R: Geometric operations on pairs of simple feature geometry sets

geos_binary_ops {sf} | R Documentation

## Geometric operations on pairs of simple feature geometry sets

### Description

Perform geometric set operations with simple feature geometry collections

### Usage

    st_intersection(x, y, ...)

    ## S3 method for class 'sfc'
    st_intersection(x, y, ...)

    ## S3 method for class 'sf'
    st_intersection(x, y, ...)

    st_difference(x, y, ...)

    ## S3 method for class 'sfc'
    st_difference(x, y, ...)

    st_sym_difference(x, y, ...)

    st_snap(x, y, tolerance)

### Arguments

`x` | object of class `sf`, `sfc` or `sfg`
`y` | object of class `sf`, `sfc` or `sfg`
`...` | arguments passed on to s2_options
`tolerance` | tolerance values used for `st_snap`; numeric value or object of class `units`; may have tolerance values for each feature in `x`

### Details

When using GEOS and not using s2, a spatial index is built on argument `x`; see https://www.r-spatial.org/r/2017/06/22/spatial-index.html. The reference for the STR tree algorithm is: Leutenegger, Scott T., Mario A. Lopez, and Jeffrey Edgington. "STR: A simple and efficient algorithm for R-tree packing." Data Engineering, 1997. Proceedings. 13th international conference on. IEEE, 1997. For the pdf, search Google Scholar.

When called with missing `y`, the `sfc` method for `st_intersection` returns all non-empty intersections of the geometries of `x`; an attribute `idx` contains a list-column with the indexes of contributing geometries.

when called with a missing `y`, the `sf` method for `st_intersection` returns an `sf` object with attributes taken from the contributing feature with lowest index; two fields are added: `n.overlaps` with the number of overlapping features in `x`, and a list-column `origins` with indexes of all overlapping features.

When `st_difference` is called with a single argument, overlapping areas are erased from geometries that are indexed at greater numbers in the argument to `x`; geometries that are empty or contained fully inside geometries with higher priority are removed entirely. The `st_difference.sfc` method with a single argument returns an object with an `"idx"` attribute with the orginal index for returned geometries.

`st_snap` snaps the vertices and segments of a geometry to another geometry's vertices. If `y` contains more than one geometry, its geometries are merged into a collection before snapping to that collection.

(from the GEOS docs:) "A snap distance tolerance is used to control where snapping is performed. Snapping one geometry to another can improve robustness for overlay operations by eliminating nearly-coincident edges (which cause problems during noding and intersection calculation). Too much snapping can result in invalid topology being created, so the number and location of snapped vertices is decided using heuristics to determine when it is safe to snap. This can result in some potential snaps being omitted, however."

### Value

The intersection, difference or symmetric difference between two sets of geometries. The returned object has the same class as that of the first argument (`x`) with the non-empty geometries resulting from applying the operation to all geometry pairs in `x` and `y`. In case `x` is of class `sf`, the matching attributes of the original object(s) are added. The `sfc` geometry list-column returned carries an attribute `idx`, which is an `n`-by-2 matrix with every row the index of the corresponding entries of `x` and `y`, respectively.

### Note

To find whether pairs of simple feature geometries intersect, use the function `st_intersects` instead of `st_intersection`.

### See Also

st_union for the union of simple features collections; intersect and setdiff for the base R set operations.

### Examples

    set.seed(131)
    library(sf)
    m = rbind(c(0,0), c(1,0), c(1,1), c(0,1), c(0,0))
    p = st_polygon(list(m))
    n = 100
    l = vector("list", n)
    for (i in 1:n)
      l[[i]] = p + 10 * runif(2)
    s = st_sfc(l)
    plot(s, col = sf.colors(categorical = TRUE, alpha = .5))
    title("overlapping squares")
    d = st_difference(s) # sequential differences: s1, s2-s1, s3-s2-s1, ...
    plot(d, col = sf.colors(categorical = TRUE, alpha = .5))
    title("non-overlapping differences")
    i = st_intersection(s) # all intersections
    plot(i, col = sf.colors(categorical = TRUE, alpha = .5))
    title("non-overlapping intersections")
    summary(lengths(st_overlaps(s, s))) # includes self-counts!
    summary(lengths(st_overlaps(d, d)))
    summary(lengths(st_overlaps(i, i)))
    sf = st_sf(s)
    i = st_intersection(sf) # all intersections
    plot(i["n.overlaps"])
    summary(i$n.overlaps - lengths(i$origins))
    # A helper function that erases all of y from x:
    st_erase = function(x, y) st_difference(x, st_union(st_combine(y)))
    poly = st_polygon(list(cbind(c(0, 0, 1, 1, 0), c(0, 1, 1, 0, 0))))
    lines = st_multilinestring(list(
     cbind(c(0, 1), c(1, 1.05)),
     cbind(c(0, 1), c(0, -.05)),
     cbind(c(1, .95, 1), c(1.05, .5, -.05))
    ))
    snapped = st_snap(poly, lines, tolerance=.1)
    plot(snapped, col='red')
    plot(poly, border='green', add=TRUE)
    plot(lines, lwd=2, col='blue', add=TRUE)

[Package sf version 0.9-7 Index]