Learn R Programming
⚠️There's a newer version (0.6.1) of this package.Take me there.

sdcSpatial (version 0.5.2)

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) .

Copy Link

Version

Install

install.packages('sdcSpatial')

Monthly Downloads

252

Version

0.5.2

License

GPL-2

Issues

11

Pull Requests

0

Stars

9

Forks

2

Maintainer

Edwin Jonge

Last Published

March 24th, 2022

Functions in sdcSpatial (0.5.2)

plot_sensitive

Plot the sensitive cells of the sdc_raster.
sensitivity_score

Mean sensitivity for raster
sdc_raster

Create a raster map with privacy awareness
protect_quadtree

Protect a raster with a quadtree method.
protect_smooth

Protect a sdc_raster by smoothing
smooth_raster

Create kde density version of a raster
protect_neighborhood

protects raster by summing over the neighborhood
topdown_code

Remove revealing high and low values
is_sensitive_at

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

Remove sensitive cells from raster
sdcSpatial-package

Privacy Protected maps
plot.sdc_raster

Plot a sdc_raster object
max2

returns one but highest contribution
mask_random

Mask coordinates using random pertubation
enterprises

Simulated data set with enterprise locations.
mask_voronoi

Mask coordinates using voronoi masking
disclosure_risk

Calculate disclosure risk for raster cells
mask_grid

Mask coordinates using a grid
is_sensitive

Return raster with sensitive locations.
mask_weighted_random

Mask coordinates using weighted random pertubation
dwellings

Simulated dwellings data set
create_raster

Create a raster at a certain resolution