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A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)

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This paper develops methodology that provides a toolbox for routinely fitting complex models to realistic spatial point pattern data. We consider models that are based on log-Gaussian Cox processes and include local interaction in these by considering constructed covariates. This enables us to use integrated nested Laplace approximation and to considerably speed up the inferential task. In addition, methods for model comparison and model assessment facilitate the
modelling process. The performance of the approach is assessed in a simulation study. To demonstrate the versatility of the approach, models are tted to two rather dierent examples, a large rainforest data set with covariates and a point pattern with multiple marks.


Original languageEnglish
Pages (from-to)1499-1530
Number of pages32
JournalAnnals of Applied Statistics
Issue number4
Publication statusPublished - Dec 2012

    Research areas

  • Cox processes, Marked point patterns, Model assessment, Model comparison

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