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Annals of Operations Research

, Volume 219, Issue 1, pp 187–202 | Cite as

Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms

  • Carlos R. García-Alonso
  • Leonor M. Pérez-Naranjo
  • Juan C. Fernández-Caballero
Article

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.

Keywords

Multiobjective evolutionary algorithms Spatial analysis Local indicators of spatial aggregation Fuzzy hot-spots Financially compromised areas 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Carlos R. García-Alonso
    • 1
  • Leonor M. Pérez-Naranjo
    • 2
  • Juan C. Fernández-Caballero
    • 3
  1. 1.ETEA, Business Administration FacultyUniversity of Córdoba (SPAIN)CórdobaSpain
  2. 2.Business Administration DepartmentUniversity Pablo de OlavideSevillaSpain
  3. 3.Computer Science and Numeric Analysis DepartmentUniversity of CórdobaCórdobaSpain

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