sdc_raster: Create a raster map with privacy awareness

Description Usage Arguments Details Value See Also Examples

View source: R/sdc_raster.R

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 location.

Usage

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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":

See Also

Other sensitive: disclosure_risk, is_sensitive, plot_sensitive, remove_sensitive, sensitivity_score

Examples

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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 July 20, 2019, 1:04 a.m.