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Fusing point and areal level space-time data with application to wet deposition.

Fusing point and areal level space-time data with application to wet deposition.
Fusing point and areal level space-time data with application to wet deposition.
Motivated by the problem of predicting chemical deposition in eastern USA at weekly, seasonal and annual scales, the paper develops a framework for joint modelling of point- and grid-referenced spatiotemporal data in this context.

The hierarchical model proposed can provide accurate spatial interpolation and temporal aggregation by combining information from observed point-referenced monitoring data and gridded output from a numerical simulation model known as the 'community multi-scale air quality model'. The technique avoids the change-of-support problem which arises in other hierarchical models for data fusion settings to combine point- and grid-referenced data.

The hierarchical space-time model is fitted to weekly wet sulphate and nitrate deposition data over eastern USA. The model is validated with set-aside data from a number of monitoring sites. Predictive Bayesian methods are developed and illustrated for inference on aggregated summaries such as quarterly and annual sulphate and nitrate deposition maps.

The highest wet sulphate deposition occurs near major emissions sources such as fossil-fuelled power plants whereas lower values occur near background monitoring sites.
change-of-support problem, hierarchical model, markov chain monte carlo sampling, measurement error model, spatial interpolation, stochastic integrals
0035-9254
77-103
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Gelfand, Alan E.
1dc59cf1-5e5f-4001-b1f9-92b0a8e2f64f
Holland, David M.
a7040f79-48c3-42f3-a449-137888cbcf28
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Gelfand, Alan E.
1dc59cf1-5e5f-4001-b1f9-92b0a8e2f64f
Holland, David M.
a7040f79-48c3-42f3-a449-137888cbcf28

Sahu, Sujit K., Gelfand, Alan E. and Holland, David M. (2010) Fusing point and areal level space-time data with application to wet deposition. Journal of the Royal Statistical Society: Series C (Applied Statistics), 59 (1), 77-103. (doi:10.1111/j.1467-9876.2009.00685.x).

Record type: Article

Abstract

Motivated by the problem of predicting chemical deposition in eastern USA at weekly, seasonal and annual scales, the paper develops a framework for joint modelling of point- and grid-referenced spatiotemporal data in this context.

The hierarchical model proposed can provide accurate spatial interpolation and temporal aggregation by combining information from observed point-referenced monitoring data and gridded output from a numerical simulation model known as the 'community multi-scale air quality model'. The technique avoids the change-of-support problem which arises in other hierarchical models for data fusion settings to combine point- and grid-referenced data.

The hierarchical space-time model is fitted to weekly wet sulphate and nitrate deposition data over eastern USA. The model is validated with set-aside data from a number of monitoring sites. Predictive Bayesian methods are developed and illustrated for inference on aggregated summaries such as quarterly and annual sulphate and nitrate deposition maps.

The highest wet sulphate deposition occurs near major emissions sources such as fossil-fuelled power plants whereas lower values occur near background monitoring sites.

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Published date: January 2010
Keywords: change-of-support problem, hierarchical model, markov chain monte carlo sampling, measurement error model, spatial interpolation, stochastic integrals
Organisations: Statistics

Identifiers

Local EPrints ID: 147817
URI: http://eprints.soton.ac.uk/id/eprint/147817
ISSN: 0035-9254
PURE UUID: abb08644-f404-4fc7-8278-0117dd6ef64c
ORCID for Sujit K. Sahu: ORCID iD orcid.org/0000-0003-2315-3598

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Date deposited: 26 Apr 2010 13:37
Last modified: 14 Mar 2024 02:44

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Contributors

Author: Sujit K. Sahu ORCID iD
Author: Alan E. Gelfand
Author: David M. Holland

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