This function prepares spatially explicit planning unit, species data, and landscape data layers for RAP processing.
make.RapData(
pus,
species,
spaces = NULL,
amount.target = 0.2,
space.target = 0.2,
n.demand.points = 100L,
kernel.method = c("ks", "hypervolume")[1],
quantile = 0.5,
species.points = NULL,
n.species.points = ceiling(0.2 * terra::global(species, "sum", na.rm = TRUE)[[1]]),
include.geographic.space = TRUE,
scale = TRUE,
verbose = FALSE,
...
)
sf::st_as_sf()
with planning unit data.
terra::rast()
with species probability
distribution data.
list
of/or terra::rast()
representing
projects of attribute space over geographic space. Use a list
to
denote separate attribute spaces.
numeric
vector for area targets (%) for each
species. Defaults to 0.2 for each attribute space for each species.
numeric
vector for attribute space targets (%)
for each species. Defaults to 0.2 for each attribute space for each
species and each space.
integer
number of demand points to use for
each attribute space for each species. Defaults to 100L.
character
name of kernel method to use to
generate demand points. Use either "ks"
or "hypervolume"
.
numeric
quantile to generate demand points within. If
species.points
intersect. Defaults to 0.5.
list
of/or sf::st_sf()
object species presence
records. Use a
list
of objects to represent different species. Must have the same
number of elements as species
. If not supplied then use
n.species.points
to sample points from the species distributions.
numeric
vector specifying the number points
to sample the species distributions to use to generate demand points.
Defaults to 20% of the distribution.
logical
should the geographic space
be considered an attribute space?
logical
scale the attribute spaces to unit mean and
standard deviation? This prevents overflow. Defaults to TRUE
.
logical
print statements during processing?
additional arguments to calcBoundaryData()
and
calcSpeciesAverageInPus()
.
A new RapData
object.
# \dontrun{
# load data
cs_pus <- sf::read_sf(
system.file("extdata", "cs_pus.gpkg", package = "raptr")
)
cs_spp <- terra::rast(
system.file("extdata", "cs_spp.tif", package = "raptr")
)
cs_space <- terra::rast(
system.file("extdata", "cs_space.tif", package = "raptr")
)
# make RapData object using the first 10 planning units in the dat
x <- make.RapData(cs_pus[1:10,], cs_spp, cs_space,
include.geographic.space = TRUE)
# print object
print(x)
#> RapData object.
#> Number of planning units: 10
#> Number of species: 4
#> Number of attribute spaces: 2
# }