sdcSpatial: Statistical Disclosure Control for Spatial Data

Privacy protected raster maps can be created from spatial point data. Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.

Version: 0.5.2
Depends: R (≥ 3.5.0)
Imports: raster, methods
Suggests: testthat, knitr, rmarkdown, sp, sf, FNN
Published: 2022-03-24
DOI: 10.32614/CRAN.package.sdcSpatial
Author: Edwin de Jonge ORCID iD [aut, cre], Peter-Paul de Wolf [aut], Douwe Hut [ctb], Sapphire Han [ctb]
Maintainer: Edwin de Jonge <edwindjonge at>
License: GPL-2
NeedsCompilation: no
Materials: NEWS
In views: OfficialStatistics
CRAN checks: sdcSpatial results


Reference manual: sdcSpatial.pdf
Vignettes: Introduction sdcSpatial: privacy protected density maps


Package source: sdcSpatial_0.5.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sdcSpatial_0.5.2.tgz, r-oldrel (arm64): sdcSpatial_0.5.2.tgz, r-release (x86_64): sdcSpatial_0.5.2.tgz, r-oldrel (x86_64): sdcSpatial_0.5.2.tgz
Old sources: sdcSpatial archive


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