Details of Award
NERC Reference : NE/T004150/1
A Bayesian approach to complex land use change modelling
Grant Award
- Principal Investigator:
- Dr A H Hagen-Zanker, University of Surrey, Civil and Environmental Engineering
- Co-Investigator:
- Dr S J Hughes, University of Surrey, Civil and Environmental Engineering
- Co-Investigator:
- Dr N Santitissadeekorn, University of Surrey, Mathematics
- Grant held at:
- University of Surrey, Civil and Environmental Engineering
- Science Area:
- Terrestrial
- Overall Classification:
- Unknown
- ENRIs:
- Biodiversity
- Environmental Risks and Hazards
- Global Change
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Urban & Land Management
- Land Predictive Models
- Spatial Planning
- Statistics & Appl. Probability
- Environmental Informatics
- Abstract:
- The UK is currently facing a housing shortage and the government plan is to increase house building to a rate of 300,000 per year in the mid-2020s. This is largely supported through policy instruments related to improving access to personal finance and reducing planning barriers. According to the government, over 40 per cent of local planning authorities do not have a plan that meets the projected growth in households in their area. Against this backdrop of announced substantial, poorly understood and more liberally regulated land use change, there is a clear need to understand possible patterns of future urban growth. There exist computational models that take economic and demographic forecasts and use these to explore possible trajectories of land use change. A particular family of such models are Cellular Automata (CA) land use models; these models are complex in a mathematical sense as they represent the chaotic and spatially uneven processes by which cities and towns can grow. A major hurdle preventing the widespread use of these models is that the better performing models are calibrated based on 'expert judgement'. This reliance on manual intervention is not only resource intensive but also casts a shadow on the objective reliability of model results. In this project we will investigate the use of a statistical technique of Approximate Bayesian Computation to automatically calibrate CA land use models. We aim to deliver a proof-of-concept demonstrating the methods on a CA model that is stripped down to its fundamental essentials, and applied to a variety of UK and European urban areas. A further advantage of the Bayesian approach is that it will not just give the best-fitting set of parameters, but also the associated uncertainty. This will allow analysts to use the model to arrive to support decisions with confidence based on a range of possible outcomes and their likelihood.
- NERC Reference:
- NE/T004150/1
- Grant Stage:
- Completed
- Scheme:
- Directed (RP) - NR1
- Grant Status:
- Closed
- Programme:
- Landscape Decisions
This grant award has a total value of £50,070
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DA - Estate Costs | DI - Staff | DA - Other Directly Allocated | DI - T&S |
|---|---|---|---|---|---|---|
| £403 | £22,483 | £7,212 | £4,737 | £12,345 | £471 | £2,419 |
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