Title: Statistical Disclosure Control for Spatial Data
Version: 0.6.1
Description: Privacy protected raster maps can be created from spatial point data. Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.
License: GPL-2
Encoding: UTF-8
LazyData: true
URL: https://github.com/edwindj/sdcSpatial
BugReports: https://github.com/edwindj/sdcSpatial/issues
RoxygenNote: 7.3.2
Suggests: testthat, knitr, rmarkdown, sp, sf, FNN
Imports: raster, methods
Depends: R (≥ 3.5.0)
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2025-03-15 09:07:06 UTC; edwin
Author: Edwin de Jonge ORCID iD [aut, cre], Peter-Paul de Wolf [aut], Douwe Hut [ctb], Sapphire Han [ctb]
Maintainer: Edwin de Jonge <edwindjonge@gmail.com>
Repository: CRAN
Date/Publication: 2025-03-15 09:30:02 UTC

Privacy Protected maps

Description

sdcSpatial contains functions to create spatial distribution maps, assess the risk of disclosure on a location and to suppress or adjust revealing values at certain locations.

Details

sdcSpatial working horse is the sdc_raster() object upon which the following methods can be applied:

Sensitivity assessment

Protection methods

Extraction

Author(s)

Maintainer: Edwin de Jonge edwindjonge@gmail.com (ORCID)

Authors:

Other contributors:

References

de Jonge, E., & de Wolf, P. P. (2016, September). Spatial smoothing and statistical disclosure control. In International Conference on Privacy in Statistical Databases (pp. 107-117). Springer, Cham.

de Wolf, P. P., & de Jonge, E. (2018, September). Safely Plotting Continuous Variables on a Map. In International Conference on Privacy in Statistical Databases (pp. 347-359). Springer, Cham.

Suñé, E., Rovira, C., Ibáñez, D., Farré, M. (2017). Statistical disclosure control on visualising geocoded population data using a structure in quadtrees, NTTS 2017

See Also

Useful links:

Create a raster at a certain resolution

Description

Utility function to generate a raster at a certain resolution (with rounded coordinates).

Usage

create_raster(x, res = 200, ...)

Arguments

x

geographical object (that supports extent).

res

desired resolution (default 200).

...

passed through to raster::raster() function.

Value

raster::raster() object

Calculate disclosure risk for raster cells

Description

The disclosure risk function is used by is_sensitive() to determine the risk of a raster cell. It returns a score between 0 and 1 for cells that have a finite value (otherwise NA).

Usage

disclosure_risk(x, risk_type = x$risk_type)

Arguments

x

sdc_raster object.

risk_type

character: "external", "internal", "discrete".

Details

Different risk functions include:

Value

raster::raster object with the disclosure risk.

See Also

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

Simulated dwellings data set

Description

The data are generated with residence/household locations from the Dutch open data BAG register. The locations are realistic, but the associated data is simulated.

Usage

dwellings

Format

a data.frame with 90603 rows and 4 columns.

x

integer, x coordinate of dwelling (crs 28992)

y

integer, y coordinate of dwelling (crs 28992)

consumption

numeric, simulated continuous value

unemployed

logical, simulated discrete value

Source

Basisregistratie Adressen en Gebouwen https://www.kadaster.nl/zakelijk/registraties/basisregistraties/bag/bag-producten

Examples


# dwellings is a data.frame, the best way is to first turn it
# into a sf or sp object.

# create an sf object from our data
if (requireNamespace("sf")){
  dwellings_sf <- sf::st_as_sf(dwellings, coords=c("x", "y"), crs=28992)

  unemployed <- sdc_raster( dwellings_sf
                          , "unemployed"
                          , r=200
                          , max_risk = 0.9
                          )

  plot(unemployed)
  sensitivity_score(unemployed)

  unemployed_smoothed <- protect_smooth(unemployed, bw = 0.4e3)
  plot(unemployed_smoothed, main="Employment rate")
  plot(unemployed_smoothed, "sum", main = "Employment")
} else {
  message("Package 'sf' was not installed.")
}

dwellings_sp <- dwellings
# or change a data.frame into a sp object
sp::coordinates(dwellings_sp) <- ~ x + y
tryCatch(
  # not working on some OS versions.
  sp::proj4string(dwellings_sp) <- "+init=epsg:28992"
)
consumption <- sdc_raster(dwellings_sp, dwellings_sp$consumption, r = 500)
consumption

plot(consumption)

# but we can also create a raster directly from a data.frame
unemployed <- sdc_raster( dwellings[c("x","y")], dwellings$unemployed)

Simulated data set with enterprise locations.

