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

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; and`dodgr_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.