  This paper discusses infill asymptotics for logistic regression estimators
for spatio-temporal point processes whose intensity functions are of log-linear
form. We establish strong consistency and asymptotic normality for the
parameters of a Poisson point process model and demonstrate how these results
can be extended to general point process models. Additionally, under proper
conditions, we also extend our central limit theorem to other unbiased
estimating equations that are based on the Campbell--Mecke theorem.
