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Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms
dc.contributor.author | García-Alonso, Carlos R. | |
dc.contributor.author | Pérez Naranjo, Leonor | |
dc.contributor.author | Fernández-Caballero, Juan Carlos | |
dc.date.accessioned | 2024-02-11T11:53:54Z | |
dc.date.available | 2024-02-11T11:53:54Z | |
dc.date.issued | 2011 | |
dc.identifier.uri | http://hdl.handle.net/10396/27396 | |
dc.description.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 Algo rithm (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. | es_ES |
dc.format.mimetype | application/pdf | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0/ | es_ES |
dc.source | García‐Alonso, C. R., Pérez Naranjo, L. M., & Fernández-Caballero, J.C. (2011). Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms. Annals Of Operations Research, 219(1), 187-202. https://doi.org/10.1007/s10479-011-0841-3 | es_ES |
dc.subject | Multiobjective evolutionary algorithms | es_ES |
dc.subject | Spatial analysis | es_ES |
dc.subject | Local indicators of spatial aggregation | es_ES |
dc.subject | Fuzzy hot-spots | es_ES |
dc.subject | Financially compromised areas | es_ES |
dc.title | Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherversion | https://doi.org/10.1007/s10479-011-0841-3 | es_ES |
dc.relation.projectID | Gobierno de España. TIN2005-08386-C05-02 | es_ES |
dc.relation.projectID | Junta de Andalucía. P05-TIC-00531 | es_ES |
dc.relation.projectID | Junta de Andalucía. P08-TIC-3745 | es_ES |
dc.relation.projectID | Gobierno de España. PI08/90752 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |