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
#> 6207 203 6207 1388481924 76.38383 15.34743 1388482509 76.38860 15.34745
#> 6259 203 6259 1388481903 76.43011 15.35046 3921525779 76.43165 15.35029
#> 3403 89 3403 7793366198 76.42424 15.31863 2588146138 76.42439 15.31886
#> 3395 89 3395 2588119056 76.42341 15.31717 2588146107 76.42349 15.31746
#> 5875 183 5875 313796426 76.47957 15.31553 1128374399 76.47924 15.31509
#> 6201 203 6201 7769271419 76.37263 15.34499 7769190961 76.38203 15.34708
#> 1281 35 1281 1206252958 76.48546 15.35660 571423205 76.48656 15.35631
#> 2399 70 2399 7799711029 76.47534 15.33166 7799711030 76.47548 15.33171
#> 5301 158 5301 3921524453 76.44483 15.34252 3921524452 76.44502 15.34246
#> 2615 70 2615 1376768826 76.48596 15.33405 1376769204 76.48618 15.33392
#> d d_weighted highway way_id component time
#> 6207 511.92811 1023.85621 primary 835018468 2 368.58824
#> 6259 166.13032 332.26064 primary 835018468 2 119.61383
#> 3403 31.37821 39.22276 unclassified 252786290 1 22.59231
#> 3395 33.00845 41.26056 unclassified 252786290 1 23.76608
#> 5875 60.34322 120.68645 primary 652570479 1 43.44712
#> 6201 1035.68141 2071.36283 primary 835018468 2 745.69062
#> 1281 122.10375 244.20750 primary 53658844 2 87.91470
#> 2399 16.06702 16.06702 path 123463598 1 11.56826
#> 5301 20.81961 21.91537 track 388983289 2 14.99012
#> 2615 26.74674 26.74674 path 123463598 1 19.25765
#> time_weighted
#> 6207 737.17647
#> 6259 239.22766
#> 3403 28.24039
#> 3395 29.70760
#> 5875 86.89424
#> 6201 1491.38124
#> 1281 175.82940
#> 2399 11.56826
#> 5301 15.77907
#> 2615 19.25765