Spatial Temporal Modelling

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Here objective is to understand the interaction between space and time; to predict at not only different locations but also at different times. Our examples depict the data for a particular pollution study of Leeds. Important to bear in mind is whether spatial locations are rich or poor, time series is rich or poor.

Map of the 14 sites where the data sulphur dioxide levels (pollution) was recorded in the neighbourhood of Leeds, March 29 1983 – March 5 1985 (see Mardia et al, 1998).

Log transformed Sulphur Dioxide time series for fourteen sites in the neighbourhood of Leeds for the period March 29 1983 – March 5 1985 (see Mardia et al, 1998).

Spatial Temporal Modelling:

Kriged-Kalman filter, strategies for spatial temporal modelling.

Read Paper:

1998 The Kriged Kalman Filter (with Goodall, C., Redfern, E. J. and Alonso, F. J.).

Discussion paper. Presented to the Spanish Statistical Society. TEST 7, pp. 217-276.

This is the first paper of its kind on kriged Kalman filter for spatial temporal modelling where kriging is merged with dynamic models. Discussants provided further insight into this approach.

Edited Volume:

1999 Spatial Temporal Modelling and its Applications, co-editors R. G. Aykroyd & I. L. Dryden. LASR Proceedings: Leeds University Press.

Paper in Journal:

2005 A Bayesian kriged Kalman model for short-term forecasting of air pollution levels (with S.K. Sahu). J. Roy. Statist. Soc. Ser. C 54, 223-244.

Papers in Edited Volumes:

1994 Challenges in multivariate spatio-temporal modeling (with C. R. Goodall). Proc. XVII Internat. Biom. Conf. 1, 1-17. Hamilton, Canada.

2002 Modelling strategies of spatial-temporal data (with J. T. Kent). In Spatial Cluster Modelling. Eds. A. B. Lawson and D. G. T. Denison. Chapman & Hall/CRC, London, pp 213-226.

2004 Spatiotemporal analysis of airflow over hills (with L. Quinn, S. J. Mobbs, and S. B. Vosper). LASR2004 Proceedings. (Editors: R.G.Aykroyd, K.V. Mardia and S. Barber), Leeds University Press, pp143-145.

2005 Recent trends in modelling spatio-temporal data (with S. K. Sahu). Proceedings of Italian Statistical Society, CLEUP scarl, Padova, pp69-83.

2007 Modeling rainfall data using a Bayesian Kriged-Kalman model (with G. J. Lasinio, and S. K. Sahu). In Bayesian Statistics and its Applications, pp301-318, Eds. S. K Upadhaya, U. Singh, and D. K. Dey. Anamaya Publishers, New Delhi, India.