Quantitative Spatial Science

The Quantitative Spatial Sciences group (QuSS) assesses a wide range of social, political, economic, demographic, health, and environmental research questions using innovative quantitative methods across statistics, spatial analysis, machine learning, and artificial intelligence. 

The quantitative spatial sciences group (QuSS) in the School of Geographical Sciences is drawn together by a common interest in using theoretically-informed empirical analysis to examine problems in human and environmental geography. The group is loosely composed of approximately thirteen core staff spread across different career pathways and seniority levels, and many postdoctoral researchers and PhD students (as well as staff in other schools and alumni) who participate in QuSSevents and comprise the QuSS intellectual community in total. For academic staff, the group has six lecturer-level staff members (Day, Zhu, Zhang Shi, Timmerman, Collins), two senior lecturers (Robinson, Wang), two associate professors (Fox, Wolf) and three full professors (Harris, Manley, and Tranos), with staff members across research-only, teaching-only, and research-and-teaching tracks.  

For its research, the group is united by its interest in statistical, spatial analytical, AI, and machine learning approaches to understanding economic processes (Tranos, Fox, Wolf, Robinson, Zhu, Shi), social segregation (Robinson, Manley, Harris, Wolf), politics (Fox, Wolf), demography (Fox, Shi, Robinson, Wang, Manley, Harris), and energy (Robinson, Fox). The group holds weekly seminars and reading group sessions, as well as organizes the annual Johnston Memorial Lecture, in memoriam for former group member Ron Johnston. Members of the group also work with other research groups within the school on research in political economy, personal finance, and ecological modelling. Overall, the group is a center of excellence in spatial modelling and analysis for human and environmental systems. 

QuSS blog

This project will use the internet, one of the largest sources of smart data, to map economic activities in the UK at an unprecedented level of detail. The project aims to show the value of the internet as an untapped source of smart data and create tools for the broader science community to use. New tools will allow for the continuous monitoring and mapping of economic activities. These can support policymakers to understand how economic activity evolves over time and in different geographic places.  

Mapping ambient vulnerabilities (MAV) explores how different dimensions of ambient vulnerability are likely to intersect – whether in a person’s home, local neighbourhood, or on a journey through the city. A growing body of evidence confirms that ambient environments are directly linked to the health, comfort, and well-being of citizens. The interplay of poor air quality, energy poverty and climate-related heat is a pressing challenge in urban areas, raising questions of social justice. We combine approaches from spatial data science, with social science theory and extensive stakeholder collaboration to define, analyse and map ambient vulnerabilities at different scales.

The UK's journey towards net-zero emissions is entering a crucial phase, where innovative and technological solutions are of paramount importance. In this context, the strategic implementation of heat networks is key, and the use of Artificial Intelligence (AI) and Earth Observation (EO) data in identifying recoverable heat sources stands as a game-changer in this endeavour. This project will harness the latest advancements in AI and EO, and apply the already developed techniques by the PI for heat identification, optimising the heat network design, and developing a prototype AI tool for policy and decision making in public sector.