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

Áreas de investigación:

• Methodology - Machine learning

Año:

2014

Tipo de publicación:

Artículo

Palabras clave:

Multiobjective, evolutionary, hot-spots

Autores:

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

Journal:

Annals of Operations Research

Volumen:

219

Número:

1

Páginas:

187-202

Mes:

August

ISSN:

0254-5330

BibTex:

@article{farms, author = "Carlos Garc{\'i}a-Alonso and Leonor M P{\`e}rez-Naranjo and Juan Carlos Fern{\'a}ndez", 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. ", awards = "JCR (2014): 1.217 Position: 38/81 (Q2) Category: OPERATIONS RESEARCH AND MANAGEMENT SCIENCE", comments = "JCR (2014): 1.217 Position: 38/81 (Q2) Category: OPERATIONS RESEARCH AND MANAGEMENT SCIENCE", doi = "10.1007/s10479-011-0841-3", issn = "0254-5330", journal = "Annals of Operations Research", keywords = "Multiobjective, evolutionary, hot-spots", month = "August", note = "JCR (2014): 1.217 Position: 38/81 (Q2) Category: OPERATIONS RESEARCH AND MANAGEMENT SCIENCE", number = "1", pages = "187-202", title = "{M}ultiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms", url = "http://dx.doi.org/10.1007/s10479-011-0841-3", volume = "219", year = "2014", }

Nota:

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.

Comentarios:

JCR (2014): 1.217 Position: 38/81 (Q2) Category: OPERATIONS RESEARCH AND MANAGEMENT SCIENCE