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).
| Version: | 1.0.0 |
| Depends: | R (≥ 3.4.0), ggplot2, data.table, kohonen, maps |
| Suggests: | knitr, rmarkdown, testthat |
| Published: | 2019-05-13 |
| Author: | Yannis Markonis [aut, cre], Filip Strnad [aut], Simon Michael Papalexiou [aut] |
| Maintainer: | Yannis Markonis <imarkonis at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| In views: | Cluster, Hydrology |
| CRAN checks: | somspace results |
| Reference manual: | somspace.pdf |
| Vignettes: |
Vignette Title |
| Package source: | somspace_1.0.0.tar.gz |
| Windows binaries: | r-devel: somspace_1.0.0.zip, r-devel-gcc8: somspace_1.0.0.zip, r-release: somspace_1.0.0.zip, r-oldrel: somspace_1.0.0.zip |
| OS X binaries: | r-release: somspace_1.0.0.tgz, r-oldrel: somspace_1.0.0.tgz |
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