Using statistics and analytical data to predict criminal activity has become standard practice in many police departments across the United States. Crime forecasting may get more accurate as new computer algorithms are developed, but experts believe that fresh data streams, not technology advancements, will drive innovation in predictive policing during the next 20 years.
Analysts currently identify crime trends using statistical data on arrests and 911 calls. Based on that information, police commanders deploy officers to areas they believe will be hot spots for illegal activities. But while predictive in nature, the effort is largely reactionary based on past events.
In the future, behavioral data and clues from virtual interactions may help cops stop bad guys before they’ve even drawn up a plan. Think Minority Report — the 2002 film where a police unit was able to arrest murderers before they committed a crime — on a more realistic scale.
We’re not quite there yet, however. The ability to accurately state that a crime will occur at a specific time in a small area is still very much science fiction. In reality, the process is similar to economic forecasting where different factors are compiled to build a statistical model to predict outcomes.
More sophisticated modeling can be done, but the return on investment is likely marginal as we’re close to the limits of accuracy with current data, according to John Hollywood, operations researcher with RAND Corp., a nonprofit organization that helps improve policy and decision-making through research and analysis.
“In order to really get into crystal-ball accuracy, you basically need to get inside peoples’ heads,” Hollywood said. “That is getting you out of the realm of statistics and computer science and much more into the realm of behavioral and social science.”
Noah Fritz, past president of the International Association of Crime Analysts and crime analysis manager for the San Diego County, Calif., Sheriff’s Department, agreed. He said there is potential for growth in the area of environmental criminology where you examine a person’s journey to a life of crime and peoples’ routine activities and habits.
“We all have routines, and if we make better sense of those routines I think we can then predict and forecast how many days out a person is going to commit another crime,” Fritz said. “Whether this is because they are addicted to drugs or because they have a propensity, in some ways we [need to] just do a better job of matching the temporal pattern and the geographic patterns together.”
Some hurdles remain, however. Fritz said privacy rights may impede some behavioral data progress and the U.S. doesn’t invest enough in behavioral data research and how it ties with predictive analytics.
But the work isn’t being ignored.
Overland Park, Kan., Police Chief John Douglass likened the efforts being made in behavioral data and its relation to predictive policing to cancer research. Just as cancer scientists look back in time through genetics to find common denominators so they can create a cure, Douglass said data scientists are doing the same thing by looking for criminal signatures and those factors that will help better predict criminal behavior.
One potential source for new data could be the Level of Service Inventory-Revised (LSI-R) Assessment. An internationally recognized quantitative survey of offender attributes and offender situations relevant for making decisions about levels of supervision and treatment, the LSI-R results could provide valuable data on what motivates a criminal to commit a crime. The assessment is typically given to those going on parole.
Dawn Clausius, police intelligence analyst with the Olathe, Kan., Police Department, believes that the assessment holds a mountain of untapped data for predictive policing efforts. She said that currently a prisoner’s assessment results are used only by parole officers or counselors within specific facilities. But eventually the data could be shared with detectives or police officers.
Local, state and federal government entities must get together with state corrections departments and law enforcement personnel and make an effort to share the information, Clausius said.
Instead of just identifying and arresting the bad guy, Clausius believes that if cops had the resources and ability to sit down with criminals and find out what motivated them, they could acquire data that could help prevent future crimes.
Some work is being done in the U.S. to examine how offenders behave and places they frequent in a community. Applications exist where an algorithm can provide an idea where an offender might live in relation to where crimes are occurring. But Clausius would like to see that work done on a more micro-level.