@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",
}