Using AMOEBA to create a spatial weights matrix and identify spatial clusters
Using AMOEBA to create a spatial weights matrix and identify spatial clusters
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DOI：
10.1111/j.1538-4632.2006.00689.x
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发表时间：
2006-10-01
影响因子：
3.6
通讯作者：
Getis, Arthur
中科院分区：
文献类型：
--
作者：
Aldstadt, Jared;Getis, Arthur
通过称为AMOEBA（一种基于多向最优生态环境的算法）的过程创建空间权重矩阵依赖于局部空间自相关统计量的使用。结果是(1)一个向量，用于识别那些与相邻空间单元相关或不相关的空间单元；(2)一个权重矩阵，其值是第i个空间单元与所有其他附近有空间关联的空间单元之间关系的函数。此外，阿米巴过程有助于划分相关空间单位的集群，称为生态群落。实验表明，变形虫是一种有效的聚类识别工具。与扫描统计程序（SaTScan）的比较给出了AMOEBA值的证据。在约旦安曼的普查区，总生育率被用来展示一个使用AMOEBA构建空间权重矩阵和识别集群的实际例子。再一次，比较揭示了阿米巴程序的有效性。
The creation of a spatial weights matrix by a procedure called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, is dependent on the use of a local spatial autocorrelation statistic. The result is (1) a vector that identifies those spatial units that are related and unrelated to contiguous spatial units and (2) a matrix of weights whose values are a function of the relationship of the ith spatial unit with all other nearby spatial units for which there is a spatial association. In addition, the AMOEBA procedure aids in the demarcation of clusters, called ecotopes, of related spatial units. Experimentation reveals that AMOEBA is an effective tool for the identification of clusters. A comparison with a scan statistic procedure (SaTScan) gives evidence of the value of AMOEBA. Total fertility rates in enumeration districts in Amman, Jordan, are used to show a real-world example of the use of AMOEBA for the construction of a spatial weights matrix and for the identification of clusters. Again, comparisons reveal the effectiveness of the AMOEBA procedure.