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Training Event / Apr 26 @ 9:30 am - Apr 27 @ 4:30 pm

R for Transport Applications: Handling Big Data in Spatial World


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:

  • How to effectively handle large transport datasets?
  • Where to locate new transport infrastructure?
  • How to develop automated and reproducible transport planning workflows?
  • How can increasingly available datasets on air quality, traffic and active travel be used to inform policy?
  • How to visualise results in an attractive and potentially on-line and interactive manner?

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.

Learning objectives

Day 1:

  • Learn and consolidate the basics of R’s syntax
  • Discover time-saving tips for efficient programming
  • Discover how add-on R packages such as dplyr can be used to improve productivity
  • Understand how R can be used to read, process and save transport-related datasets
  • Understand the structure of spatial data in R

Day 2:

  • Be able to query, subset and analyse spatial objects
  • Have a working knowledge of fundamental GIS functions such as changing projections
  • Be proficient in the use of R to create maps using add-on packages such as tmap
  • Have some experience with transport planning functions provided by stplanr


Prior experience with transport datasets or common geographic data formats is essential.

Some exposure to software with a command-line interface, such as Stata, Python or R is highly recommended.

Attendees who are already proficient with their R programming skills are welcome to attend just the second day, although attendance of both days is recommended for most attendees: even advanced R users are likely to learn something on the first day.

Computers with RStudio installed will be available for course attendees. However, for maximum benefit, we recommend participants bring their own laptops, with the latest version of R installed. Steps to set-up a suitable R/RStudio environment are described in sections 2.3 and 2.5 of the book Efficient R Programming. It is also recommended the following packages are installed prior to attending the course:


Course tutors

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.

Craig Morton is a Research Fellow at the Institute for Transport Studies where he conducts research on the adoption of new technologies by individuals, such as household interest with cars in general and the emerging demand for Electric Vehicles in particular, alongside research which investigates household interest in energy efficiency technologies and building retrofits. Craig draws from a spectrum of methodologies to conduct his research covering socio-psychological theories of human behaviour, market segmentation analysis of citizen archetypes and spatial analysis of transport and energy demand patterns.

Further information & how to book

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 students, academic staff 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.

Booking is available via the online store.  For enquiries please contact e.a.pound@leeds.ac.uk


Early bird prices (valid until 27 March)

External: £800
Academic: £600
Student: £400

Price (valid 27 March – 26 April)

External: £900
Academic: £700
Student: £500





Leeds Institute for Data Analytics
Level 11, Worsley Building
Leeds, LS2 9JT United Kingdom
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