  We introduce a simple and wide class of multifractal spatial point patterns
as Cox processes which intensity is multifractal, i.e., the class of Poisson
processes with a stochastic intensity corresponding to a random multifractal
measure. We then propose a maximum likelihood approach by means of a standard
Expectation-Maximization procedure in order to estimate the distribution of
these intensities at all scales. This provides, as validated on various
numerical examples, a simple framework to estimate the scaling laws and
therefore the multifractal properties for this class of spatial point
processes. The wildfire distribution gathered in the Prom\'eth\'ee French
Mediterranean wildfire database is investigated within this approach that
notably allows us to compute the statistical moments associated with the
spatial distribution of annual likelihood of fire event occurence. We show that
for each order $q$, these moments display a well defined scaling behavior with
a non-linear spectrum of scaling exponents $\zeta_q$. From our study, it thus
appears that the spatial distribution of the widlfire ignition annual risk can
be described by a non-trivial, multifractal singularity spectrum and that this
risk cannot be reduced to providing a number of events per $km^2$. Our analysis
is confirmed by a direct spatial correlation estimation of the intensity
logarithms whose the peculiar slowly decreasing shape corresponds to the
hallmark of multifractal cascades. The multifractal features appear to be
constant over time and similar over the three regions that are studied.
