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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-post-staging / r-tangles
Type Size Name Uploaded Downloads Labels
conda 1.4 MB | noarch/r-tangles-0.8.1-r43hc72bb7e_3.conda  1 year and 1 month ago 1015 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r44hc72bb7e_3.conda  1 year and 1 month ago 1007 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r42hc72bb7e_2.conda  2 years and 3 months ago 1484 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r43hc72bb7e_2.conda  2 years and 3 months ago 1504 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r41hc72bb7e_1.tar.bz2  2 years and 11 months ago 1771 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r42hc72bb7e_1.tar.bz2  2 years and 11 months ago 1786 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r40hc72bb7e_0.tar.bz2  4 years and 2 months ago 2318 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r41hc72bb7e_0.tar.bz2  4 years and 2 months ago 2350 main

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