| spsample {sp} | R Documentation |
sample point locations within a square area, a ring, or on a spatial line, using regular or random sampling
spsample(x, n, type, ...)
sample.Spatial(x, n, type, bb = bbox(x), offset = runif(2), cellsize, ...)
sample.Line(x, n, type, offset = runif(1), proj4string=CRS(as.character(NA)), ...)
sample.Polygon(x, n, type = "random", bb = bbox(x), offset = runif(2), proj4string=CRS(as.character(NA)), ...)
sample.Polygons(x, n, type = "random", bb = bbox(x), offset = runif(2), proj4string=CRS(as.character(NA)), ...)
sample.Sgrid(x, n, type = "random", bb = bbox(x), offset = runif(2), ...)
makegrid(x, n = 10000, nsig = 2, cellsize, offset = c(0.5,0.5),
type = "regular", ...)
x |
Spatial object; spsample(x,...) is a generic method for the
existing sample.Xxx fumctions |
... |
optional arguments, passed to the appropriate sample.Xxx
functions |
n |
(approximate) sample size |
type |
character; "random" for completely spatial random;
"regular" for regular (systematically aligned) sampling;
"stratified" for stratified random (one single random location in
each "cell"); or "nonaligned" for nonaligned systematic sampling
(nx random y coordinates, ny random x coordinates) |
bb |
bounding box of the sampled domain; setting this to a smaller value leads to sub-region sampling |
offset |
for regular sampling only: the offset (position) of the regular
grid; the default for spsample methods is a random location in
the unit cell $[0,1] times [0,1]$, leading to a different grid after
each call; if this is set to c(0.5,0.5), the returned grid is
not random (but, in Ripley's wording, "centric systematic") |
cellsize |
if missing, a cell size is derived from the sample size
n; otherwise, this cell size is used for all sampling methods
except "random" |
proj4string |
Object of class "CRS";
holding a valid proj4 string |
nsig |
for "pretty" coordinates; spsample do not result in
pretty grids |
an object of class SpatialPoints-class. The number of
points is only guaranteed to equal n when sampling is done in a
square box, i.e. (sample.Spatial). Otherwise, the obtained number
of points will have expected value n.
When x is of a class deriving from Spatial-class for which
no spsample-methods exists, sampling is done in the bounding box
of the object, using spsample.Spatial. An overlay may be
necessary to select afterwards.
Sampling type "nonaligned" is not implemented for line objects.
If an Polygon-class object has zero area (i.e. is a line), samples on this line element are returned. If the area is very close to zero, the algorithm taken here (generating points in a square area, selecting those inside the polygon) may be very resource intensive
Edzer J. Pebesma, e.pebesma@geo.uu.nl
Chapter 3 in B.D. Ripley, 1981. Spatial Statistics, Wiley
overlay-methods, point.in.polygon, sample
data(meuse.riv) meuse.sr = SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)), "x"))) plot(meuse.sr) points(spsample(meuse.sr, n = 1000, "regular"), pch = 3) plot(meuse.sr) points(spsample(meuse.sr, n = 1000, "random"), pch = 3) plot(meuse.sr) points(spsample(meuse.sr, n = 1000, "stratified"), pch = 3) plot(meuse.sr) points(spsample(meuse.sr, n = 1000, "nonaligned"), pch = 3) plot(meuse.sr) points(spsample(meuse.sr@polygons[[1]], n = 100, "stratified"), pch = 3, cex=.5) data(meuse.grid) gridded(meuse.grid) = ~x+y image(meuse.grid) points(spsample(meuse.grid,n=1000,type="random"), pch=3, cex=.5) image(meuse.grid) points(spsample(meuse.grid,n=1000,type="stratified"), pch=3, cex=.5) image(meuse.grid) points(spsample(meuse.grid,n=1000,type="regular"), pch=3, cex=.5) image(meuse.grid) points(spsample(meuse.grid,n=1000,type="nonaligned"), pch=3, cex=.5) fullgrid(meuse.grid) = TRUE image(meuse.grid) points(spsample(meuse.grid,n=1000,type="stratified"), pch=3,cex=.5)