We summarize and discuss the current state of spatial point process
theory and directions for future research, making an analogy with generalized
linear models and random effect models, and illustrating the theory with various
examples of applications. In particular, we consider Poisson, Gibbs, and Cox
process models, diagnostic tools and model checking, Markov chain Monte Carlo
algorithms, computational methods for likelihood-based inference, and quick
non-likelihood approaches to inference.