Geometric binary predicates on pairs of simple feature geometry sets

Usage

st_intersects(x, y, sparse = TRUE, ...)

st_disjoint(x, y = x, sparse = TRUE, prepared = TRUE, ...)

st_touches(x, y, sparse = TRUE, prepared = TRUE, ...)

st_crosses(x, y, sparse = TRUE, prepared = TRUE, ...)

st_within(x, y, sparse = TRUE, prepared = TRUE, ...)

st_contains(x, y, sparse = TRUE, prepared = TRUE, ..., model = "open")

st_contains_properly(x, y, sparse = TRUE, prepared = TRUE, ...)

st_overlaps(x, y, sparse = TRUE, prepared = TRUE, ...)

st_equals(
  x,
  y,
  sparse = TRUE,
  prepared = FALSE,
  ...,
  retain_unique = FALSE,
  remove_self = FALSE
)

st_covers(x, y, sparse = TRUE, prepared = TRUE, ..., model = "closed")

st_covered_by(x, y = x, sparse = TRUE, prepared = TRUE, ..., model = "closed")

st_equals_exact(x, y, par, sparse = TRUE, prepared = FALSE, ...)

st_is_within_distance(x, y = x, dist, sparse = TRUE, ..., remove_self = FALSE)

Arguments

x

object of class sf, sfc or sfg

y

object of class sf, sfc or sfg; if missing, x is used

sparse

logical; should a sparse index list be returned (TRUE) or a dense logical matrix? See below.

...

Arguments passed on to s2::s2_options

snap: Use s2_snap_identity(), s2_snap_distance(), s2_snap_level(), or s2_snap_precision() to specify how or if coordinate rounding should occur.
snap_radius: As opposed to the snap function, which specifies the maximum distance a vertex should move, the snap radius (in radians) sets the minimum distance between vertices of the output that don't cause vertices to move more than the distance specified by the snap function. This can be used to simplify the result of a boolean operation. Use -1 to specify that any minimum distance is acceptable.
duplicate_edges: Use TRUE to keep duplicate edges (e.g., duplicate points).
edge_type: One of 'directed' (default) or 'undirected'.
validate: Use TRUE to validate the result from the builder.
polyline_type: One of 'path' (default) or 'walk'. If 'walk', polylines that backtrack are preserved.
polyline_sibling_pairs: One of 'discard' (default) or 'keep'.
simplify_edge_chains: Use TRUE to remove vertices that are within snap_radius of the original vertex.
split_crossing_edges: Use TRUE to split crossing polyline edges when creating geometries.
idempotent: Use FALSE to apply snap even if snapping is not necessary to satisfy vertex constraints.
dimensions: A combination of 'point', 'polyline', and/or 'polygon' that can used to constrain the output of s2_rebuild() or a boolean operation.

prepared

logical; prepare geometry for x, before looping over y? See Details.

model

character; polygon/polyline model; one of "open", "semi-open" or "closed"; see Details.

retain_unique

logical; if TRUE (and y is missing) return only indexes of points larger than the current index; this can be used to select unique geometries, see examples. This argument can be used for all geometry predicates; see also distinct.sf to find records where geometries AND attributes are distinct.

remove_self

logical; if TRUE (and y is missing) return only indexes of geometries different from the current index; this can be used to omit self-intersections; see examples. This argument can be used for all geometry predicates

par

numeric; parameter used for "equals_exact" (margin);

dist

distance threshold; geometry indexes with distances smaller or equal to this value are returned; numeric value or units value having distance units.

Value

If sparse=FALSE, st_predicate (with predicate e.g. "intersects") returns a dense logical matrix with element i,j equal to TRUE when predicate(x[i], y[j]) (e.g., when geometry of feature i and j intersect); if sparse=TRUE, an object of class sgbp is returned, which is a sparse list representation of the same matrix, with list element i an integer vector with all indices j for which predicate(x[i],y[j]) is TRUE (and hence a zero-length integer vector if none of them is TRUE). From the dense matrix, one can find out if one or more elements intersect by apply(mat, 1, any), and from the sparse list by lengths(lst) > 0, see examples below.

