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This paper develops methodology that provides a toolbox for routinely fittingcomplex models to realistic spatial point pattern data. We consider models thatare based on log-Gaussian Cox processes and include local interaction in theseby considering constructed covariates. This enables us to use integrated nestedLaplace approximation and to considerably speed up the inferential task. Inaddition, methods for model comparison and model assessment facilitate themodelling process. The performance of the approach is assessed in a simulationstudy. To demonstrate the versatility of the approach, models are fitted to tworather different examples, a large rainforest data set with covariates and apoint pattern with multiple marks.

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