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Bayesian spatial analysis of hardwood tree counts in forests via MCMC

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  • Reihaneh Entezari
  • Patrick E. Brown
  • Jeffrey S. Rosenthal

Abstract

In this paper, we use a Bayesian spatial model to spatially interpolate forest inventory data from the Timiskaming and Abitibi River forests in Ontario, Canada. We consider a Bayesian generalized linear geostatistical model and implement a Markov chain Monte Carlo algorithm to sample from its posterior distribution. How spatial predictions for new sites in the forests change as the amount of training data is reduced is studied and compared with a Bayesian logistic regression model without a spatial effect. Finally, we discuss a stratified sampling approach for selecting subsets of data that allows for potential better predictions.

Suggested Citation

  • Reihaneh Entezari & Patrick E. Brown & Jeffrey S. Rosenthal, 2020. "Bayesian spatial analysis of hardwood tree counts in forests via MCMC," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
    • Handle: RePEc:wly:envmet:v:31:y:2020:i:4:n:e2608
    DOI: 10.1002/env.2608
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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. 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. Giorgi, Emanuele & Diggle, Peter J., 2017. "PrevMap: An R Package for Prevalence Mapping," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i08).
    4. Lelys Bravo Guenni & Susan J. Simmons & Benjamin A. Shaby & Brian J. Reich, 2012. "Bayesian spatial extreme value analysis to assess the changing risk of concurrent high temperatures across large portions of European cropland," Environmetrics, John Wiley & Sons, Ltd., vol. 23(8), pages 638-648, December.
    5. Gareth O. Roberts & Jeffrey S. Rosenthal, 1998. "Optimal scaling of discrete approximations to Langevin diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 255-268.
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    Cited by:

    1. Leonie Netter & Eike Luedeling & Cory Whitney, 2022. "Agroforestry and reforestation with the Gold Standard-Decision Analysis of a voluntary carbon offset label," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(2), pages 1-26, February.
    2. KWON, Heeeun & HWANG, Beom Seuk, 2023. "Do Spatial Characteristics Affect Housing Prices in Korea? : Evidence from Bayesian Spatial Models," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 64(2), pages 109-124, December.
    3. Jaewoo Park & Sangwan Lee, 2022. "A projection‐based Laplace approximation for spatial latent variable models," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.

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