`R/geom.R`

`geos_binary_pred.Rd`

Geometric binary predicates on pairs of simple feature geometry sets

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) 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) st_covers(x, y, sparse = TRUE, prepared = TRUE) st_covered_by(x, y, sparse = TRUE, prepared = TRUE) st_equals_exact(x, y, par, sparse = TRUE, prepared = FALSE) st_is_within_distance(x, y, dist, sparse = TRUE)

x | object of class |
---|---|

y | object of class |

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

... | ignored |

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

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. |

If `sparse=FALSE`

, `st_predicate`

(with `predicate`

e.g. "intersects") returns a dense logical matrix with element `i,j`

`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`

with 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 `integer(0)`

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.

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 http://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 `st_is_within_distance`

do not.

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.

`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.

For intersection on pairs of simple feature geometries, use
the function `st_intersection`

instead of `st_intersects`

.

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#> [1] TRUE TRUE FALSE#> [1] TRUE TRUE FALSE# which points fall inside the first polygon? st_intersects(pol, pts)[[1]]#> [1] 1 2