Using AMOEBA to Create a Spatial Weights Matrix and Identify Spatial Clusters

• Published in: Geographical analysis Vol. 38; no. 4; pp. 327 - 343
• Main Authors: Aldstadt, Jared, Getis, Arthur
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.10.2006; Ohio State University Press
• Subjects: Algorithms; Bgi / Prodig; Cluster analysis; Fertility rate; General methodology; Geography; Mathematical methods; Methodology; Spatial analysis; Statistical and stochastic methods; Cluster analysis; Matrix analysis; Spatial analysis; Statistics; Spatial statistics
• ISSN: 0016-7363; 1538-4632
• DOI: 10.1111/j.1538-4632.2006.00689.x

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.