Description

enterprises is generated from the dutch open data BAG register. The locations are realistic, but the associated data is simulated.

Usage

enterprises

Format

An object of class SpatialPointsDataFrame with 8348 rows and 2 columns.

production

numeric simulated production (lognormal).

fined

logical simulated variable if an enterprise is fined or not.

Source

Basisregistratie Adressen en Gebouwen: https://www.kadaster.nl/zakelijk/registraties/basisregistraties/bag/bag-producten

Examples


library(sdcSpatial)
library(raster)

data("enterprises")

production <- sdc_raster(enterprises, "production", min_count = 10)
print(production)

# show the average production per cell
plot(production, "mean")
production$min_count <- 2 # adjust norm for sdc
plot(production)

production_safe <- remove_sensitive(production)
plot(production_safe)

Return raster with sensitive locations.

Description

Create a binary raster with sensitive locations.

Usage

is_sensitive(
  x,
  max_risk = x$max_risk,
  min_count = x$min_count,
  risk_type = x$risk_type
)

Arguments

x

sdc_raster object.

max_risk

a risk value higher than max_risk will be sensitive.

min_count

a count lower than min_count will be sensitive.

risk_type

what kind of measure should be used (see details).

Details

By default the risk settings are taken from x, but they can be overriden.

Different risk functions can be used:

See Also

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

Examples


dwellings_sp <- dwellings
sp::coordinates(dwellings_sp) <- ~ x + y
tryCatch(
  # does not work on some OS versions
  sp::proj4string(dwellings_sp) <- "+init=epsg:28992"
)
# create a 1km grid
unemployed <- sdc_raster(dwellings_sp, dwellings_sp$unemployed, r = 1e3)
print(unemployed)

# retrieve the sensitive cells
is_sensitive(unemployed)

Calculate sensitivity from a sdc_raster at x,y locations.

Description

Calculate sensitivity from a sdc_raster at x,y locations. A typical use is to calculate the sensitivity for each of the locations x was created with (see example).

Usage

is_sensitive_at(x, xy, ...)

Arguments

x

sdc_raster()

xy

matrix of x and y coordinates, or a SpatialPoints or SpatialPointsDataFrame object

...

Arguments passed on to is_sensitive

max_risk

a risk value higher than max_risk will be sensitive.

min_count

a count lower than min_count will be sensitive.

risk_type

what kind of measure should be used (see details).

Value

logical vector with

See Also

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

Examples


production <- sdc_raster(enterprises, "production")

# add the sensitive variable to original data set.
enterprises$sensitive <- is_sensitive_at(production, enterprises)

Mask coordinates using a grid

Description

Pertubates coordinates by rounding coordinates to grid coordinates

Usage

mask_grid(x, r, plot = FALSE)

Arguments

x

coordinates

r

grid resolution

plot

if TRUE the points (black) and the pertubation (red) will be plotted

See Also

Other point pertubation: mask_random(), mask_voronoi(), mask_weighted_random()

Examples

x <- cbind(
  x = c(2.5, 3.5, 7.2, 1.5),
  y = c(6.2, 3.8, 4.4, 2.1)
)

# plotting is only useful from small datasets!

# grid masking
x_g <- mask_grid(x, r=1, plot=TRUE)

# random pertubation
set.seed(3)
x_r <- mask_random(x, r=1, plot=TRUE)


  if (requireNamespace("FNN", quietly = TRUE)){
    # weighted random pertubation
    x_wr <- mask_weighted_random(x, k = 2, r = 4, plot=TRUE)
  }

  if ( requireNamespace("FNN", quietly = TRUE)
    && requireNamespace("sf", quietly = TRUE)
     ){
    # voronoi masking, plotting needs package `sf`
    x_vor <- mask_voronoi(x, r = 1, plot=TRUE)
  }

Mask coordinates using random pertubation

Description

Pertubates points with a uniform pertubation in a circle. Note that r can either be one distance, or a distance per data point.

