Simulating (burglar) behaviour with agent-based modelling
Improved models of offending behaviour
Modelling everyone else...
Home Office / Innovation Fund supported.
Aims to implement predictive policing through apps.
Leeds running statistical tests on the various algorithms, looking at different crimes and urban (and rural) structure.
Also testing how crime theory plays out in predictability; for example, how do algorithms respond to crimes matching different theories.
To do this, probably use ABM to generate datasets in different localities and of different types, with different underlying theory.
Exploring theory (‘explanatory’ models)
Simulation as a virtual laboratory:
Linking theory with crime patterns to test it.
Making predictions (‘predictive’ models)
Forecasting social / environmental change.
Exploring aspects of current data patterns through prediction.
Spatially patterned therefore predictable(?)
Spatio-temporally variations key to understanding system.
System with history of qualitative theorisation that needs testing.
Data good (geocoding, reporting).
Largely individually initiated in UK therefore don’t need so much data-poor social interaction modelling.
Should be possible to run “what if” tests (specifically, urban regeneration in Leeds).
Significant component of fear of crime in UK.
Extremely complex system:
Attributes of the individual houses.
Personal characteristics of the potential offender.
Features of the local community.
Physical layout of the neighbourhood.
Potential offender’s knowledge of the environment.
Traditional approaches often work at large scales, struggle to predict local effects
But cannot capture non-linear, complex systems.
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.
Environmental Criminology theories emphasise importance of
Individual behaviour (offenders, victims guardians)
Individual geographical awareness
Analysis of Modus Operandi.
Interviews with prisoners.
Risk for areas of different MOs and target characteristics.
Daily flows of people have a significant impact on crime rates
But usually have to fall back on residential populatin for want of data
“The general patterns of movement towards and away from activity nodes such as work or school locations, major shopping areas, entertainment districts or bedroom suburbs provide a very general image of where crimes will concentrate” (Kinney et al., 2008)
Malleson, N., and Andresen, M.A. (2016) Exploring the impact of ambient population measures on London crime hotspots. Journal of Criminal Justice 46 pp 52-63
A city-wide dynamic ABM, constantly re-calibrated to streaming data
New insights into urban mobility patterns and footfall estimates.
Simulating Urban Flows (surf) prototype