Spatial stochastic population models for the analysis of city-scale systems

• Main Author: Günther, Marcel Christoph
• Other Authors: Bradley, Jeremy
• Published: Imperial College London 2014
• Subjects: 004
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.656804

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.