spatial_node_predicates: Query nodes with spatial predicates

In sfnetworks: Tidy Geospatial Networks

Query nodes with spatial predicates

Description

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.

Usage

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, ...)

Arguments

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.

Details

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.

Value

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

Note

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.

Examples

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)

sfnetworks documentation built on March 31, 2023, 9:51 p.m.