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Modelling wildfire occurrences is important for disaster management includingprevention, detection and suppression of large catastrophic events. We presenta spatial Poisson hurdle model for exploring geographical variation of monthlycounts of wildfire occurrences and apply it to Indonesia and Australia. Themodel includes two a priori independent spatially structured latent effectsthat account for residual spatial variation in the probability of wildfireoccurrence, and the positive count rate given an occurrence. Inference isprovided by empirical Bayes using the Laplace approximation to the marginalposterior which provides fast inference for latent Gaussian models with sparsestructures. In both cases, our model matched several empirically known factsabout wildfires. We conclude that elevation, percentage tree cover, relativehumidity, surface temperature, and the interaction between humidity andtemperature to be important predictors of monthly counts of wildfireoccurrences. Further, our findings show opposing effects for surfacetemperature and its interaction with relative humidity.

A spatial Poisson hurdle model with application to wildfires