Government Technology

Los Angeles to Test Simulation Model That Predicts Criminals' Reactions to Increased Policing



July 23, 2010 By

Hot-spot policing, in which officers increase patrols in areas identified as having a disproportionate amount of crime is used by law enforcement agencies to proactively protect communities. But use of the technique has triggered questions about whether it eliminates crime or simply pushes criminals into other areas.

Now researchers from the University of California, Los Angeles (UCLA) have developed a computer simulation model that answers those questions -- and it could help police departments target their crime-fighting resources more effectively.

"It's been known for decades, probably at least since the 1930s, that crime shows very strong spatial and temporal patterning, meaning it forms hot spots," said Jeffrey Brantingham, an associate professor of anthropology at UCLA. "Beginning in about the 1970s, criminologists and law enforcement officials started to say, 'Well, since crime patterns in this way, wouldn't it be reasonable to put extra policing and crime-prevention strategies and direct them right at those hot spots?'"

Many assumed hot spot policing would just cause the criminals to relocate. However, Brantingham said there actually are only a few instances where crime displacement has been observed.

About four years ago, UCLA researchers created a mathematical computer simulation model of crime pattern formation. The model led them to identify two types of crime hot spots that react differently to increased policing - one that relocates and another that dissolves.

"Once we noticed these large-scale patterns forming in the simulation model, we started working on a theory to explain them," said Andrea Bertozzi, a professor of mathematics and director of applied mathematics at UCLA. The researchers published their analysis in February.

Different Outcomes, Same Intervention

Using the mathematical analysis, the researchers can predict how a crime hot spot will respond to increased policing. "It provides that mechanistic model that says these are exactly the conditions under which you should get hot spots forming," Brantingham said. "So it provides boundaries on what sorts of conditions produce hot spots."

That's how the researchers arrived at a "surprising conclusion," he said. The model suggested that there were at least two different types of hot spots - known as super-critical and subcritical - that form under different circumstances. Small spikes in crime form super-critical hot spots. "Little crime events that individually don't seem to be all that significant or all that big, nucleate into a hot spot," Brantingham said. The second type, subcritical, forms during a large, significant spike in crime. The two may appear similar to the public's eye, but they respond differently to intensified policing.

Using 10 years of data from the Los Angeles and Long Beach police departments, the researchers tested the model with information from burglaries. "We did the mathematical equivalent of experiments to test what would happen if you went in and tried to suppress these different types of hot spots," he said. "And lo and behold, you get the two characteristic behaviors that we observed in real experiments of hot spot policing."

The model revealed that when given additional policing, super-critical hot spots displace and form in adjacent regions because they're developed from small crime spikes. "You can't just go and suppress all those small spikes in crime; you're going to suppress the big hot spot," Brantingham said. "The small spikes in crime that are out there in the environment are ready to nucleate into a new one." However, the larger, subcritical hot spots do not re-emerge after increased policing.

Although researchers used burglary data to test the simulation, Bertozzi said the model can apply to other crimes like gang violence or improvised explosive devices in the Middle East.

"Ideally from the technological and crime-prevention strategy perspective, we're pointing in the direction of saying, 'OK, we need to be aware of the different types of hot spots out there,


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