Create a raster map with privacy awareness

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.

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())

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()).

See also

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

Examples

# \donttest{
library(raster)
prod <- sdc_raster(enterprises, field = "production", r = 500)
print(prod)
#> numeric sdc_raster object: 
#>    resolution: 500 500 ,  max_risk: 0.95 , min_count: 10 
#>    mean sensitivity score [0,1]:  0.6432039

prod <- sdc_raster(enterprises, field = "production", r = 1e3)
print(prod)
#> numeric sdc_raster object: 
#>    resolution: 1000 1000 ,  max_risk: 0.95 , min_count: 10 
#>    mean sensitivity score [0,1]:  0.255814

# get raster with the average production per cell averaged over the enterprises
prod_mean <- mean(prod)
summary(prod_mean)
#>              layer
#> Min.      383.4914
#> 1st Qu.  1425.1032
#> Median   2463.0075
#> 3rd Qu.  4150.8754
#> Max.    11871.0465
#> NA's        6.0000

# get raster with the total production per cell
prod_total <- sum(prod)
summary(prod_total)
#>                 sum
#> Min.       1115.291
#> 1st Qu.   14887.530
#> Median    58206.438
#> 3rd Qu.  178065.698
#> Max.    2435076.047
#> NA's          6.000
# }