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

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osc.pdf |osc.html
osc/json (API)

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

Peer review:

Bug tracker:https://github.com/steffenkriewald/osc/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:

3.78 score 12 scripts 132 downloads 5 exports 5 dependencies

Last updated 5 years agofrom:211b0e6c20. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-win-x86_64NOTEOct 25 2024
R-4.5-linux-x86_64NOTEOct 25 2024
R-4.4-win-x86_64NOTEOct 25 2024
R-4.4-mac-x86_64NOTEOct 25 2024
R-4.4-mac-aarch64NOTEOct 25 2024
R-4.3-win-x86_64NOTEOct 25 2024
R-4.3-mac-x86_64NOTEOct 25 2024
R-4.3-mac-aarch64NOTEOct 25 2024

Exports:assign.dataccacca.singlecoordinate.listosc.buffer

Dependencies:latticerasterRcppspterra

Using the City Clustering Algorithm

Rendered frompaper.rnwusingutils::Sweaveon Oct 25 2024.

Last update: 2018-05-02
Started: 2018-05-02