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Research BRIEF

C O R P O R A T I O N

Predictive Policing Forecasting for Enforcement

redictive policing—the application of analytical tech- niques, particularly quantitative techniques, to identify Key findings: promising targets for intervention and prevent or P • is the application of analytical tech- solve crime—can offer several advantages to niques to identify promising targets for police intervention, agencies. Policing that is smarter, more effective, and more with the goal of reducing crime risk or solving past . proactive is clearly preferable to simply reacting to criminal acts. Predictive methods also allow police to make better use • Predictive policing techniques can be used to identify of limited resources. places and times with the highest risk of crime, people at To increase understanding of how predictive policing risk of being offenders or victims, and people who most methods can be used, RAND researchers, with sponsorship likely committed a past crime. from the National Institute of , reviewed the literature • To be effective, predictive policing must include interven- on predictive policing tools, compiled case studies of depart- tions based on analytical findings. Successful interven- ments that have used techniques that appear promising, and tions typically have top-level support, sufficient resources, developed a taxonomy of approaches to predictive policing. automated systems to provide needed information, and assigned personnel with both the freedom to resolve Predictive Policing Taxonomy crime problems and accountability for doing so. The researchers found four broad categories of predictive policing methods, with approaches varying in the amount • Many agencies may find simple heuristics sufficient for and complexity of the data involved: their predictive policing needs, though larger agencies • Methods for predicting crimes: These are approaches used to that collect large amounts of data may benefit from more forecast places and times with an increased risk of crime. complex models. • Methods for predicting offenders:These approaches iden- tify individuals at risk of offending in the future. • Methods for predicting perpetrators’ identities: These tech- niques are used to create profiles that accurately match Predictive Policing Process likely offenders with specific past crimes. Making “predictions” is only half of prediction-led policing. • Methods for predicting victims of crime: Similar to those The other half is carrying out interventions based on the methods that focus on offenders, crime locations, and predictions to reduce criminal activity or solve crimes. times of heightened risk, these approaches are used to At the core of the process is a four-step cycle, as shown identify groups or, in some cases, individuals who are in the figure on page 3. The first two steps involve collect- likely to become victims of crime. ing and analyzing data on crimes, incidents, and offenders to produce predictions. The third step is conducting police The table on the next page summarizes each category operations to intervene on the basis of the predictions. Such and shows the range of approaches that law enforcement interventions, as shown at the bottom of the figure, may be agencies have employed to predict crimes, offenders, perpe- generic (i.e., an increase in resources), crime-specific, or prob- trators’ identities, and victims. The researchers found a near lem-specific. Ideally, these interventions will reduce criminal one-to-one correspondence between conventional crime activity or lead police to solve crimes, the fourth step. Law analysis and investigative methods and the more recent enforcement agencies should assess the immediate effects “predictive analytics” methods that mathematically extend or of the intervention to ensure that there are no immediately automate the earlier methods. visible problems. Agencies should also track longer-term – 2 –

Problem Conventional Predictive Analytics Predicting crimes Identify areas at increased risk Using historical crime data (hot spot identification) Advanced hot spot identification models, risk terrain analysis Using a range of additional data Basic regression models created in a Regression, classification, and clustering (e.g., 911 call records, economics) spreadsheet program models Accounting for increased risk from a Assumption of increased risk in areas Near-repeat modeling recent crime immediately surrounding a recent crime Determine when areas will be at most Graphing/mapping frequency of crimes Spatiotemporal analysis methods risk of crime in a given area by time/date (or specific events) Identify geographic features that increase Finding locations with the greatest Risk-terrain analysis the risk of crime frequency of crime incidents and drawing inferences Predicting offenders Find a high risk of a violent outbreak Manual review of incoming /criminal Near-repeat modeling on recent between criminal groups intelligence reports intergroup Identify individuals who may become Clinical instruments that summarize known Regression and classification models using offenders risk factors for various types of offenders the risk factors Predicting perpetrator identities Identify using a victim’s criminal Manually reviewing Computer-assisted queries and analysis of history or other partial data reports and drawing inferences intelligence and other databases Determine which crimes are part of a Crime linking (use a table to compare Statistical modeling to perform crime series (most likely committed by the same attributes of crimes known to be in a series linking perpetrator) with other crimes) Find a perpetrator’s most likely anchor Locating areas both near and between Geographic profiling tools to statistically point crimes in a series infer the most likely anchor points Find suspects using sensor information Manual requests and review of sensor data Computer-assisted queries and analyses of around a crime scene (GPS tracking, license sensor databases plate reader) Predicting crime victims Identify groups likely to be victims Crime mapping (identifying hot spots for Advanced hot spot identification models; of various types of crime (vulnerable different types of crimes) risk terrain analysis ) Identify people directly affected by at-risk Manually graphing or mapping most Advanced crime-mapping tools to generate locations frequent crime sites and identifying people crime locations and identify workers, most likely to be at these locations residents, and others who frequent these locations Identify people at risk for victimization Review of criminal records of individuals Multi-database queries to identify those at (e.g., people engaged in high-risk criminal known to be engaged in repeated criminal risk; regression and classification models to behavior) activity assess individuals’ risk Identify people at risk of Manual review of domestic disturbance Computer-assisted queries of multiple incidents to identify those at most risk databases to identify domestic and other disturbances involving local residents – 3 –

