Following from Nigel Gilbert's recent email, there are some other modelling PhD projects in the School of Geography, University of Leeds, that readers might be interested in. The titles and abstracts are below. 

The projects will be conducted in the Centre for Spatial Analysis and Policy (CSAP) which is a leading research cluster for spatial analysis and agent-based modelling. The research council deadline for applications is in early February, so if you are interested please get in contact soon so that we have time to help with the application.

For more information, please contact myself ([log in to unmask]), Andrew Evans ([log in to unmask]) or Alison Heppenstall ([log in to unmask]). All the projects will be up on our website shortly:

http://www.geog.leeds.ac.uk/research/csap/phd.html

Kind regards,
Nick Malleson
http://www.geog.leeds.ac.uk/people/n.malleson


Project 1 - Dynamic Data Assimilation and Error Management in Agent-based Socioeconomic Systems. 
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Traditionally social science models have been either static (in the sense that they replicate or predict a single time slice of a social system), or have dynamics that work with a ‘fixed rudder’ (in the sense that they are initiated and then run over time to replicate or predict some end-point, with no opportunity for adjusting the running model). This is actually rather unusual for geographical models as both these model pathways have considerable error issues. In the case of static models, there is almost no way to appropriately check the error involved in new predictions. In the case of ‘fixed rudder’ models, the errors involved in modelling non-linear systems ‘explode’ as the model runs, making most predictions extremely dubious (the potential error variance is generally far larger than the potential range of model results). 

In most communities modelling real-world systems these issues have been addressed through data assimilation: the adjustment of the running model with dynamically arriving real-world data. For example, in meteorological models new air pressure, temperature, and other field data are integrated into running models, and the current errors used to recalibrate the running model. Social science models, however, have generally not taken this step, in part because of limited access to continuous datasets. Concentration on datasets such as decadal censuses, and the continual development of new collection schemes for quasi-longitudinal datasets, has made data assimilation to all intents impossible. However, the last five years has seen an explosion in the availability of continuous and spatially-linked datasets, both formal (for example, economic time series) and informal (for example crowd-sourced geolocated twitter feeds). Social science is now in a position to better constrain model errors using dynamic data assimilation, indeed, it is essential if socio-economic modelling is to fulfil its considerable potential. However, social scientists are now so tied into a small number of static modelling traditions that this is often hard to see.

This PhD will utilise methodologies found in other modelling communities to enhance socio-economic agent-based models (for a review of the techniques, see Evans, 2011). It will additionally develop new techniques to visualise the evolution over time of error surfaces, and investigate the unique opportunities agent-based modelling can bring to bear on the issue of error propagation in non-linear systems (most notably the accurate replication of those relationships in human society that tend to dampen the propagation of instabilities – social negotiation, compromise, group decision making).  The example system used will depend on student interests, however, could be in the areas of retail pricing, crime, or the housing market. The PhD would be suitable for anyone with a computational, mathematical, or physics-centred background, or anyone with a geographical, geological, meteorological, environmental, or sociological background and a strong interest in modelling human or physical environments.

References:
Evans, A.J. (2011) Uncertainty and Error.  In Heppenstall, A.J., Crooks, A.T., See, L.M., and Batty, M. (2011) Agent-Based Models of Geographical Systems. Springer.



Project 2 - Capturing and simulating criminal behaviour through advanced spatial analysis and agent-based modelling
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Classifications of criminal behaviour are largely limited through one of the following: size of the group of individuals; poor consideration of spatio-temporal movement and often poor, or no linkage to geodemographics or other indicators of socio-economic status. This project will apply advanced spatial analysis methodologies to a database of tens of thousands of individual reported crime events over a 5-10 year period of time to develop a more realistic and detailed classification of criminal behaviour based on spatial movement. The classification will be further enhanced through the inclusion of literature and other national data sets such as the Index of Multiple Deprivation and those provided by local government such as unemployment data, abandoned buildings, council administered benefits and health status information.

This new behavioural classification will be used to inform the construction of a customised individual-level synthetic population. This, in turn, will be incorporated into an existing individual-level computer model to test whether the patterns of criminal behaviour/social unrest are more effectively captured than present techniques. For example, if a new housing development is constructed, can we estimate the potential for attracting burglars?

The overarching aim of this PhD is the creation of a new classification of criminal behaviour that will be used for creation of a customised population for an existing agent-based model. The objectives can be broken down into:

i.	A critique of existing classifications and an identification of their shortcomings in terms of grouping types of criminal behaviour;

ii.	Detailed spatiotemporal analysis of the known movement of criminals;

iii.	Linkage of movements to geodemographics to create a new behaviour classification;

iv.	Testing of the new classification through (i) creation of a customised population and (ii) benchmarking against an existing models.