R package for calculating pairwise distances on dual-weighted directed graphs using Priority Queue Shortest Paths. Dual-weighted directed graphs are directed graphs with two sets of weights so that weight1(A->B) != weight1(B->A)
—the directed property—and weight2(A->B) != weight1(A->B)
—the dual property. dodgr
calculates shortest paths according to one weight, while distances along paths are calculating using the other weight. A canonical example of a dual-weighted directed graph is a street network to be used for routing. Routes are usually calculated by weighting different kinds of streets or ways according to a particular mode of transport, while the desired output is a direct, unweighted distance.
But wait, there’s more … dodgr
can also aggregate flows throughout a network through specifying origins, destinations, and flow densities. Or even apply a network dispersal model from a set of origin points only.
You can install dodgr
with:
install.packages("dodgr") # current CRAN version
# install.packages("remotes")
remotes::install_github("ATFutures/dodgr") # Development version
Then load with
library (dodgr)
The primary functions are,
d <- dodgr_dists (graph = graph, from = pts, to = pts)
flows <- array (runif (length (pts) ^ 2), dim = rep (length (pts, 2)))
f <- dodgr_flows_aggregate (graph = graph, from = pts, to = pts, flows = flows)
f <- dodgr_flows_disperse (graph = graph, from = pts, to = pts,
dens = runif (length (pts)))
The first function, dodgr_dists()
, produces a square matrix of distances between all points listed in pts
and routed along the dual-weighted directed network given in graph
. An even simpler usage allows calculation of pair-wise distances between a set of geographical coordinates (here, for a sizey chunk of New York City):
xlim <- c (-74.12931, -73.99214)
ylim <- c (40.70347, 40.75354)
npts <- 1000
pts <- data.frame (x = xlim [1] + runif (npts) * diff (xlim),
y = ylim [1] + runif (npts) * diff (ylim))
system.time (
d <- dodgr_dists (from = pts)
)
#> user system elapsed
#> 107.530 0.602 19.418
range (d, na.rm = TRUE)
#> [1] 0.00000 21.68109
This will automatically download the street network (using osmdata
), and even then calculating distances between 1,000 points – that’s 1,000,000 pairwise distances! – can be done in around 20 seconds.
The second function, dodgr_flows_aggregate()
, aggregates the densities specified in the matrix flows
between all pairs of from
and to
points, and returns a modified version of the input network with an additional column containing aggregated flows (see below). The equivalent function, dodgr_flows_disperse()
, does an equivalent thing for network dispersal models from known points of origin.
dodgr
graph structureA graph or network in dodgr
is represented as a flat table (data.frame
, tibble
, data.table
, whatever) of minimally four columns: from
, to
, weight
, and distance
. The first two can be of arbitrary form (numeric
or character
); weight
is used to evaluate the shortest paths, and the desired distances are evaluated by summing the values of distance
along those paths. For a street network example, weight
will generally be the actual distance multiplied by a priority weighting for a given mode of transport and type of way, while distance
will be the pysical distance.
dodgr
includes the conversion functions:
dodgr_to_sfc
to convert spatial dodgr
graphs into Simple Features format used by the sf
package.dodgr_to_igraph
to convert (not necessarily spatial) dodgr
graphs into igraph
format; anddodgr_to_tidygraph
to convert (not necessarily spatial) dodgr
graphs into tidygraph
format.For more detail, see the main package vignette, along with a second vignette detailing benchmark timings, showing that under many circumstances, dodgr
performs considerably faster than equivalent routines from the igraph
package.