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Updated: Jun 4, 2026
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Development of a new spatial analysis tool in mental health: identification of highly autocorrelated areas
Carlos R García-Alonso1, Luis Salvador-Carulla, Miguel A Negrín-Hernández
1ETEA, Department of Management and Quantitative Methods, University of Córdoba, Córdoba, Spain. cgarcia@etea.com
Aims:
This study had two objectives: (1) to design and develop a computer-based tool, called Multi-Objective Evolutionary Algorithm/Hot-Spots (MOEA/HS), to identify and geographically locate highly autocorrelated zones or hot-spots and which merges different methods, and (2) to carry out a demonstration study in a geographical area where previous information about the distribution of schizophrenia prevalence is available and which can therefore be compared.
Methods:
Local Indicators of Spatial Aggregation (LISA) models as well as the Bayesian Conditional Autoregressive Model (CAR) were used as objectives in a multicriteria framework when highly autocorrelated zones (hot-spots) need to be identified and geographically located. A Multi-Objective Evolutionary Algorithm (MOEA) model was designed and used to identify highly autocorrelated areas of the prevalence of schizophrenia in Andalusia. Hot-spots were statistically identified using exponential-based QQ-Plots (statistics of extremes).
Results:
Efficient solutions (Pareto set) from MOEA/HS were analysed statistically and one main hot-spot was identified and spatially located. Our model can be used to identify and locate geographical hot-spots of schizophrenia prevalence in a large and complicated region.
Conclusions:
MOEA/HS enables a compromise to be achieved between different econometric methods by highlighting very special zones in complex areas where schizophrenia shows a high autocorrelation.