Details

If prepared is TRUE, and x contains POINT geometries and y contains polygons, then the polygon geometries are prepared, rather than the points.

For most predicates, a spatial index is built on argument x; see https://r-spatial.org/r/2017/06/22/spatial-index.html. Specifically, st_intersects, st_disjoint, st_touches st_crosses, st_within, st_contains, st_contains_properly, st_overlaps, st_equals, st_covers and st_covered_by all build spatial indexes for more efficient geometry calculations. st_relate, st_equals_exact, and do not; st_is_within_distance uses a spatial index for geographic coordinates when sf_use_s2() is true.

If y is missing, st_predicate(x, x) is effectively called, and a square matrix is returned with diagonal elements st_predicate(x[i], x[i]).

Sparse geometry binary predicate (sgbp) lists have the following attributes: region.id with the row.names of x (if any, else 1:n), ncol with the number of features in y, and predicate with the name of the predicate used.

for model, see https://github.com/r-spatial/s2/issues/32

st_contains_properly(A,B) is true if A intersects B's interior, but not its edges or exterior; A contains A, but A does not properly contain A.

See also st_relate and https://en.wikipedia.org/wiki/DE-9IM for a more detailed description of the underlying algorithms.

st_equals_exact returns true for two geometries of the same type and their vertices corresponding by index are equal up to a specified tolerance.

Note

For intersection on pairs of simple feature geometries, use the function st_intersection instead of st_intersects.

Examples

pts = st_sfc(st_point(c(.5,.5)), st_point(c(1.5, 1.5)), st_point(c(2.5, 2.5)))
pol = st_polygon(list(rbind(c(0,0), c(2,0), c(2,2), c(0,2), c(0,0))))
(lst = st_intersects(pts, pol))
#> Sparse geometry binary predicate list of length 3, where the predicate
#> was `intersects'
#>  1: 1
#>  2: 1
#>  3: (empty)
(mat = st_intersects(pts, pol, sparse = FALSE))
#>       [,1]
#> [1,]  TRUE
#> [2,]  TRUE
#> [3,] FALSE
# which points fall inside a polygon?
apply(mat, 1, any)
#> [1]  TRUE  TRUE FALSE
lengths(lst) > 0
#> [1]  TRUE  TRUE FALSE
# which points fall inside the first polygon?
st_intersects(pol, pts)[[1]]
#> [1] 1 2
# remove duplicate geometries:
p1 = st_point(0:1)
p2 = st_point(2:1)
p = st_sf(a = letters[1:8], geom = st_sfc(p1, p1, p2, p1, p1, p2, p2, p1))
st_equals(p)
#> Sparse geometry binary predicate list of length 8, where the predicate
#> was `equals'
#>  1: 1, 2, 4, 5, 8
#>  2: 1, 2, 4, 5, 8
#>  3: 3, 6, 7
#>  4: 1, 2, 4, 5, 8
#>  5: 1, 2, 4, 5, 8
#>  6: 3, 6, 7
#>  7: 3, 6, 7
#>  8: 1, 2, 4, 5, 8
st_equals(p, remove_self = TRUE)
#> Sparse geometry binary predicate list of length 8, where the predicate
#> was `equals', with remove_self = TRUE
#>  1: 2, 4, 5, 8
#>  2: 1, 4, 5, 8
#>  3: 6, 7
#>  4: 1, 2, 5, 8
#>  5: 1, 2, 4, 8
#>  6: 3, 7
#>  7: 3, 6
#>  8: 1, 2, 4, 5
(u = st_equals(p, retain_unique = TRUE))
#> Sparse geometry binary predicate list of length 8, where the predicate
#> was `equals', with retain_unique = TRUE
#>  1: 2, 4, 5, 8
#>  2: 4, 5, 8
#>  3: 6, 7
#>  4: 5, 8
#>  5: 8
#>  6: 7
#>  7: (empty)
#>  8: (empty)
# retain the records with unique geometries:
p[-unlist(u),]
#> Simple feature collection with 2 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 0 ymin: 1 xmax: 2 ymax: 1
#> CRS:           NA
#>   a        geom
#> 1 a POINT (0 1)
#> 3 c POINT (2 1)