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 |
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