Map: point density

map_dens() creates a smoothed utilisation distribution (UD).

as.im.SpatRaster(.map)

as.owin.SpatRaster(.map, .im = NULL)

as.owin.sf(.poly, .bbox = sf::st_bbox(.poly), .invert = TRUE)

map_dens(
  .map,
  .owin = as.owin.SpatRaster(.map),
  .coord = NULL,
  .discretise = FALSE,
  .shortcut = list(),
  ...,
  .plot = TRUE,
  .use_tryCatch = TRUE,
  .verbose = getOption("patter.verbose")
)

Arguments

.map

A SpatRaster that defines the grid on which the UD is represented. If .coord = NULL, .map also defines the points (and associated weights) that are smoothed (see .map_coord()). The coordinate reference system of .map must be planar and specified.

.im, .owin

A pixel image representation of .map (see as.im.SpatRaster() and spatstat.geom::im()) and an observation window (see as.owin.SpatRaster(), as.owin.sf() and spatstat.geom::owin()). If un-supplied, .owin is defined automatically from .map via as.owin.SpatRaster(), which uses as.im.SpatRaster() internally (see Details). For faster results, use a rectangular or polygon observation window (see as.owin.sf()).

.poly, .bbox, .invert

For as.owin.sf() to construct observation windows from sf objects.

.poly is an sf polygon object;
.bbox is the bounding of a simple feature (see sf::st_bbox());
.invert is a logical variable that defines whether or not to invert .poly (e.g., to turn a terrestrial polygon into an aquatic polygon);

.coord

(optional) Coordinates for density estimation, provided in any format accepted by .map_coord(). Coordinates must be planar.

.discretise

If .coord is provided, .discretise is a logical variable that defines whether or not to discretise coordinates on .map (see .map_coord()).

.shortcut

(optional) A named list from a previous call to map_dens(). If supplied, the function short-cuts straight to smoothing (.owin, .coord and .discretise are silently unused).

...

Arguments for density estimation, passed to spatstat.explore::density.ppp(), such as sigma (i.e., the bandwidth). at and se are not permitted.

.plot

A logical variable that defines whether or not to plot the output.

.use_tryCatch

A logical variable that controls error handling:

If .use_tryCatch = FALSE, if density estimation fails with an error, the function fails with the same error.
If .use_tryCatch = TRUE, if density estimation fails with an error, the function produces a warning with the error message and returns NULL.

.verbose

User output control (see patter-progress for supported options).

Value

The function returns a named list, with the following elements:

x: a spatstat.geom::ppp object that defines points for density estimation;
D: a spatstat.geom::im object of estimated intensities, from spatstat.explore::density.ppp();
ud: a normalised SpatRaster;

D and ud are NULL if spatstat.explore::density.ppp() fails and .use_tryCatch = TRUE.

Details

map_dens() smooths (a) a SpatRaster or (b) a set of inputted coordinates.

.map_coord() (and .map_mark()) are used to define coordinates and weights:

If .coords is NULL, .map cell coordinates are used for density estimation and cell values are used as weights.
If coordinates are supplied, coordinates are optionally re-expressed on .map and then used for density estimation. This option is generally faster. Coordinate weights are defined by .map_mark().

Cell coordinates and associated weights are converted to a spatstat.geom::ppp() object, which is passed, alongside the observation window (.owin), to spatstat.explore::density.ppp() for the estimation. Weights must sum to one.

as.im.SpatRaster(), as.owin.SpatRaster() and as.owin.sf() are helper functions that convert a SpatRaster to a pixel image and an observation window (see spatstat.geom::owin()). as.im.SpatRaster() is based on maptools::as.im.RasterLayer(). as.owin.SpatRaster() either defines a rectangular window, if there are no NAs on .map, or converts .map directly to an owin object. Gridded observation windows, especially if high resolution, considerably slow down density estimation and may exhaust vector memory. Use rectangular windows, or convert sf objects to polygon windows (via as.owin.sf()) if possible.

If .shortcut is supplied, the preceding steps can be skipped and the function short-cuts straight to smoothing. Use this option if the preceding steps are slow and you want to trial different smoothing options (such as sigma functions).

