Título Hot-spots identified in the spatial distribution of financial risk in agrarian enterprises
Publicación externa No
Medio Acta Hortic.
Alcance Conference Paper
Naturaleza Científica
Cuartil SJR 3
Impacto SJR 0.27100
Ámbito Internacional
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855392273&doi=10.17660%2fActaHortic.2008.802.56&partnerID=40&md5=d8cac4290edf11552eee063542a9df0f
Fecha de publicacion 01/01/2008
ISI 000305212500056
Scopus Id 2-s2.0-84855392273
DOI 10.17660/ActaHortic.2008.802.56
Abstract Horticulture is the most important sub-sector in Andalusian agriculture. The geographical identification of financially compromised and/or sustainable areas can be strategic for determining spatially structural and political measures. Different methodologies have been used to detect highly (positively or negatively) autocorrelated zones in space (hot-spots) but all of them find it quite difficult to precisely pinpoint the spatial units included. This paper describes a Multi-Objective Evolutionary Algorithm (MOEA) designed to identify hot-spots at municipal level based on the Bayesian Conditional Auto-regressive (CAR) model. Our MOEA (SPEA2 model) evaluates the probability each spatial unit has of belonging to a potential hot-spot and results can be represented on a map. Hot-spots were identified by optimizing the spatial distribution of Bayesian risks, minimizing their standard deviations and minimizing the minimum path (distances) that links all municipality capitals included in the potential hot-spot. The results lead to a better understanding of problems related to rural sustainability.
Palabras clave Financial risk in horticultural farms; Hierarchical bayesian model; Multi-Objective Evolutionary Algorithm; Spatial analysis
Miembros de la Universidad Loyola

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