Environmental Statistics

Environmental Statistics

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.


Dr Linda Altieri  Environmental Research Associate

Dr Craig Anderson  Lecturer

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 James Campbell Gemmell  Honorary Professor

    Prof Gemmell is chief executive of the Environment Protection Agency of South Australia.

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  • Dr Dimitra Kosta  LKAS Fellowship

    Algebraic statistics; Markov bases techniques for statistical models.

    Member of other research groups: Statistical Methodology, Biostatistics and Statistical Genetics, Geometry and Topology, Algebra

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  • Dr Duncan Lee  Reader

    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 , Kamol Sanittham, Yoana Borisova, Cillian Doherty
    Postgraduate opportunities: Mapping disease risk in space and time

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  • Publications
  • Dr Claire Miller (née Ferguson)  Reader

    Environmental and ecological modelling; nonparametric smoothing; time series analysis; brain imaging applications

    Research students: Mengyi Gong, Craig Wilkie, Michael Currie, Andrew Gilliland, Anna Sehn

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  • Dr Gary Napier  Research Assistant

    Member of other research groups: Biostatistics and Statistical Genetics
    Supervisor: Duncan Lee

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  • Dr Ruth O'Donnell  Lecturer

    Research student: Anna Sehn

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  • Dr Surajit Ray  Senior lecturer

    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.

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  • Prof Marian Scott OBE  Professor of Environmental Statistics

    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 students: Yoana Borisova, David Carr, Michael Currie, Cillian Doherty, Andrew Gilliland, Anna Sehn, Qingying Shu, Katie Stewart<

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  • Dr Ron Smith  Honorary Senior Research Fellow

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  • Postgraduates

    Yoana Borisova  PhD Student

    Supervisors: Marian Scott OBE, Duncan Lee

  • David Carr  MSc Student

    Research Topic: Development of environmental indicators
    Supervisor: Marian Scott OBE

  • Michael Currie  PhD Student

    Supervisors: Marian Scott OBE, Claire Miller (née Ferguson)

  • Mengyi Gong  PhD Student

    Research Topic: Modelling coherence and evidence for long-term change in environmental time series
    Supervisors: Claire Miller (née Ferguson), Marian Scott OBE

  • Marnie McLean  PhD Student

    Research Topic: Optimal spatio-temporal modelling and monitoring of groundwater
    Supervisors: Ludger Evers, Adrian Bowman

  • Kamol Sanittham  PhD Student

    Supervisors: Duncan Lee, Craig Anderson

  • Anna Sehn  PhD Student

    Supervisors: Marian Scott OBE, Ruth O'Donnell, Claire Miller (née Ferguson)

  • Qingying Shu  PhD Student

    Research Topic: Design and statistical modelling of space weather
    Supervisor: Marian Scott OBE

  • Katie Stewart  PhD Student

    Member of other research groups: Biostatistics and Statistical Genetics
    Supervisors: Marian Scott OBE, Dirk Husmeier

  • George Vazanellis  PhD Student

    Research Topic: Spatiotemporal models for environmental data
    Supervisor: Adrian Bowman

  • Craig Wilkie  PhD Student

    Supervisor: Claire Miller (née Ferguson)

  • Postgraduate opportunities

    Analysis of Spatially correlated functional data objects. (PhD)

    Supervisors: Surajit Ray
    Relevant research groups: Environmental Statistics, Statistical Methodology

    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
    • analysing sparsely observed functional data by borrowing information from neighbours
    • visualisation of data summaries associated with dependent functional data
    • Clustering of functional data


    Mapping disease risk in space and time (PhD)

    Supervisors: Duncan Lee
    Relevant research groups: Biostatistics and Statistical Genetics, Environmental Statistics, Statistical Modelling

    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.