Is the Mahalanobis distance based method a good approach for regionalization?
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The Mahalanobis distance based method is a useful approach for regionalization . It has been applied in various fields such as statistics, data science, ecology, and climatology . The method allows for the quantification of species-environment relationships, outlier detection, and identification of homogeneous regions based on multivariate data . It has been shown to perform well in handling dynamic multi-objective optimization problems with stochastic changes . The Mahalanobis distance method can effectively process test data and improve the consistency of results in trajectory tests . Additionally, it has been used to identify homogeneous regions in climatology research, considering seasonal and intra-annual variations . Overall, the Mahalanobis distance based method has demonstrated its effectiveness and versatility in regionalization tasks across different disciplines.
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