Joey Talbot

With a background in Geography, I took a PhD in tropical ecology at the University of Leeds, where I studied relations between the tree species diversity and carbon dynamics of tropical forests. Working with the extensive ForestPlots.net database (https://www.forestplots.net/) and the RAINFOR and AfriTRON networks of permanent tropical forest plots, I was introduced to issues surrounding the large, complex datasets and multiple variables of real-world observational studies, such as the importance of clear data standardisation protocols.

Since then, I have shifted my research focus, while continuing to work on issues with a direct impact on our ability to mitigate climate change and move towards a low carbon future. I worked for three years for Transport for New Homes (see http://www.transportfornewhomes.org.uk/), a project funded by the Foundation for Integrated Transport that aims to bring together transport and planning, with the mission of improving the way in which we design and build new housing developments. Too often, new homes are sited in remote locations, far from the jobs, shops and services residents rely on, and effectively inaccesible to those who do not own a car. This has a wide range of social, economic and environmental impacts, not least contributing to air pollution and carbon emissions, helping to make transport the UK’s highest emitting sector.

At ITS, I research sustainable transport with a focus on using Big Data to investigate potential shifts to sustainable modes and guide future infrastructure investment. This work is centred on two key online tools. The Propensity to Cycle Tool (https://www.pct.bike/) is a popular online tool for cycle planning, with scenarios modelling future increases in cycle uptake at high spatial resolutions. The Cycling Infrastructure Prioritisation Toolkit (https://www.cyipt.bike/) complements this by further assessing the potential for cycle improvements at the street level. My work is developing both of these tools, mainly using the R open source statistical computing environment. This has included research into the potential for cycling to school, sustainable transport access to rail stations, improving the accessibility of new housing developments by walking and cycling, and a project focused on road safety for active travel.

Most recently, I have been developing a tool to help local authorities rapidly prioritise where to build new cycleways as part of the response to the COVID-19 pandemic.