Precrime is a system which punishes people with imprisonment for murders they would have committed, had they not been prevented.
Singularity Hub – In July, 2011 the Santa Cruz Police Department began experimenting with an interesting bit of software developed by scientists at Santa Clara University. The researchers behind the software are a team of specialists: two mathematicians, an anthropologist and a criminologist.
Artificial intelligence augments human intuition. Predictive policing software avoids human bias.
Geographic profiling is the problem of estimating the residence (or place of work) of a criminal offender given the locations of crimes committed by the offender. We have developed an agent-based, Bayesian method for geographic profiling that calculates a prior distribution of residences using housing/population density and a prior distribution of foraging parameters using historic crime data. The method attempts to take into account how criminals interact with their heterogeneous environment.
Our research incorporates observed patterns of criminal behavior into macroscopic models of crime in a systematic way. One successful approach so far is to model crime as a Poisson process of “background” events, each of which can trigger a sequence of aftershocks analogous to those in seismology. Such a point process is referred to as “self-exciting”. Predictions can then be made by flagging the neighborhoods in the city where the conditional intensity takes on its highest values:
Below is the conditional intensity for burglaries in the San Fernando Valley of Los Angeles for three consecutive days. The intensity acts as an evolving risk surface that could be used by police to direct patrols. The five percent of cells with the highest estimated risk are flagged as the most likely burglary targets. Red indicates newly flagged cells and yellow indicates the removal of a flag. Our findings indicate that forecasting strategies based upon self-exciting point processes significantly outperform recently proposed strategies based upon Crime Hotspot Maps.
The Santa Clara software isn’t the first of its kind. Other police departments have been experimenting with their own predictive software. But according to Dr. Mohler, comparisons show that their software outpredicts the others. And they plan to develop software that predicts crimes other than burglaries. Because gang violence begets more gang violence it is amenable to the same type of chain reaction-dependent analysis. Dr. Mohler and his colleagues have already begun working on a gang violence model using the activities of three gang rivalries in Los Angeles. Evidently retaliations commonly occur within days of and at nearly the same location as the initial attack. Dr. Mohler hopes software might be developed for still other types of crimes in the future.
One impetus for adopting predictive policing is the downturn in the US economy. As police departments are pressured to downsize it becomes that much more important to patrol intelligently and efficiently. With only 26 officers for every 10,000 residents Los Angeles is particularly short-handed (Chicago has 46). “We’re facing a situation where we have 30 percent more calls for service but 20 percent less staff than in the year 2000, and that is going to continue to be our reality,” Mr. Friend told the New York Times. “So we have to deploy our resources in a more effective way, and we thought this model would help.”