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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
message = FALSE,
collapse = TRUE,
comment = "#>"
)
```
# Network Planning Tool for Scotland
<!-- badges: start -->
<!-- badges: end -->
The aim of this project is to generate evidence to support strategic cycle network planning in Scotland.
# Reproducibility: basic
To reproduce the basic results in this repository, hosted on GitHub at nptscot/npt, first download and unzip or clone it and then open the 'npt' folder in your favourite integrated development environment (IDE) for R, such as RStudio.
- You can download the repository as a zip file from the GitHub website by clicking on the green 'Code' button and selecting 'Download ZIP', hosted at this URL: https://github.com/nptscot/npt/archive/refs/heads/main.zip
- You can clone the repo with Git as follows:
```bash
git clone https://github.com/nptscot/npt.git
```
- Or (recommended for future-proof workflows) you can install the GitHub CLI tool and clone the repo as follows:
```bash
gh repo clone nptscot/npt
```
Install the GitHub CLI tools by following instructions here: https://cli.github.com/manual/installation
Note: you can check to see if you have the GitHub CLI tools installed by running the following command from the R console:
```{r}
gh_version = try({
system("gh --version", intern = TRUE)
})
gh_version
if(is(gh_version, "try-error")) {
message("You don't have the GitHub CLI tools installed. Please install them by following instructions here: https://cli.github.com/manual/installation")
}
```
## Set-up
We will use the following packages:
```{r, message=FALSE, warning=FALSE}
library(tidyverse)
library(tmap)
library(sf)
```
## Reading in input datasets
Read-in the input datasets as follows:
```{r}
list.files("data-raw")
od_data = read_csv("data-raw/od_subset.csv")
zones = read_sf("data-raw/zones_edinburgh.geojson")
desire_lines = od::od_to_sf(od_data, zones)
plot(desire_lines)
```
Generating route networks:
```{r}
# Create a subset of the desire lines for speed of computation:
desire_lines_subset = desire_lines |>
slice_max(n = 30, order_by = all)
library(stplanr)
routes_subset = desire_lines_subset |>
route(l = _, route_fun = route_osrm)
route_network = overline(routes_subset, attrib = "bicycle")
plot(route_network)
```
## Exercises to test your knowledge of the NPT approach
1. Read-in the [`od_subset.csv`](https://github.com/nptscot/npt/blob/main/data-raw/od_subset.csv) file containing origin-destination data in the [`data-raw`](https://github.com/nptscot/npt/blob/main/data-raw/) folder in this repo with the following command:
```{r, message=FALSE}
od_data = read.csv("https://github.com/nptscot/npt/raw/main/data-raw/od_subset.csv")
```
2. What's the average distance of trips according to the `dist_euclidean` column in that dataset?
3. The values in the `dist_euclidean` column represent straight line distance between zone centroids. In what way could the values be over or under-estimates of the distances of trips taken between the OD pairs?
4. Use the function `od_to_sf()` to convert the OD data into desire lines. Hint: a starting point could be:
```{r, message=FALSE}
zones = sf::read_sf("https://github.com/nptscot/npt/raw/main/data-raw/zones_edinburgh.geojson")
plot(zones$geometry)
```
**Bonus exercises**
1. Using interactive geographic data visualisation packages such as `tmap` and `leaflet`, visualise the desire lines and route network on a map? Which road links are most important for cycling according to the network results?
2. Using the `pct::uptake_pct_godutch_2020()` function estimate the potential uptake of cycling based on the subset of desire lines created above. What is the total level of cycling resulting from thhat uptake?
3. Using the `osmextract` or `osmdata` packages, or equivalent packages written in another language, identify the OSM ways that lie completely within a 500m buffer of the route network. How can you ensure that the results are reproducible?
4. Bonus: think about ways to identify weak links or gaps in the cycle network based on the small datasets analysed. How could you scale this up to the whole of Scotland?
# Reproducibility
Note: to reproduce the full build process currently depends on data sources and dependencies that are not publicly available. This is a work in progress.
Then run the following command (requires dependencies and data to be present):
```{r, eval=FALSE, echo=FALSE}
targets::tar_make()
```
Note: you need to have a CYCLESTREETS API key for this, see here for details: https://rpackage.cyclestreets.net/reference/journey.html#details-1
This project uses `targets` for data processing pipeline management, with outputs like this:
Visualise the project as follows:
```{r visnet}
# targets::tar_visnetwork(targets_only = TRUE)
```
The zones in the case study region are as follows:
```{r zones, echo=FALSE, eval=FALSE}
# targets::tar_load(zones)
# knitr::include_graphics("figures/test-plot.png")
tmap_mode("plot")
tm_shape(zones) +
tm_polygons(col = "TotPop2011", palette = "viridis")
```
Baseline cycling levels are shown below:
```{r overline, eval=FALSE}
tm_shape(rnet) +
tm_lines(lwd = "bicycle", scale = 9)
```