Recent advances in technology have led to a surge in innovations in the area of
spatially aware applications such as locally operating social networks, retail,
advertising, local weather and traffic services. Such applications are often
supported by large data-collection and dissemination processes, designed to work
on large-scale, inexpensive, infrastructure-light wireless \adhoc networks. As
a consequence, novel modelling techniques are required for the purpose of
capacity planning and in order to build on-line prediction models based on large
quantities of location-aware data.

In this thesis we study the spatio-temporal evolution of population systems
related to such city-scale challenges. In particular we focus on large-scale,
spatial population processes that are not amenable to fluid-flow or mean-field
approximation techniques because of locally or temporarily varying population
sizes. Our main contributions are

- Providing novel ways of incorporating space and mobility in large-scale
spatial populations models.
- Illustrating how, for a certain class of spatial population processes, the
time-evolution of higher-order population moments can be obtained
efficiently using hybrid-simulation analysis.
- Presenting case studies for realistic spatial systems from different
application areas to show that our modelling techniques are well-suited
for the analysis of network and protocol performance of static and mobile
\adhoc communication networks as well as for building fast on-line
prediction models.