`dodgr`

is an R package for efficient calculation of many-to-many pairwise distances on dual-weighted directed graphs, for aggregation of flows throughout networks, and for highly realistic routing through street networks (time-based routing considering incline, turn-angles, surface quality, everything).

Note that most `dodgr`

algorithms implement parallel computation with the `RcppParallel`

library, and by default use the maximal number of available cores or threads. If you do not wish `dodgr`

to use all available threads, please reduce the number manually by first specifying a value via

Four aspects. First, while other packages exist for calculating distances on directed graphs, notably `igraph`

, even that otherwise fabulous package does not (readily) permit analysis of *dual-weighted* graphs. Dual-weighted graphs have two sets of weights for each edge, so routing can be evaluated with one set of weights, while distances can be calculated with the other. A canonical example is a street network, where *weighted distances* are assigned depending on mode of transport (for example, weighted distances for pedestrians on multi-lane vehicular roads are longer than equivalent distances along isolated walking paths), yet the desired output remains direct, unweighted distances. Accurate calculation of distances on street networks requires a dual-weighted representation. In **R**, `dodgr`

is currently the only package that offers this functionality (without excessive data wrangling).

Second, while `igraph`

and almost all other routing packages are primarily designed for one-to-one routing, `dodgr`

is specifically designed for many-to-many routing, and will generally outperform equivalent packages in large routing tasks.

Third, `dodgr`

goes beyond the functionality of comparable packages through including routines to aggregate flows throughout a network, through specifying origins, destinations, and flow densities between each pair of points. Alternatively, flows can be aggregated according to a network dispersal model from a set of origin points and associated densities, and a user-specified dispersal model.

Fourth and finally, `dodgr`

implements highly realistic and fully-customisable profiles for routing through street networks with various modes of transport, and using either distance- or time-based routing. Routing can include such factors as waiting times at traffic lights, delays for turning across oncoming traffic, access restrictions, and the effects of elevation on both cyclists and pedestrians. See the dedicated vignette on street networks and time-based routing for more detail.

You can install latest stable version of `dodgr`

from CRAN with:

Alternatively, current development versions can be installed using any of the following options:

```
# install.packages("remotes")
remotes::install_git("https://git.sr.ht/~mpadge/dodgr")
remotes::install_bitbucket("atfutures/dodgr")
remotes::install_gitlab("atfutures1/dodgr")
remotes::install_github("ATFutures/dodgr")
```

Then load with

While `dodgr`

works with any arbitrary networks, it also includes numerous functions explicitly intended to be applied to geodesic coordinates, which are identified whenever input data have columns labelled “longitude” and “latitude”, or similar. Coordinates for such data must be in the EPSG:4326 (WGS84) coordinate system. `dodgr`

treats coordinates as numbers only, and it is up to the user to ensure appropriate transformation to WGS84 coordinates prior to submitting data to `dodgr`

functions.

`dodgr`

networksTo illustrate functionality, the package includes an example data set containing the Open Street Map network for Hampi, India (a primarily pedestrian village in the middle of a large World Heritage zone). These data are in Simple Features (`sf`

) format, as a collection of `LINESTRING`

objects. `dodgr`

represents networks as a simple rectangular graph, with each row representing an edge segment between two points or vertices. `sf`

-format objects can be converted to equivalent `dodgr`

representations with the `weight_streetnet()`

function:

```
class (hampi)
#> [1] "sf" "data.frame"
dim (hampi)
#> [1] 236 15
graph <- weight_streetnet (hampi, wt_profile = "foot")
class (graph)
#> [1] "data.frame" "dodgr_streetnet"
dim (graph)
#> [1] 6813 15
```

The `sf`

-format network contained 236 `LINESTRING`

objects, with the `weight_streetnet()`

function decomposing these into 6,813 distinct edges, indicating that the `sf`

representation had around 29 edges or segments in each `LINESTRING`

object. The `dodgr`

network then looks like this:

geom_num | edge_id | from_id | from_lon | from_lat | to_id | to_lon | to_lat | d | d_weighted | highway | way_id | component | time | time_weighted |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 1 | 339318500 | 76.47491 | 15.34167 | 339318502 | 76.47612 | 15.34173 | 129.972181 | 129.972181 | path | 28565950 | 1 | 93.579970 | 93.579970 |

1 | 2 | 339318502 | 76.47612 | 15.34173 | 339318500 | 76.47491 | 15.34167 | 129.972181 | 129.972181 | path | 28565950 | 1 | 93.579970 | 93.579970 |