Coordinates and associated weights are smoothed via spatstat.explore::density.ppp() into an image. Pixel resolution and smoothing parameters such as bandwidth can be controlled via ... arguments which are passed directly to this function. The output is translated into a gridded probability density surface (on the geometry defined by .map).

This function replaces flapper::kud*() and flapper::pf_kud*() routines based on adehabitatHR (see here).

Examples

library(data.table)
library(dtplyr)
library(dplyr, warn.conflicts = FALSE)
library(spatstat.explore)
#> Warning: package ‘spatstat.explore’ was built under R version 4.3.3
#> Loading required package: spatstat.data
#> Warning: package ‘spatstat.data’ was built under R version 4.3.3
#> Loading required package: spatstat.univar
#> Warning: package ‘spatstat.univar’ was built under R version 4.3.3
#> spatstat.univar 3.0-0
#> Loading required package: spatstat.geom
#> Warning: package ‘spatstat.geom’ was built under R version 4.3.3
#> spatstat.geom 3.3-2
#> 
#> Attaching package: ‘spatstat.geom’
#> The following object is masked from ‘package:data.table’:
#> 
#>     shift
#> Loading required package: spatstat.random
#> Warning: package ‘spatstat.random’ was built under R version 4.3.3
#> spatstat.random 3.3-1
#> Loading required package: nlme
#> Warning: package ‘nlme’ was built under R version 4.3.3
#> 
#> Attaching package: ‘nlme’
#> The following object is masked from ‘package:dplyr’:
#> 
#>     collapse
#> spatstat.explore 3.3-1

#### Define map
map <- dat_gebco()

#### Example (1): Use sample coordinates
# Sample example coordinates
coord <-
  map |>
  terra::spatSample(size = 5000L, xy = TRUE, cell = TRUE, na.rm = TRUE) |>
  select("x", "y") |>
  as.data.table()
# Use x, y coordinates
map_dens(map, .coord = coord)
#> `patter::map_dens()` called @ 2024-08-01 11:41:26... 
#> ... 11:41:26: Processing `.map`... 
#> ... 11:41:26: Building XYM... 
#> ... 11:41:26: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:26: Estimating density surface... 
#> ... 11:41:26: Scaling density surface... 
#> `patter::map_dens()` call ended @ 2024-08-01 11:41:26 (duration: ~0 sec(s)). 
#> $x
#> Marked planar point pattern: 5000 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :        lyr.1 
#> min value   : 5.607983e-05 
#> max value   : 6.495713e-05 
#> 
# Other formats are acceptable
map_dens(map, .coord = as.matrix(coord))
#> `patter::map_dens()` called @ 2024-08-01 11:41:26... 
#> ... 11:41:26: Processing `.map`... 
#> ... 11:41:26: Building XYM... 
#> ... 11:41:26: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:26: Estimating density surface... 
#> ... 11:41:26: Scaling density surface... 

#> `patter::map_dens()` call ended @ 2024-08-01 11:41:27 (duration: ~1 sec(s)). 
#> $x
#> Marked planar point pattern: 5000 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :        lyr.1 
#> min value   : 5.607983e-05 
#> max value   : 6.495713e-05 
#> 
map_dens(map, .coord = as.data.frame(coord))
#> `patter::map_dens()` called @ 2024-08-01 11:41:27... 
#> ... 11:41:27: Processing `.map`... 
#> ... 11:41:27: Building XYM... 
#> ... 11:41:27: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:27: Estimating density surface... 
#> ... 11:41:27: Scaling density surface... 
#> `patter::map_dens()` call ended @ 2024-08-01 11:41:27 (duration: ~0 sec(s)). 
#> $x
#> Marked planar point pattern: 5000 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :        lyr.1 
#> min value   : 5.607983e-05 
#> max value   : 6.495713e-05 
#> 
# `cell_x` and `cell_y` coordinates are acceptable
map_dens(map, .coord = coord[, .(cell_x = x, cell_y = y)])
#> `patter::map_dens()` called @ 2024-08-01 11:41:27... 
#> ... 11:41:27: Processing `.map`... 
#> ... 11:41:27: Building XYM... 
#> ... 11:41:27: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:27: Estimating density surface... 
#> ... 11:41:27: Scaling density surface... 
#> `patter::map_dens()` call ended @ 2024-08-01 11:41:27 (duration: ~0 sec(s)). 
#> $x
#> Marked planar point pattern: 5000 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :        lyr.1 
#> min value   : 5.607983e-05 
#> max value   : 6.495713e-05 
#> 
# A SpatRaster is also acceptable
pou <- map_pou(map, .coord = coord, .plot = FALSE)$ud
#> `patter::map_pou()` called @ 2024-08-01 11:41:27... 
#> ... Building XYM... 
#> ... Building SpatRaster... 
#> `patter::map_pou()` call ended @ 2024-08-01 11:41:27 (duration: ~0 sec(s)). 
map_dens(pou)
#> `patter::map_dens()` called @ 2024-08-01 11:41:27... 
#> ... 11:41:27: Processing `.map`... 
#> ... 11:41:27: Building XYM... 
#> ... 11:41:27: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:27: Estimating density surface... 
#> ... 11:41:27: Scaling density surface... 
#> `patter::map_dens()` call ended @ 2024-08-01 11:41:27 (duration: ~0 sec(s)). 
#> $x
#> Marked planar point pattern: 5000 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :        lyr.1 
#> min value   : 5.607983e-05 
#> max value   : 6.495713e-05 
#> 

