  We introduce a class of proper scoring rules for evaluating spatial point
process forecasts based on summary statistics. These scoring rules rely on
Monte-Carlo approximations of expectations and can therefore easily be
evaluated for any point process model that can be simulated. In this regard,
they are more flexible than the commonly used logarithmic score and other
existing proper scores for point process predictions. The scoring rules allow
for evaluating the calibration of a model to specific aspects of a point
process, such as its spatial distribution or tendency towards clustering. Using
simulations we analyze the sensitivity of our scoring rules to different
aspects of the forecasts and compare it to the logarithmic score. Applications
to earthquake occurrences in northern California, USA and the spatial
distribution of Pacific silver firs in Findley Lake Reserve in Washington, USA
highlight the usefulness of our scores for scientific model selection.