Usage

mask_random(x, r, plot = FALSE)

Arguments

x

coordinates, matrix or data.frame (first two columns)

r

numeric pertubation distance (vectorized)

plot

if TRUE points will be plotted.

Value

adapted x with perturbed coordinates

See Also

Other point pertubation: mask_grid(), mask_voronoi(), mask_weighted_random()

Examples

x <- cbind(
  x = c(2.5, 3.5, 7.2, 1.5),
  y = c(6.2, 3.8, 4.4, 2.1)
)

# plotting is only useful from small datasets!

# grid masking
x_g <- mask_grid(x, r=1, plot=TRUE)

# random pertubation
set.seed(3)
x_r <- mask_random(x, r=1, plot=TRUE)


  if (requireNamespace("FNN", quietly = TRUE)){
    # weighted random pertubation
    x_wr <- mask_weighted_random(x, k = 2, r = 4, plot=TRUE)
  }

  if ( requireNamespace("FNN", quietly = TRUE)
    && requireNamespace("sf", quietly = TRUE)
     ){
    # voronoi masking, plotting needs package `sf`
    x_vor <- mask_voronoi(x, r = 1, plot=TRUE)
  }

Mask coordinates using voronoi masking

Description

Pertubates points by using voronoi masking. Each point is moved at its nearest voronoi boundary.

Usage

mask_voronoi(x, r = 0, k = 10, plot = FALSE)

Arguments

x

coordinates

r

tolerance, nearest voronoi should be at least r away.

k

number of neighbors to consider when determining nearest neighbors

plot

if TRUE plots the voronoi tesselation, points (black), and perturbated points (red), needs package sf.

Value

adapted x with perturbed coordinates

See Also

Other point pertubation: mask_grid(), mask_random(), mask_weighted_random()

Examples

x <- cbind(
  x = c(2.5, 3.5, 7.2, 1.5),
  y = c(6.2, 3.8, 4.4, 2.1)
)

# plotting is only useful from small datasets!

# grid masking
x_g <- mask_grid(x, r=1, plot=TRUE)

# random pertubation
set.seed(3)
x_r <- mask_random(x, r=1, plot=TRUE)


  if (requireNamespace("FNN", quietly = TRUE)){
    # weighted random pertubation
    x_wr <- mask_weighted_random(x, k = 2, r = 4, plot=TRUE)
  }

  if ( requireNamespace("FNN", quietly = TRUE)
    && requireNamespace("sf", quietly = TRUE)
     ){
    # voronoi masking, plotting needs package `sf`
    x_vor <- mask_voronoi(x, r = 1, plot=TRUE)
  }

Mask coordinates using weighted random pertubation

Description

This method uses per point the distance to the kth neighbor as the maximum pertubation distance. Parameter r can be used to restrict the maximum distance of the kth neighbor.

Usage

mask_weighted_random(x, k = 5, r = NULL, plot = FALSE)

Arguments

x

coordinates, matrix or data.frame (first two columns)

k

integer number of neighbors to be used as the maximum distance

r

numeric pertubation distance (vectorized)

plot

if TRUE points will be plotted.

Value

adapted x with perturbed coordinates

References

Spatial obfuscation methods for privacy protection of household-level data

See Also

Other point pertubation: mask_grid(), mask_random(), mask_voronoi()

Examples

x <- cbind(
  x = c(2.5, 3.5, 7.2, 1.5),
  y = c(6.2, 3.8, 4.4, 2.1)
)

# plotting is only useful from small datasets!

# grid masking
x_g <- mask_grid(x, r=1, plot=TRUE)

# random pertubation
set.seed(3)
x_r <- mask_random(x, r=1, plot=TRUE)


  if (requireNamespace("FNN", quietly = TRUE)){
    # weighted random pertubation
    x_wr <- mask_weighted_random(x, k = 2, r = 4, plot=TRUE)
  }

  if ( requireNamespace("FNN", quietly = TRUE)
    && requireNamespace("sf", quietly = TRUE)
     ){
    # voronoi masking, plotting needs package `sf`
    x_vor <- mask_voronoi(x, r = 1, plot=TRUE)
  }

returns one but highest contribution

Description

returns one but highest contribution

Usage

max2(x, default_value = 0, na.rm = TRUE)

Plot a sdc_raster object

Description

Plot a sdc_raster object together with its sensitivity.

