The environmental statistics theme by definition is the development and application of statistical methodology to environmental issues- these can be based in the natural environment (both undisturbed and perturbed) or the urban environment. Environmental statistics is a broad discipline stretching from how and what to sample, through to modelling impacts on human and ecosystem health and ultimately to providing predictions of what changes might occur in the future. Statistical methodology being used include time series analysis, spatial modelling, Bayesian methods, wavelet analysis, extreme value modelling and non-parametric (particularly regression and additive) modelling.
The school also heads the EPSRC funded SECURE nework which brings together the environmental and statistical communities to provide fresh intelligence and new insights into environmental change and society's management of that change.
Research student: Kamol Sanittham
Member of other research groups: Statistical Methodology, Scholarship of Learning and Teaching in Statistics, Biostatistics and Statistical Genetics
Supervisor: Dirk Husmeier
Prof Gemmell is chief executive of the Environment Protection Agency of South Australia.
Algebraic statistics; Markov bases techniques for statistical models.
Spatiotemporal modelling; Bayesian methods; environmental epidemiology and disease mapping
Member of other research groups: Biostatistics and Statistical Genetics
Research staff: Gary Napier
Research students: Eilidh Jack, Kamol Sanittham, Yoana Borisova, Cillian Doherty
Postgraduate opportunities: Mapping disease risk in space and time
Environmental and ecological modelling; nonparametric smoothing; time series analysis; brain imaging applications
Research student: Anna Sehn
Functional Data Analysis; Analysis of mixture models; high-dimensional data; medical image analysis; analysis of earth systems data; immunoinformatics
Radio-carbon and cosmogenic dating-design and analysis of proficiency trials; environmental radioactivity; sensitivity and uncertainty analysis applied to complex environmental models; spatial and spatiotemporal modeling of water quality; flood risk modeling; environmental indicators; developing the evidence base for environmental policy and regulation
Supervisor: Xiaoyu Luo
Research Topic: Development of environmental indicators
Supervisor: Marian Scott OBE
Research Topic: Spatiotemporal models for environmental data
Supervisor: Adrian Bowman
Supervisor: Claire Miller (née Ferguson)
Mapping disease risk in space and time (PhD)
Disease risk varies over space and time, due to similar variation in environmental exposures such as air pollution and risk inducing behaviours such as smoking. Modelling the spatio-temporal pattern in disease risk is known as disease mapping, and the aims are to: quantify the spatial pattern in disease risk to determine the extent of health inequalities, determine whether there has been any increase or reduction in the risk over time, identify the locations of clusters of areas at elevated risk, and quantify the impact of exposures, such as air pollution, on disease risk. I am working on all these related problems at present, and I have PhD projects in all these areas.