This is a GitHub Repository (repo for short) that supports teaching of the Transport Data Science module at the University of Leeds. The module can be taken by students on the Data Science and Analytics and the Transport Planning and the Environment MSc courses.
- On the University’s system (official): mytimetable.leeds.ac.uk
- In ical format (for import into Google/Outlook/other Calendar systems): timetable.ics
- As a .csv file (for easy reading as data): https://github.com/ITSLeeds/TDS/blob/master/timetable.csv (see table below)
| Summary | Description | Date | Location | Duration (Hours) |
|---|---|---|---|---|
| TDS deadline 1 | Computer set-up | 2024-02-02 | Online - Teams | 0 |
| TDS Practical 1: intro | Introduction to transport data science | 2024-02-08 | Irene Manton North Cluster (7.96) | 3 |
| TDS Practical 2: od | Origin-destination data | 2024-02-15 | Irene Manton North Cluster (7.96) | 3 |
| TDS Practical 3: routing | Routing | 2024-02-22 | Irene Manton North Cluster (7.96) | 3 |
| TDS seminar 1 | Seminar 1: Tom Van Vuren, Amey and ITS | 2024-02-22 | Institute for Transport Studies 1.11 | 3 |
| TDS Practical 4: getting | Getting transport data | 2024-02-29 | Irene Manton North Cluster (7.96) | 3 |
| TDS seminar 2 | Seminar 2 Will Deakin, Network Rail | 2024-03-21 | Institute for Transport Studies 1.11 | 3 |
| TDS deadline 2 | Draft portfolio | 2024-04-22 | Online - Teams | 0 |
| TDS Practical 5: visualisation | Visualising transport data | 2024-05-02 | Irene Manton North Cluster (7.96) | 3 |
| TDS Practical 6: project | Project work | 2024-05-09 | Irene Manton North Cluster (7.96) | 3 |
| TDS deadline 3 | Deadline: coursework, 2pm | 2024-05-17 | Online - Teams | 0 |
Although you are free to use any software for the course, the emphasis on reproducibility means that popular data science languages such as R and Python are highly recommended.
The teaching will be delivered in R. For this module you therefore need to have up-to-date versions of R and RStudio installed on a computer you have access to:
- R from cran.r-project.org
- RStudio from rstudio.com
- R packages, which can be installed by opening RStudio and typing
install.packages("stats19")in the R console, for example.
You should have the latest stable release of R (4.3.0 or above) and be comfortable setting-up any addition software tools you need for your work. Should have access to a computer with decent resources (e.g. a laptop with 8 GB of more RAM).
See Section 1.5 of the online guide Reproducible Road Safety Research with R for instructions on how to install key packages we will use in the module.1
It is also recommended that you have installed and have experience with GitHub Desktop (or command line git on Linux and Mac), Docker, Python, QGIS and transport modelling tools such as SUMO and A/B Street. These software packages will help with the course but are not essential.
Attending the Introduction to R one-off 3 hour workshop (semester 1 Computer Skills workshop) and experience of using R (e.g. having used it for work, in previous degrees or having completed an online course) is essential. Students can demonstrate this by showing evidence that they have worked with R before, have completed an online course such as the first 4 sessions in the RStudio Primers series https://rstudio.cloud/learn/primers or DataCamp’s Free Introduction to R course: https://www.datacamp.com/courses/free-introduction-to-r. This is an advanced and research-led module. Evidence of substantial programming and data science experience in previous professional or academic work, in languages such as R or Python, also constitutes sufficient pre-requisite knowledge for the course.
See the handbook.
- You will build-up a portfolio of work
- 100% coursework assessed, you will submit by Friday 17th May:
- a pdf document up to 10 pages long with figures, tables, references explaining how you used data science to research a transport problem
- reproducible code contained in an RMarkdown (.Rmd) document that produced the report
- Written in RMarkdown - will be graded for reproducibility
- Code chunks and figures are encouraged
- You will submit a non-assessed 2 page pdf + Rmd report by Friday 23rd February
Any feedback or contributions to this repo are welcome. If you have a question please open an issue here (you’ll need a GitHub account): https://github.com/ITSLeeds/TDS/issues
Footnotes
-
For further guidance on setting-up your computer to run R and RStudio for spatial data, see these links, we recommend Chapter 2 of Geocomputation with R (the Prerequisites section contains links for installing spatial software on Mac, Linux and Windows): https://geocompr.robinlovelace.net/spatial-class.html and Chapter 2 of the online book Efficient R Programming, particularly sections 2.3 and 2.5, for details on R installation and set-up and the project management section. ↩