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

Research areas:

• Methodology - Machine learning

Year:

2014

Type of Publication:

Article

Keywords:

Multiobjective, evolutionary, hot-spots

Authors:

• García-Alonso, Carlos
• Pèrez-Naranjo, Leonor M
• Fernández, Juan Carlos

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