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

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  • Sujit K. Sahu
  • Alan E. Gelfand
  • David M. Holland

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. Copyright (c) 2010 Royal Statistical Society.

Suggested Citation

  • Sujit K. Sahu & Alan E. Gelfand & David M. Holland, 2010. "Fusing point and areal level space-time data with application to wet deposition," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 77-103.
  • Handle: RePEc:bla:jorssc:v:59:y:2010:i:1:p:77-103
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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9876.2009.00685.x
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    References listed on IDEAS

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    1. Zhang, Hao, 2004. "Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 250-261, January.
    2. Ana G. Rappold & Alan E. Gelfand & David M. Holland, 2008. "Modelling mercury deposition through latent space-time processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(2), pages 187-205.
    3. Le, Nhu D. & Zidek, James V., 1992. "Interpolation with uncertain spatial covariances: A Bayesian alternative to Kriging," Journal of Multivariate Analysis, Elsevier, vol. 43(2), pages 351-374, November.
    4. Sujit K. Sahu & Kanti V. Mardia, 2005. "A Bayesian kriged Kalman model for short-term forecasting of air pollution levels," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 223-244.
    5. Christopher K. Wikle, 2003. "Hierarchical Models in Environmental Science," International Statistical Review, International Statistical Institute, vol. 71(2), pages 181-199, August.
    6. Montserrat Fuentes & Adrian E. Raftery, 2005. "Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models," Biometrics, The International Biometric Society, vol. 61(1), pages 36-45, March.
    7. Sahu, Sujit K. & Gelfand, Alan E. & Holland, David M., 2007. "High-Resolution SpaceTime Ozone Modeling for Assessing Trends," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1221-1234, December.
    8. Gabriel Huerta & Bruno Sansó & Jonathan R. Stroud, 2004. "A spatiotemporal model for Mexico City ozone levels," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 231-248.
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    Cited by:

    1. Daisuke Murakami & Morito Tsutsumi, 2015. "Area-to-point parameter estimation with geographically weighted regression," Journal of Geographical Systems, Springer, vol. 17(3), pages 207-225, July.

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