| EPSRC Reference: |
EP/C531965/1 |
| Title: |
Recent advances in modelling spatio-temporal data |
| Principal Investigator: |
Sahu, Professor SK |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
School of Mathematics |
| Organisation: |
University of Southampton |
| Scheme: |
Mathematics Small Grant PreFEC |
| Starts: |
09 June 2005 |
Ends: |
08 November 2005 |
Value (£): |
4,507
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| EPSRC Research Topic Classifications: |
| Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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Recall the last weather forecast map you saw on television. The map shows how aspects of the weather vary both in space and time. It is important to be able to forecast, or predict, many such response variables that describe the weather, such as the amount of rainfall, sunshine, and levels of air pollution. Typically, such predictions are made from data observed on a large number of variables which themselves vary over time and space. These spatio-temporal data sets can be very large, for instance, air pollution measurements are often observed every day at over one hundred locations in the UK and the last ten years' data may be available.There are many other important areas that affect our day-to-day lives in which spatio-temporal data are used to detect recognisable and meaningful patterns as well as to make predictions. Examples include the study of house prices, ecology, geology and many areas of medicine such as brain imaging.In order to obtain a high degree of accuracy in the analysis and prediction of a response variable, such as the level of air pollution, mathematical models are employed which explicitly include the underlying uncertainty in the data. Such models are statistical in nature and, if appropriately chosen, allow accurate forecasting in future time periods and interpolation over the entire spatial region of interest. In addition, they allow us to estimate the size of possible errors associated with our forecasts. Essential to this process is the use of modern statistical modelling techniques.Such statistical modelling of spatio-temporal data is a challenging task which requires the manipulation of large data sets and the ability to fit realistic and complex models. Often, the required solutions are not available in closed mathematical form and computer intensive methods are needed. In addition, various associated questions have to be addressed: Is the model adequate for the data? Can a better model be found? Can data values that are outliers relative to the model be identified? How accurate are the predictions? Will the model be able to cope with future, possibly more complex, data sets?We are able to answer these questions, far more comprehensively than ever before and across a far wider range of real-life applications, by harnessing the greatly increased computer power now available. The purpose of the two-day meeting of researchers from the UK and abroad is to present and discuss recent exciting developments in modelling spatio-temporal data. The experts work in a wide variety of practical areas and across many different disciplines. They will share ideas, learn from one another, instigate new collaborations and identify important avenues for future research, through debate and roundtable discussions. In particular, young researchers will have the opportunity to present their work and benefit from interactions with more experienced researchers.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.soton.ac.uk |