Funded by the Human Social Dynamics Program at NSF, the UC MaSC
Project centers on theoretical, methodological and empirical work
to develop analytical and computational models of crime pattern
formation.
Crime mapping forms a key feature of current approaches to understanding
offender behavior and is a tool used increasingly by police departments
and policy makers for strategic crime prevention. However, despite
the availability of sophisticated digital mapping and analysis tools
there is a substantial gap in our understanding of how low-level
behaviors of offenders lead to aggregate crime patterns such as
crime hot spots. Thus, for example, we are unable to specify exactly
why directed police action at crime hot spots sometimes leads to
displacement of crime in space but, surprisingly, often can also
lead to hot spot dissipation and a real reduction in crime incidences.
Agent-based
modeling offers a potential avenue for developing a quantitative
understanding of crime hot spot formation built from the bottom-up
around offender behavior. Agent-based models are not only more consistent
with the scale of decisions that offenders actually take, but they
also open the door to the development of custom statistics that
are designed to answer specific behavioral questions less tractable
in general statistical models. However, there is also concern that
agent-based simulations can lead to erroneous results either because
of poor model design or errors in model implementation that go undetected.
A solution to this problem is to design simulations around well-studied
analytical models where the model behavior can be tested against
sound analytical expectations. Only following such testing should
simulation models be extended into areas that cannot be treated
analytically and, only subsequent to this, into applied contexts.
The
UC MaSC Project has four components.
1.
Drawing on methods in statistical physics and the mathematics
of swarms, we are developing formal models of offender movement
and target selection in variously structured environments.
2.
We plan to extend these baseline models to consider offender behavior
on abstract urban street networks.
3.
We will then integrate both model types with Geographic Information
Systems (GIS) by exploring the spatial properties of simulated
crime maps.
4.
At each stage of model development, empirical tests will be conducted
against spatial crime data provided by the Los Angeles, San Diego
and Long Beach police departments. We will concentrate empirical
testing on comparing simulated crime prevention interventions
with known changes in urban planning and policing strategies within
these southern Californian cities.
