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>).

Version:	1.2.4
Depends:	R (≥ 3.5.0), ggplot2, data.table, kohonen
Imports:	maps, reshape2
Suggests:	knitr, rmarkdown, testthat
Published:	2023-04-28
DOI:	10.32614/CRAN.package.somspace
Author:	Yannis Markonis [aut, cre], Filip Strnad [aut], Simon Michael Papalexiou [aut], Mijael Rodrigo Vargas Godoy [ctb]
Maintainer:	Yannis Markonis <imarkonis at gmail.com>
License:	GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	somspace results

Documentation:

Reference manual:	somspace.pdf
Vignettes:	somspace: Spatial classification with Self-Organizing Maps

Downloads:

Package source:	somspace_1.2.4.tar.gz
Windows binaries:	r-devel: somspace_1.2.4.zip, r-release: somspace_1.2.4.zip, r-oldrel: somspace_1.2.4.zip
macOS binaries:	r-release (arm64): somspace_1.2.4.tgz, r-oldrel (arm64): somspace_1.2.4.tgz, r-release (x86_64): somspace_1.2.4.tgz, r-oldrel (x86_64): somspace_1.2.4.tgz
Old sources:	somspace archive

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