The distribution of incidents in time and space is a central issue in the study of crime, for both theoretical and practical reasons. It is also a context in which quantitative analysis and modelling has significant potential value: such research represents a means by which the implications of theory can be examined rigorously, and can also provide tools which support both policing and policy-making. The nature of the field, however, presents a number of challenges, particularly with regard to the incorporation of complex environmental factors and the modelling of individual-level behaviour. In this thesis, the techniques of complexity science are used to overcome these issues, and the approach is demonstrated using a number of examples from a range of crime types. The thesis begins by presenting a network-based framework for the analysis of spatio-temporal clustering. It is demonstrated that signature `motifs' can be identified in patterns of offending for burglary and maritime piracy, and that the technique provides a more nuanced characterisation of clustering than existing approaches. Analysis is then presented of the relationship between street network structure and the distribution of urban crime. It is shown that burglary risk is predicted by the graph-theoretic properties of street segments; in particular, those which correspond to levels of street usage. It is further demonstrated that the `near-repeat' phenomenon in burglary displays a form of directionality, which can be reconciled with a novel street network metric. These results are then used to inform a mathematical model of burglary, which is situated on a network and which may be used for prediction. This model is analysed and its behaviour characterised in terms of urban form. Finally, a model is presented for a contrasting crime problem, the London riots of 2011, and used to examine a number of policy questions.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:639628 |
| Date | January 2015 |
| Creators | Davies, T. P. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/1460300/ |
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