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Model-Based Geostatistics the Easy Way

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  • Brown, Patrick E.

Abstract

This paper briefly describes geostatistical models for Gaussian and non-Gaussian data and demonstrates the geostatsp and dieasemapping packages for performing inference using these models. Making use of R’s spatial data types, and raster objects in particular, makes spatial analyses using geostatistical models simple and convenient. Examples using real data are shown for Gaussian spatial data, binomially distributed spatial data, a logGaussian Cox process, and an area-level model for case counts.

Suggested Citation

  • Brown, Patrick E., 2015. "Model-Based Geostatistics the Easy Way," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i12).
    • Handle: RePEc:jss:jstsof:v:063:i12
    DOI: http://hdl.handle.net/10.18637/jss.v063.i12
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    References listed on IDEAS

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    1. Taylor, Benjamin M. & Davies, Tilman M. & Rowlingson, Barry S. & Diggle, Peter J., 2015. "Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i07).
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. Huan Jiang & Patrick E. Brown & Håvard Rue & Silvia Shimakura, 2014. "Geostatistical survival models for environmental risk assessment with large retrospective cohorts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(3), pages 679-695, June.
    4. 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.
    5. Ye Li & Patrick Brown & Håvard Rue & Mustafa al‐Maini & Paul Fortin, 2012. "Spatial modelling of lupus incidence over 40 years with changes in census areas," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(1), pages 99-115, January.
    6. Taylor, Benjamin M. & Davies, Tilman M. & Rowlingson, Barry S. & Diggle, Peter J., 2013. "lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i04).
    7. Schlather, Martin & Malinowski, Alexander & Menck, Peter J. & Oesting, Marco & Strokorb, Kirstin, 2015. "Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i08).
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    Cited by:

    1. Marein, Brian, 2022. "Colonial Roads and Regional Inequality," Journal of Urban Economics, Elsevier, vol. 131(C).
    2. Bernardi, Mara S. & Carey, Michelle & Ramsay, James O. & Sangalli, Laura M., 2018. "Modeling spatial anisotropy via regression with partial differential regularization," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 15-30.
    3. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).

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