  We consider the problem of categorizing and describing the dynamic properties
and behaviours of crowds over time. Previous work has tended to focus on a
relatively static "typology"-based approach, which does not account for the
fact that crowds can change, often quite rapidly. Moreover, the labels attached
to crowd behaviours are often subjective and/or value-laden. Here, we present
an alternative approach, loosely based on the statechart formalism from
computer science. This uses relatively "agnostic" labels, which means that we
do not prescribe the behaviour of an individual, but provide a context within
which an individual might behave. This naturally describes the time-series
evolution of a crowd as "threads" of states, and allows for the dynamic
handling of an arbitrary number of "sub-crowds".
