Funded by the US National Science Foundation

 

 

 

Monthly auto theft hot spots in Los Angeles (LAPD jurisdiction) January to August 2003.
 
 

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.


Simulated offender movement and abstract mapping of crime locations. (a) Simulated offender following a Lévy mobility strategy. Lévy mobility shows clusters of short distance flights interspersed with longer distance flights. (b) Mapped crime locations assuming that criminal opportunities are uniformly distributed in space and that offenses occur only at the end points of Lévy flights. (c) Inverse distance weighted interpolation of offense locations showing crime hot spots generated by the underlying Lévy mobility strategy.


The UC MaSC Project will help clarify the quantitative relationships between criminal behavior, criminal opportunities and policing and may provide insight into how to design better crime prevention strategies, contributing to a broader dialog on homeland security. Simultaneous development of mathematical and simulation models, as well as continuous empirical testing, will provide a guide for the experimental use of these tools in the social sciences, while the broad interdisciplinary foundation of the project will provide a model for collaboration between mathematicians and social scientists. The educational component, including planned supplemental REU training, will provide an excellent venue for developing the research careers of students and postdoctoral associates at all levels.

Simulation of spontaneous burglary hotspot nucleation. Panels on the left show the cumulative distribution of locations visited in a two-dimensional plane. Panels on the right show the cumulative distribution of burgled houses. Simulation code written by Jon Azose, a Harvey Mudd undergraduate Math student participating in the NSF Harvey Mudd-UCLA Research Training Group program run by Andy Bernoff.




P. Jeffrey Brantingham (PI)
UCLA Anthropology
branting@ucla.edu
webpage


Andrea Bertozzi
UCLA Mathematics
bertozzi@math.ucla.edu
webpage


George Tita
UCI Criminology, Law and Society
gtita@uci.edu
webpage


Lincoln Chayes
UCLA Mathematics
lchayes@math.ucla.edu
webpage

Los Angeles Police Department


Long Beach Police Department
   


Maria D'Orsogna 2005-2007
Now @ CSUN
homepage

Virginia Pasour 2006-2008
Now @ Army Research Office

Martin Short 2007-
mbshort@math.ucla.edu
homepage



George Mohler 2008-2010
Now @ Santa Clara University
gmohler@math.ucla.edu
homepage

 

Alethea Barbaro 2008-
alethea@math.ucla.edu
homepage
 

   

Nancy Rodriguez
UCLA Mathematics
PhD Candidate
homepage


Charles Perreault
UCLA Anthropology
PhD Candidate
homepage

   

Erik Lewis
UCLA Mathematics
PhD Candidate
homepage

Laura Smith
UCLA Mathematics
PhD Candidate
homepage

   

Rachel Danson
UCLA Mathematics
PhD Candidate
homepage

Nancy Rodriguez
UCLA Mathematics
PhD Candidate
homepage

   
 
 

Paul A. Jones
UCLA Mathematics
PhD 2010
Now working @ Facebook

homepage

 

Ethan Brown
UCLA Mathematics
PhD Candidate
NSF VIGRE Participant 2005

Silas Boren
UCLA Anthropology
Undergraduate Major
Autotheft Research 2004-2006
   
Shao Shuanglin
UCLA Mathematics
PhD Candidate
NSF VIGRE Participant 2005

Kenn Tevin
Harvey Mudd Math/Computer Science
Undergraduate Major
Harvey Mudd-UCLA Summer Research 2005





 

hits since 06-22-06