Jeffrey Brantingham, UCLA associate professor of anthropology, has been working for years with Andrea Bertozzi, director of applied mathematics and professor at UCLA, to apply sophisticated math to urban crime patterns. Their goal is simply to gain a better understanding and definition of different types of criminal hotspots—areas where crime frequently occurs.
They use the systematic methods commonly applied to math and science to assist law enforcement prevent and reduce crime.
“We have a key to understand real-world phenomena,” Bertozzi was quoted as saying in a UCLA press release.
“Predicting crime and devising better crime-prevention strategies requires a mechanistic explanation for how and why crime occurs where it does and when it does,” said Brantingham.
That is where the math comes in.
Brantingham and Bertozzi’s mathematical model goes beyond crime mapping, and locating hotspots, commonly done through Geographic Information System (GIS) technology, to identifying different types of hotspots and understanding how each type may respond to targeted police action.
In Brantingham and Bertozzi’s latest research, to be featured in the March 2 issue of the Proceedings of the National Academy of Sciences (PNAS), they assert that there are at least two different types of crime hot spots. Some are generated by small spikes in crime that grow (sub-critical hotspots). In others, a large spike in crime pulls offenders into a central location (supercritical hotspots).
The two types look the same from the surface, but they are not.
“If you were to send police into a hotspot without knowing which kind it is, you would not be able to predict whether you will just cause displacement of crime—moving it somewhere else, which is what our model predicts if it’s a hotspot generated by small fluctuations in crime—or whether you will actually reduce crime,” said Brantigham of the study.
Brantingham acknowledges the argument that human behavior is far too inconsistent to be predicted with mathematical formulas.
“Many social scientists say human behavior and criminal behavior are too complex to be explained with a mathematical model,” said Brantingham, who was trained as an archaeologist. “But it’s not too complex. We’re not trying to explain everything, but there are many aspects of human behavior that are easily understood in a formal mathematical structure. There are regularities to human behavior that we can understand mathematically.”
Brantingham currently works with the Los Angeles Police Department (LAPD) to analyze crime patterns, and he utilized LAPD data to conduct his recent research.
The PNAS report includes the work of collaborators, Martin Short, UCLA assistant adjunct professor of mathematics, and George Tita, associate professor of criminology, law and society at UC Irvine.
The research was funded by the National Science Foundation.
They use the systematic methods commonly applied to math and science to assist law enforcement prevent and reduce crime.
“We have a key to understand real-world phenomena,” Bertozzi was quoted as saying in a UCLA press release.
“Predicting crime and devising better crime-prevention strategies requires a mechanistic explanation for how and why crime occurs where it does and when it does,” said Brantingham.
That is where the math comes in.
Brantingham and Bertozzi’s mathematical model goes beyond crime mapping, and locating hotspots, commonly done through Geographic Information System (GIS) technology, to identifying different types of hotspots and understanding how each type may respond to targeted police action.
In Brantingham and Bertozzi’s latest research, to be featured in the March 2 issue of the Proceedings of the National Academy of Sciences (PNAS), they assert that there are at least two different types of crime hot spots. Some are generated by small spikes in crime that grow (sub-critical hotspots). In others, a large spike in crime pulls offenders into a central location (supercritical hotspots).
The two types look the same from the surface, but they are not.
“If you were to send police into a hotspot without knowing which kind it is, you would not be able to predict whether you will just cause displacement of crime—moving it somewhere else, which is what our model predicts if it’s a hotspot generated by small fluctuations in crime—or whether you will actually reduce crime,” said Brantigham of the study.
Brantingham acknowledges the argument that human behavior is far too inconsistent to be predicted with mathematical formulas.
“Many social scientists say human behavior and criminal behavior are too complex to be explained with a mathematical model,” said Brantingham, who was trained as an archaeologist. “But it’s not too complex. We’re not trying to explain everything, but there are many aspects of human behavior that are easily understood in a formal mathematical structure. There are regularities to human behavior that we can understand mathematically.”
Brantingham currently works with the Los Angeles Police Department (LAPD) to analyze crime patterns, and he utilized LAPD data to conduct his recent research.
The PNAS report includes the work of collaborators, Martin Short, UCLA assistant adjunct professor of mathematics, and George Tita, associate professor of criminology, law and society at UC Irvine.
The research was funded by the National Science Foundation.
