This is a modification of distanceFromPoints for the case of many points.
This version can often be faster for a single point because it does not return a RasterLayer. This is
different than distanceFromPoints because it does not take the minimum
distance from the set of points to all cells. Rather this returns the every pair-wise point distance.
As a result, this can be used for doing inverse distance weightings, seed rain, cumulative effects
of distance-based processes etc. If memory limitation is an issue, maxDistance will keep memory
use down, but with the consequences that there will be a maximum distance returned. This function
has the potential to use a lot of memory if there are a lot of from and to points.
distanceFromEachPoint(from, to = NULL, landscape, angles = NA_real_,
maxDistance = NA_real_, cumulativeFn = NULL, distFn = function(dist)
1/(1 + dist), cl, ...).pointDistance(from, to, angles = NA, maxDistance = NA_real_)
matrix with 2 or 3 columns, x and y, representing x and y coordinates of "from" cell,
and optional "id" which will be matched with "id" from to
matrix with 2 or 3 columns (or optionally more, all of which will be returned),
x and y, representing x and y coordinates of "to" cells, and
optional "id" which will be matched with "id" from from. Default is all cells.
RasterLayer. optional. This is only used if to is NULL, in which case
all cells are considered to
Logical. If TRUE, then the function will return angles in radians,
as well as distances.
Numeric in units of number of cells. The algorithm will build the whole surface
(from from to to), but will remove all distances that are above
this distance. Using this will keep memory use down.
A function that can be used to incrementally accumulate values in each to
location, as the function iterates through each from. See Details.
A function. This can be a function of landscape, fromCells, toCells, and
dist. If cumulativeFn is supplied, then this will be used to convert
the distances to some other set of units
that will be accumulated by the cumulativeFn. See Details and examples.
A cluster object. Optional. This would generally be created using
parallel::makeCluster or equivalent. This is an alternative way, instead
of beginCluster(), to use parallelism for this function, allowing for
more control over cluster use.
Any additional objects needed for distFn.
A sorted matrix on id with same number of rows as to,
but with one extra column, "dists"
indicating the distance between from and to.
This function is cluster aware. If there is a cluster running, it will use it. To start a cluster
use beginCluster, with N being the number of cores to use. See examples in
experiment.
If the user requires an id (indicating the from cell for each to cell) to be returned with
the fuction, the user must add an identifier to the from matrix, such as "id".
Otherwise, the function will only return the coordinates and distances.
distanceFromEachPoint calls .pointDistance, which is not intended to be called
directly by the user.
This function has the potential to return a very large object, as it is doing pairwise
distances (and optionally directions) between from and to. If there are memory
limitations because there are many
from and many to points, then cumulativeFn and distFn can be used.
These two functions together will be used iteratively through the from points. The
distFn should be a transformation of distances to be used by the
cumulativeFn function. For example, if distFn is 1/(1+x), the default,
and cumulativeFn is `+`, then it will do a sum of inverse distance weights.
See examples.
rings, cir, distanceFromPoints,
which can all be made to do the same thing, under specific combinations of arguments.
But each has different primary use cases. Each is also faster under different conditions.
For instance, if maxDistance is relatively small compared to the number of cells
in the landscape, then cir will likely be faster. If a minimum
distance from all cells in the landscape to any cell in from, then
distanceFromPoints will be fastest. This function scales best when there are
many to points or all cells are used to = NULL (which is default).
# NOT RUN {
library(raster)
N <- 2
distRas <- raster(extent(0,40,0,40), res = 1)
coords <- cbind(x = round(runif(N, xmin(distRas), xmax(distRas)))+0.5,
y = round(runif(N, xmin(distRas), xmax(distRas)))+0.5)
# inverse distance weights
dists1 <- distanceFromEachPoint(coords, landscape = distRas)
indices <- cellFromXY(distRas,dists1[,c("x","y")])
invDist <- tapply(dists1[,"dists"], indices, function(x) sum(1/(1+x))) # idw function
distRas[] <- as.vector(invDist)
if (interactive()) {
clearPlot()
Plot(distRas)
}
# With iterative summing via cumulativeFn to keep memory use low, with same result
dists1 <- distanceFromEachPoint(coords[, c("x", "y"), drop = FALSE],
landscape = distRas, cumulativeFn = `+`)
idwRaster <- raster(distRas)
idwRaster[] <- dists1[,"val"]
if (interactive()) Plot(idwRaster)
all(idwRaster[] == distRas[]) # TRUE
# A more complex example of cumulative inverse distance sums, weighted by the value
# of the origin cell
ras <- raster(extent(0,34, 0,34), res = 1, val = 0)
rp <- randomPolygons(ras, numTypes = 10) ^ 2
N <- 15
cells <- sample(ncell(ras), N)
coords <- xyFromCell(ras, cells)
distFn <- function(landscape, fromCell, dist) landscape[fromCell] / (1 + dist)
# beginCluster(3) # can do parallel
dists1 <- distanceFromEachPoint(coords[, c("x", "y"), drop = FALSE],
landscape = rp, distFn = distFn, cumulativeFn = `+`)
# endCluster() # if beginCluster was run
idwRaster <- raster(ras)
idwRaster[] <- dists1[,"val"]
if (interactive()) {
clearPlot()
Plot(rp, idwRaster)
sp1 <- SpatialPoints(coords)
Plot(sp1, addTo="rp")
Plot(sp1, addTo="idwRaster")
}
# }
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