  Due to climate change and human activity, wildfires are expected to become
more frequent and extreme worldwide, causing economic and ecological disasters.
The deployment of preventive measures and operational forecasts can be aided by
stochastic modeling that helps to understand and quantify the mechanisms
governing the occurrence intensity. We here develop a point process framework
for wildfire ignition points observed in the French Mediterranean basin since
1995, and we fit a spatio-temporal log-Gaussian Cox process with monthly
temporal resolution in a Bayesian framework using the integrated nested Laplace
approximation (INLA). Human activity is the main direct cause of wildfires and
is indirectly measured through a number of appropriately defined proxies
related to land-use covariates (urbanization, road network) in our approach,
and we further integrate covariates of climatic and environmental conditions to
explain wildfire occurrences. We include spatial random effects with Mat\'ern
covariance and temporal autoregression at yearly resolution. Two major
methodological challenges are tackled: first, handling and unifying multi-scale
structures in data is achieved through computer-intensive preprocessing steps
with GIS software and kriging techniques; second, INLA-based estimation with
high-dimensional response vectors and latent models is facilitated through
intra-year subsampling, taking into account the occurrence structure of
wildfires.