Data Collection collectionData Altered

environment Data fusion

Criminal Analysis Response Assessment

Intervention

Police Prediction Operations

Situational • Increase resources in areas at greater risk Awareness Generic

Crime- • Conduct crime-specific interventions Provide tailored specific information to all levels • Address specific locations and factors Problem- driving crime risk specific

RAND RR233-S.1

changes by examining collected data, performing additional pret analysis findings (and exclude erroneous findings), analysis, and modifying operations as needed. recommend interventions, and take action to exploit the findings and assess the impact of interventions. Predictive Policing Myths and Pitfalls • You need a high-powered (and expensive) model. In fact, While predictive policing has much promise and has received most departments do not need the most sophisticated much attention, there are myths to be aware of and pitfalls to software packages or computers to launch a predictive avoid when adopting these approaches. Many of the myths policing program. In several of the case studies, simple stem from unrealistic expectations: Predictive policing has heuristics were nearly as good as analytic software in been so hyped that the reality cannot match the hyperbole. performing some tasks for predictive policing. While There are four common myths when it comes to predictive complex models can offer increased predictive power, policing: there may be diminishing returns. • The omputerc actually knows the future. Although much • Predictions automatically lead to crime reductions. news coverage promotes the meme that predictive polic- Predictions are just that. Actual decreases in crime ing is a “crystal ball,” the resulting algorithms predict the require taking action based on predictions. Predictive risk of future events, not actual events. Computers can policing is part of an end-to-end process. dramatically simplify the search for patterns, but their predictions will be only as good as the data used to make To ensure that predictive policing realizes its potential, them. law enforcement agencies need to avoid some common pitfalls: • The computer will do everything for you. On the con- • Focusing on accuracy instead of utility. It may be accurate trary, even with the most complete software suites, to characterize an entire as a crime hot spot, but humans must find the relevant data, process these data such a large area is not “actionable” when it comes to for analysis, design and conduct analyses in response to planning police interventions with limited resources. ever-changing crime characteristics, review and inter- To identify hot spots that are small enough for police to – 4 –

realistically take action, analysts must accept some limits Conclusions on accuracy as measured by the proportion of overall For law enforcement agencies that are considering adopt- crime captured by the data. ing predictive policing tools, the key value is in situational • Relying on poor-quality data. Three typical deficiencies awareness. Small agencies with relatively few crimes and in data quality are data censoring, or omitting data on reasonably understandable crime patterns may need only incidents of interest in particular places or at particular relatively simple capabilities, such as those provided by basic times; systematic , which may result from how data spreadsheet or statistical programs. Larger agencies with are collected; and irrelevant data, or data that are not higher data demands may need more sophisticated systems useful for the specific problem being addressed. that are interoperable with existing systems and those in • Misunderstanding the factors behind the prediction. When other . applying techniques like regression or data mining, For the developer, the researchers suggest that vendors analysts should use common sense in selecting factors for describe their systems as identifying crime risks, not foretell- analysis to avoid acting on spurious relationships. ing them. Developers must also be aware of the financial • Underemphasizing assessment and evaluation. During limitations that law enforcement agencies face in procuring interviews with practitioners, RAND researchers found and maintaining new systems. Vendors should consider busi- that very few respondents had evaluated the effectiveness ness models that make predictive systems more affordable for of their departments’ predictions. Measuring effective- smaller agencies, such as regional cost sharing. ness is key to identifying areas for improvement and For the crime fighter, the researchers emphasize that allocating resources efficiently. predictive policing must complement actions taken to inter- • Overlooking civil and rights. Designating cer- dict crimes. Successful interventions typically have top-level tain areas or individuals as meriting law enforcement support, sufficient resources, automated systems provid- action raises civil and privacy rights concerns. The U.S. ing needed information, and assigned personnel with both Supreme has ruled that standards for reasonable the freedom to resolve crime problems and accountability suspicion are relaxed in “high-crime areas,” but what for doing so. Designing intervention programs with such constitutes such an area and what measures may be attributes, combined with solid predictive analytics, can go a taken are open questions. long way toward ensuring that predicted crime risks do not become real crimes.

This research brief describes work done for RAND Justice, Infrastructure, and Environment and documented in Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations, by Walter L. Perry, Brian McInnis, Carter C. , Susan C. Smith, and John S. Hollywood, RR-233-NIJ (available at http://www.rand.org/pubs/research_reports/RR233.html), 2013. The RAND Corporation is a nonprofit research institution that helps improve and decisionmaking through research and analysis. RAND’s publications do not necessarily reflect the opinions of its research clients and sponsors. R® is a registered trademark. © RAND 2013

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