Usage

## S3 method for class 'sdc_raster'
plot(
  x,
  value = "mean",
  sensitive = TRUE,
  ...,
  main = paste(substitute(x)),
  col
)

Arguments

x

sdc_raster object to be plotted

value

character which value layer to be used for plotting, e.g. "sum", "count", "mean" (default).

sensitive

logical show the sensitivity in the plot?

...

passed on to raster::plot()

main

title of plot

col

color palette to be used, passed on to raster::plot().

Details

When sensitive is set to TRUE, a side-by-side plot will be made of the value and its sensitivity.

See Also

Other plotting: plot_sensitive()

Plot the sensitive cells of the sdc_raster.

Description

Plots t the sensitive cells of the sdc_raster. The sensitive cells are plotted in red. The sensitive cells are determined using is_sensitive.

Usage

plot_sensitive(x, value = "mean", main = "sensitive", col, ...)

Arguments

x

sdc_raster object

value

character which value layer to be used for values, e.g. "sum", "count", "mean" (default).

main

character title of map.

col

color palette to be used, passed on to raster::plot().

...

passed on to plot.sdc_raster.

See Also

Other plotting: plot.sdc_raster()

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

protects raster by summing over the neighborhood

Description

protects raster by summing over the neighborhood

Usage

protect_neighborhood(x, radius = 10 * raster::res(x$value)[1], ...)

Arguments

x

sdc_raster() object to be protected

radius

of the neighborhood to take

...

not used at the moment

Value

sdc_raster object

Examples


data(enterprises)

# create a sdc_raster from point data with raster with
# a resolution of 200m
production <- sdc_raster(enterprises, variable = "production"
                        , r = 200, min_count = 3)

print(production)

# plot the raster
zlim <- c(0, 3e4)
# show which raster cells are sensitive
plot(production, zlim=zlim)

# let's smooth to reduce the sensitivity
smoothed <- protect_smooth(production, bw = 400)
plot(smoothed)

neighborhood <- protect_neighborhood(production, radius=1000)
plot(neighborhood)

# what is the sensitivy fraction?
sensitivity_score(neighborhood)

Protect a raster with a quadtree method.

Description

protect_quadtree reduces sensitivy by aggregating sensisitve cells with its three neighbors, and does this recursively until no sensitive cells are left or when the maximum zoom levels has been reached.

Usage

protect_quadtree(x, max_zoom = Inf, ...)

Arguments

x

sdc_raster object to be protected.

max_zoom

numeric, restricts the number of zoom steps and thereby the max resolution for the blocks. Each step will zoom with a factor of 2 in x and y so the max resolution = resolution * 2^max_zoom.

...

Arguments passed on to is_sensitive

max_risk

a risk value higher than max_risk will be sensitive.

min_count

a count lower than min_count will be sensitive.

risk_type

what kind of measure should be used (see details).

Details

This implementation generalizes the method as described by Suñé et al., in which there is no risk function, and only a min_count to determine sensitivity. Furthermore the method the article only handles count data (x$value$count), not mean or summed values. Currently the translation feature of the article is not (yet) implemented, for the original method does not take the disclosure_risk into account.

Value

a sdc_raster object, in which sensitive cells have been recursively aggregated until not sensitive or when max_zoom has been reached.

