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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. Furong Li & Huiyan Sang, 2019. "Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1050-1062, July.
    2. Marianna Siino & Francisco J. Rodríguez‐Cortés & Jorge Mateu & Giada Adelfio, 2018. "Testing for local structure in spatiotemporal point pattern data," Environmetrics, John Wiley & Sons, Ltd., vol. 29(5-6), August.
    3. Jesper Møller & Jakob Gulddahl Rasmussen, 2012. "A Sequential Point Process Model and Bayesian Inference for Spatial Point Patterns with Linear Structures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(4), pages 618-634, December.
    4. Rasmus Plenge Waagepetersen, 2007. "An Estimating Function Approach to Inference for Inhomogeneous Neyman–Scott Processes," Biometrics, The International Biometric Society, vol. 63(1), pages 252-258, March.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    6. Benjamin D. Youngman & Theodoros Economou, 2017. "Generalised additive point process models for natural hazard occurrence," Environmetrics, John Wiley & Sons, Ltd., vol. 28(4), June.
    7. Taddy, Matthew A., 2010. "Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1403-1417.
    8. Marianna Siino & Francisco J. Rodríguez‐Cortés & Jorge Mateu & Giada Adelfio, 2020. "Spatio‐temporal classification in point patterns under the presence of clutter," Environmetrics, John Wiley & Sons, Ltd., vol. 31(2), March.
    9. J. Møller & A. N. Pettitt & R. Reeves & K. K. Berthelsen, 2006. "An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants," Biometrika, Biometrika Trust, vol. 93(2), pages 451-458, June.
    10. Schoenberg F.P., 2003. "Multidimensional Residual Analysis of Point Process Models for Earthquake Occurrences," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 789-795, January.
    11. Fangzheng Xie & Yanxun Xu, 2020. "Bayesian Repulsive Gaussian Mixture Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 187-203, January.
    12. Jeffrey W. Miller & Matthew T. Harrison, 2018. "Mixture Models With a Prior on the Number of Components," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 340-356, January.
    13. Yu Ryan Yue & Ji Meng Loh, 2011. "Bayesian Semiparametric Intensity Estimation for Inhomogeneous Spatial Point Processes," Biometrics, The International Biometric Society, vol. 67(3), pages 937-946, September.
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