  In this paper we focus on spatial Markov population models, describing the
stochastic evolution of populations of agents, explicitly modelling their
spatial distribution, representing space as a discrete, finite graph. More
specifically, we present a heuristic approach to aggregating spatial locations,
which is designed to preserve the dynamical behaviour of the model whilst
reducing the computational cost of analysis. Our approach combines stochastic
approximation ideas (moment closure, linear noise), with computational
statistics (spectral clustering) to obtain an efficient aggregation, which is
experimentally shown to be reasonably accurate on two case studies: an instance
of epidemic spreading and a London bike sharing scenario.
