Package: osc 1.0.5
osc: Orthodromic Spatial Clustering
Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for a matrix, raster and single coordinates on a plain (Euclidean distance) or on a sphere (great-circle or orthodromic distance).
Authors:
osc_1.0.5.tar.gz
osc_1.0.5.zip(r-4.7)osc_1.0.5.zip(r-4.6)osc_1.0.5.zip(r-4.5)
osc_1.0.5.tgz(r-4.6-x86_64)osc_1.0.5.tgz(r-4.6-arm64)osc_1.0.5.tgz(r-4.5-x86_64)osc_1.0.5.tgz(r-4.5-arm64)
osc_1.0.5.tar.gz(r-4.7-arm64)osc_1.0.5.tar.gz(r-4.7-x86_64)osc_1.0.5.tar.gz(r-4.6-arm64)osc_1.0.5.tar.gz(r-4.6-x86_64)
osc_1.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
osc/json (API)
| # Install 'osc' in R: |
| install.packages('osc', repos = c('https://steffenkriewald.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/steffenkriewald/osc/issues
- exampledata - Example data for the clustering algorithm.
- landcover - Fictional landcover data to demonstrate the cca for Raster-Data
- population - Example population data for the city clustering algorithm
Last updated from:211b0e6c20. Checks:11 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | NOTE | 225 | ||
| linux-devel-x86_64 | NOTE | 236 | ||
| source / vignettes | OK | 203 | ||
| linux-release-arm64 | NOTE | 207 | ||
| linux-release-x86_64 | NOTE | 202 | ||
| macos-release-arm64 | NOTE | 172 | ||
| macos-release-x86_64 | NOTE | 317 | ||
| macos-oldrel-arm64 | NOTE | 284 | ||
| macos-oldrel-x86_64 | NOTE | 417 | ||
| windows-devel | NOTE | 201 | ||
| windows-release | NOTE | 204 | ||
| windows-oldrel | NOTE | 185 | ||
| wasm-release | OK | 123 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Assign data to clusters | assign.data |
| City Clustering Algorithm (CCA) | cca cca.single |
| List of coordinates for clustering | coordinate.list |
| Example data for the clustering algorithm. | exampledata |
| Fictional landcover data to demonstrate the cca for Raster-Data | landcover |
| Simple Buffer algorithm | osc.buffer |
| Example population data for the city clustering algorithm | population |
