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

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Research areas:
Year:
2014
Type of Publication:
Article
Keywords:
Multiobjective, evolutionary, hot-spots
Authors:
Journal:
Annals of Operations Research
Volume:
219
Number:
1
Pages:
187-202
Month:
August
ISSN:
0254-5330
Note:
JCR (2014): 1.217 Position: 38/81 (Q2) Category: OPERATIONS RESEARCH AND MANAGEMENT SCIENCE
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 Algo- rithm (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.
Comments:
JCR (2014): 1.217 Position: 38/81 (Q2) Category: OPERATIONS RESEARCH AND MANAGEMENT SCIENCE
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