A fuzzy clustering model for multivariate spatial time series. (English) Zbl 1337.62305
Summary: Clustering of multivariate spatial-time series should consider: 1) the spatial nature of the objects to be clustered; 2) the characteristics of the feature space, namely the space of multivariate time trajectories; 3) the uncertainty associated to the assignment of a spatial unit to a given cluster on the basis of the above complex features. The last aspect is dealt with by using the Fuzzy \(C\)-Means objective function, based on appropriate measures of dissimilarity between time trajectories, by distinguishing the cross-sectional and longitudinal aspects of the trajectories. In order to take into account the spatial nature of the statistical units, a spatial penalization term is added to the above function, depending on a suitable spatial proximity/ contiguity matrix. A tuning coefficient takes care of the balance between, on one side, discriminating according to the pattern of the time trajectories and, on the other side, ensuring an approximate spatial homogeneity of the clusters. A technique for determining an optimal value of this coefficient is proposed, based on an appropriate spatial autocorrelation measure. Finally, the proposed models are applied to the classification of the Italian provinces, on the basis of the observed dynamics of some socio-economical indicators.
MSC:
| 62M86 | Inference from stochastic processes and fuzziness |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
| 62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
Keywords:
array of space time data; fuzzy clustering; spatial autocorrelation function; spatial penalization term; dissimilarity measures between multivariate time trajectoriesReferences:
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