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".
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