文献类型： 外文期刊
作者： Ren, Xuhong 1 ; Wei, Ning 1 ; Gao, Bingbo 2 ; Pan, Yuchun 2 ; Guo, Qing 1 ; Gao, Yunbing 2 ;
作者机构： 1.China Three Gorges Univ, Coll Comp & Informat Technol, Yichang 443002, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
关键词： Preferential sampling;Global mean estimation;Self-organizing dual-zoning method;Polygonal declustering;Cell declustering
期刊名称：INTELLIGENT AUTOMATION AND SOFT COMPUTING （ 影响因子：1.647； 五年影响因子：1.469 ）
ISSN： 1079-8587
年卷期： 2014 年 20 卷 4 期
页码：
收录情况： SCI
摘要： The conventional approach to estimate the global mean under preferential spatial sampling gets a larger deviation and further influences the precision of the subsequent model calculation and analysis. Zoning or declustering methods can effectively improve the estimation precision of preferential sampling. In this paper, we propose a novel method, which uses self-organizing dual-zoning method to estimate the global mean, in which the Self-Organizing Feature Map (SOFM) and the Voronoi diagram are utilized to realize classification and zoning. By comparing with arithmetic mean method, polygonal declustering method, and cell declustering method, we got that arithmetic mean method could not satisfy the special properties of the preferential sampling, and self-organizing dual-zoning method gets more accurate zoning results and more stable global means with different sample sizes and Feature Deviation Index (FDI).
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