1 | 3 | 339318502 | 76.47612 | 15.34173 | 2398958028 | 76.47621 | 15.34174 | 8.888670 | 8.888670 | path | 28565950 | 1 | 6.399843 | 6.399843 |

1 | 4 | 2398958028 | 76.47621 | 15.34174 | 339318502 | 76.47612 | 15.34173 | 8.888670 | 8.888670 | path | 28565950 | 1 | 6.399843 | 6.399843 |

1 | 5 | 2398958028 | 76.47621 | 15.34174 | 1427116077 | 76.47628 | 15.34179 | 9.326536 | 9.326536 | path | 28565950 | 1 | 6.715106 | 6.715106 |

1 | 6 | 1427116077 | 76.47628 | 15.34179 | 2398958028 | 76.47621 | 15.34174 | 9.326536 | 9.326536 | path | 28565950 | 1 | 6.715106 | 6.715106 |

The `geom_num`

column maps directly onto the sequence of `LINESTRING`

objects within the `sf`

-formatted data. The `highway`

column is taken directly from Open Street Map, and denotes the kind of “highway” represented by each edge. The `component`

column is an integer value describing which of the connected components of the network each edge belongs to (with `1`

always being the largest component; `2`

the second largest; and so on).

Note that the `d_weighted`

values are often greater than the geometric distances, `d`

. In the example shown, `service`

highways are not ideal for pedestrians, and so weighted distances are slightly greater than actual distances. Compare this with:

geom_num | edge_id | from_id | from_lon | from_lat | to_id | to_lon | to_lat | d | d_weighted | highway | way_id | component | time | time_weighted |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 1 | 339318500 | 76.47491 | 15.34167 | 339318502 | 76.47612 | 15.34173 | 129.972181 | 129.972181 | path | 28565950 | 1 | 93.579970 | 93.579970 |

1 | 2 | 339318502 | 76.47612 | 15.34173 | 339318500 | 76.47491 | 15.34167 | 129.972181 | 129.972181 | path | 28565950 | 1 | 93.579970 | 93.579970 |

1 | 3 | 339318502 | 76.47612 | 15.34173 | 2398958028 | 76.47621 | 15.34174 | 8.888670 | 8.888670 | path | 28565950 | 1 | 6.399843 | 6.399843 |

1 | 4 | 2398958028 | 76.47621 | 15.34174 | 339318502 | 76.47612 | 15.34173 | 8.888670 | 8.888670 | path | 28565950 | 1 | 6.399843 | 6.399843 |

1 | 5 | 2398958028 | 76.47621 | 15.34174 | 1427116077 | 76.47628 | 15.34179 | 9.326536 | 9.326536 | path | 28565950 | 1 | 6.715106 | 6.715106 |

1 | 6 | 1427116077 | 76.47628 | 15.34179 | 2398958028 | 76.47621 | 15.34174 | 9.326536 | 9.326536 | path | 28565950 | 1 | 6.715106 | 6.715106 |

A `"path"`

offers ideal walking conditions, and so weighted distances are equal to actual distances.

The many-to-many nature of `dodgr`

means that the function to calculate distances, `dodgr_distances()`

or, for street networks, times, `dodgr_times()`

, accepts two vectors or matrices of routing points as inputs (describing origins and destinations), and returns a corresponding matrix of pairwise distances. If an input graph has columns for both distances and weighted distances, and/or times and weighted times, the weighted versions are used to determine the effectively shortest or fastest routes through a network, while actual distances or times are summed along the routes to calculate final values. It is of course also possible to calculate distances along fastest routes, times along shortest routes, or any combination thereof, as detailed in the package vignette on street networks and time-based routing.

Routing points can, for example, be randomly selected from the vertices of a graph. The vertices can in turn be extracted with the `dodgr_vertices()`

function:

id | x | y | component | n | |
---|---|---|---|---|---|

1 | 339318500 | 76.47491 | 15.34167 | 1 | 0 |

2 | 339318502 | 76.47612 | 15.34173 | 1 | 1 |

4 | 2398958028 | 76.47621 | 15.34174 | 1 | 2 |

6 | 1427116077 | 76.47628 | 15.34179 | 1 | 3 |

8 | 7799710916 | 76.47634 | 15.34184 | 1 | 4 |

10 | 339318503 | 76.47641 | 15.34190 | 1 | 5 |

For OSM data extracted with the `osmdata`

package (or, equivalently, via the `dodgr::dodgr_streetnet()`

function), each object (vertices, ways, and high-level relations between these objects) is assigned a unique identifying number. These are retained both in `osmdata`

and `dodgr`

, as the `way_id`

column in the above `graph`

, and as the `id`

column in the vertices. Random vertices may be generated in this case through selecting `id`

values:

```
from <- sample (v$id, size = 20)
to <- sample (v$id, size = 50)
d <- dodgr_dists (graph = graph, from = from, to = to)
dim (d)
#> [1] 20 50
```

