Title Multiobjective evolutionary algorithms to identify highly autocorrelated areas: The case of spatial distribution in financially compromised farms
Authors GARCÍA ALONSO, CARLOS, Pérez-Naranjo L.M., Fernández-Caballero J.C., GARCÍA ALONSO, CARLOS
External publication No
Means Ann. Oper. Res.
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 2
JCR Impact 1.21700
SJR Impact 0.94600
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904548266&doi=10.1007%2fs10479-011-0841-3&partnerID=40&md5=4175c8c75ab6da7a6d4e8748cbc6a06e
Publication date 01/01/2014
ISI 000339726600011
Scopus Id 2-s2.0-84904548266
DOI 10.1007/s10479-011-0841-3
Abstract Local Indicators of Spatial Aggregation (LISA) can be used as objectives in a multicriteria framework when highly autocorrelated areas (hot-spots) must be identified and geographically located in complex areas. To do so, a Multi-Objective Evolutionary Algorithm (MOEA) based on SPEA2 (Strength Pareto Evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fine-grained strength, the weighted sum of objectives and fuzzy evaluation of weighted objectives) and three LISA methods. MOEA makes it possible to achieve a compromise between spatial econometric methods as it highlights areas where a specific phenomenon shows significantly high autocorrelation. The spatial distribution of financially compromised olive-tree farms in Andalusia (Spain) was selected for analysis and two fuzzy hot-spots were statistically identified and spatially located. Hot-spots can be considered to be spatial fuzzy sets where the spatial units have a membership degree that can also be calculated. © 2011 Springer Science+Business Media, LLC.
Keywords Financially compromised areas; Fuzzy hot-spots; Local indicators of spatial aggregation; Multiobjective evolutionary algorithms; Spatial analysis
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