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.
My main research interests lie in developing statistical methodology for health data, with a focus on health inequalities both in Scotland and globally. I have a particular interest in spatial statistics, and the development of methodology for disease mapping. Disease maps are an important public health tool in compare the risks of a disease across a geographic region, and in particular to identify regions of high risk which require additional health care resources. I also have an interest in growth modelling, where I work on identifying and developing suitable modelling strategies to accurately capture the growth trajectories of young children in low and middle income countries. By doing so, one can often start to identify the social and environmental factors which may lead to poor growth in these regions.
Research student: Kamol Sanittham
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, Aisyah Binti Nawawi , Francesca Pannullo, Kamol Sanittham, Yoana Borisova, Cillian Doherty
Environmental and ecological modelling; nonparametric smoothing; time series analysis; brain imaging applications
Functional Data Analysis; Analysis of mixture models; high-dimensional data; medical image analysis; analysis of earth systems data; immunoinformatics
Member of other research groups: Statistical Methodology, Biostatistics and Statistical Genetics
Research students: Maryam Al Alawi , Salihah Alghamdi, Bader Lafi Q Alruwaili, Flynn Gewirtz-O'Reilly
Postgraduate opportunities: Analysis of Spatially correlated functional data objects.
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
Research Topic: Development of environmental indicators
Supervisor: Marian Scott OBE
Supervisor: Duncan Lee
Research Topic: Design and statistical modelling of space weather
Supervisor: Marian Scott OBE
Research Topic: Spatiotemporal models for environmental data
Supervisor: Adrian Bowman
Supervisor: Claire Miller (née Ferguson)
Analysis of Spatially correlated functional data objects. (PhD)
Historically, functional data analysis techniques have widely been used to analyze traditional time series data, albeit from a different perspective. Of late, FDA techniques are increasingly being used in domains such as environmental science, where the data are spatio-temporal in nature and hence is it typical to consider such data as functional data where the functions are correlated in time or space. An example where modeling the dependencies is crucial is in analyzing remotely sensed data observed over a number of years across the surface of the earth, where each year forms a single functional data object. One might be interested in decomposing the overall variation across space and time and attribute it to covariates of interest. Another interesting class of data with dependence structure consists of weather data on several variables collected from balloons where the domain of the functions is a vertical strip in the atmosphere, and the data are spatially correlated. One of the challenges in such type of data is the problem of missingness, to address which one needs develop appropriate spatial smoothing techniques for spatially dependent functional data. There are also interesting design of experiment issues, as well as questions of data calibration to account for the variability in sensing instruments. Inspite of the research initiative in analyzing dependent functional data there are several unresolved problems, which the student will work on:
- robust statistical models for incorporating temporal and spatial dependencies in functional data
- developing reliable prediction and interpolation techniques for dependent functional data
- developing inferential framework for testing hypotheses related to simplified dependent structures
- analyzing sparsely observed functional data by borrowing information from neighbors
- visualization of data summaries associated with dependent functional data
- Clustering of functional data