Introduction

This vignette supports workshops on advanced usage and development of the Propensity to Cycle Tool (PCT). Beginner and intermediate PCT events focus on using the PCT via the web application hosted at www.pct.bike and the data provided by the PCT in QGIS.

The focus here is on analysing cycling potential in the open source statistical programming language R, in which the majority of the PCT was built. It will show how the code underlying the PCT works, how the underlying data can be accessed for reproducible analysis, and how the methods can be used to generate new scenarios of cycling uptake.

If you are an intermediate user, it may be worth brushing-up on your R skills, e.g. by taking a free online course such as that provided by DataCamp or by working through Chapter 2 onwards of the open source book Geocomputation with R (see reading list below for more transport-specific resources).

Prior reading

In addition to computer hardware (a laptop) and software (an up-to-date R set-up and experience using R) pre-requisites, you should have read, or at least have working knowledge of the contents of, the following publications, all of which are freely available online:

The original paper on the Propensity to Cycle Tool (Lovelace et al. 2017)
The transport chapter (12) in the open source book Geocomputation with R (Lovelace, Nowosad, and Meunchow 2019)
The vignette that describes the pct R package, which can be found here: https://itsleeds.github.io/pct/articles/pct.html

Prerequisites

To ensure your computer is ready for the course, you should be able to run the following lines of R code on your computer:

install.packages("remotes")
pkgs = c(
  "cyclestreets",
  "mapview",
  "pct",
  "sf",
  "stats19",
  "stplanr",
  "tidyverse",
  "devtools"
)
remotes::install_cran(pkgs)
# remotes::install_github("ITSLeeds/pct")

To test your computer is ready to work with PCT data in R, try running the following command:

source("https://github.com/ITSLeeds/pct/raw/master/inst/test-setup.R")

Agenda

Part 1: how the Propensity to Cycle Tool works + demo: ‘I do’

Introductions (14:00 - 14:15)
Walk through of test-setup.R (14:15 - 14:30)
Demonstration of calculating cycling potential in a new context (14:30 - 14:40)
Questions, break and debugging the test code in breakout rooms (14:40 - 15:00)
- Room 1: technical questions and debugging code (Joey)
- Room 2: methodological questions about the PCT (Robin)
- Room 3: applying the PCT in new contexts (Rosa)
- Room 4: networking

Part 2: Co-coding session: getting started with transport data in R: ‘we do’

Working through the code in pct_training.R (15:00 - 15:30)
- Breakout rooms 2 and 3 open for people who have questions
Live demo: overlaying PCT data with data from the Rapid tool and OSM (15:30 - 15:45)

☕☕☕ 15 minute break ☕☕☕

Part 3: using PCT data for local transport planning: ‘you do’

Getting set-up with RStudio and input data (16:00 - 16:15, Robin)
Break-out rooms (16:15 - 17:00)
- Getting and visualising PCT data (e.g. get_pct_rnet)
- Running the PCT for new areas
- Advanced topics (different routing, uptake and route network summary methods - developments in stplanr)
Presentation of work and next steps (17:00 - 17:15)
Networking and ‘ideas with beers’ 🍻🍻🍻 (17:20 - 18:00)

Exercises

Useful links

These links may be useful when working through the exercises below:

The codebook describing each variable in the PCT route: od_l_rf_codebook.csv
Folder containing all codebooks, including variables in zone, line and centroid datasets
The PCT tool itself, which can be used to cross-compare results from the analysis steps below: www.pct.bike

How the PCT works and what you can use it for

Take a look at the image below from (Lovelace et al. 2017). Which parts of the process are of most interest for your work?

The PCT provides data at 4 geographic levels:

Zones
Desire lines
Routes
Route networks

Which types of data are most appropriate to tackle each of the questions/problems you identified?

Name 3 limitations of the data currently provided by the PCT and discuss how you could overcome them

Getting PCT data

In this section we will get PCT data at the MSOA level and plot the result in a simple map.

G1: The first stage is to load packages we will use:

library(pct)
library(dplyr)   # in the tidyverse
library(tmap)    # installed alongside mapview

G2: After setting the region name (to avoid re-typing it many times) run the following commands to download data at the four main levels used in the PCT:

region_name = "isle-of-wight"
zones_all = get_pct_zones(region_name, geography = "msoa")
lines_all = get_pct_lines(region_name, geography = "msoa")
routes_all = get_pct_routes_fast(region_name, geography = "msoa")
rnet_all = get_pct_rnet(region_name)

G3: Check the downloads worked by plotting the result as follows:

plot(zones_all$geometry)
plot(lines_all$geometry, col = "blue", add = TRUE)
plot(routes_all$geometry, col = "green", add = TRUE)
plot(rnet_all$geometry, col = "red", lwd = rnet_all$bicycle / 10, add = TRUE)

Visualising PCT data

At its heart, the PCT is a data visualisation tool.

V1: Create a static plot showing the route network layer with the tmap package

tm_shape(rnet_all) +
  tm_lines(lwd = "dutch_slc", scale = 9)
#> Legend labels were too wide. Therefore, legend.text.size has been set to 0.47. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.

V2: Create an interactive map showing the route network layer with the tmap package

# interactive plot
tmap_mode("view")
#> tmap mode set to interactive viewing
tm_shape(rnet_all) +
  tm_lines(lwd = "dutch_slc", scale = 9)
#> Legend for line widths not available in view mode.

Propensity to Cycle Tool Advanced Workshop