References

Suñé, E., Rovira, C., Ibáñez, D., Farré, M. (2017). Statistical disclosure control on visualising geocoded population data using a structure in quadtrees, NTTS 2017

See Also

Other protection methods: protect_smooth(), remove_sensitive()

Examples

# library(raster)
#
# fined <- sdc_raster(enterprises, enterprises$fined)
# plot(fined)
# fined_qt <- protect_quadtree(fined)
# plot(fined_qt)
#
# fined <- sdc_raster(enterprises, enterprises$fined, r=50)
# plot(fined)
# fined_qt <- protect_quadtree(fined)
# plot(fined_qt)
#
#
#
# library(sf)
# gemeente_2019 <- st_read("https://cartomap.github.io/nl/rd/gemeente_2019.geojson")
# st_crs(gemeente_2019) <- 28992
# nbl <- st_touches(gemeente_2019)
#
# coords <- st_coordinates(st_centroid(gemeente_2019))
# l <- lapply(seq_along(nbl), function(i){
#   nb <- nbl[[i]]
#   st_sfc(lapply(nb, function(j){
#     st_linestring(coords[c(i,j),])})
#   )
# })
# l2 <- do.call(c, l)
#
# edge_list <- as.data.frame(nbl)
# library(data.table)
# el <- as.data.table(edge_list)
# names(el) <- c("from", "to")
#
# edge_list$from <- gemeente_2019$id[edge_list$row.id]
# edge_list$to <- gemeente_2019$id[edge_list$col.id]
# edge_list <- subset(edge_list, row.id < col.id)
# edge_list <- edge_list[,c("from", "to")]
#
# g <- igraph::graph_from_data_frame(edge_list, directed = FALSE)
# plot(g)
# library(igraph)
# i <- match(names(V(g)), gemeente_2019$id)
#
# c2 <- igraph::layout_with_fr(g, coords[i,])
# plot(g, layout = c2)
#
# buurt_2019 <- st_read("https://cartomap.github.io/nl/rd/buurt_2019.geojson")
# st_crs(buurt_2019) <- 28992
# system.time({
#   nbl <- st_touches(buurt_2019)
# })
#
# coords <- st_coordinates(st_centroid(buurt_2019))
# l <- lapply(seq_along(nbl), function(i){
#   nb <- nbl[[i]]
#   st_sfc(lapply(nb, function(j){
#     st_linestring(coords[c(i,j),])})
#   )
# })
# l2 <- do.call(c, l)
#
# plot(l2)

Protect a sdc_raster by smoothing

Description

protect_smooth reduces the sensitivity by applying a Gaussian smoother, making the values less localized.

Usage

protect_smooth(x, bw = raster::res(x$value), ...)

Arguments

x

raster object

bw

bandwidth

...

passed through to raster::focal().

Details

The sensitivity of a raster can be decreased by applying a kernel density smoother as argued by de Jonge et al. (2016) and de Wolf et al. (2018). Smoothing spatially spreads localized values, reducing the risk for location disclosure. Note that smoothing often visually enhances detection of spatial patterns. The kernel applied is a Gaussian kernel with a bandwidth bw supplied by the user. The smoother acts upon the x$value$count and x$value$sum from which a new x$value$mean is derived.

References

de Jonge, E., & de Wolf, P. P. (2016, September). Spatial smoothing and statistical disclosure control. In International Conference on Privacy in Statistical Databases (pp. 107-117). Springer, Cham.

de Wolf, P. P., & de Jonge, E. (2018, September). Safely Plotting Continuous Variables on a Map. In International Conference on Privacy in Statistical Databases (pp. 347-359). Springer, Cham.

See Also

Other protection methods: protect_quadtree(), remove_sensitive()

Examples


library(sdcSpatial)
library(raster)

data(enterprises)

# create a sdc_raster from point data with raster with
# a resolution of 200m
production <- sdc_raster(enterprises, variable = "production"
                        , r = 200, min_count = 3)

print(production)

# plot the raster
zlim <- c(0, 3e4)
# show which raster cells are sensitive
plot(production, zlim=zlim)

# but we can also retrieve directly the raster
sensitive <- is_sensitive(production, min_count = 3)
plot(sensitive, col = c('white', 'red'))

# what is the sensitivy fraction?
sensitivity_score(production)
# or equally
cellStats(sensitive, mean)

# let's smooth to reduce the sensitivity
smoothed <- protect_smooth(production, bw = 400)
plot(smoothed)

# let's smooth to reduce the sensitivity, with higher resolution
smoothed <- protect_smooth(production, bw = 400, smooth_fact=4, keep_resolution=FALSE)
plot(smoothed)

