This page contains brief information about research areas that I am currently involved in. For most of these, more information can be found on here.
In summary, I am interested in the following:
- development of individual-based modelling techniques
- how to exploit Big Data for use in these models
- simulating realistic human behaviour
Agent-Based Modelling of Crime
Crime is an extremely complex phenomenon which is driven by a wide range of environmental and human factors. Traditional techniques that use statistical methods to investigate crime have difficulties including the highly detailed, low-level factors which will determine whether or not a crime is likely to occur. These factors include the design of buildings, the structure of the road network and the behaviour of individual people going about their daily business (whether they are possible offenders, victims, or people who might prevent a crime).
This research, in conjunction with Nick Malleson, uses agent-based modelling which is a type of computer simulation that simulates the behaviour of individuals (virtual people in this case). By incorporating detailed information about human behaviour into a simulation consisting of many “intelligent” agents it might be possible to better understand how people behave in the real world, which factors determine their movements, and where crimes are ultimately most likely to be committed.
The product of the work will be an application which could be used by local authorities to predict the effects of new environmental developments or policies. Specifically, the model will be used to experiment with the effects that a major development project will have on rates of residential burglary in Leeds. For more information, see Nick Malleson’s CrimeSim blog.
Modelling Retail Markets Using Agent-Based Models
Despite seeming quite dull, I can tell you that modelling petrol prices is rather interesting! This was my PhD and subsequent postdoc work. It also had a collection of awful maps, like this one…
Many geographical systems can be viewed as complex entities containing numerous non-linear processes which may be interrelated at different spatial and temporal scales. This is particularly evident with retail markets. Here, companies make decisions on pricing whilst consumers make decisions on where to purchase one or more commodities. The competition between retailers to attract customers, and therefore make profits, can result in complex spatial interactions between all participants within the system. This is especially true when companies’ product prices are set dependant on competitors’ prices within geographically localised markets. Given the high degree of complexity within such geographical systems, it is unsurprising that traditional equation-based techniques have failed to produce realistic representations of the behaviour involved.
This work developed an ABM representing the petrol stations that was linked to a network model (for redistributing individuals), a spatial interaction model (for calculating demand) and a genetic algorithm (for optimising rules).