Contemporary flow data are used to investigate a number of social science phenomena: examples include flows inferred from mobile phone data, migration flows, commuting flows, or taxi flows between pick-up and set-down points. This PhD thesis will develop new bespoke advanced quantitative methods for flow data to better understand patterns in flows and their relationship with geography and environment. Flow data are mathematically represented as geographic networks and are due to their size and complicated structure a typical example of big data. In geography, there is a lack of appropriate methods for their analysis. While some disciplines, such as physics, have recently developed flow methods, these methods are not suitable for flow networks which are geographically constrained, as the methods are not scalable to large geographic networks, nor do they consider the effects of geographic location. Further, in physics there is no consideration for environmental conditions which affect human mobility, such as the effect of weather on commuting behaviour, as reflected in commuters' flows. In this project we will develop new analytical methods for geographic flows which will 1) inherently incorporate geography into flow analysis (location-aware methods) and 2) integrate flow data with environmental data (context-aware methods). We will evaluate our new location-aware methods on migration flows from UK censuses in 1981, 1991, 2001 and 2011. The context-aware methods will be used to integrate human mobility data from the iMCD project at the Urban Big Data Centre with open environmental and contextual data, such as data from the Open Data Glasgow portal, meteorological data and remotely sensed satellite data. All methods will be implemented as FOSS software to achieve maximum impact. The ultimate goal is to provide new rigorous and extensively tested methods for flow data and make them accessible to any social scientist interested in flows and related phenomena.

I'll be applying novel techniques involving tensor networks to improve the computational efficiency in simulation of open quantum systems involving coupled qubits connected to a Non-Markovian bath. The effect of such baths on the system is dependent on what the system has dissipated into them and therefore the former states of these baths. Thus when simulating these systems one has to store a certain number of past states of the system to accurately predict the next step in the evolution. This typically requires large amounts of memory and I'll be working on techniques that look to drastically reduce this demand. This will see me applying code already written to systems already studied and then adapting it to be more versatile in it's application to potentially more useful systems. These systems are relevant to the fast evolving area of quantum computing and any hopes of application of such systems will be reliant on good knowledge of how they evolve.

Influenza virus epidemics are responsible for hundreds of thousands of deaths worldwide per year. Occasionally, a new virus starts to circulate which causes a pandemic resulting in ten of millions of deaths. There were three pandemics in the last century, each caused by a different type of Influenza virus; H1N1 in 1918, H2N2 in 1957 and H3N2 in 1968. Influenza viruses have two proteins on their surface, Haemagglutinin (H) and Neuraminidase (N), and there are 16 subtypes of H (H1-H16) and 9 subtypes of N (N1-N9). All combinations are found naturally in the bird population. Since 2003, a highly pathogenic avian H5N1 virus has caused in excess of 140 human deaths and threatens to cause the next pandemic. At present, there are two clinically licensed anti-Influenza drugs (Tamiflu and Relenza) and both of these were designed through knowledge of the three-dimensional structure of the N protein. Although effective upon prompt administration, mutant viruses have arisen that are resistant to these drugs. Therefore, there is an urgent need to develop novel drugs against Influenza viruses. We have recently elucidated the three-dimensional structure of N1 from a H5N1 virus that infected a person in Vietnam. Surprisingly, the structure revealed a novel pocket in the N1 protein next to the binding site of Tamiflu and Relenza. We propose to use the knowledge of this structure to design novel anti-Influenza drugs.

Holocene perspectives on the ecology of moorland burning in northern England