth nearest distance (the distance between a point and its kth nearest neighbor) at various values of k. Then, CLNN selects those classifications in which the separated classes (i.e. features and noise) are homogenous Poisson processes and cannot be further divided. Finally, CLNN identifies clustered points by averaging the selected classifications. Evaluation of CLNN using simulated data shows that CLNN reduces the number of false points significantly. The comparison between CLNN, the shared nearest neighbor, the spatial scan and the classification entropy method shows that CLNN produced the fewest false points. A case study using seismic data in southwestern China showed that CLNN is able to identify foreshocks of the Songpan earthquake (M=7.2), which may help to locate the epicenter of the Songpan earthquake. (C) 2009 Elsevier Ltd. All rights reserved.
出处	Computers Environment and Urban Systems
卷	33
期	6
页	435-447
收录类别	SCI
语种	英语
ISSN号	0198-9715
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/23217]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Pei T.. Detecting feature from spatial point processes using Collective Nearest Neighbor. 2009.

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