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.

Staff

Dr Linda Altieri : Environmental Research Associate

Dr Craig Anderson : Lecturer

Research student: Kamol Sanittham

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  • Dr Agnieszka Borowska : Research Assistant

    Member of other research groups: Statistical Methodology, Scholarship of Learning and Teaching in Statistics, Biostatistics and Statistical Genetics
    Supervisor: Dirk Husmeier

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

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  • Dr Claire Miller (née Ferguson): Reader

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

    Research students: Craig Wilkie, Jafet Belmont Osuna, 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

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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: Jafet Belmont Osuna, Yoana Borisova, David Carr, Michael Currie, Cillian Doherty, Andrew Gilliland, Anna Sehn, Katie Stewart

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  • Qingying Shu : Postdoctoral Research Fellow

    Supervisor: Xiaoyu Luo

  • Dr Ron Smith : Honorary Senior Research Fellow

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  • Postgraduate opportunities

    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.