
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.
sdc_raster(
x,
variable,
r = 200,
max_risk = 0.95,
min_count = 10,
risk_type = c("external", "internal", "discrete"),
...,
field = variable
)
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()
)
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 numeric
with same length as x
to be used for the data in the raster map.
either a desired resolution or a pre-existing raster::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.
numeric
, the maximum_risk score (disclosure_risk
)
before a cell in the map is considered sensitive.
numeric
, a raster cell with less then min_count
observations is considered sensitived.
passed on to disclosure_risk()
.
passed through to raster::rasterize()
synonym for variable
. If both supplied, field
has precedence.
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
(raster::brick()
).
Other sensitive:
disclosure_risk()
,
is_sensitive()
,
is_sensitive_at()
,
plot_sensitive()
,
remove_sensitive()
,
sensitivity_score()
# \donttest{
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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