Moderator: Joey Talbot, Research Fellow in Transport Data Science, University of Leeds
Speakers: Joey Talbot and Robin Lovelace, Associate Professor of Transport Data Science at the Leeds Institute for Transport Studies, University of Leeds
The Propensity to Cycle Tool (PCT) is widely used to provide an evidence base to inform cycling investment. In this training session you will learn how to download and use these open datasets. This may be of use to anyone interested in data-driven planning for sustainable and active travel futures.
The focus here is on analysing cycling potential in the open source statistical programming language R. We use R because the PCT was developed in, and can be extended with, R code.
Using open source software with a command-line interface reduces barriers to entry, enabling the development of open access transport models for more citizen-led and participatory transport planning, such as integration with the A/B Street city simulation and editing software (Lovelace 2021).
To view a video of our previous advanced training workshop at the Cycle Active City 2021 Conference, see https://www.youtube.com/watch?v=OiLzjrBMQmU.
14:00 - Intro to the workshop and team
14:05 - Demo of PCT web app
14:15 - Working together: Section 1 - downloading PCT data from the website and downloading PCT data in R (Section 1.2)
14:40 - Break
14:50 - Section 2 - Cycling uptake scenarios and joining commute and school data
16:00 - Questions and solo work
17.00 - Networking and social
The training covers:
• How to download data from the PCT using the web interface
• How to access PCT data on travel to work and travel to school, using R
• How to recreate existing PCT scenarios of cycling uptake
• How to create your own scenarios of cycling uptake
• How to join together commute and school travel data
The training team recently hosted an 'introductory' training event which can now be viewed here (66 minutes).
Further preparation information and reading has been made available here for those you wish to take part.
R is a powerful statistical programming language for data science and a wide range of other applications and, like any language, takes time to learn. To get started we recommend the following free resources:
If you want to calculate cycle routes from within R, you are recommended to sign-up for a CycleStreets API key. See here to apply and see here for instructions on creating a ‘environment variable’ (recommended for experienced R users only).
It may also be worth taking a read about the PCT if you’re not familiar with it before the course starts.
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:
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, you can also try running the code hosted at https://raw.githubusercontent.com/ITSLeeds/pct/master/inst/test-setup.R to check everything is working:
If you have any questions before the workshop, feel free to ask a question on the package’s issue tracker (requires a GitHub login): https://github.com/ropensci/slopes/issues
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