Alternatively, the points may be specified as matrices of geographic coordinates:

```
from_x <- min (graph$from_lon) + runif (20) * diff (range (graph$from_lon))
from_y <- min (graph$from_lat) + runif (20) * diff (range (graph$from_lat))
to_x <- min (graph$from_lon) + runif (50) * diff (range (graph$from_lon))
to_y <- min (graph$from_lat) + runif (50) * diff (range (graph$from_lat))
d <- dodgr_dists (graph = graph, from = cbind (from_x, from_y), to = cbind (to_x, to_y))
```

In this case, the random points will be mapped on to the nearest points on the street network. This may, of course, map some points onto minor, disconnected components of the graph. This can be controlled either by reducing the graph to it’s largest connected component only:

or by explicitly using the `match_points_to_graph()`

function with the option `connected = TRUE`

:

```
from <- match_points_to_graph (v, cbind (from_x, from_y), connected = TRUE)
to <- match_points_to_graph (v, cbind (to_x, to_y), connected = TRUE)
```

This function returns an index into the result of `dodgr_vertices`

, and so points to use for routing must then be extracted as follows:

```
from <- v$id [from] # or from <- v [from, c ("x", "y")]
to <- v$id [to]
d <- dodgr_dists (graph = graph, from = from, to = to)
```

Flow aggregation refers to the procedure of routing along multiple ways according to specified densities of flow between defined origin and destination points, and aggregating flows along each edge of the network. The procedure is functionally similar to the above procedure for distances, with the addition of a matrix specifying pairwise flow densities between the input set of origin (`from`

) and destination (`to`

) points. The following example illustrates use with a random “flow matrix”:

```
flows <- array (runif (length (from) * length (to)), dim = c (length (from), length (to)))
length (from); length (to); dim (flows)
#> [1] 20
#> [1] 50
#> [1] 20 50
f <- dodgr_flows_aggregate (graph = graph, from = from, to = to, flows = flows)
```

The result is simply the input `graph`

with an additional column quantifying the aggregate flows along each edge:

geom_num | edge_id | from_id | from_lon | from_lat | to_id | to_lon | to_lat | d | d_weighted | highway | way_id | component | time | time_weighted | flow |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 1 | 339318500 | 76.47491 | 15.34167 | 339318502 | 76.47612 | 15.34173 | 129.972181 | 129.972181 | path | 28565950 | 1 | 93.579970 | 93.579970 | 0.1046131 |

1 | 2 | 339318502 | 76.47612 | 15.34173 | 339318500 | 76.47491 | 15.34167 | 129.972181 | 129.972181 | path | 28565950 | 1 | 93.579970 | 93.579970 | 0.1310037 |

1 | 3 | 339318502 | 76.47612 | 15.34173 | 2398958028 | 76.47621 | 15.34174 | 8.888670 | 8.888670 | path | 28565950 | 1 | 6.399843 | 6.399843 | 0.1046131 |

1 | 4 | 2398958028 | 76.47621 | 15.34174 | 339318502 | 76.47612 | 15.34173 | 8.888670 | 8.888670 | path | 28565950 | 1 | 6.399843 | 6.399843 | 0.1310037 |

1 | 5 | 2398958028 | 76.47621 | 15.34174 | 1427116077 | 76.47628 | 15.34179 | 9.326536 | 9.326536 | path | 28565950 | 1 | 6.715106 | 6.715106 | 0.1046131 |

1 | 6 | 1427116077 | 76.47628 | 15.34179 | 2398958028 | 76.47621 | 15.34174 | 9.326536 | 9.326536 | path | 28565950 | 1 | 6.715106 | 6.715106 | 0.1310037 |

An additional flow aggregation function can be applied in cases where only densities at origin points are known, and movement throughout a graph is dispersive:

For more detail, see the main package vignette, and the second vignette on street networks and time-based routing

All contributions to this project are gratefully acknowledged using the `allcontributors`

package following the all-contributors specification. Contributions of any kind are welcome!

mpadge |
karpfen |
Robinlovelace |
agila5 |
JimShady |
layik |
virgesmith |

richardellison |
coatless |
znmeb |
yihui |