set_points: Simulate points in a landscape

In NINAnor/oneimpact: Tools for the assessment of the cumulative impacts of anthropogenic features in ecological studies

Simulate points in a landscape

Description

This function simulates point patterns in space and rasterize them. The idea is to mimic the spatial distribution of point-type infrastructure, such as houses, cabins, or turbines, for instance. The function returns a list with the position of the points and a binary raster with 1 where there are points and NA elsewhere. If created with a raster to define the weights, this base raster is also returned in the output.

Usage

set_points(
  n_features = 1000,
  method = c("mobsim", "regular", "random", "raster")[1],
  centers = 1,
  width = 0.05,
  base_raster = NULL,
  point_coordinates = NULL,
  res = 0.1,
  extent_x = c(0, 1),
  extent_y = c(0, 1),
  buffer_around = 0,
  return_base_raster = TRUE,
  use_terra = TRUE,
  crs = ""
)

Arguments

n_features	⁠[integer(1)=1000]⁠ Total number of features to spread in space.
method	⁠[character(1)]{"mobsim", "regular", "random", "raster"}⁠ Method used to simulate points in space. mobsim uses the function mobsim::sim_thomas_community() from the mobsim package to simulate points. raster uses a base raster map as input to define weights and simulate the random points.
centers	⁠[integer(1)=1]⁠ Number of centers around which the features will be placed. Used only if method = "mobsim".
width	⁠[numeric(1)=0.05]⁠ Mean distance between each of the features in a cluster and the center of the cluster. Used only if method = "mobsim".
base_raster	⁠[RasterLayer=NULL]⁠ Base raster to define weights for creating the random points. Used only if method = "raster".
point_coordinates	⁠[data.frame=NULL]⁠ data.frame with (x,y) columns with coordinates already taken from elsewhere. This option is intended for when the points' coordinates were already generated or taken from a real landscape. In this case, no points are simulated and they are just rasterized (so that distances or other derived variables might be calculated).
res	⁠[numeric(1)=0.1]⁠ Resolution of the output raster.
extent_x, entent_y	⁠[numeric vector(2)=c(0,1)]⁠ Vector representing the minimum and maximum extent in x and y within which the points should be placed, in the format c(min,max).
buffer_around	⁠[numeric(1)=0.1]⁠ Size of the buffer around the extent of the landscape, to avoid edge effects when calculating densities using neighborhood analysis.
return_base_raster	⁠[logical(1)=TRUE]⁠ Whether the base_raster should be returned in the output list. This is NULL for method = "mobsim".
use_terra	⁠[logical(1)=TRUE]⁠ If TRUE (default), the rast element created from the points is a SpatRaster object from terra package is created. If FALSE, it is a RasterLayer from raster package is created.
crs	⁠[character(1)]⁠ Specification for the coordinate reference system of the rast object created from the points. Default is "+proj=utm +zone=1 +datum=WGS84". argument.

Details

If method = "mobsim", the function builds upon the function mobsim::sim_thomas_community() from the mobsim package. Originally the function is intended to simulate positions of multiple species in the context of species abundance distribution studies, but it fits well in case of a single species (or point patterns for a single type of feature). In this case, the points are simulated based on the number of centers/patches of points and their width.

If method = "raster", the function uses an input raster (defined by the argument base_raster) to define the probabilities of setting a given point in a certain pixel in space.

Value

A list with three elements: (1) pts, the coordinates (x,y) of the simulated points; (2) rast, a binary raster containing the landscape, with 1 where there points and NA elsewhere; (3) base_rast, the base raster used to weigh the simulation of points. If method = "mobsim" or "regular" or "random", base_rast is NULL.

Examples

#-----
# using mobsim
library(terra)
library(mobsim)

set.seed(1234)

# gradient distribution
ext <- 30000
wd <- ext/5
pts <- set_points(n_features = 1000, centers = 1,
                  width = wd, res = 100,
                  extent_x = c(0, ext), extent_y = c(0, ext))
plot(pts$pts)
plot(pts$rast, col = "black")

# one focus of features, with buffer around
wd <- ext/20
pts <- set_points(n_features = 1000, centers = 1,
                  width = wd, res = 100,
                  extent_x = c(0, ext), extent_y = c(0, ext),
                  buffer_around = 10000)
plot(pts$pts)
plot(pts$rast, col = "black")

#-----
# using base raster

# raster
set.seed(12)
r <- raster::raster(matrix(runif(12),3,4)) |>
  raster::disaggregate(fact = 10)

# points from raster
pts <- set_points(n_features = 100, method = "raster",
                  base_raster = r)
plot(pts$base_rast)
plot(pts$pts)
plot(pts$rast, col = "black")

#-----
# using random or regular

set.seed(123)
ext <- 30000
pts <- set_points(n_features = 1000, method = "random",
                  res = 100,
                  extent_x = c(0, ext), extent_y = c(0, ext))
plot(pts$pts)
plot(pts$rast, col = "black")

pts <- set_points(n_features = 1000, method = "regular",
                  res = 100,
                  extent_x = c(0, ext), extent_y = c(0, ext))
plot(pts$pts)
plot(pts$rast, col = "black")

#-----
# using point coordinates as input
pt_input <- data.frame(x = c(0.5, 0.7), y = c(0.5, 0.3))
pts <- set_points(point_coordinates = pt_input)
plot(pts$pts)
plot(pts$rast, col = "black")

NINAnor/oneimpact documentation built on June 14, 2025, 12:27 a.m.