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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 454 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r44hc72bb7e_3.conda  5 months and 9 days ago 440 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r42hc72bb7e_2.conda  1 year and 7 months ago 949 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r43hc72bb7e_2.conda  1 year and 7 months ago 961 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r41hc72bb7e_1.tar.bz2  2 years and 3 months ago 1325 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r42hc72bb7e_1.tar.bz2  2 years and 3 months ago 1327 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r40hc72bb7e_0.tar.bz2  3 years and 5 months ago 1861 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r41hc72bb7e_0.tar.bz2  3 years and 5 months ago 1914 main

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