  Spatial trajectories are ubiquitous and complex signals. Their analysis is
crucial in many research fields, from urban planning to neuroscience. Several
approaches have been proposed to cluster trajectories. They rely on
hand-crafted features, which struggle to capture the spatio-temporal complexity
of the signal, or on Artificial Neural Networks (ANNs) which can be more
efficient but less interpretable. In this paper we present a novel ANN
architecture designed to capture the spatio-temporal patterns characteristic of
a set of trajectories, while taking into account the demographics of the
navigators. Hence, our model extracts markers linked to both behaviour and
demographics. We propose a composite signal analyser (CompSNN) combining three
simple ANN modules. Each of these modules uses different signal representations
of the trajectory while remaining interpretable. Our CompSNN performs
significantly better than its modules taken in isolation and allows to
visualise which parts of the signal were most useful to discriminate the
trajectories.
