A collaboration between the N8 (8 research-intensive universities in the north of England), 12 police forces, and others
support data sharing, analysis and use
Dynamic Simulation - Agent Based Modelling
Predictive Intelligent Policing
Create an urban (or other) environment in a computer model.
Stock it with buildings, roads, houses, etc.
Create individuals to represent offenders, victims, guardians.
Give them backgrounds and drivers.
See what happens.
Modelling complexity, non-linearity, emergence
Natural description of a system
Bridge between verbal theories and mathematical models
Produces a history of the evolution of the system
Computationally expensive (not amenable to optimisation)
Complicated agent decisions
Lots of decisions!
Multiple model runs (robustness)
Modelling "soft" human factors
Need detailed, high-resolution, individual-level data
Explanatory: exploring theory
Randomly generated abstract environments
Rational choice perspective
Routine activity theory
Geometric theory of crime
Validation against stylized facts:
Spatial crime concentration
Journey to crime curve
Predictive: exploring the real world
ABM to explore the impacts of real-world policies
Urban regeneration in Leeds
This is not minority report!
We can't (and wouldn't want to!) predict when/where/who will commit a crime.
Academics have a role to set the boundaries on what is ethically acceptable
Dynamic simulation models have great potential, we need to make the case that they can be used responsibly
Particularly relevant in the 'big data' / 'smart cities' era
This is not predictive policing
It could be, in the future, maybe, but not yet
This is a useful tool for exploring the crime system.
It might lead to a better understanding of:
How different behavioural characteristics (offender, victim, or guardian) influence crime outcomes
How the physical infrastructure can be organised to discourage crime
How patrol routes might be most effective in circumstances