Journal of Geographical Systems

, Volume 13, Issue 4, pp 355–372 | Cite as

A computationally efficient method for delineating irregularly shaped spatial clusters

  • Juan C. Duque
  • Jared Aldstadt
  • Ermilson Velasquez
  • Jose L. Franco
  • Alejandro Betancourt
Original Article


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.


AMOEBA Cluster detection Local G statistic Ecotope 

JEL Classification

C02 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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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Juan C. Duque
    • 1
  • Jared Aldstadt
    • 2
  • Ermilson Velasquez
    • 3
  • Jose L. Franco
    • 3
  • Alejandro Betancourt
    • 3
  1. 1.Research in Spatial Economics (RISE-group), Department of EconomicsEAFIT UniversityMedellinColombia
  2. 2.Department of GeographyUniversity at BuffaloBuffaloUSA
  3. 3.Research in Spatial Economics (RISE-group), Department of Fundamental SciencesEAFIT UniversityMedellinColombia

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