I've just finished the GIS version of my prototype using Repast Simphony, here's a short video showing the simple burglar agents moving around their environment (part of East Leeds).
This is a prototype at the moment, so the behaviour of the agents and the virtual environment are both relatively simple. The model is still able to produce crime hotspots which appear similar to those we find in real data (I haven't done any proper statistical analysis yet). These images show some preliminary model results and clusters produced by analysing real burglary data:
To model human behaviour I'm using the PECS framework (Physical Conditions, Emotional States, Cognitive Capabilities and Social Status) and I'm using Mastermap GIS data along with census boundaries and statistics to describe houses and local communities. I'm using findings from criminology to make the model as realistic as possible, both from the point-of-view of how the burglars should behave and which aspects of the environment are important to a potential burglar. I'm hoping to publish a couple of papers which have more details about the model, one is in review at the moment.
I'm working in the School of Geography at the University of Leeds and I'm part of the Centre for Spatial Analysis and Policy (CSAP) research cluster. We're working closely with Safer Leeds the crime and disorder reduction partnership. They are kindly supplying a range of data from essential crime statistics to detailed information about their crime-reduction projects. If anyone has any comments / criticisms / suggestions about the work please either email me (my address is on the school website) or leave comments here.
The Environment
In my model, the environment will be made up of three layers:
- The "community" or "neighbourhood" layer
- Individual properties
- Transport
The community layer is designed to bring in all the aspects of a neighbourhood which will influence how a potential burglar behaves. This layer consists of four sub-layers which will each influence a potential burglar differently. Collective efficacy (or community spirit) is designed to give an idea of how likely it is that people in the neighbourhood will notice an outsider and keep an eye on them, a highly cohesive neighbourhood will make a burglar agent more cautious. In general, criminologists have found that a high collective efficacy is inversely proportional to crime rates. Attractiveness gives an estimation of the abundance of goods available to a potential burglary (student households, for example, often have lots of expensive goods in them). The final two layers: traffic volume and occupancy are designed to give an estimate of how busy the area is. If there are lots of passers-by it will be more difficult to break into a property unnoticed and many burglars have expressed a preference for empty houses so they will not have to confront the owners. Most of the data for these layers can be gathered from the (2001) UK census.
The individual property layer provides individual information about each potential victim (household). For UK studies I will be using mastermap data which is a detailed, individual-level GIS dataset containing information about roads, buildings, parks etc. By analysing of each individual house it is possible to estimate the number of possible entrances to the property (a terraced house, for example, will usually have fewer possible entrances than a detached house) and how visible the property is to its' neighbours. This figure shows some of the different features available as part of the Topographic Layer.
Finally, the transport layer will be used by the agents to move around the environment. Different methods will include public transport, walking and driving. This will be particularly useful for policy scenarios. For example, it might be useful to ask "what might happen to burglary rates in this area if we built this new rapid-transit line".
Burglar Agents
Potential burglars are the only agents in the model. The actions of other important actors (passers-by, residents etc) can be simulated in the virtual environment. To make the agents as realistic as possible the research will draw on findings from criminology and use an advanced behavioural framework to model their behaviour.
