What does an anthropologist standing on a windswept Tibetan plateau have in common with a Los Angeles police detective investigating a crime scene?
To P. Jeffrey Brantingham, assistant professor of anthropology, the two scenarios have definite similarities because both involve piecing together information.
“The process underlying crime analysis is like an archaeological process, starting with a fragmentary record and trying to infer behavior that led to a pattern,” says Brantingham, who does fieldwork on the prehistoric hunter-gatherers of Tibet.
Here in Los Angeles, Brantingham has partnered with two UCLA colleagues in mathematics—Andrea Bertozzi and Professor Lincoln Chayes—and Southern California police departments to bring novel techniques from mathematics to the study of crime. Together with a criminologist at UC Irvine, the team began the UC Mathematical and Simulation Modeling of Crime Project in 2004.
The researchers began with a theory that suggests that the formation of short-term crime patterns are the result of random interactions that play out among three types of individuals—offenders, victims and law-enforcement officers—performing everyday activities and moving through their routines.
See simulations evolve in real time on Brantingham's web site
Most people follow fairly predictable routes and patterns in their daily lives; people go from home to work, work to home, and generally visit the same restaurants, malls, gas stations and gyms. Extending those patterns to the study of crime, “the concept is that offenders, victims and law enforcers move and mix in relatively simple ways that can be described mathematically, and out of that interaction, crime patterns emerge.”
Delving more deeply into the idea, the researchers then analyze past data and can predict future patterns using a series of models from fields as diverse as chemistry, biology and microscale physics that describe mixing patterns.
Police departments in Los Angeles and Long Beach cooperate with Brantingham and his team, opening their records and providing the data for testing the models. As populations grow and cities expand geographically, the police face ever-greater challenges of allocating their manpower to prevent crime. To the extent that Brantingham’s models can predict where future crimes are likely to occur, police will be able to allocate their resources more efficiently. Essentially, the police will change the dynamic of interactions by inserting additional manpower into the equation.
It sounds good in theory, but will it work in practice? Brantingham said that there is already plenty of evidence that Situational Crime Prevention can reduce crime by altering the environment. Situational Crime Prevention can also mean improving street lighting, adding video surveillance cameras—or just getting more pedestrians on the streets.
“In China, urban street crime is growing, but it’s less of a problem than in U.S. urban environments simply because in China so many people sit outside and watch everything that is going on,” said Brantingham. “All those watchers alter the dynamic by removing opportunities to commit crime unnoticed.”
With support from the National Science Foundation and the College of Letters and Science, Brantingham and his team continue to delve into the dynamics of how and why crime patterns cluster in time and space in the ways that they do. “I don’t want to promise a crime-fighting silver bullet,” Brantingham said, ‘but I’m cautiously optimistic that the models we create will give police advance notice of where to put patrols on the ground.”
Adapted from a story by Aaron Dalton in the UCLA College Report