sdc_raster: Create a raster map with privacy awareness

In sdcSpatial: Statistical Disclosure Control for Spatial Data

Create a raster map with privacy awareness

Description

sdc_raster creates multiple raster::raster objects ("count", "mean", "sum") from supplied point data x and calculates the sensitivity to privacy disclosure for each raster location.

Usage

sdc_raster(
  x,
  variable,
  r = 200,
  max_risk = 0.95,
  min_count = 10,
  risk_type = c("external", "internal", "discrete"),
  ...,
  field = variable
)

Arguments

x	sp::SpatialPointsDataFrame, sf::sf or a two column matrix or data.frame that is used to create a raster map.
variable	name of data column or numeric with same length as x to be used for the data in the raster map.
r	either a desired resolution or a pre-existing raster object. In the first case, the crs of x (if present) will be used, in the latter the properties of the r will be kept.
max_risk	numeric, the maximum_risk score (disclosure_risk) before a cell in the map is considered sensitive.
min_count	numeric, a raster cell with less then min_count observations is considered sensitived.
risk_type	passed on to disclosure_risk().
...	passed through to raster::rasterize()
field	synonym for variable. If both supplied, field has precedence.

Details

A sdc_raster object is the vehicle that does the book keeping for calculating sensitivity. Protection methods work upon a sdc_raster and return a new sdc_raster in which the sensitivity is reduced. The sensitivity of the map can be assessed with sensitivity_score, plot.sdc_raster(), plot_sensitive() or print. Reducing the sensitivity can be done with protect_smooth(), protect_quadtree() and remove_sensitive(). Raster maps for mean, sum and count data can be extracted from the $value (brick()).

Value

object of class "sdc_raster":

• $value: raster::brick() object with different layers e.g. count, sum, mean, scale.

• $max_risk: see above.

• $min_count: see above. of protection operation protect_smooth() or protect_quadtree().

• $type: data type of variable, either numeric or logical

• $risk_type, "external", "internal" or "discrete" (see disclosure_risk())

See Also

Other sensitive: disclosure_risk(), is_sensitive_at(), is_sensitive(), plot_sensitive(), remove_sensitive(), sensitivity_score()

Examples

library(raster)
prod <- sdc_raster(enterprises, field = "production", r = 500)
print(prod)

prod <- sdc_raster(enterprises, field = "production", r = 1e3)
print(prod)

# get raster with the average production per cell averaged over the enterprises
prod_mean <- mean(prod)
summary(prod_mean)

# get raster with the total production per cell
prod_total <- sum(prod)
summary(prod_total)

sdcSpatial documentation built on March 24, 2022, 5:05 p.m.