Route networks represent the network of highways, cycleways, footways and other ways along which transport happens. You can get route network data from OpenStreetMap (e.g. via the `osmdata`

R package) and other providers or transport network data.

Unlike routes, each segment geometry in a route network can only appear once.

**stplanr** can be used to convert a series of routes into a route network, using the function `overline()`

, as illustrated below:

library(stplanr) library(sf) sample_routes <- routes_fast_sf[2:6, 1] sample_routes$value <- rep(1:3, length.out = 5) rnet <- overline(sample_routes, attrib = "value") plot(sample_routes["value"], lwd = sample_routes$value, main = "Routes") plot(rnet["value"], lwd = rnet$value, main = "Route network")

The above figure shows how `overline()`

breaks the routes into segments with the same values and removes overlapping segments. It is a form of geographic aggregation.

Route networks can be represented as a graph. Usually all segments are connected together, meaning the graph is connected. We can show that very simple network above is connected as follows:

touching_list = st_intersects(sample_routes) #> although coordinates are longitude/latitude, st_intersects assumes that they are planar g = igraph::graph.adjlist(touching_list) igraph::is_connected(g) #> [1] TRUE

A more complex network may not be connected in this way, as shown in the example below:

# piggyback::pb_download_url("r_key_roads_test.Rds") u = "https://github.com/ropensci/stplanr/releases/download/0.6.0/r_key_roads_test.Rds" rnet_disconnected = readRDS(url(u)) touching_list = sf::st_intersects(rnet_disconnected) g = igraph::graph.adjlist(touching_list) igraph::is_connected(g) #> [1] FALSE sf:::plot.sfc_LINESTRING(rnet_disconnected$geometry)

The elements of the network are clearly divided into groups. We can identify these groups as follows:

rnet_disconnected$group = rnet_igroup(rnet_disconnected)

An important feature of route networks is that they are simultaneously spatial and graph entities. This duality is captured in `sfNetwork`

objects, which can be created by the function `SpatialLinesNetwork()`

:

sln <- SpatialLinesNetwork(rnet) class(sln) #> [1] "sfNetwork" #> attr(,"package") #> [1] "stplanr"

`sln`

has both spatial and graph components, with the number of lines equal to the number graph edges:

class(sln@sl) #> [1] "sf" "data.frame" nrow(sln@sl) #> [1] 8 class(sln@g) #> [1] "igraph" length(igraph::edge.attributes(sln@g)[["weight"]]) #> [1] 8 class(sln@nb) #> [1] "list" length(unique(unlist(sln@nb))) #> [1] 8 identical(sln@sl$geometry, rnet$geometry) #> [1] TRUE

sln_nodes <- sln2points(sln) nrow(sln_nodes) #> [1] 9 length(sln@nb) #> [1] 9

rnet_coordinates <- sf::st_coordinates(rnet) set.seed(85) x <- runif(n = 2, min = min(rnet_coordinates[, 1]), max = max(rnet_coordinates[, 1])) y <- runif(n = 2, min = min(rnet_coordinates[, 2]), max = max(rnet_coordinates[, 2])) crs <- sf::st_crs(rnet) xy_sf <- sf::st_as_sf(data.frame(n = 1:2, x, y), coords = c("x", "y"), crs = crs) xy_nodes <- stplanr::find_network_nodes(sln = sln, x = x, y = y)

Currently not running due to issues with dev version of `dplyr`

:

https://github.com/ropensci/stplanr/issues/383

# plot(rnet$geometry) # plot(sln_nodes, add = TRUE) # xy_path <- sum_network_routes(sln = sln, start = xy_nodes[1], end = xy_nodes[2], sumvars = "length") # # xy_path = sum_network_links(sln = sln, start = xy_nodes[1], end = xy_nodes[2]) # plot(rnet$geometry) # plot(xy_sf$geometry, add = TRUE) # plot(xy_path$geometry, add = TRUE, lwd = 5)

New nodes can be added to the network, although this should be done before the graph representation is created. Imagine we want to create a point half way along the the most westerly route segment in the network, near the coordinates -1.540, 53.826:

new_point_coordinates <- c(-1.540, 53.826) p <- sf::st_sf(geometry = sf::st_sfc(sf::st_point(new_point_coordinates)), crs = crs)

We can identify the nearest point on the network at this point and use that to split the associated linestring:

sln_new <- sln_add_node(sln = sln, p = p) #> although coordinates are longitude/latitude, st_nearest_points assumes that they are planar route_new <- route_local(sln = sln_new, from = p, to = xy_sf[1, ]) plot(sln_new) plot(p, add = TRUE) plot(route_new, lwd = 5, add = TRUE) #> Warning in plot.sf(route_new, lwd = 5, add = TRUE): ignoring all but the first #> attribute

Other approaches to working with route networks include:

- sDNA, an open source C++ library for analysing route networks and estimating flows at segments across network segments
- sfnetworks, an R package that provides an alternative igraph/sf spatial network class
- dodgr, an R package providing functions for calculating distances on directed graphs
- cppRouting, a package for routing in C++
- Chapter 10 of Geocomputation with R, which provides context and demonstrates a transport planning workflow in R.