DOI: 10.3886/icpsr37959.v1
title: Policing Predicted Crime Areas: An Operationally-Realistic Randomized, Controlled Field Experiment, Philadelphia, Pennsylvania, 2015-2016
title: Version 1

description: The Philadelphia Predictive Policing Experiment was a place-based, randomized control trial to study the impact of different patrol strategies on violent and property crime in predicted crime areas. The experiment's goal was to learn whether different operationally-realistic police responses to crime forecasts, estimated by a predictive policing software program, would reduce crime. Specifically, the study tested whether greater awareness among general duties patrol officers of the predicted crime areas would be sufficient to deter crime, whether a dedicated uniform patrol attendance in predictive areas would increase visible police presence sufficiently in the local area to deter crime, or if dedicated plain-clothes units performing surveillance and unmarked patrol would increase interdiction and offender incapacitation sufficiently to reduce crime. With support of the Philadelphia Police Department, the study took place over two, three-month periods between 2015 and 2016.
description: The goal of this study was to investigate the crime reduction link between certain hot spot policing tactics associated with predictive policing and guided by a predictive policing algorithm.
description: The experiment took place in Philadelphia, Pennsylvania and was conducted in two phases. The property crime phase ran for one shift only for 90 days from June 1, 2015 through August 29, 2015. After a scheduled break, the violent crime phase ran for one shift only for 92 days from November 1, 2015 through January 31, 2016. Predicted crime areas were generated by the HunchLab program designed by Azavea, which predicted three 500 x 500 square foot high-crime grid cells, for each eight-hour experimental shift, occurring once per day, for each district. Twenty Philadelphia police districts were randomly assigned to one of four experimental conditions using block randomization with a 1:1:1:1 allocation ratio across the four conditions. These conditions included (1) a <em>control</em> condition, with a business-as-usual patrol strategy, (2) an <em>awareness</em> condition where officers were made aware of the predicted high crime areas and asked to focus on those areas when able, (3) an enhanced awareness model with a dedicated <em>marked</em> patrol car to exclusively patrol the predicted crime areas, and (4) an enhanced awareness model with a dedicated <em>unmarked</em> vehicle to exclusively patrol the predicted areas.
All recorded police activity and crime incidents in Philadelphia are geolocated with the incident type. This incident database is the city's record of officer-initiated activity, and recorded crime and calls for service that have been verified by a police officer. Two outcome variables were examined, one for each experimental phase. Uniform Crime Reporting (UCR) part one property crimes, excluding arson, were included in the property crime analysis (specifically UCR codes 500-725). For the violent crime phase, only part one violent crimes were included (specifically UCR codes 111-116, 211, 231, 300-315, 350-399 and 411-416).
During the course of the experiment, the research team also collected observational data during ride-alongs with officers who were assigned to patrol the grid areas for the entirety of their eight-hour shift. The field observations comprised of two elements; structured and systematic detailing of officer patrol behavior which was noted on a form in 15-minute increments, and the use of open-ended ethnographic field notes. The observation form was filled out every 15 minutes by the researcher, with multiple options to describe the police activity, as well as administrative information such as date, district, car type (uniform or unmarked), number of officers, and space to detail any other pertinent information relevant to the police activity on the shift. For the open-ended ethnographic field notes, the researchers were instructed to observe the actions of the officers and discuss topics related to the experiment and the software grid placement.
description: <strong>Daily Grid Property Crime Data (Dataset 1):</strong> property crime counts for predicted crime grids per day during the property crime experimental phase.
<strong>Daily District Property Crime Data (Dataset 2):</strong> property crime counts for each study police district per day during the property crime experimental phase.
<strong>Daily Grid Violent Crime Data (Dataset 3):</strong> violent crime counts for predicted crime grids per day during the violent crime experimental phase.
<strong>Daily District Violent Crime Data (Dataset 4):</strong> violent crime counts for each study police district per day during the violent crime experimental phase.
description: The Philadelphia Police Department comprises 22 police districts. One of these districts is the city's international airport, which was excluded from this study, leaving 21 eligible districts. In order to allocate the districts to four treatment conditions using a 1:1:1:1 ratio, the district with the lowest crime frequency in the assignment phase was dropped from the study. When districts were ranked using property crime, this excluded district number 7. When districts were rank ordered using violent crime, this excluded district number 5.
The sample in this study is termed the 'district day' -- that is, an eight-hour shift during each day nested within districts. For each experimental condition there were five districts across a three-month experimental phase. This equates to 450 district days (five districts x 90 days = 450) in each property crime experimental condition and 460 days for the violent crime phase (five districts x 92 days = 460).
description: Datasets: 
DS0: Study-Level Files
DS1: Daily Grid Property Crime Data
DS2: Daily District Property Crime Data
DS3: Daily Grid Violent Crime Data
DS4: Daily District Violent Crime Data
description: Property and violent crime in Philadelphia, Pennsylvania over two, three-month periods between June 2015 and January 2016. Smallest Geographic Unit: Police district
description: coded on-site observation; mixed modeICPSR received GIS data, but it is not available at this time. Other qualitative components of this study (e.g., observational field notes) were not provided.