  This paper is the second in a series of papers which combine graphical
modelling and marked spatial point patterns. Extending the previous results of
\cite Eckardt (2016a), we introduce a marked spatial dependence graph model
which depicts the global dependence structure of quantitatively marked
multi-type points that occur in space based on the marked conditional partial
spectral coherence. Most beneficial, no structural assumption with respect to
the characteristics in the data are to be made prior to analysis. This approach
presents a computationally efficient method of pattern recognition in highly
structured and high dimensional multi-type spatial point processes where also
quantitative marks are available. Unlike all previous methods, our new model
permits the simultaneous analysis of all multivariate conditional
interrelations. The new technique is illustrated analysing the diameter at
breast hight of 37 different tree species recorded at 10053 locations in Duke
Forest.
