Sample points on or in (sets of) spatial features.
By default, returns a pre-specified number of points that is equal to
size
(if type = "random"
and exact = TRUE
) or an approximation of
size
otherwise. spatstat
methods are
interfaced and do not use the size
argument, see examples.
st_sample(x, size, ...)
# S3 method for sf
st_sample(x, size, ...)
# S3 method for sfc
st_sample(
x,
size,
...,
type = "random",
exact = TRUE,
warn_if_not_integer = TRUE,
by_polygon = FALSE,
progress = FALSE
)
# S3 method for sfg
st_sample(x, size, ...)
object of class sf
or sfc
sample size(s) requested; either total size, or a numeric vector with sample sizes for each feature geometry. When sampling polygons, the returned sampling size may differ from the requested size, as the bounding box is sampled, and sampled points intersecting the polygon are returned.
passed on to sample for multipoint
sampling, or to spatstat
functions for spatstat sampling types (see details)
character; indicates the spatial sampling type; one of random
, hexagonal
(triangular really), regular
, Fibonacci
,
or one of the spatstat
methods such as Thomas
for calling spatstat.random::rThomas
(see Details).
logical; should the length of output be exactly
logical; if FALSE
then no warning is emitted if size
is not an integer
logical; for MULTIPOLYGON
geometries, should the effort be split by POLYGON
? See https://github.com/r-spatial/sf/issues/1480
the same as specified by size
? TRUE
by default. Only applies to polygons, and
when type = "random"
.
logical; if TRUE
show progress bar (only if size
is a vector).
an sfc
object containing the sampled POINT
geometries
The function is vectorised: it samples size
points across all geometries in
the object if size
is a single number, or the specified number of points
in each feature if size
is a vector of integers equal in length to the geometry
of x
.
if x
has dimension 2 (polygons) and geographical coordinates (long/lat), uniform random sampling on the sphere is applied, see e.g. http://mathworld.wolfram.com/SpherePointPicking.html.
For regular
or hexagonal
sampling of polygons, the resulting size is only an approximation.
As parameter called offset
can be passed to control ("fix") regular or hexagonal sampling: for polygons a length 2 numeric vector (by default: a random point from st_bbox(x)
); for lines use a number like runif(1)
.
Fibonacci sampling see: Alvaro Gonzalez, 2010. Measurement of Areas on a Sphere Using Fibonacci and Latitude-Longitude Lattices. Mathematical Geosciences 42(1), p. 49-64
Sampling methods from package spatstat
are interfaced (see examples), and need their own parameters to be set.
For instance, to use spatstat.random::rThomas()
, set type = "Thomas"
.
nc = st_read(system.file("shape/nc.shp", package="sf"))
#> Reading layer `nc' from data source
#> `/tmp/RtmpxmZ5jE/temp_libpathf41261b39098/sf/shape/nc.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS: NAD27
p1 = st_sample(nc[1:3, ], 6)
p2 = st_sample(nc[1:3, ], 1:3)
plot(st_geometry(nc)[1:3])
plot(p1, add = TRUE)
plot(p2, add = TRUE, pch = 2)
x = st_sfc(st_polygon(list(rbind(c(0,0),c(90,0),c(90,90),c(0,90),c(0,0)))), crs = st_crs(4326))
plot(x, axes = TRUE, graticule = TRUE)
if (sf_extSoftVersion()["proj.4"] >= "4.9.0")
plot(p <- st_sample(x, 1000), add = TRUE)
if (require(lwgeom, quietly = TRUE)) { # for st_segmentize()
x2 = st_transform(st_segmentize(x, 1e4), st_crs("+proj=ortho +lat_0=30 +lon_0=45"))
g = st_transform(st_graticule(), st_crs("+proj=ortho +lat_0=30 +lon_0=45"))
plot(x2, graticule = g)
if (sf_extSoftVersion()["proj.4"] >= "4.9.0") {
p2 = st_transform(p, st_crs("+proj=ortho +lat_0=30 +lon_0=45"))
plot(p2, add = TRUE)
}
}
x = st_sfc(st_polygon(list(rbind(c(0,0),c(90,0),c(90,10),c(0,90),c(0,0))))) # NOT long/lat:
plot(x)
p_exact = st_sample(x, 1000, exact = TRUE)
p_not_exact = st_sample(x, 1000, exact = FALSE)
length(p_exact); length(p_not_exact)
#> [1] 1000
#> [1] 999
plot(st_sample(x, 1000), add = TRUE)
x = st_sfc(st_polygon(list(rbind(c(-180,-90),c(180,-90),c(180,90),c(-180,90),c(-180,-90)))),
crs=st_crs(4326))
# FIXME:
#if (sf_extSoftVersion()["proj.4"] >= "4.9.0") {
# p = st_sample(x, 1000)
# st_sample(p, 3)
#}
# hexagonal:
sfc = st_sfc(st_polygon(list(rbind(c(0,0), c(1,0), c(1,1), c(0,0)))))
plot(sfc)
h = st_sample(sfc, 100, type = "hexagonal")
h1 = st_sample(sfc, 100, type = "hexagonal")
plot(h, add = TRUE)
plot(h1, col = 'red', add = TRUE)
c(length(h), length(h1)) # approximate!
#> [1] 104 103
pt = st_multipoint(matrix(1:20,,2))
ls = st_sfc(st_linestring(rbind(c(0,0),c(0,1))),
st_linestring(rbind(c(0,0),c(.1,0))),
st_linestring(rbind(c(0,1),c(.1,1))),
st_linestring(rbind(c(2,2),c(2,2.00001))))
st_sample(ls, 80)
#> Geometry set for 4 features (with 1 geometry empty)
#> Geometry type: MULTIPOINT
#> Dimension: XY
#> Bounding box: xmin: 0 ymin: 0 xmax: 0.08531849 ymax: 1
#> CRS: NA
#> MULTIPOINT ((0 0.4630932), (0 0.5103067), (0 0....
#> MULTIPOINT ((0.004848793 0), (0.03337024 0), (0...
#> MULTIPOINT ((0.001932966 1), (0.08177992 1), (0...
#> MULTIPOINT EMPTY
plot(st_sample(ls, 80))
# spatstat example:
if (require(spatstat.random)) {
x <- sf::st_sfc(sf::st_polygon(list(rbind(c(0, 0), c(10, 0), c(10, 10), c(0, 0)))))
# for spatstat.random::rThomas(), set type = "Thomas":
pts <- st_sample(x, kappa = 1, mu = 10, scale = 0.1, type = "Thomas")
}