  This paper contributes to the multivariate analysis of marked spatio-temporal
point process data by introducing different partial point characteristics and
extending the spatial dependence graph model formalism. Our approach yields a
unified framework for different types of spatio-temporal data including both,
purely qualitatively (multivariate) cases and multivariate cases with
additional quantitative marks. The proposed graphical model is defined through
partial spectral density characteristics, it is highly computationally
efficient and reflects the conditional similarity among sets of spatio-temporal
sub-processes of either points or marked points with identical discrete marks.
The paper considers three applications, two on crime data and a third one on
forestry.
