  This paper reviews developments in statistics for spatial point processes
obtained within roughly the last decade. These developments include new classes
of spatial point process models such as determinantal point processes, models
incorporating both regularity and aggregation, and models where points are
randomly distributed around latent geometric structures. Regarding parametric
inference the main focus is on various types of estimating functions derived
from so-called innovation measures. Optimality of such estimating functions is
discussed as well as computational issues. Maximum likelihood inference for
determinantal point processes and Bayesian inference are briefly considered
too. Concerning non-parametric inference, we consider extensions of functional
summary statistics to the case of inhomogeneous point processes as well as new
approaches to simulation based inference.
