Moving window approach to get distance from source

This function provides three different methods for calculating the distance of all points on a landscape from "source" locations. This is a computationally intensive process so the function arguments can be used to balance the tradeoffs between speed and accuracy. Note the pfocal versions are only available in the development version of the package.

Usage

getDistFromSource(src, maxDist, kwidth = 3, method = "terra")

Arguments

src: SpatRaster or RasterLayer, where all values > 0 are treated as source locations. NA values are treated as 0s.
maxDist: Numeric, maximum distance that should be calculated in units of the CRS.
kwidth: Integer, for the "pfocal" and "terra" methods the width of the moving window. For the "pfocal2" method the aggregation factor.
method: Character, the method to use, currently only "terra" supported with the CRAN version, while "pfocal" or "pfocal2" are available with the development version. See below for details.

Value

A SpatRaster

Details

The "terra" and "pfocal" methods use an iterative moving window approach and assign each cell a distance based on the number of times the moving window is repeated before it is included. This means that the moving window function is run many times but for a small window relative to the size of the raster. The maxDist argument determines the maximum distance calculated and affects the number of iterations of the moving window that are needed. kwidth is the radius of the moving window in number of cells, with larger values reducing the number of iterations needed but also reducing the granularity of the distances produced. The resulting distances will be in increments of kwidth

the resolution of the raster. The total number of iterations is maxDist/ kwidth * resolution. The only difference in these methods is the underlying package used to do the moving window. The terra package has methods for handling large rasters by writing them to disk, while the pfocal package requires that the raster can be held in memory as a matrix.

The third method "pfocal2" uses a global moving window to calculate the distance to the source. This means that the moving window only needs to be applied once but the window size can be very large. In this case maxDist determines the total size of the window. kwidth can be used to reduce the number of cells included in the moving window by aggregating the source raster by a factor of kwidth. This will increase the speed of computation but will produce results with artefacts of the larger grid and which may be less accurate since the output raster is disaggregated using bilinear interpolation.

Examples


CLUSexample <-  prepExData(CLUSexample)
getDistFromSource(CLUSexample$roads, 5, 2)
#> class       : SpatRaster 
#> dimensions  : 5, 5, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : 0, 5, 0, 5  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 
#> source(s)   : memory
#> name        : lyr.1 
#> min value   :     0 
#> max value   :     4 

# \donttest{
 library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
 library(terra)
#> terra 1.7.71

#make example roads from scratch
rds <- data.frame(x = 1:1000/100, y = cos(1:1000/100)) %>%
  st_as_sf(coords = c("x", "y")) %>%
  st_union() %>%
  st_cast("LINESTRING")

rds_rast <- rasterize(vect(rds),
                      rast(nrows = 50, ncols = 50,
                           xmin = 0, xmax = 10,
                           ymin = -5, ymax = 5),
                      touches = TRUE)

getDistFromSource(rds_rast, 5, 2)
#> class       : SpatRaster 
#> dimensions  : 50, 50, 1  (nrow, ncol, nlyr)
#> resolution  : 0.2, 0.2  (x, y)
#> extent      : 0, 10, -5, 5  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 
#> source(s)   : memory
#> name        : layer 
#> min value   :   0.0 
#> max value   :   5.2 
# }