sdc_raster creates multiple
("count", "mean", "sum") from supplied point data
x and calculates
the sensitivity to privacy disclosure for each raster location.
sdc_raster( x, variable, r = 200, max_risk = 0.95, min_count = 10, risk_type = c("external", "internal", "discrete"), ..., field = variable )
sp::SpatialPointsDataFrame, sf::sf or a two column matrix or data.frame that is used to create a raster map.
name of data column or
either a desired resolution or a pre-existing raster object.
In the first case, the crs of
passed on to
passed through to
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,
remove_sensitive(). Raster maps for
count data can be extracted from the
raster::brick() object with different layers e.g.
$max_risk: see above.
$min_count: see above.
of protection operation
$type: data type of
$risk_type, "external", "internal" or "discrete" (see
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)
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