Decision-making for urban planning under demographic uncertainty
The increasing variability in the demographic composition of urban areas in Scotland through time makes taking planning decisions a challenge. Uncertainties such as the size of the population and the movement of people into urban areas can have huge impacts on the requirements for building and transport infrastructure in urban environments. This project addresses how to model this demographic uncertainty and then how to make sensible decisions for sustainable urban planning that are robust to this uncertainty. We will consider two key exemplars – school placements and the development of sustainable transport networks.
Scottish councils must ensure that school places are available for all students in the catchment areas that want one - hence good projections of school placement requirements are needed. There is considerable uncertainty in these projections as data might not be directly relevant (e.g. birth rates are needed per catchment area but are only available city-wide) and do not tell us how demographics might change in future. This project will investigate methods for identifying and modelling the uncertainties in the projections.
A better understanding of demographic uncertainty also has implications for other areas of urban planning, for example, transport use. This project will work with local councils to determine statistical models for how changes in demographics will impact the modal split in transport use over time.
Both school placement and transport projections are key when making decisions about the landscape of future cities. For example, how many new schools should be built, and where should electric vehicle charging points be located? This project will develop a modelling framework that can help councils take decisions that account for multiple sources of uncertainty.
Project Team and where the student will be based
The student will be based in the School of Mathematics at the University of Edinburgh and will be part of the statistics group but will make regular research visits to the Urban Big Data Centre at the University of Glasgow.
Dr Amy Wilson is a lecturer in Industrial Mathematics with experience in applied statistical problems in industry and government. Dr Chris Dent is Chancellor’s Fellow and Reader in Industrial Mathematics, and a Turing Fellow at the Alan Turing Institute, with extensive experience in the energy industry and in wider government modelling. Dr Gail Robertson is a statistical consultant in the Edinburgh Statistical Consultancy Unit and has worked on many applied modelling problems with the private and public sector. Professor Nick Bailey is a professor of Urban Studies and the Director of the Urban Big Data Centre. Dr Jing Yao is a senior lecturer in Urban Studies with an interest in spatial modelling and optimisation. Dr Jinhyun Hong is a senior lecturer in Urban Studies with research in transport planning and surveys.
The student should have a degree in a quantitative subject and should have a good background in statistics. For example, a degree in Mathematics or a degree in Social Statistics with modules in statistics. A postgraduate qualification in a quantitative subject is advantageous.
The student should have experience in statistics or data science and an interest in public policy.
Programming skills are essential, experience with the R programming language is advantageous but not necessary.
Enquiries about this project should be directed to Dr Amy Wilson - Amy.L.Wilson@ed.ac.uk.