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

Prof James Campbell Gemmell  Honorary Professor

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

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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 , Francesca Pannullo, Yoana Borisova, Cillian Doherty

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  • Dr Claire Miller (née Ferguson)  Reader

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

    Research students: Mengyi Gong, Craig Wilkie, Amira El-Ayouti, Andrew Gilliland

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  • Dr Ruth O'Donnell  Lecturer

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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
    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 staff: Scott McGrane
    Research students: Yoana Borisova, David Carr, Cillian Doherty, Amira El-Ayouti, Andrew Gilliland, Qingying Shu, Katie Stewart, Mengyi Gon

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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

  • Amira El-Ayouti  PhD Student

    Research Topic: Statistical modelling of rivers and river networks.
    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

  • Francesca Pannullo  PhD Student

    Supervisor: Duncan Lee

  • Publications
  • 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
      • analyzing sparsely observed functional data by borrowing information from neighbors
      • visualization of data summaries associated with dependent functional data
      • Clustering of functional data