My research focuses on the analysis of performance measures, such as passage times and steady state metrics in large spatial Continuous Time Markov-Chains (CTMC)s. Such models naturally arise from systems that feature interactions between a large number of individual agents, for instance transmitters in massively parallel telecommunication networks, vehicles in a transport system or organisms in ecology. In particular I investigate applications of approximative mean field solution techniques to evaluate realistic spatial models that are otherwise too computationally expensive to solve. A good example is where metrics of interest could be message throughput as well as battery consumption. As the state space is the superposition of the states of all individual nodes in the network, it is impossible to apply numerical solution techniques that explore the entire state-space and extremly costly to use techniques such as tensor algebra. Similarly the cost of simulation hugely depends on the frequency of interaction between individuals, making the model very expensive to evaluate as the rate of communication increases.