  Transport in crowded, complex environments occurs across many spatial scales.
Geometric restrictions can hinder the motion of individuals and, combined with
crowding between individuals, can have drastic effects on global transport
phenomena. However, in general, the interplay between crowding and geometry in
complex real-life environments is poorly understood. Existing analytical
methodologies are not always readily extendable to heterogeneous environments:
in these situations predictions of crowded transport behaviour within
heterogeneous environments rely on computationally intensive mesh-based
approaches. Here, we take a different approach by employing networked
representations of complex environments to provide an efficient framework
within which the interactions between networked geometry and crowding can be
explored. We demonstrate how the framework can be used to: extract detailed
information at the level of the whole population or an individual within it;
identify the topological features of environments that enable accurate
prediction of transport phenomena; and, provide insights into the design of
optimal environments.
