spatial_edge_predicates | R Documentation |

These functions allow to interpret spatial relations between edges and
other geospatial features directly inside `filter`

and `mutate`

calls. All functions return a logical
vector of the same length as the number of edges in the network. Element i
in that vector is `TRUE`

whenever `any(predicate(x[i], y[j]))`

is
`TRUE`

. Hence, in the case of using `edge_intersects`

, element i
in the returned vector is `TRUE`

when edge i intersects with any of
the features given in y.

edge_intersects(y, ...) edge_is_disjoint(y, ...) edge_touches(y, ...) edge_crosses(y, ...) edge_is_within(y, ...) edge_contains(y, ...) edge_contains_properly(y, ...) edge_overlaps(y, ...) edge_equals(y, ...) edge_covers(y, ...) edge_is_covered_by(y, ...) edge_is_within_distance(y, ...)

`y` |
The geospatial features to test the edges against, either as an
object of class |

`...` |
Arguments passed on to the corresponding spatial predicate
function of sf. See |

See `geos_binary_pred`

for details on each spatial
predicate. Just as with all query functions in tidygraph, these functions
are meant to be called inside tidygraph verbs such as
`mutate`

or `filter`

, where
the network that is currently being worked on is known and thus not needed
as an argument to the function. If you want to use an algorithm outside of
the tidygraph framework you can use `with_graph`

to
set the context temporarily while the algorithm is being evaluated.

A logical vector of the same length as the number of edges in the network.

Note that `edge_is_within_distance`

is a wrapper around the
`st_is_within_distance`

predicate from sf. Hence, it is based on
'as-the-crow-flies' distance, and not on distances over the network.

library(sf, quietly = TRUE) library(tidygraph, quietly = TRUE) # Create a network. net = as_sfnetwork(roxel) %>% st_transform(3035) # Create a geometry to test against. p1 = st_point(c(4151358, 3208045)) p2 = st_point(c(4151340, 3207520)) p3 = st_point(c(4151756, 3207506)) p4 = st_point(c(4151774, 3208031)) poly = st_multipoint(c(p1, p2, p3, p4)) %>% st_cast('POLYGON') %>% st_sfc(crs = 3035) # Use predicate query function in a filter call. intersects = net %>% activate(edges) %>% filter(edge_intersects(poly)) oldpar = par(no.readonly = TRUE) par(mar = c(1,1,1,1)) plot(st_geometry(net, "edges")) plot(st_geometry(intersects, "edges"), col = "red", lwd = 2, add = TRUE) par(oldpar) # Use predicate query function in a mutate call. net %>% activate(edges) %>% mutate(disjoint = edge_is_disjoint(poly)) %>% select(disjoint)

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