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Bayesian Geostatistical Design

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  • PETER DIGGLE
  • SØREN LOPHAVEN

Abstract

. This paper describes the use of model‐based geostatistics for choosing the set of sampling locations, collectively called the design, to be used in a geostatistical analysis. Two types of design situation are considered. These are retrospective design, which concerns the addition of sampling locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model parameter values are unknown. The results show that in this situation a wide range of inter‐point distances should be included in the design, and the widely used regular design is often not the best choice.

Suggested Citation

  • Peter Diggle & Søren Lophaven, 2006. "Bayesian Geostatistical Design," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(1), pages 53-64, March.
    • Handle: RePEc:bla:scjsta:v:33:y:2006:i:1:p:53-64
    DOI: 10.1111/j.1467-9469.2005.00469.x
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    Cited by:

    1. Qian Ren & Sudipto Banerjee, 2013. "Hierarchical Factor Models for Large Spatially Misaligned Data: A Low-Rank Predictive Process Approach," Biometrics, The International Biometric Society, vol. 69(1), pages 19-30, March.
    2. K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
    3. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    4. Erum Zahid & Ijaz Hussain & Gunter Spöck & Muhammad Faisal & Javid Shabbir & Nasser M. AbdEl-Salam & Tajammal Hussain, 2016. "Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-16, September.
    5. Hartman, Linda & Hossjer, Ola, 2008. "Fast kriging of large data sets with Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2331-2349, January.
    6. Arbia, Giuseppe & Lafratta, Giovanni & Simeoni, Carla, 2007. "Spatial sampling plans to monitor the 3-D spatial distribution of extremes in soil pollution surveys," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 4069-4082, May.
    7. Jo Eidsvik & Sara Martino & Håvard Rue, 2009. "Approximate Bayesian Inference in Spatial Generalized Linear Mixed Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 1-22, March.
    8. Wang, Craig & Furrer, Reinhard, 2021. "Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    9. Finley, Andrew O. & Sang, Huiyan & Banerjee, Sudipto & Gelfand, Alan E., 2009. "Improving the performance of predictive process modeling for large datasets," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2873-2884, June.
    10. Elizabeth G. Ryan & Christopher C. Drovandi & James M. McGree & Anthony N. Pettitt, 2016. "A Review of Modern Computational Algorithms for Bayesian Optimal Design," International Statistical Review, International Statistical Institute, vol. 84(1), pages 128-154, April.
    11. Victor De Oliveira & Zifei Han, 2022. "On Information About Covariance Parameters in Gaussian Matérn Random Fields," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 690-712, December.
    12. Eidsvik, Jo & Finley, Andrew O. & Banerjee, Sudipto & Rue, Håvard, 2012. "Approximate Bayesian inference for large spatial datasets using predictive process models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1362-1380.
    13. Letícia Ellen Dal Canton & Luciana Pagliosa Carvalho Guedes & Miguel Angel Uribe-Opazo, 2021. "Reduction of Sample Size in the Soil Physical-Chemical Attributes Using the Multivariate Effective Sample Size," Journal of Agricultural Studies, Macrothink Institute, vol. 9(1), pages 357-376, June.
    14. Jacopo Paglia & Jo Eidsvik & Juha Karvanen, 2022. "Efficient spatial designs using Hausdorff distances and Bayesian optimization," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1060-1084, September.
    15. Borgoni, Riccardo & Radaelli, Luigi & Tritto, Valeria & Zappa, Diego, 2013. "Optimal reduction of a spatial monitoring grid: Proposals and applications in process control," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 407-419.
    16. Shengde Liang & Sudipto Banerjee & Bradley P. Carlin, 2009. "Bayesian Wombling for Spatial Point Processes," Biometrics, The International Biometric Society, vol. 65(4), pages 1243-1253, December.
    17. Elizabeth Eisenhauer & Ephraim Hanks, 2020. "A lattice and random intermediate point sampling design for animal movement," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.

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