  Modelling wildfire occurrences is important for disaster management including
prevention, detection and suppression of large catastrophic events. We present
a spatial Poisson hurdle model for exploring geographical variation of monthly
counts of wildfire occurrences and apply it to Indonesia and Australia. The
model includes two a priori independent spatially structured latent effects
that account for residual spatial variation in the probability of wildfire
occurrence, and the positive count rate given an occurrence. Inference is
provided by empirical Bayes using the Laplace approximation to the marginal
posterior which provides fast inference for latent Gaussian models with sparse
structures. In both cases, our model matched several empirically known facts
about wildfires. We conclude that elevation, percentage tree cover, relative
humidity, surface temperature, and the interaction between humidity and
temperature to be important predictors of monthly counts of wildfire
occurrences. Further, our findings show opposing effects for surface
temperature and its interaction with relative humidity.
