杨学习, 石岩, 邓敏, 唐建波. 一种基于多层次专题属性约束的空间异常探测方法[J]. 武汉大学学报 ( 信息科学版), 2016, 41(6): 810-817. DOI: 10.13203/j.whugis20140425
引用本文: 杨学习, 石岩, 邓敏, 唐建波. 一种基于多层次专题属性约束的空间异常探测方法[J]. 武汉大学学报 ( 信息科学版), 2016, 41(6): 810-817. DOI: 10.13203/j.whugis20140425
YANG Xuexi, SHI Yan, DENG Min, TANG Jianbo. A New Method of Spatial Outlier Detection by Considering Multi-level Thematic Attribute Constraints[J]. Geomatics and Information Science of Wuhan University, 2016, 41(6): 810-817. DOI: 10.13203/j.whugis20140425
Citation: YANG Xuexi, SHI Yan, DENG Min, TANG Jianbo. A New Method of Spatial Outlier Detection by Considering Multi-level Thematic Attribute Constraints[J]. Geomatics and Information Science of Wuhan University, 2016, 41(6): 810-817. DOI: 10.13203/j.whugis20140425

一种基于多层次专题属性约束的空间异常探测方法

A New Method of Spatial Outlier Detection by Considering Multi-level Thematic Attribute Constraints

  • 摘要: 针对现有空间异常探测方法在构建空间邻近域以及准确探测各类空间异常模式方面的局限性,本文提出了一种基于多层次专题属性约束的空间异常探测方法(spatial outlier detection by considering multi-level thematic attribute constraints,MTACSOD)。首先采用层次约束Delaunay三角网构建合理、稳定的空间邻近域;进而根据空间邻接实体间的专题属性距离,针对各空间连通子图施加全局和局部约束;最后通过一个异常模式识别指标提取各类空间异常模式。该方法不需要人为输入参数,通过模拟实验比较和实际应用验证了本文方法的优越性和有效性。

     

    Abstract: Spatial outlier detectionis an important approach in spatial data mining and knowledge discovery. Spatial outliers are entities whose non-spatial attributes are significantly different from the value of other entities in their spatial neighborhoods. The current methods have limitations in capturing spatial neighborhoods and detecting small abnormal clusters. In order to solve this problem, we develops a new method of spatial outlier detection that considers thematic attributes, named MTACSOD. Firstly, a constrained Delaunay triangulation is used to construct reasonable and stable spatial proximity relationship. Then, for the thematic attribute distance between adjacent spatial entities, global and local constraints are imposed consecutively to refine spatial adjacency. Finally, a spatial outlier identification index is proposed to detect spatial outliers. Both simulated and real-life datasets are used to illustrate the advance and practicability of the MTACSOD proposed in this paper.

     

/

返回文章
返回