Journal
ANNALS OF OPERATIONS RESEARCH
Volume 219, Issue 1, Pages 187-202Publisher
SPRINGER
DOI: 10.1007/s10479-011-0841-3
Keywords
Multiobjective evolutionary algorithms; Spatial analysis; Local indicators of spatial aggregation; Fuzzy hot-spots; Financially compromised areas
Categories
Funding
- Spanish Department of Research of the Ministry of Education and Science [TIN2005-08386-C05-02]
- Junta de Andalucia [P05-TIC-00531, P08-TIC-3745]
- Ministry of Health Care [PI08/90752]
- FEDER
- Consejeria de Agricultura y Pesca (Junta de Andalucia)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available