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

and `mutate`

calls. All functions return a logical
vector of the same length as the number of nodes 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 `node_intersects`

, element i
in the returned vector is `TRUE`

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

```
node_intersects(y, ...)
node_is_disjoint(y, ...)
node_touches(y, ...)
node_is_within(y, ...)
node_equals(y, ...)
node_is_covered_by(y, ...)
node_is_within_distance(y, ...)
```

- y
The geospatial features to test the nodes against, either as an object of class

`sf`

or`sfc`

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

`geos_binary_pred`

.

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

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.

Note that `node_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. For
distances over the network, use `node_distance_to`

with edge lengths as weights argument.

```
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.
within = net %>%
activate("nodes") %>%
filter(node_is_within(poly))
disjoint = net %>%
activate("nodes") %>%
filter(node_is_disjoint(poly))
oldpar = par(no.readonly = TRUE)
par(mar = c(1,1,1,1))
plot(net)
plot(within, col = "red", add = TRUE)
plot(disjoint, col = "blue", add = TRUE)
par(oldpar)
# Use predicate query function in a mutate call.
net %>%
activate("nodes") %>%
mutate(within = node_is_within(poly)) %>%
select(within)
#> # A sfnetwork with 701 nodes and 851 edges
#> #
#> # CRS: EPSG:3035
#> #
#> # A directed multigraph with 14 components with spatially explicit edges
#> #
#> # A tibble: 701 × 2
#> within geometry
#> <lgl> <POINT [m]>
#> 1 TRUE (4151491 3207923)
#> 2 TRUE (4151474 3207946)
#> 3 TRUE (4151398 3207777)
#> 4 TRUE (4151370 3207673)
#> 5 TRUE (4151408 3207539)
#> 6 TRUE (4151421 3207592)
#> # ℹ 695 more rows
#> #
#> # A tibble: 851 × 5
#> from to name type geometry
#> <int> <int> <chr> <fct> <LINESTRING [m]>
#> 1 1 2 Havixbecker Strasse residential (4151491 3207923, 4151474 32079…
#> 2 3 4 Pienersallee secondary (4151398 3207777, 4151390 32077…
#> 3 5 6 Schulte-Bernd-Strasse residential (4151408 3207539, 4151417 32075…
#> # ℹ 848 more rows
```