Prepare the structural raster
This function prepares the structural raster for the follow-up analyses. The size and extent of the structural raster defines the resolution at which the isoscapes and the assignments are defined.
prepraster( raster, isofit = NULL, margin_pct = 5, aggregation_factor = 0L, aggregation_fn = mean, manual_crop = NULL, verbose = interactive() )
raster |
The structural raster (RasterLayer) |
isofit |
The fitted isoscape model returned by the function
|
margin_pct |
The percentage representing by how much the space should extend outside the range of the coordinates of the sources (default = 5). |
aggregation_factor |
The number of neighbouring cells (integer) to merge during aggregation |
aggregation_fn |
The function used to aggregate cells |
manual_crop |
A vector of four coordinates (numeric) for manual cropping, e.g. the spatial extent |
verbose |
A logical indicating whether information about the progress of the procedure should be displayed or not while the function is running. By default verbose is TRUE if users use an interactive R session, and FALSE otherwise. |
This functions allows the user to crop a raster according to
either the extent of the isoscape or manually. If a fitted isoscape object
is provided (see isofit), the function extracts the observed
locations of isotopic sources from the model object and crops the structural
raster accordingly. Alternatively, manual_crop allows you to crop the
structural raster to a desired extent. If no model and no coordinates for
manual cropping are provided, no crop will be performed. Importantly,
cropping is recommended as it prevents extrapolations outside the
latitude/longitude range of the source data. Predicting outside the range of
the source data may lead to highly unreliable predictions.
Aggregation changes the spatial resolution of the raster, making computation faster and using less memory (this can affect the assignment; see note below). An aggregation factor of zero (or one) keeps the resolution constant (default).
This function relies on calls to the functions
aggregate and crop from the
package raster. It thus share the limitations of these
functions. In particular, crop expects extents with
increasing longitudes and latitudes. We have tried to partially relax this
constrains for longitude and you can use the argument manual_crop to
provide longitudes in decreasing order, which is useful to centre a isoscape
around the pacific for instance. But this fix does not solve all the
limitations as plotting polygons or points on top of that remains problematic
(see example bellow). We will work on this on the future but we have other
priorities for now (let us know if you really need this feature).
The prepared structural raster of class RasterLayer
Aggregating the raster may lead to different results for the assignment, because the values of raster cells changes depending on the aggregation function (see example below), which in turn affects model predictions.
ElevRasterDE for information on elevation rasters, which
can be used as structural rasters.
## The examples below will only be run if sufficient time is allowed
## You can change that by typing e.g. options_IsoriX(example_maxtime = XX)
## if you want to allow for examples taking up to ca. XX seconds to run
## (so don't write XX but put a number instead!)
if(getOption_IsoriX("example_maxtime") > 30) {
## We fit the models for Germany
GNIPDataDEagg <- prepsources(data = GNIPDataDE)
GermanFit <- isofit(data = GNIPDataDEagg,
mean_model_fix = list(elev = TRUE, lat_abs = TRUE))
### Let's explore the difference between aggregation schemes
## We aggregate and crop using different settings
ElevationRaster1 <- prepraster(
raster = ElevRasterDE,
isofit = GermanFit,
margin_pct = 0,
aggregation_factor = 0)
ElevationRaster2 <- prepraster(
raster = ElevRasterDE,
isofit = GermanFit,
margin_pct = 5,
aggregation_factor = 5)
ElevationRaster3 <- prepraster(
raster = ElevRasterDE,
isofit = GermanFit,
margin_pct = 10,
aggregation_factor = 5, aggregation_fn = max)
## We build the plots of the outcome of the 3 different aggregation schemes
PlotAggregation1 <- levelplot(ElevationRaster1,
margin = FALSE, main = "Original small raster") +
layer(sp.polygons(CountryBorders)) +
layer(sp.polygons(OceanMask, fill = "blue"))
PlotAggregation2 <- levelplot(ElevationRaster2,
margin = FALSE, main = "Small raster aggregated (by mean)") +
layer(sp.polygons(CountryBorders)) +
layer(sp.polygons(OceanMask, fill = "blue"))
PlotAggregation3 <- levelplot(ElevationRaster3,
margin = FALSE, main = "Small raster aggregated (by max)") +
layer(sp.polygons(CountryBorders)) +
layer(sp.polygons(OceanMask, fill = "blue"))
## We plot as a panel using lattice syntax:
print(PlotAggregation1, split = c(1, 1, 3, 1), more = TRUE)
print(PlotAggregation2, split = c(2, 1, 3, 1), more = TRUE)
print(PlotAggregation3, split = c(3, 1, 3, 1))
}
## The examples below will only be run if sufficient time is allowed
## You can change that by typing e.g. options_IsoriX(example_maxtime = XX)
## if you want to allow for examples taking up to ca. XX seconds to run
## (so don't write XX but put a number instead!)
if(getOption_IsoriX("example_maxtime") > 10) {
### Let's create a raster centered around the pacific
## We first create an empty raster
EmptyRaster <- raster(matrix(0, ncol = 360, nrow = 180))
extent(EmptyRaster) <- c(-180, 180, -90, 90)
projection(EmptyRaster) <- CRS("+proj=longlat +datum=WGS84")
## We crop it around the pacific
PacificA <- prepraster(EmptyRaster, manual_crop = c(110, -70, -90, 90))
extent(PacificA) # note that the extent has changed!
## We plot (note the use of the function shift()!)
levelplot(PacificA, margin = FALSE, colorkey = FALSE, col = "blue") +
layer(sp.polygons(CountryBorders, fill = "black")) +
layer(sp.polygons(shift(CountryBorders, dx = 360), fill = "black"))
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