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Big data sampling and spatial analysis: “which of the two ladles, of fig-wood or gold, is appropriate to the soup and the pot?”

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  • Bivand, Roger
  • Krivoruchko, Konstantin

Abstract

Following from Krivoruchko and Bivand (2009), we consider some general points related to challenges to the usefulness of big data in spatial statistical applications when data collection is compromised or one or more model assumptions are violated. We look further at the desirability of comparison of new methods intended to handle large spatial and spatio-temporal datasets.

Suggested Citation

  • Bivand, Roger & Krivoruchko, Konstantin, 2018. "Big data sampling and spatial analysis: “which of the two ladles, of fig-wood or gold, is appropriate to the soup and the pot?”," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 87-91.
  • Handle: RePEc:eee:stapro:v:136:y:2018:i:c:p:87-91
    DOI: 10.1016/j.spl.2018.02.012
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    References listed on IDEAS

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    1. 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.
    2. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    3. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
    4. Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
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    Cited by:

    1. Reid, Nancy, 2018. "Statistical science in the world of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 42-45.

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