A computationally efficient method for delineating irregularly shaped spatial clusters
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In this paper, we present an efficiency improvement for the algorithm called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, devised by Aldstadt and Getis (Geogr Anal 38(4):327–343, 2006). AMOEBA embeds a local spatial autocorrelation statistic in an iterative procedure in order to identify spatial clusters (ecotopes) of related spatial units. We provide an analysis of the computational complexity of the original AMOEBA and develop an alternative formulation that reduces computational time without losing optimality. Empirical evidence is provided using georeferenced socio-demographic data in Accra, Ghana.
KeywordsAMOEBA Cluster detection Local G statistic Ecotope
JEL ClassificationC02 mathematical methods C4 econometric and statistical methods: special topics
The authors thank Professor Dr. John Weeks, director of the International Population Center at San Diego State University, for providing us with the data for our empirical application. The usual disclaimer applies.
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