outputs

This analysis co-authored with Paul Bridgen (University of Southampton) and published in Energy Research and Social Science, explores the spatio-temporal dynamics of fuel poverty estimates and energy efficiency policy targeting in England since 2010.

This ‘how to’ guide outlines the Critical Spatial Data Science research method, authored by Caitlin Robinson in collaboration with illustrator Jack Brougham. Critical Spatial Data Science (or Geographic Data Science) analyses quantitative data with some form of spatial identifier – for example, a coordinate, a street name, or a census block – to generate new knowledge.

This paper, published in Transactions of the Institute of British Geographers, conceptualises and analyses localised heat-housing trajectories for neighbourhoods in England and Wales. It considers housing-related vulnerabilities that are likely to be reinforced, produced or remediated by extreme summer temperatures. 

Published in Energy Research and Social Science, this paper centres understanding of how their subjective experiences of overheating vary. We analyse the largest recent sample of English dwellings, comprising 11,152 households. Methodologically, using Latent Class Analysis, four classes are derived which highlight specific housing characteristics that increase the likelihood of a household experiencing overheating.

This paper, published in Environment and Planning B, aims to better understand the complex social and spatial drivers of vulnerability to poor indoor air quality. This is done by establishing a conceptual framing and building a new classification of vulnerability to indoor air pollution at the neighbourhood scale across England and Wales.