TY - JOUR
T1 - A computationally efficient method for delineating irregularly shaped spatial clusters
AU - Duque, J.C.
AU - Aldstadt, J.
AU - Velasquez, E.
AU - Franco, Jose L.
AU - Betancourt Arango, A.
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 - http://www.scopus.com/inward/record.url?scp=80855130145&partnerID=8YFLogxK
U2 - 10.1007/s10109-010-0137-1
DO - 10.1007/s10109-010-0137-1
M3 - Article
VL - 13
SP - 355
EP - 372
JO - Journal of Geographical Systems
JF - Journal of Geographical Systems
SN - 1435-5949
IS - 4
ER -