  We define several new models for how to define anomalous regions among
enormous sets of trajectories. These are based on spatial scan statistics, and
identify a geometric region which captures a subset of trajectories which are
significantly different in a measured characteristic from the background
population. The model definition depends on how much a geometric region is
contributed to by some overlapping trajectory. This contribution can be the
full trajectory, proportional to the time spent in the spatial region, or
dependent on the flux across the boundary of that spatial region. Our methods
are based on and significantly extend a recent two-level sampling approach
which provides high accuracy at enormous scales of data. We support these new
models and algorithms with extensive experiments on millions of trajectories
and also theoretical guarantees.
