GARCÍA ALONSO, CARLOS, Pérez-Naranjo L.M. , Fernández-Caballero J.C.
No
Ann. Oper. Res.
Article
Científica
1.217
0.946
01/01/2014
000339726600011
2-s2.0-84904548266
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.
Financially compromised areas; Fuzzy hot-spots; Local indicators of spatial aggregation; Multiobjective evolutionary algorithms; Spatial analysis