| Título |
Multiobjective evolutionary algorithms to identify highly autocorrelated areas: The case of spatial distribution in financially compromised farms |
| Autores |
GARCÍA ALONSO, CARLOS, Pérez-Naranjo L.M. , Fernández-Caballero J.C. |
| Publicación externa |
No |
| Medio |
Ann. Oper. Res. |
| Alcance |
Article |
| Naturaleza |
Científica |
| Cuartil JCR |
2 |
| Cuartil SJR |
2 |
| Impacto JCR |
1.21700 |
| Impacto SJR |
0.94600 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904548266&doi=10.1007%2fs10479-011-0841-3&partnerID=40&md5=4175c8c75ab6da7a6d4e8748cbc6a06e |
| Fecha de publicacion |
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. |
| Palabras clave |
Financially compromised areas; Fuzzy hot-spots; Local indicators of spatial aggregation; Multiobjective evolutionary algorithms; Spatial analysis |
| Miembros de la Universidad Loyola |
|
Gestionar cookies