geos_binary_pred | R Documentation |

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, ..., 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, ...)
```

`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. |

`...` |
passed on to s2_options |

`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 predictates; see als 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 predictates |

`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 `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.

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 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.

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))
(mat = st_intersects(pts, pol, sparse = FALSE))
# which points fall inside a polygon?
apply(mat, 1, any)
lengths(lst) > 0
# which points fall inside the first polygon?
st_intersects(pol, pts)[[1]]
# 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)
st_equals(p, remove_self = TRUE)
(u = st_equals(p, retain_unique = TRUE))
# retain the records with unique geometries:
p[-unlist(u),]
```

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