Decomposition of Repulsive Clusters in Complex Point Processes with Heterogeneous Components
Decomposition of Repulsive Clusters in Complex Point Processes with Heterogeneous Components
复制标题
具有异质成分的复杂点过程中排斥簇的分解
DOI：
10.3390/ijgi8080326
复制
发表时间：
2019-07
影响因子：
3.4
通讯作者：
Pei Tao
中科院分区：
文献类型：
--
作者：
Song Ci;Pei Tao
点过程的分解对于分析空间格局和发现地理现象的潜在机制是有用的。然而，当一个局部排斥集群存在于一个复杂的异质点过程中，传统的解决方案，这是基于聚类，可能是无效的分解，因为排斥模式是不受一个特定的概率分布函数和聚合和排斥组件的影响可能会被抵消。针对这一问题，本文提出了一种分解多个异质分量的复杂点过程中排斥簇的方法。排斥性团簇是指在小尺度上以一定距离间隔开的、同时在大尺度上聚集的、具有排斥性的、密度连通的点的集合。H-函数用于确定排斥距离和提取排斥点以进一步聚类来识别排斥簇。基于三个数据集的仿真实验表明，该方法能有效地对异质点过程进行排斥性聚类分解。对北京地区的兴趣点数据集进行的实例研究表明，该方法能够从不同区域的兴趣点中识别出具有排斥性的聚类，从而反映出不同区域的商店的不同服务特征。
The decomposition of a point process is useful for the analysis of spatial patterns and in the discovery of potential mechanisms of geographic phenomena. However, when a local repulsive cluster is present in a complex heterogeneous point process, the traditional solution, which is based on clustering, may be invalid for decomposition because a repulsive pattern is not subject to a specific probability distribution function and the effects of aggregative and repulsive components may be counterbalanced. To solve this problem, this paper proposes a method of decomposing repulsive clusters in complex point processes with multiple heterogeneous components. A repulsive cluster is defined as a set of repulsive density-connected points that are separated by a certain distance at a small scale and aggregated at a large scale simultaneously. The H-function is used to identify repulsive clusters by determining the repulsive distance and extracting repulsive points for further clustering. Through simulation experiments based on three datasets, the proposed method has been shown to effectively perform repulsive cluster decomposition in heterogeneous point processes. A case study of the point of interest (POI) dataset in Beijing also indicates that the method can identify meaningful repulsive clusters from types of POIs that represent different service characteristics of shops in different local regions.
登录
查看更多内容
DOI：
--
发表时间：
2003
期刊：
--
影响因子：
--
作者：
V. Guralnik;D. Wijesekera;J. Srivastava
通讯作者：
V. Guralnik;D. Wijesekera;J. Srivastava
DOI：
--
发表时间：
--
期刊：
--
影响因子：
--
作者：
T. Pei;Chenghu Zhou;A. Zhu;Baolin Li;C. Qin
通讯作者：
T. Pei;Chenghu Zhou;A. Zhu;Baolin Li;C. Qin
影响因子：
--
作者：
J. Neyman;E. Scott
通讯作者：
J. Neyman;E. Scott
DOI：
10.1201/b12382-18
发表时间：
2001-10
期刊：
--
影响因子：
--
作者：
H. Miller;Jiawei Han
通讯作者：
H. Miller;Jiawei Han
点过程数据的多尺度分解
影响因子：
2
作者：
Pei, Tao;Gao, Jianhuan;Zhou, Chenghu
通讯作者：
Zhou, Chenghu