Match spatial points to the edges of a spatial graph, through finding the
edge with the closest perpendicular intersection. NOTE: Intersections are
calculated geometrically, and presume planar geometry. It is up to users of
projected geometrical data, such as those within a dodgr_streetnet object,
to ensure that either: (i) Data span an sufficiently small area that errors
from presuming planar geometry may be ignored; or (ii) Data are re-projected
to an equivalent planar geometry prior to calling this routine.
Arguments
- graph
A
dodgrgraph with spatial coordinates, such as adodgr_streetnetobject.- xy
coordinates of points to be matched to the vertices, either as matrix or sf-formatted
data.frame.- connected
Should points be matched to the same (largest) connected component of graph? If
FALSEand these points are to be used for adodgrrouting routine (dodgr_dists, dodgr_paths, or dodgr_flows_aggregate), then results may not be returned if points are not part of the same connected component. On the other hand, forcing them to be part of the same connected component may decrease the spatial accuracy of matching.
Value
For distances = FALSE (default), a vector index matching the xy
coordinates to nearest edges. For bi-directional edges, only one match is
returned, and it is up to the user to identify and suitably process matching
edge pairs. For 'distances = TRUE', a 'data.frame' of four columns:
"index" The index of closest edges in "graph", as described above.
"d_signed" The perpendicular distance from ech point to the nearest edge, with negative distances denoting points to the left of edges, and positive distances denoting points to the right. Distances of zero denote points lying precisely on the line of an edge (potentially including cases where nearest point of bisection lies beyond the actual edge).
"x" The x-coordinate of the point of intersection.
"y" The y-coordinate of the point of intersection.
See also
Other match:
add_nodes_to_graph(),
match_points_to_verts(),
match_pts_to_graph(),
match_pts_to_verts()
Examples
graph <- weight_streetnet (hampi, wt_profile = "foot")
# Then generate some random points to match to graph
verts <- dodgr_vertices (graph)
npts <- 10
xy <- data.frame (
x = min (verts$x) + runif (npts) * diff (range (verts$x)),
y = min (verts$y) + runif (npts) * diff (range (verts$y))
)
edges <- match_pts_to_graph (graph, xy)
graph [edges, ] # The edges of the graph closest to `xy`
#> geom_num edge_id from_id from_lon from_lat to_id to_lon to_lat
#> 6257 203 6257 1204772830 76.42729 15.35071 1388481903 76.43011 15.35046
#> 485 18 485 339574181 76.44887 15.33268 339574182 76.44817 15.33271
#> 3395 89 3395 2588119056 76.42341 15.31717 2588146107 76.42349 15.31746
#> 859 34 859 2398957540 76.44876 15.30889 676635813 76.44890 15.30911
#> 819 34 819 286632878 76.44887 15.30104 676635779 76.44832 15.30230
#> 3467 89 3467 5351719173 76.43085 15.31804 5351719172 76.43136 15.31796
#> 6201 203 6201 7769271419 76.37263 15.34499 7769190961 76.38203 15.34708
#> 5675 171 5675 5974426256 76.44354 15.31398 5351820892 76.44350 15.31399
#> 761 31 761 571423430 76.47690 15.35360 571423433 76.47649 15.35310
#> 6203 203 6203 7769190961 76.38203 15.34708 1204772804 76.38329 15.34736
#> d d_weighted highway way_id component time
#> 6257 304.133008 608.266016 primary 835018468 2 218.975766
#> 485 74.866713 78.807066 track 30704678 1 53.904033
#> 3395 33.008446 41.260558 unclassified 252786290 1 23.766081
#> 859 28.521630 47.536050 secondary 53626074 1 20.535573
#> 819 151.598902 252.664836 secondary 53626074 1 109.151209
#> 3467 55.711284 69.639105 unclassified 252786290 1 40.112125
#> 6201 1035.681414 2071.362827 primary 835018468 2 745.690618
#> 5675 4.550499 5.688124 unclassified 554572321 1 3.276359
#> 761 70.305169 78.116855 service 45064036 2 50.619722
#> 6203 138.666644 277.333288 primary 835018468 2 99.839984
#> time_weighted
#> 6257 437.951532
#> 485 56.741088
#> 3395 29.707601
#> 859 34.225956
#> 819 181.918682
#> 3467 50.140156
#> 6201 1491.381236
#> 5675 4.095449
#> 761 56.244135
#> 6203 199.679967