The Philadelphia Predictive Policing Experiment was a city-wide, place-based, randomized experiment to study the impact of different police strategies on violence and property crime in predicted criminal activity areas.
The experiment was designed to test two theoretically-relevant operational questions about police patrol. If police are able to dedicate a car to predicted crime areas, would it be better to use a marked car or an unmarked car? The visible police car would emphasize deterrence and prevention. The plain-clothes car would allow officers to conduct surveillance and approach criminal activity undetected. The experiment also examined if it was effective to simply tell officers on roll call where the predicted grids were for the day without having a car dedicated to the task. These three interventions were compared to control areas where ‘business-as-usual’ policing was conducted.
The experiment ran in two phases; a three month property crime phase, and a three month violent crime phase. For each day during these phases, predictive software identified three small grid cells in each district for an eight-hour tour. Districts responded to these predicted crime areas depending on their experimental assignment. The Research and Analysis section of the Philadelphia Police Department worked with the academic researchers to randomly assigned 20 Philadelphia Police Department (PPD) districts into one of four experimental conditions.
- In five districts, officers were made aware of the predicted high crime activity area at roll call and asked to concentrate there when able (the awareness treatment).
- Five districts received the awareness model treatment as well as a patrol car solely dedicated to the predicted crime area (marked car).
- Finally, five districts received an intelligence-led, investigative response with an unmarked unit dedicated to the predicted area (unmarked car).
- Control districts were areas where police personnel did not have access to the crime prediction software, so they maintained a standard patrol strategy.
A three page pdf has a fuller description of the research methodology.
When examining both predicted high-crime grid cells and the grids cells immediately surrounding them, the marked car patrols resulted in a 31% reduction in property crime counts, or a 36% reduction in the number of cells experiencing at least one crime. While this sounds substantial, the specific numbers are small. Small micro-grids will never generate a massive volume of crime in a short time frame, therefore the raw numbers will remain small. This translates to a reduction in three crimes over 3 months for an average city district patrolling around three grids. To extrapolate, if each of the 21 geographic districts dedicated a marked car to three grids for an 8 hour shift each day, we estimate a reduction in 256 Part I property crimes per year.
There were also signs of a temporal diffusion of benefits to the eight hours after the property crime marked car patrols. While the percentages were substantial, the results were not statistically significant due to floor effects. There were no crime reduction benefits associated with the violent phase of the experiment, nor were there any benefits with the property crime awareness or unmarked car interventions. A two page pdf has a more detailed description of the experimental results.
And at the district level?
We also explored what happened at the district level. During the property experiment, expected weekly property crime counts for the entire district were between three and eight percent lower in the marked car districts compared to the control districts.
In summary, because of statistical limitations we should recognize the limitations in categorically stating that the implementation of a dedicated marked car to property crime predicted micro-grid areas reduced crime; however, both the micro-level grid analysis and the broader district analysis are supportive of this implication. It appears that marked police cars dedicated to predictive policing areas were effective at reducing some property crime. Unmarked cars, and efforts to combat violence, were not shown to be effective in the Philadelphia Predictive Policing Experiment.
A note on software efficacy
We used the HunchLab program designed by Azavea. HunchLab is a web-based predictive policing system that accesses real-time Philadelphia Police data to produce crime forecasts for the city. It incorporates statistical modeling that considers seasonality, risk terrain modeling, near repeats, and collective efficacy. Azavea adapted the software at the request of the Philadelphia Police Department and the research team to generate three predicted 500 feet square grids per district per shift. They also included a slight randomization component to reduce the possibility that the same grid cells were predicted every day. It is important to note therefore that the experiment artificially reduced the efficiency of the software, because it forced the software to choose grids in low crime districts, and limited the number of grids it could assign in high crime districts.
The software was able to predict twice as much crime as we would expect if crime were spread uniformly across the districts, even when artificially constrained by our experiment to be less effective than designed. A two page pdf has more details of the software efficacy.
Downloadable study reports
- A two-page pdf summary of the experimental results.
- A three-page pdf summary of the experimental design.
- A two-page pdf overview of the prediction software.