Three-Way Data Analysis for Multivariate Spatial Time Series
We discuss several methods to realize three-way (three mode) approaches to clustering using the INDCLUS model and multidimensional scaling using the INDSCAL model, which assumes that the objects are embedded in a discrete or continuous space common to all data, including individual differences obtained by weighting each dimension. We apply some effective dynamic graphical approaches using two methods to perform a time-space structural analysis for multivariate spatial time series. The clustering and scaling of multivariate spatial time series consider: (1) the spatial nature of the objects to be clustered geometrically (discrete); (2) the characteristics of the feature space with the time series (continuous); (3) the latent structure between space and time. The last aspect is addressed using dynamic graphics with a matrix-type presentation. We can simultaneously observe the spatial nature, move the feature space and can zoom in/out of the results using a suitable size. The proposed analysis can be applied to the classification and scaling of the prefectures of Japan on the basis of the observed dynamics of some safety indicators.
This work was supported by the MEXT-Supported Program for the Strategic Research Foundation at Private Universities, 2008–2012.
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