This course teaches two skill-sets that are fundamental in modern transport research: programming and data analytics, with a focus on spatial data. Combining these enables powerful transport planning and analysis workflows for tackling a wide range of problems, including:
This course will provide tools, example code and data and above all face-to-face teaching to empower participants with new software to answer these questions and more. The focus is on the programming language R (we will briefly look at visualising results in QGIS). However, the principles and skills learned will be cross-transferable to other languages. By providing strong foundations in spatial data handling and the use of an up-coming language for statistical computing, R for Transport Applications aims to open a world of possibilities for generating insight from your transport datasets for researchers in the public sector, academia and industry alike.
As with any language, it is important to gain a strong understanding of the underlying syntax and structure before moving on to complex uses. This course therefore starts with the foundations: how R can be used to load, manipulate, process, transform and visualise spatial data.
In terms of content, the first day will focus on how the R language works, general concepts in efficient R programming, and spatial and non-spatial data classes in R. Building on this strong foundation the second day will cover the application of the skills developed in Day 1 to transport datasets, with a focus on geographical transport data.
Robin Lovelace is a researcher at the Leeds Institute for Transport Studies (ITS) and the Leeds Institute for Data Analytics (LIDA). Robin has many years of experience of using R for academic research and has taught numerous R courses at all levels. He has developed popular R resources including the recently published book Efficient R Programming (Gillespie and Lovelace 2016), Introduction to Visualising Spatial Data in R and Spatial Microsimulation with R (Lovelace and Dumont 2016). These skills have been applied on a number of projects with real-world applications, including the Propensity to Cycle Tool, a nationally scalable interactive online mapping application, and the stplanr package.
James Tate is a vehicle emissions and air quality expert focussing on the impacts of road transport on the environment. He has developed and deployed new approaches to survey and model the emission performance of the UK/ EU road transport fleet. James has been using R as the primary tool in his data analysis workflow for a decade and has developed popular modules teaching R to Master’s students in ITS.
Please note: Prior experience with transport datasets or common geographic data formats is essential. View the full prerequisites here.
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The course will be held in the Leeds Institute for Data Analytics (see lida.leeds.ac.uk/about-lida/contact/ for details and a map).
The course is open to ITS Masters and PhD students, and external delegates. The fee includes learning materials, lunch and refreshments during the course, but not overnight accommodation. The course is also available as bespoke or in-company training.
For enquiries please contact Kylie Norman.
Lovelace, Robin, and Morgane Dumont. 2016. Spatial Microsimulation with R. Available from CRC Press.
Gillespie, C., Lovelace, R., 2016. Efficient R Programming: A Practical Guide to Smarter Programming. Available from O’Reilly Media.