# what is the sensitivy fraction?
sensitivity_score(smoothed)

# let's remove the sensitive data.
smoothed_safe <- remove_sensitive(smoothed, min_count = 3)
plot(smoothed_safe)

# let's communicate!
production_mean <- mean(smoothed_safe)
production_total <- sum(smoothed_safe)

# and create a contour plot
raster::filledContour(production_mean, nlevels = 6, main = "Mean production")


# generated with R 3.6 >=
#col <- hcl.colors(11, rev=TRUE)
col <- c("#FDE333", "#C2DE34", "#7ED357", "#00C475", "#00B28A", "#009B95"
        ,  "#008298", "#006791", "#274983", "#44286E", "#4B0055"
        )
raster::filledContour(production_total, nlevels = 11
             , col = col
             , main="Total production")

Remove sensitive cells from raster

Description

remove_sensitive removes sensitive cells from a sdc_raster. The sensitive cells, as found by is_sensitive() are set to NA.

Usage

remove_sensitive(x, max_risk = x$max_risk, min_count = x$min_count, ...)

mask_sensitive(x, max_risk = x$max_risk, min_count = x$min_count, ...)

Arguments

x

sdc_raster object.

max_risk

a risk value higher than max_risk will be sensitive.

min_count

a count lower than min_count will be sensitive.

...

passed on to is_sensitive.

Details

Removing sensitive cells is a protection method, which often is useful to finalize map protection after other protection methods have been applied. mask_sensitive and remove_sensitive are synonyms, to accommodate both experienced raster users as well as sdc users.

Value

sdc_raster object with sensitive cells set to NA.

See Also

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

Other protection methods: protect_quadtree(), protect_smooth()

Examples


library(raster)

unemployed <- sdc_raster(dwellings[1:2], dwellings$unemployed, r=200)

# plot the normally rastered data
plot(unemployed, zlim=c(0,1))
plot_sensitive(unemployed)

unemployed_safe <- remove_sensitive(unemployed, risk_type="discrete")
plot_sensitive(unemployed_safe, zlim=c(0,1))
print(unemployed)
unemployed$value

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 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::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 (raster::brick()).

Value

object of class "sdc_raster":

See Also

Other sensitive: disclosure_risk(), is_sensitive(), is_sensitive_at(), 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)

Mean sensitivity for raster

Description

sensitivity_score calculates the fraction of cells (with a value) that are considered sensitive according to the used disclosure_risk

Usage

sensitivity_score(x, max_risk = x$max_risk, min_count = x$min_count, ...)

Arguments

x

sdc_raster object.

max_risk

a risk value higher than max_risk will be sensitive.

min_count

a count lower than min_count will be sensitive.

...

passed on to is_sensitive

See Also

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

Examples


consumption <- sdc_raster(dwellings[1:2], variable = dwellings$consumption, r = 500)

sensitivity_score(consumption)
# same as
print(consumption)

# change the rules! A higher norm generates more sensitive cells
sensitivity_score(consumption, min_count = 20)

Create kde density version of a raster

Description

Create kde density version of a raster

Usage

smooth_raster(
  x,
  bw = raster::res(x),
  smooth_fact = 5,
  keep_resolution = TRUE,
  na.rm = TRUE,
  pad = TRUE,
  padValue = NA,
  threshold = NULL,
  type = c("Gauss", "circle", "rectangle"),
  ...
)

Arguments

x

raster object

bw

bandwidth

smooth_fact

integer, disaggregate factor to have a better smoothing

keep_resolution

integer, should the returned map have same resolution as x or keep the disaggregated raster resulting from smooth_fact?

na.rm

should the NA value be removed from the raster?

pad

should the data be padded?

padValue

what should the padding value be?

threshold

cells with a lower (weighted) value of this threshold will be removed.

type

what is the type of smoothing (see raster::focal())

...

passed through to raster::focal().

Remove revealing high and low values

Description

Remove revealing high and low values

Usage

topdown_code(r, top = Inf, down = -Inf, ..., upper = top, lower = down)