  Lasso and Dantzig selector are standard procedures able to perform variable
selection and estimation simultaneously. This paper is concerned with extending
these procedures to spatial point process intensity estimation. We propose
adaptive versions of these procedures, develop efficient computational
methodologies and derive asymptotic results for a large class of spatial point
processes under an original setting where the number of parameters, i.e. the
number of spatial covariates considered, increases with the expected number of
data points. Both procedures are compared theoretically, in a simulation study,
and in a real data example.
