| 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. |
| External publication | No |
| Means | Ann. Oper. Res. |
| Scope | Article |
| Nature | Científica |
| JCR Quartile | 2 |
| SJR Quartile | 2 |
| JCR Impact | 1.217 |
| SJR Impact | 0.946 |
| 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 |
| Universidad Loyola members |