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 * cellStats(species, "sum")),
include.geographic.space = TRUE, scale = TRUE, verbose = FALSE, ...)
SpatialPolygons
with planning unit data.
RasterLayer
, RasterStack
, RasterBrick
with species probability distribution data.
list
of/or RasterLayer
, RasterStack
, RasterBrick
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 0 then demand points are generated across the full range of values the species.points
intersect. Defaults to 0.5.
list
of/or SpatialPointsDataFrame
or SpatialPoints
with 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 specifiying 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 calcPuVsSpeciesData
.
# NOT RUN {
# load data
data(cs_pus, cs_spp, cs_space)
# make RapData object using the 1st 10 planning units
x <- make.RapData(cs_pus[1:10,], cs_spp, cs_space, include.geographic.space=TRUE)
print(x)
# }
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