Sep 25, 2013
Predictive policing — the application of analytical techniques, particularly quantitative techniques, to identify promising targets for police intervention and prevent or solve crime — can offer several advantages to law enforcement agencies. Policing that is smarter, more effective, and more proactive is clearly preferable to simply reacting to criminal acts. Predictive methods also allow police to make better use of limited resources.
To increase understanding of how predictive policing methods can be used, RAND researchers, with sponsorship from the National Institute of Justice, reviewed the literature on predictive policing tools, compiled case studies of departments that have used techniques that appear promising, and developed a taxonomy of approaches to predictive policing.
The researchers found four broad categories of predictive policing methods, with approaches varying in the amount and complexity of the data involved:
The table summarizes each category and shows the range of approaches that law enforcement agencies have employed to predict crimes, offenders, perpetrators' identities, and victims. The researchers found a near one-to-one correspondence between conventional crime analysis and investigative methods and the more recent "predictive analytics" methods that mathematically extend or automate the earlier methods.
|Problem||Conventional Crime Analysis||Predictive Analytics|
|Identify areas at increased risk|
|Using historical crime data||Crime mapping (hot spot identification)||Advanced hot spot identification models, risk terrain analysis|
|Using a range of additional data (e.g., 911 call records, economics)||Basic regression models created in a spreadsheet program||Regression, classification, and clustering models|
|Accounting for increased risk from a recent crime||Assumption of increased risk in areas immediately surrounding a recent crime||Near-repeat modeling|
|Determine when areas will be at most risk of crime||Graphing/mapping frequency of crimes in a given area by time/date (or specific events)||Spatiotemporal analysis methods|
|Identify geographic features that increase the risk of crime||Finding locations with the greatest frequency of crime incidents and drawing inferences||Risk-terrain analysis|
|Find a high risk of a violent outbreak between criminal groups||Manual review of incoming gang/criminal intelligence reports||Near-repeat modeling on recent intergroup violence|
|Identify individuals who may become offenders||Clinical instruments that summarize known risk factors for various types of offenders||Regression and classification models using the risk factors|
|Predicting perpetrator identities|
|Identify suspects using a victim's criminal history or other partial data||Manually reviewing criminal intelligence reports and drawing inferences||Computer-assisted queries and analysis of intelligence and other databases|
|Determine which crimes are part of a series (most likely committed by the same perpetrator)||Crime linking (use a table to compare attributes of crimes known to be in a series with other crimes)||Statistical modeling to perform crime linking|
|Find a perpetrator's most likely anchor point||Locating areas both near and between crimes in a series||Geographic profiling tools to statistically infer the most likely anchor points|
|Find suspects using sensor information around a crime scene (GPS tracking, license plate reader)||Manual requests and review of sensor data||Computer-assisted queries and analyses of sensor databases|
|Predicting crime victims|
|Identify groups likely to be victims of various types of crime (vulnerable populations)||Crime mapping (identifying hot spots for different types of crimes)||Advanced hot spot identification models; risk terrain analysis|
|Identify people directly affected by at-risk locations||Manually graphing or mapping most frequent crime sites and identifying people most likely to be at these locations||Advanced crime-mapping tools to generate crime locations and identify workers, residents, and others who frequent these locations|
|Identify people at risk for victimization (e.g., people engaged in high-risk criminal behavior)||Review of criminal records of individuals known to be engaged in repeated criminal activity||Multi-database queries to identify those at risk; regression and classification models to assess individuals' risk|
|Identify people at risk of domestic violence||Manual review of domestic disturbance incidents to identify those at most risk||Computer-assisted queries of multiple databases to identify domestic and other disturbances involving local residents|
Making "predictions" is only half of prediction-led policing. The other half is carrying out interventions based on the predictions to reduce criminal activity or solve crimes.
At the core of the process is a four-step cycle, as shown in the figure. The first two steps involve collecting and analyzing data on crimes, incidents, and offenders to produce predictions. The third step is conducting police operations to intervene on the basis of the predictions. Such interventions, as shown at the bottom of the figure, may be generic (i.e., an increase in resources), crime-specific, or problem-specific. Ideally, these interventions will reduce criminal activity or lead police to solve crimes, the fourth step. Law enforcement agencies should assess the immediate effects of the intervention to ensure that there are no immediately visible problems. Agencies should also track longer-term changes by examining collected data, performing additional analysis, and modifying operations as needed.
While predictive policing has much promise and has received much attention, there are myths to be aware of and pitfalls to avoid when adopting these approaches. Many of the myths stem from unrealistic expectations: Predictive policing has been so hyped that the reality cannot match the hyperbole. There are four common myths when it comes to predictive policing:
To ensure that predictive policing realizes its potential, law enforcement agencies need to avoid some common pitfalls:
For law enforcement agencies that are considering adopting predictive policing tools, the key value is in situational awareness. Small agencies with relatively few crimes and reasonably understandable crime patterns may need only relatively simple capabilities, such as those provided by basic spreadsheet or statistical programs. Larger agencies with higher data demands may need more sophisticated systems that are interoperable with existing systems and those in other jurisdictions.
For the developer, the researchers suggest that vendors describe their systems as identifying crime risks, not foretelling them. Developers must also be aware of the financial limitations that law enforcement agencies face in procuring and maintaining new systems. Vendors should consider business models that make predictive systems more affordable for smaller agencies, such as regional cost sharing.
For the crime fighter, the researchers emphasize that predictive policing must complement actions taken to interdict crimes. Successful interventions typically have top-level support, sufficient resources, automated systems providing needed information, and assigned personnel with both the freedom to resolve crime problems and accountability for doing so. Designing intervention programs with such attributes, combined with solid predictive analytics, can go a long way toward ensuring that predicted crime risks do not become real crimes.