Abstract
Representing synthetically the complexity of multidimensional phenomena is especially challenging when comparing different territorial areas, according to multiple factors. Attention can be focused either on the performance shown by Countries, usually evaluated through rankings, or on the structural models which characterize different compositions of the various indicators. We present here a technique which combines cluster analysis with the use of a composite indicator, thus permitting to classify Countries, both according to their structural characteristics and to their overall performance. We test the analysis on the Europe 2020 set of indicators, obtaining interesting results both with respect to the whole set of indicators and sub-sets regarding sustainability, smartness and inclusion.
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- 1.
Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. In order to decide which clusters should be combined (for agglomerative, “bottom up” approaches), or where a cluster should be split (for divisive, “top down” approaches), a measure of dissimilarity between sets of observations is required. In most methods of hierarchical clustering, this is achieved by use of an appropriate measure of distance, and a linkage criterion which specifies the dissimilarity of sets as a function of the pairwise distances of observations in the sets. In Ward’s minimum-variance method, the distance between two clusters is the ANOVA sum of squares between the two clusters added up over all the variables. At each generation, the within-cluster sum of squares is minimized over all partitions obtainable by merging two clusters from the previous generation (Milligan 1980).
- 2.
With regards to as the analysis of each single Country, we refer to the group where the country is most frequently included (mode).
References
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Milligan, G. W. (1980). An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika, 45(3), 325–342.
OECD and JRC. (2008). Handbook on constructing composite indicators. Methodology and user guide. Paris: OECD.
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Rondinella, T., Grimaccia, E. (2017). Joint Analysis of Structural Models and Performance: Merging Clustering and Composite Indicators in the Analysis of Europe 2020 Strategy. In: Maggino, F. (eds) Complexity in Society: From Indicators Construction to their Synthesis. Social Indicators Research Series, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-319-60595-1_13
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DOI: https://doi.org/10.1007/978-3-319-60595-1_13
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