gorillas_sf | R Documentation |
This is the gorillas
dataset from the package spatstat.data
, reformatted
as point process data for use with inlabru
.
gorillas_sf
data(gorillas_sf, package = "inlabru")
gorillas_sf_gcov()
The data are a list that contains these elements:
nests
: An sf
object containing the locations of
the gorilla nests.
boundary
: An sf
object defining the boundary
of the region that was searched for the nests.
mesh
: An fm_mesh_2d
object containing a mesh that can be used
with function lgcp
to fit a LGCP to the nest data.
gcov_file
: The in-package filename of a terra::SpatRaster
object,
with one layer for each of these spatial covariates:
aspect
Compass direction of the terrain slope. Categorical, with levels N, NE, E, SE, S, SW, W and NW, which are coded as integers 1 to 8.
elevation
Digital elevation of terrain, in metres.
heat
Heat Load Index at each point on the surface (Beer's aspect), discretised. Categorical with values Warmest (Beer's aspect between 0 and 0.999), Moderate (Beer's aspect between 1 and 1.999), Coolest (Beer's aspect equals 2). These are coded as integers 1, 2 and 3, in that order.
slopangle
Terrain slope, in degrees.
slopetype
Type of slope. Categorical, with values Valley, Toe (toe slope), Flat, Midslope, Upper and Ridge. These are coded as integers 1 to 6.
vegetation
Vegetation type: a categorical variable with 6 levels coded as integers 1 to 6 (in order of increasing expected habitat suitability)
waterdist
Euclidean distance from nearest water body, in metres.
Loading of the covariates can be done with gorillas_sf_gcov()
or
gorillas_sf$gcov <- terra::rast( system.file(gorillas_sf$gcov_file, package = "inlabru") )
plotsample
Plot sample of gorilla nests, sampling 9x9 over the region, with 60\
counts
A SpatialPointsDataFrame frame with elements x
, y
, count
,
exposure
, being the x- and y-coordinates of the centre of each plot, the count in each
plot and the area of each plot.
plots
A SpatialPolygonsDataFrame
defining the individual plot boundaries.
nests
A SpatialPointsDataFrame
giving the locations of each detected nest.
gorillas_sf_gcov()
: Access the gorillas_sf
covariates data as a
terra::rast()
object.
Library spatstat.data
.
Funwi-Gabga, N. (2008) A pastoralist survey and fire impact assessment in the Kagwene Gorilla Sanctuary, Cameroon. M.Sc. thesis, Geology and Environmental Science, University of Buea, Cameroon.
Funwi-Gabga, N. and Mateu, J. (2012) Understanding the nesting spatial behaviour of gorillas in the Kagwene Sanctuary, Cameroon. Stochastic Environmental Research and Risk Assessment 26 (6), 793-811.
if (interactive() &&
bru_safe_inla() &&
bru_safe_sp() &&
require("sp") &&
require(ggplot2, quietly = TRUE) &&
requireNamespace("terra")) {
# plot all the nests, mesh and boundary
ggplot() +
gg(gorillas_sf$mesh) +
geom_sf(
data = gorillas_sf$boundary,
alpha = 0.1, fill = "blue"
) +
geom_sf(data = gorillas_sf$nests)
# Plot the elevation covariate
gorillas_sf$gcov <- terra::rast(
system.file(gorillas_sf$gcov_file, package = "inlabru")
)
plot(gorillas_sf$gcov$elevation)
# Plot the plot sample
ggplot() +
geom_sf(data = gorillas_sf$plotsample$plots) +
geom_sf(data = gorillas_sf$plotsample$nests)
}
## Not run:
gorillas_sf$gcov <- gorillas_sf_gcov()
## End(Not run)
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