Professor Robin Lovelace

Profile

I research and teach data science, with a focus on geographic and computational methods for sustainable transport policies. I am a Geographer and Environmental Scientist by training with expertise in geocomputation, data analysis and modelling. Through my PhD on the Energy Costs of Commuting, I developed a strong interest in modelling large transport systems at high levels of resolution. This interest continued to grow during the 3 post-doctorate positions I held before becoming an Associate Professor, as Research Fellow in Geospatial Data Analysis and Simulation (TALISMAN, 2013 – 2015), Research Fellow in the Leeds Institute for Data Analytics (LIDA 2015 – 2017), and as a University Academic Fellow (UAF) in Transport and Big Data (2017 – 2020).

In my current role as Associate Professor in Transport Data Science (January 2020 to present) I aim to put the Institute for Transport Studies at the forefront of new methods in transport research. I have applied my skills to tackle a range of policy and academic research challenges in transportation, including:

• How to make use of new technologies to enable wider participation in transport planning through the development of online, interactive tools (see the PCT and ACTON project websites for examples of tools in use).
• Analysis of the potential for shifts to sustainable transport modes at high levels of geographical resolution, down to the road segment level (e.g. Lovelace et al. 2017).
• Developing and teaching new open source software for transport planning (e.g. stplanr and stats19 R packages).
• Modelling decarbonisation pathways for city, national and global transport systems as part of a global transition away from fossil fuels (watch this space).

An ongoing project that illustrates each of these facets of my research profile is the Propensity to Cycle Tool (PCT), which has been described by the Department for Transport as “a brilliant example of using Big Data to better plan infrastructure investment” (Shane Snow, Head of Seamless Travel at the Department for Transport) and which is being used to inform strategic cycle network plans in cities and regions across the UK. To test out the PCT please see www.pct.bike. For more up-to-date information about my work, please check out my academic publications, my website, and my Twitter feed @robinlovelace.

I have developed many popular workshops, module sessions and tutorials in the areas of transport, GIS and data analysis. A selection of the teaching materials that have been made available is provided below:

• Efficient R Programming (Gillespie and Lovelace, 2016) provides strong foundations for reproducible data analysis research, and is freely available online and via O'Reilly Press.
• Spatial Microsimulation with R (Lovelace and Dumont, 2016) is an open source book and website teaching methods to simulate individual processes over geographical space.
• Geocomputation with R (Lovelace et al., 2019) is an acclaimed and heavily used open source text book that teaches how to analyze, visualize and model geographic data with open source software.

At ITS I focus on up-skilling students' ability to use and program open source software packages such as QGIS and R, through my roles teaching on the module Sustainable Spatial Planning and Analysis and leading the successful Transport Data Science module.

Responsibilities

• Module Lead, Transport Data Science
• GIS Development Lead

• Network Planning Tools for Scotland (NPTSCOT)

Research groups and institutes

• Spatial Modelling and Dynamics
• Social and Political Sciences