Geographic profiling in biology.

Abstract

In Chapter one I introduce the subject of geographic profiling, its use in criminology and its previous application to biology. I go on in Chapter two to examine the original model and develop a likelihood-based approach to fit the parameters to data from 53 UK invasive species. GP performs well on this novel problem, and outperforms other simple spatial modelling techniques. Using simulations I show that GP is particularly efficient at locating sources when there is more than a single source. Chapter three develops a Bayesian approach using Dirichlet Processes to account for the problem of multiple sources. This model was developed in collaboration with Robert Verity. This new Bayesian model outperforms the original model used in criminology and offers a range of additional information from the data. The Bayesian GP model is then used to determine the sources of malaria outbreaks in Cairo. These developments significantly improve and extend the theory and application of GP. In Chapter four I discuss the possible shapes of dispersal functions. I conduct a review of the literature and find a geometric mistake in the way linear distributions have been extracted from two-dimensional data. The correct back-transformation allows these dispersal distributions to be properly generated. Using this information; ecologists, conservationists and resources managers can now apply GP to real world problems and effectively allocate limited resources to locate sources of species invasions and disease outbreaks. I go on in Chapter five to develop a method for fitting the primary parameter sigma from the point pattern data and run simulations to show the effectiveness of this new approach. In Chapter six I illustrate the application of GP to three problems, one in criminology, one in ecology and one in epidemiology. I finish by summarising the work in this thesis and discussing the potential future developments and applications of GP.