Detecting nominal variables' spatial associations using conditional probabilities of neighboring surface objects' categories
Detecting nominal variables' spatial associations using conditional probabilities of neighboring surface objects' categories
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DOI：
10.1016/j.ins.2015.10.003
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发表时间：
2016-02
期刊：
影响因子：
--
通讯作者：
Hexiang Bai;Deyu Li;Y. Ge;Jinfeng Wang
中科院分区：
文献类型：
--
作者：
Hexiang Bai;Deyu Li;Y. Ge;Jinfeng Wang
如何自动挖掘空间数据中的空间关联模式是空间数据挖掘中的一个具有挑战性的课题。在本文中，我们提出了三个指数，代表每类，类间和整体空间关联的名义变量，这是基于表面对象类别的条件概率。这些指标表示相对量，并归一化到区域[-1，1]，这更符合人们的直观认知雅阁。我们提出了一些算法，用于检测空间关联，是基于这些指数。该方法可以看作是连接计数统计和传输图的扩展。通过几个工程实例说明了该方法的优越性。利用两个真实的数据集--山西青县植被类型和山西和顺县神经管出生缺陷数据集，我们与其他常用的方法进行了比较实验，包括连接计数统计、共位商和Q（m）统计。实验结果表明，该方法能检测出更细微的空间关联，且对相邻节点的顺序不敏感。
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.