Spatial Analysis with Self-Organizing Maps

Application of the Self-Organizing Maps technique for spatial classification of time series. The package uses spatial data, point or gridded, to create clusters with similar characteristics. The clusters can be further refined to a smaller number of regions by hierarchical clustering and their spatial dependencies can be presented as complex networks. Thus, meaningful maps can be created, representing the regional heterogeneity of a single variable. More information and an example of implementation can be found in Markonis and Strnad (2019).


Reference manual

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1.0.0 by Yannis Markonis, a year ago

Browse source code at

Authors: Yannis Markonis [aut, cre] , Filip Strnad [aut] , Simon Michael Papalexiou [aut]

Documentation:   PDF Manual  

Task views: Cluster Analysis & Finite Mixture Models, Hydrological Data and Modeling

GPL-3 license

Depends on ggplot2, data.table, kohonen, maps

Suggests knitr, rmarkdown, testthat

See at CRAN