sdc_raster {sdcSpatial} | R Documentation |
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 |
r |
either a desired resolution or a pre-existing raster object.
In the first case, the crs of |
max_risk |
|
min_count |
|
risk_type |
passed on to |
... |
passed through to |
field |
synonym for |
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 operationprotect_smooth()
orprotect_quadtree()
. -
$type
: data type ofvariable
, eithernumeric
orlogical
-
$risk_type
, "external", "internal" or "discrete" (seedisclosure_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)