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	Detecting nominal variables' spatial associations using conditional probabilities of neighboring surface objects' categories
	Bai H. X.; Li, D. Y.; Ge, Y.; Wang, J. F.
	2016
关键词	Spatial associations Nominal variable Conditional probability distribution Adjacency matrix neural-tube defects local indicators birth-defects risk-factors morans i autocorrelation models images land statistics
英文摘要	How to automatically mining the spatial association patterns in spatial data is a challenging task in spatial data mining. In this paper, we propose three indices that represent the per-class, inter-class, and overall spatial associations of a nominal variable, which are based on the conditional probabilities of surface object categories. These indices represent relative quantities and are normalized to the region [-1, 1], which more accord with the intuitive cognition of people. We present some algorithms for detecting spatial associations that are based on these indices. The proposed method can be regarded as an extension of join count statistics and Transiogram. Several constructive examples were used to illustrate the advantages of the new method. Using two real data sets, vegetation types in Qingxian, Shanxi, China and neural tube birth defects in Heshun, Shanxi, China, we ran comparative experiments with other commonly used methods, including join count statistics, co-location quotient, and Q(m) statistics. The experimental results show that the proposed method can detect more subtle spatial associations, and is not sensitive to the sequence of neighbors. (C) 2015 Elsevier Inc. All rights reserved.
出处	Information Sciences
卷	329
页	701-718
语种	英语
ISSN号	0020-0255
DOI标识	10.1016/j.ins.2015.10.003
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/42860]
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	Bai H. X.,Li, D. Y.,Ge, Y.,et al. Detecting nominal variables' spatial associations using conditional probabilities of neighboring surface objects' categories. 2016.

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