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Spatial data anonymization preserves confidentiality. Using methods described in Zandbergen (2014) <doi:10.1155/2014/567049>, spatial data anonymization is achieved by dithering original spatial coordinates with combinations of randomized vertical, horizontal and rotational shifts. This can apply to non-grid spatial point patterns and raster objects, and the methods preserve the same spatial characteristics and relationships of the original data. Unique hash keying enables data subjected to anonymization sequences to be re-identified where required.

copied from cf-staging / r-tangles
Type Size Name Uploaded Downloads Labels
conda 1.4 MB | noarch/r-tangles-0.8.1-r43hc72bb7e_3.conda  5 months and 9 days ago 456 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r44hc72bb7e_3.conda  5 months and 9 days ago 441 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r42hc72bb7e_2.conda  1 year and 7 months ago 951 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r43hc72bb7e_2.conda  1 year and 7 months ago 963 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r41hc72bb7e_1.tar.bz2  2 years and 3 months ago 1327 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r42hc72bb7e_1.tar.bz2  2 years and 3 months ago 1329 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r40hc72bb7e_0.tar.bz2  3 years and 5 months ago 1863 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r41hc72bb7e_0.tar.bz2  3 years and 5 months ago 1915 main

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