Package: somspace 1.2.4

somspace: 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 (2020, <doi:10.1177/0959683620913924>).

Authors:Yannis Markonis [aut, cre], Filip Strnad [aut], Simon Michael Papalexiou [aut], Mijael Rodrigo Vargas Godoy [ctb]

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Last updated 1 years agofrom:d7958c63c0. Checks:OK: 5 NOTE: 2. Indexed: yes.

Exports:cnetplot_tsregssominpsomregssomspsomspa

Dependencies:clicolorspacedata.tablefansifarverggplot2gluegtableisobandkohonenlabelinglatticelifecyclemagrittrmapsMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcppreshape2rlangscalesstringistringrtibbleutf8vctrsviridisLitewithr