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sdcSpatial (version 0.6.1)

Statistical Disclosure Control for Spatial Data

Description

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) , continuous variables by de Wolf and de Jonge (2018) , suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) .

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Install

install.packages('sdcSpatial')

Monthly Downloads

167

Version

0.6.1

License

GPL-2

Issues

11

Pull Requests

0

Stars

8

Forks

1

Maintainer

Edwin Jonge

Last Published

March 15th, 2025

Functions in sdcSpatial (0.6.1)

sdc_raster

Create a raster map with privacy awareness
protect_quadtree

Protect a raster with a quadtree method.
plot_sensitive

Plot the sensitive cells of the sdc_raster.
sensitivity_score

Mean sensitivity for raster
protect_smooth

Protect a sdc_raster by smoothing
max2

returns one but highest contribution
smooth_raster

Create kde density version of a raster
remove_sensitive

Remove sensitive cells from raster
sdcSpatial-package

Privacy Protected maps
topdown_code

Remove revealing high and low values
plot.sdc_raster

Plot a sdc_raster object
mask_voronoi

Mask coordinates using voronoi masking
create_raster

Create a raster at a certain resolution
is_sensitive_at

Calculate sensitivity from a sdc_raster at x,y locations.
disclosure_risk

Calculate disclosure risk for raster cells
mask_weighted_random

Mask coordinates using weighted random pertubation
mask_grid

Mask coordinates using a grid
is_sensitive

Return raster with sensitive locations.
mask_random

Mask coordinates using random pertubation
dwellings

Simulated dwellings data set
enterprises

Simulated data set with enterprise locations.
protect_neighborhood

protects raster by summing over the neighborhood