This repo supports teaching of the Transport Data Science module at the University of Leeds.

The module catalogue can be found at catalogue.md. The code accompanying the course can be found in the code folders. To run this code you will need R and Python installed plus various packages and libraries. This software has been packaged-up into a docker container to ease teaching.

See here for the the timetable, a basic visualisation of which is shown below:

• On the University’s system (official): http://timetable.leeds.ac.uk/
• In .ics form: https://github.com/ITSLeeds/TDS/releases/download/0.20.1/tds-timetable.ics
• As a .csv file: https://github.com/ITSLeeds/TDS/blob/master/timetable.csv

To access references collected for this course (and contribute more if you want), you can join the ‘tds’ Zotero group: https://www.zotero.org/groups/956304/tds

For this module you need to have up-to-date versions of R and RStudio. For guidance on setting-up your computer to run R and RStudio for spatial data, see these links:

• 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

• 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.

See the image below for the course locations and the following links:

The lectures will be in the Business School Maurice Keyworth SR (1.15): http://students.leeds.ac.uk/room/1-01-087-2730-01-115

The practicals will be in the West Teaching Lab Cluster (B.16): http://it.leeds.ac.uk/site/custom_scripts/clusters.php

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

Data for course can be accessed from the repos Releases page. You can, for example, download and unzip the data folder in a local version of the repo (accessed by downloading and unzipp https://github.com/ITSLeeds/TDS/archive/master.zip ) with the following R commands:

download.file("https://github.com/ITSLeeds/TDS/releases/download/0.1/data.zip", destfile = "data.zip")
unzip("data.zip")

If you want to be clever you can use the piggyback package:

install.packages("piggyback")
piggyback::pb_download("data.zip")

# (This package was used to upload the data with:)
# piggyback::pb_upload(file = "data.zip")
# piggyback::pb_upload(file = "codeExamples.zip")

import pandas as pd
e = pd.read_csv("/mnt/27bfad9a-3474-4e61-9a43-0156ebc67d67/home/robin/ITSLeeds/TDS/sample-data/everyone.csv")
pd.DataFrame.sort_values(e, "n_coffee")

• A book on R for Geocomputation: https://github.com/Robinlovelace/geocompr
• A Python package for OSM data analysis: https://github.com/gboeing/osmnx