Package: osc 1.0.5

Steffen Kriewald

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:Steffen Kriewald, Till Fluschnik, Dominik Reusser, Diego Rybski

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✨
card.svg |card.png
osc/json (API)

# Install 'osc' in R:
install.packages('osc', repos = c('https://cranhaven.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

Datasets:
  • 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

On CRAN:

Conda:

archivedpackagesr-universe

3.51 score 5 stars 13 scripts 159 downloads 5 exports 5 dependencies

Last updated from:b4c7e20b11 (on package/osc). Checks:13 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK187
linux-devel-x86_64OK217
source / vignettesOK227
linux-release-arm64OK197
linux-release-x86_64OK213
macos-release-arm64OK170
macos-release-x86_64OK269
macos-oldrel-arm64OK137
macos-oldrel-x86_64OK364
windows-develOK184
windows-releaseOK193
windows-oldrelOK190
wasm-releaseOK125

Exports:assign.dataccacca.singlecoordinate.listosc.buffer

Dependencies:latticerasterRcppspterra

Using the City Clustering Algorithm

Rendered frompaper.rnwusingutils::Sweaveon May 18 2026.

Last update: 2026-05-14
Started: 2026-05-14