A computationally efficient method for delineating irregularly shaped spatial clusters

Juan C. Duque; Jared Aldstadt; Ermilson Velasquez; Jose L. Franco; Alejandro Betancourt

doi:10.1007/s10109-010-0137-1

A computationally efficient method for delineating irregularly shaped spatial clusters

Juan C. Duque
, Jared Aldstadt
, Ermilson Velasquez
, Jose L. Franco
, Alejandro Betancourt

Geography

Universidad EAFIT

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	355-372
Number of pages	18
Journal	Journal of Geographical Systems
Volume	13
Issue number	4
DOIs	https://doi.org/10.1007/s10109-010-0137-1
State	Published - Dec 2011

Keywords

AMOEBA
Cluster detection
Ecotope
Local G statistic

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10.1007/s10109-010-0137-1

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title = "A computationally efficient method for delineating irregularly shaped spatial clusters",

abstract = "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.",

keywords = "AMOEBA, Cluster detection, Ecotope, Local G statistic",

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AU - Duque, Juan C.

AU - Aldstadt, Jared

AU - Velasquez, Ermilson

AU - Franco, Jose L.

AU - Betancourt, Alejandro

PY - 2011/12

Y1 - 2011/12

N2 - 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.

AB - 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.

KW - AMOEBA

KW - Cluster detection

KW - Ecotope

KW - Local G statistic

UR - https://www.scopus.com/pages/publications/85027956809

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SP - 355

EP - 372

JO - Journal of Geographical Systems

JF - Journal of Geographical Systems

IS - 4

ER -

A computationally efficient method for delineating irregularly shaped spatial clusters

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Keywords

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