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Heterogeneity pursuit for spatial point pattern with application to tree locations: A Bayesian semiparametric recourse

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  • Jieying Jiao
  • Guanyu Hu
  • Jun Yan

Abstract

Spatial point pattern data are routinely encountered. A flexible regression model for the underlying intensity is essential to characterizing the spatial point pattern and understanding the impacts of potential risk factors on such pattern. We propose a Bayesian semiparametric regression model where the observed spatial points follow a spatial Poisson process with an intensity function which adjusts a nonparametric baseline intensity with multiplicative covariate effects. The baseline intensity is piece‐wise constant, approached with a powered Chinese restaurant process prior which prevents an unnecessarily large number of pieces. The parametric regression part allows for variable selection through the spike‐slab prior on the regression coefficients. An efficient Markov chain Monte Carlo algorithm is developed for the proposed methods. The performance of the methods is validated in an extensive simulation study. In application to the locations of Beilschmiedia pendula trees in the Barro Colorado Island forest dynamics research plot in central Panama, the spatial heterogeneity is attributed to a subset of soil measurements in addition to geographic measurements with a spatially varying baseline intensity.

Suggested Citation

  • Jieying Jiao & Guanyu Hu & Jun Yan, 2021. "Heterogeneity pursuit for spatial point pattern with application to tree locations: A Bayesian semiparametric recourse," Environmetrics, John Wiley & Sons, Ltd., vol. 32(7), November.
    • Handle: RePEc:wly:envmet:v:32:y:2021:i:7:n:e2694
    DOI: 10.1002/env.2694
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    References listed on IDEAS

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    1. Heaton, Matthew J. & Dahl, Benjamin K. & Dayley, Caleb & Warr, Richard L. & White, Philip, 2024. "Integrating machine learning and Bayesian nonparametrics for flexible modeling of point pattern data," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).

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