---
title: "Route networks with stplanr"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Route networks with stplanr}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = all(c(curl::has_internet(), requireNamespace("igraph", quietly = TRUE)))
)
```
```{r setup, message=FALSE}
library(stplanr)
library(sf)
```
# Introduction
Route networks represent the network of highways, cycleways, footways and other ways along which transport happens.
You can get route network data from OpenStreetMap (e.g. via the `osmdata` R package) and other providers or transport network data.
# Creating route networks from overlapping routes
Unlike routes, each segment geometry in a route network can only appear once.
**stplanr** can be used to convert a series of routes into a route network, using the function `overline()`, as illustrated below:
```{r, out.width="40%", fig.show='hold', fig.width=5, message=FALSE}
library(stplanr)
library(sf)
sample_routes <- routes_fast_sf[2:6, 1]
sample_routes$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.
```{r rnets1, message=FALSE, warning=FALSE, out.width="100%", fig.width=6, fig.height=6, echo=FALSE}
# knitr::include_graphics("route-networks.png")
```
# Identifying route network groups
Route networks can be represented as a graph.
Usually all segments are connected together, meaning the graph is [connected](https://en.wikipedia.org/wiki/Connectivity_(graph_theory)#Connected_graph).
We can show that very simple network above is connected as follows:
```{r}
touching_list = st_intersects(sample_routes)
g = igraph::graph.adjlist(touching_list)
igraph::is_connected(g)
```
A more complex network may not be connected in this way, as shown in the example below:
```{r, eval=FALSE}
# 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$geometry)
```
```{r, echo=FALSE}
knitr::include_graphics("https://i.imgur.com/827f761.png")
```
The elements of the network are clearly divided into groups.
We can identify these groups as follows:
```{r, eval=FALSE}
rnet_disconnected$group = rnet_igroup(rnet_disconnected)
```
# Routing on route networks
```{r, out.width="49%", fig.show='hide'}
# 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)
```
# Adding new nodes
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:
```{r netpoint}
new_point_coordinates <- c(-1.540, 53.826)
p <- sf::st_sf(geometry = sf::st_sfc(sf::st_point(new_point_coordinates)), crs = 4326)
```
# Other approaches
Other approaches to working with route networks include:
- [sDNA](https://github.com/fiftysevendegreesofrad/sdna_open), an open source C++ library for analysing route networks and estimating flows at segments across network segments
- [sfnetworks](https://github.com/luukvdmeer/sfnetworks), an R package that provides an alternative igraph/sf spatial network class
- [dodgr](https://github.com/UrbanAnalyst/dodgr), an R package providing functions for calculating distances on directed graphs
- [cppRouting](https://cran.r-project.org/package=cppRouting), a package for routing in C++
- Chapter [10 of Geocomputation with R](https://r.geocompx.org/transport.html), which provides context and demonstrates a transport planning workflow in R.
```{r, message=FALSE, warning=FALSE, out.width="100%", fig.width=6, fig.height=6, echo=FALSE, eval=FALSE}
# Show solutions to https://github.com/ropensci/stplanr/issues/237
library(tmap)
# sample_routes = routes_fast_sf
# sample_routes$id <- 1:nrow(sample_routes)
# sample_routes <- sample_routes[!is.na(sample_routes$plan),]
sample_routes <- routes_fast_sf[c(22, 38, 39, 46, 47), NULL]
sample_routes$type <- " Routes"
v <- 1:5
n <- c(2, 3, 5)
# route_list = purrr::map(n, ~sample_routes[1:., ])
route_list <- lapply(n, function(x) {
l <- sample_routes[1:x, ]
l$n <- x
l$value <- rep(v, length.out = x)
l
})
routes_all <- do.call(rbind, route_list)
# rnet_list = purrr::map(route_list, function(x) {
rnet_list <- lapply(route_list, function(x) {
l <- overline2(x, "value")
l$n <- mean(x$n)
l
})
rnet_all <- do.call(rbind, rnet_list)
rnet_all$type <- "Route network"
all_routes <- rbind(routes_all, rnet_all)
p <- sf::st_centroid(all_routes)
tmap_mode("plot")
m <- tm_shape(all_routes, bbox = tmaptools::bb(all_routes)) +
tm_lines(
col = "value", lwd = 1, palette = "OrRd", scale = 8, alpha = 0.8, breaks = 0:6,
legend.lwd.show = FALSE, labels = as.character(1:6),
legend.col.show = FALSE
) +
tm_text("value") +
tm_facets(by = c("n", "type")) +
tm_layout(scale = 1.5)
# m
tmap_save(m, "vignettes/route-networks.png")
```
```{r, eval=FALSE, echo=FALSE}
# test code:
sample_routes2 <- sample_routes5[2:3, ]
sample_routes3 <- sample_routes5[2:4, ]
rnet2 <- overline2(sample_routes2, attrib = "value")
rnet3 <- overline2(sample_routes3, attrib = "value")
rnet5 <- overline2(sample_routes5, attrib = "value")
b <- 0:6
bb <- tmaptools::bb(rnet, ext = 1.1)
rnet5$n <- 5
rnet5$type <- "Route network"
sample_routes5$n <- 5
sample_routes5$type <- " Routes"
all_routes <- rbind(rnet5, sample_routes5)
m2 <- tm_shape(sample_routes[1:2, ], bbox = bb) +
tm_lines(col = "value", lwd = "value", palette = "magma", scale = 8, alpha = 0.5, breaks = b) +
tm_layout(title = "2 Routes", legend.show = FALSE)
r2 <- tm_shape(rnet2, bbox = bb) +
tm_lines(
col = "value", palette = "viridis", scale = 10, alpha = 0.5, breaks = b,
legend.lwd.show = FALSE, labels = as.character(1:6)
)
m3 <- tm_shape(sample_routes[1:3, ], bbox = bb) +
tm_lines(col = "value", lwd = "value", palette = "viridis", scale = 15, alpha = 0.5, breaks = b) +
tm_layout(title = "3 Routes", legend.show = FALSE)
r3 <- tm_shape(rnet3, bbox = bb) +
tm_lines(col = "value", palette = "viridis", scale = 8, alpha = 0.5, breaks = b) +
tm_layout(legend.show = FALSE)
m6 <- tm_shape(sample_routes, bbox = bb) +
tm_lines(col = "value", lwd = "value", palette = "viridis", scale = 10, alpha = 0.5, breaks = b) +
tm_layout(title = "6 Routes", legend.show = FALSE)
r6 <- tm_shape(rnet, bbox = bb) +
tm_lines(col = "value", palette = "viridis", scale = 8, alpha = 0.5, breaks = b) +
tm_layout(legend.show = FALSE)
tmap_arrange(m2, r2, m3, r3, m6, r6, nrow = 3)
```