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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  4 months and 23 days ago 421 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r44hc72bb7e_3.conda  4 months and 23 days ago 409 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r42hc72bb7e_2.conda  1 year and 6 months ago 919 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r43hc72bb7e_2.conda  1 year and 6 months ago 931 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r41hc72bb7e_1.tar.bz2  2 years and 2 months ago 1300 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r42hc72bb7e_1.tar.bz2  2 years and 2 months ago 1297 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r40hc72bb7e_0.tar.bz2  3 years and 5 months ago 1828 main
conda 1.4 MB | noarch/r-tangles-0.8.1-r41hc72bb7e_0.tar.bz2  3 years and 5 months ago 1888 main

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