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

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...

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Bibliographic Details
Published in:Annals of operations research Vol. 219; no. 1; pp. 187 - 202
Main Authors: García-Alonso, Carlos R., Pérez-Naranjo, Leonor M., Fernández-Caballero, Juan C.
Format: Journal Article
Language:English
Published: Boston Springer US 01.08.2014
Springer
Springer Nature B.V
Subjects:
Algorithms
Business and Management
Combinatorics
Econometrics
Economic aspects
Evolutionary algorithms
Farms
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Genetic algorithms
Geographic information systems
Linear programming
Mathematical analysis
Objectives
Olive
Operations research
Operations Research/Decision Theory
Optimization techniques
Spatial analysis
Spatial distribution
Strength
Studies
Theory of Computation
Spain
Andalusia Spain
Spatial analysis
Fuzzy hot-spots
Multiobjective evolutionary algorithms
Local indicators of spatial aggregation
Financially compromised areas
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary: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.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-011-0841-3