#### Example (2): Use coordinates from `sim_path_walk()`
coord <- dat_path()
map_dens(map, .coord = coord)
#> `patter::map_dens()` called @ 2024-08-01 11:41:27... 
#> ... 11:41:27: Processing `.map`... 
#> ... 11:41:27: Building XYM... 
#> ... 11:41:27: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:27: Estimating density surface... 
#> ... 11:41:27: Scaling density surface... 
#> `patter::map_dens()` call ended @ 2024-08-01 11:41:27 (duration: ~0 sec(s)). 
#> $x
#> Marked planar point pattern: 360 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :        lyr.1 
#> min value   : 1.629509e-17 
#> max value   : 9.829595e-04 
#> 
points(coord$x, coord$y, cex = 0.5)


#### Example (3): Use coordinates from `coa()`
# Use example dataset
coord <- dat_coa()
map_dens(map, .coord = coord)
#> `patter::map_dens()` called @ 2024-08-01 11:41:27... 
#> ... 11:41:27: Processing `.map`... 
#> ... 11:41:27: Building XYM... 
#> ... 11:41:27: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:27: Estimating density surface... 
#> ... 11:41:27: Scaling density surface... 
#> `patter::map_dens()` call ended @ 2024-08-01 11:41:27 (duration: ~0 sec(s)). 
#> $x
#> Marked planar point pattern: 6 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :        lyr.1 
#> min value   : 3.389772e-18 
#> max value   : 8.976006e-04 
#> 
points(coord$x, coord$y, cex = 0.5)


#### Example (4): Use a time series of coordinates from `pf_*()`
# Use example dataset
# * We use particles from the forward filter (`?pf_filter()`);
# * Particles are equally weighted b/c re-sampling is implemented every time step;
# * It is better to use outputs from the particle smoother;
coord <- dat_pff()$states
map_dens(map, .coord = coord)
#> `patter::map_dens()` called @ 2024-08-01 11:41:27... 
#> ... 11:41:27: Processing `.map`... 
#> ... 11:41:27: Building XYM... 
#> ... 11:41:28: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:28: Estimating density surface... 
#> ... 11:41:28: Scaling density surface... 

#> `patter::map_dens()` call ended @ 2024-08-01 11:41:28 (duration: ~1 sec(s)). 
#> $x
#> Marked planar point pattern: 31298 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :        lyr.1 
#> min value   : 1.787775e-17 
#> max value   : 9.909920e-04 
#> 
# points(coord$x, coord$y, cex = 0.5)

#### Example (5): Control smoothing via `spatstat.explore::density.ppp()`
# E.g. use fixed bandwidth:
map_dens(map, .coord = coord, sigma = 5)
#> `patter::map_dens()` called @ 2024-08-01 11:41:28... 
#> ... 11:41:28: Processing `.map`... 
#> ... 11:41:28: Building XYM... 
#> ... 11:41:28: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:28: Estimating density surface... 
#> ... 11:41:28: Scaling density surface... 

