Using remote sensing and machine learning to explore the spatial patterning of poverty and inequality
This interdisciplinary project will focus on studying how we can improve our understanding of the spatial patterning of poverty and inequality across the UK and possibly internationally (depending on the student’s interests/skills and data availability). You will do this by combining existing GIS and administrative data resources on area-level deprivation with novel remote sensing and machine learning/artificial intelligence (AI) approaches. The aim is to better understand how poverty and inequality manifest themselves in the urban environment and how they have changed over time. The remote sensing component will also allow us to have more synoptic, ‘big-picture’ views of areas considering regional and country-level scales. How we measure inequality at this scale will include examining inequalities in factors such as urban sprawl, housing density and access to green space and how they can complement in situ measurements of physical, environmental and socioeconomic variables1.
The UK is a highly geographically unequal country, with measures such as health, poverty, education, employment and wealth showing some of the most distinct differences across our shores, generalised as a ‘North-South’ divide2,3. These measures can be seen as both outcomes and predictors, as well as interacting with each other. Essentially these inequalities represent unequal social and economic experiences and exposures, resulting in detrimental effects on health, wellbeing, social mobility and productivity4. While we have some good evidence about patterns of geographic inequality, there remains scope to better measure and understand the true nature of inequality in the country with a much ‘finer resolution’ and how these inequalities are changing in the rapidly developing urban landscapes. Traditional mapping methods and administrative data struggle to keep up with such rapid changes and therefore limit the evidence for supporting successful and equitable and sustainable ‘smart city’ development. Satellite and remote sensing technology, combined with machine learning and spatial statistics, offer new methods and answers to old problems of how to manage sustainable and equitable urban development and meet the ever-increasing demand from city-based populations. A strong motivation for introducing satellite images into the measurement of inequality, over-and-above the possibility to realise new dimensions of inequality not possible with administrative and ‘on-the-ground’ data, is that we have entered a new era of freely available satellite data (e.g. the ESA Sentinel constellation missions). We are experiencing a rapid growth of activities in the Space industry and the Earth Observation sector and these opportunities do not just support commercial activities, but also provide more efficient tools to the environmental management community and socio-economic research.
If we are to realise the UK government’s current ‘levelling up’ focus for example, better evidence of the nature and causes of, and solutions to, UK regional inequalities are needed. This project will therefore provide robust evidence using novel data linkage of existing, siloed data sources combining satellite imagery, geospatial and administrative data to highlight the importance of where we live on patterns of poverty and inequality and how these are changing over time.
The supervisory team for this project includes expertise in: assessing inequalities and health inequalities using existing administrative and cohort data (Tony Robertson); remote sensing, machine learning and AI (Armando Marino); and spatial and spatio-temporal statistical modelling (Craig Anderson). You will be based in the Division of Biological and Environmental Sciences (BES) at the University of Stirling alongside Dr Robertson and Dr Marino. Researchers in BES have a unique focus on the relationships between people and the environment. Alongside your work and supervisory team in Stirling, you will be supported by Dr Anderson from the School of Mathematics and Statistics, University of Glasgow and will be encouraged to join and engage with research groups and opportunities across both Universities and the IAPETUS-II scheme.
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Dhaka form above, near Baridhara. Attribution: Mohammad Tauheed (no changes made) https://www.flickr.com/photos/12342805@N00/49263301482