Convert input to a SpatRaster object
rasterize(x)
# S4 method for matrix
rasterize(x)
# S4 method for RasterLayer
rasterize(x)
# S4 method for SpatRaster
rasterize(x)A matrix, RasterLayer, or SpatRaster
A SpatRaster object
This function is primarily used to convert an input matrix or raster to a SpatRaster object. The main thing it is useful for is setting a standard extent and CRS for converting matrices. It is used internally by the package to ensure consistent results for the different data types for maps.
When converting matrices, the extents are set to match the number of rows and columns of the matrix. Pixels in the result are centered on whole number coordinates with (1,1) corresponding to the bottom left pixel. The CRS is set to "local", which treats it as Euclidean (Cartesian) plane with the units in meters.
The main benefit will be for users that want an easy way to plot matrix data.
If the input type to the samc function is matrices, then the output
of map will also be matrices. Plotting these matrices can require
more work than simply using SpatRaster objects for samc and getting
SpatRaster results back from map.
The raster and terra packages both also have a rasterize function that serves a different purpose. If either of these packages are used directly, then the order of package loading becomes very important because it will determine which version of rasterize is used by default.
# "Load" the data. In this case we are using data built into the package.
# In practice, users will likely load raster data using the raster() function
# from the raster package.
res_data <- samc::example_split_corridor$res
abs_data <- samc::example_split_corridor$abs
init_data <- samc::example_split_corridor$init
# Make sure our data meets the basic input requirements of the package using
# the check() function.
check(res_data, abs_data)
#> [1] TRUE
check(res_data, init_data)
#> [1] TRUE
# Setup the details for a random-walk model
rw_model <- list(fun = function(x) 1/mean(x), # Function for calculating transition probabilities
dir = 8, # Directions of the transitions. Either 4 or 8.
sym = TRUE) # Is the function symmetric?
# Create a `samc-class` object with the resistance and absorption data using
# the samc() function. We use the recipricol of the arithmetic mean for
# calculating the transition matrix. Note, the input data here are matrices,
# not RasterLayers.
samc_obj <- samc(res_data, abs_data, model = rw_model)
# Convert the initial state data to probabilities
init_prob_data <- init_data / sum(init_data, na.rm = TRUE)
# Calculate short- and long-term metrics using the analytical functions
short_mort <- mortality(samc_obj, init_prob_data, time = 50)
short_dist <- distribution(samc_obj, origin = 3, time = 50)
long_disp <- dispersal(samc_obj, init_prob_data)
#>
#> Cached diagonal not found.
#> Performing setup. This can take several minutes... Complete.
#> Calculating matrix inverse diagonal...
#>
Computing: 50% (~10s remaining)
Computing: 100% (~0s remaining)
Computing: 100% (done)
#>
Complete
#> Diagonal has been cached. Continuing with metric calculation...
visit <- visitation(samc_obj, dest = 4)
surv <- survival(samc_obj)
# Use the map() function to turn vector results into RasterLayer objects.
short_mort_map <- map(samc_obj, short_mort)
short_dist_map <- map(samc_obj, short_dist)
long_disp_map <- map(samc_obj, long_disp)
visit_map <- map(samc_obj, visit)
surv_map <- map(samc_obj, surv)