#> `patter::map_dens()` call ended @ 2024-08-01 11:41:28 (duration: ~0 sec(s)). 
#> $x
#> Marked planar point pattern: 31298 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :         lyr.1 
#> min value   : -3.107830e-19 
#> max value   :  4.972285e-03 
#> 
map_dens(map, .coord = coord, sigma = 100)
#> `patter::map_dens()` called @ 2024-08-01 11:41:28... 
#> ... 11:41:28: Processing `.map`... 
#> ... 11:41:28: Building XYM... 
#> ... 11:41:28: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:28: Estimating density surface... 
#> ... 11:41:28: Scaling density surface... 

#> `patter::map_dens()` call ended @ 2024-08-01 11:41:29 (duration: ~1 sec(s)). 
#> $x
#> Marked planar point pattern: 31298 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :         lyr.1 
#> min value   : -1.890992e-19 
#> max value   :  3.398141e-03 
#> 
# E.g., perform automatic bandwidth selection using cross validation:
map_dens(map, .coord = coord, sigma = bw.diggle)
#> `patter::map_dens()` called @ 2024-08-01 11:41:29... 
#> ... 11:41:29: Processing `.map`... 
#> ... 11:41:29: Building XYM... 
#> ... 11:41:29: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:29: Estimating density surface... 
#> ... 11:41:30: Scaling density surface... 

#> `patter::map_dens()` call ended @ 2024-08-01 11:41:30 (duration: ~1 sec(s)). 
#> $x
#> Marked planar point pattern: 31298 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :         lyr.1 
#> min value   : -2.170756e-19 
#> max value   :  4.139222e-03 
#> 
map_dens(map, .coord = coord, sigma = bw.scott)
#> `patter::map_dens()` called @ 2024-08-01 11:41:30... 
#> ... 11:41:30: Processing `.map`... 
#> ... 11:41:30: Building XYM... 
#> ... 11:41:30: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:30: Estimating density surface... 
#> ... 11:41:31: Scaling density surface... 

#> `patter::map_dens()` call ended @ 2024-08-01 11:41:31 (duration: ~1 sec(s)). 
#> $x
#> Marked planar point pattern: 31298 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :         lyr.1 
#> min value   : -3.956010e-19 
#> max value   :  2.531056e-03 
#> 
map_dens(map, .coord = coord, sigma = bw.ppl)
#> `patter::map_dens()` called @ 2024-08-01 11:41:31... 
#> ... 11:41:31: Processing `.map`... 
#> ... 11:41:31: Building XYM... 
#> ... 11:41:31: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:41:31: Estimating density surface... 
#> ... 11:42:02: Scaling density surface... 

#> `patter::map_dens()` call ended @ 2024-08-01 11:42:02 (duration: ~31 sec(s)). 
#> $x
#> Marked planar point pattern: 31298 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :         lyr.1 
#> min value   : -2.338062e-19 
#> max value   :  4.154600e-03 
#> 
map_dens(map, .coord = coord, sigma = bw.CvL)
#> `patter::map_dens()` called @ 2024-08-01 11:42:02... 
#> ... 11:42:02: Processing `.map`... 
#> ... 11:42:02: Building XYM... 
#> ... 11:42:02: Defining `ppp` object... 
#> Observation window is gridded.
#> ... 11:42:02: Estimating density surface... 
#> ... 11:42:22: Scaling density surface... 

#> `patter::map_dens()` call ended @ 2024-08-01 11:42:22 (duration: ~20 sec(s)). 
#> $x
#> Marked planar point pattern: 31298 points
#> marks are numeric, of storage type  ‘double’
#> window: binary image mask
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695492.1, 714492.1] x [6246657, 6273057] units
#> 
#> $D
#> real-valued pixel image
#> 264 x 190 pixel array (ny, nx)
#> enclosing rectangle: [695490, 714490] x [6246700, 6273100] units
#> 
#> $ud
#> class       : SpatRaster 
#> dimensions  : 264, 190, 1  (nrow, ncol, nlyr)
#> resolution  : 100, 100  (x, y)
#> extent      : 695492.1, 714492.1, 6246657, 6273057  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / UTM zone 29N (EPSG:32629) 
#> source(s)   : memory
#> name        :        lyr.1 
#> min value   : 1.593925e-07 
#> max value   : 3.768079e-04 
#>

Arguments

Value

Details

See also

Examples