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The Philadelphia predictive policing experiment

Abstract

Objectives

This place-based, randomized experiment explored the impact of different patrol strategies on violent and property crime in microscale predicted crime areas. The experiment aimed to learn whether different but operationally realistic police responses to crime forecasts, estimated by a predictive policing software program, could reduce crime.

Methods

Twenty Philadelphia city districts were randomized to three interventions and one control condition. The three interventions comprised awareness districts (where officers were made aware of predicted areas on roll-call), marked car districts (where a marked patrol police car was dedicated to treatment areas), and unmarked car districts (a plain-clothes vehicle was dedicated to treatment areas). A business-as-usual approach represented the control condition in districts where staff had no access to the predictive software program. Two distinct 3-month phases examined crime outcomes for property and violent crime, respectively.

Results

The marked car treatment showed substantial benefits for property crime (31% reduction in expected crime count), as well as temporal diffusion of benefits to the subsequent 8-h period (40% reduction in expected crime count). No other intervention demonstrated meaningful crime reduction. These reductions were probably not substantial enough to impact city or district-wide property crime. Some violent crime results ran contrary to expectations, but this happened in a context of extremely low crime counts in predicted areas. The small grid size areas hampered achieving statistical power.

Conclusions

The experiment found reductions in property crime resulting from the marked car focused patrols. It also demonstrated the real-world challenges of estimating and preventing crime in small areas.

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Notes

  1. 1.

    http://www.nij.gov/topics/law-enforcement/strategies/predictive-policing/symposium/Pages/technical-breakout.aspx

  2. 2.

    “Predictive policing…is hot-spot policing, significantly enhanced by technology” [Bill Bratton, NPR interview (Around the Nation), November 26, 2011].

  3. 3.

    Perry et al. (2013) consolidated these approaches into what they called blended theory, but environmental criminologists usually refer to these as the opportunity theories.

  4. 4.

    Source: https://www.census.gov/quickfacts/fact/table/philadelphiacountypennsylvania/PST045216

  5. 5.

    Source: FBI Crime in the US, Table 8 (Offenses known to Law Enforcement by City) and Table 2 (“Crime in the United States, by Community Type, 2015).

  6. 6.

    While this list includes the variables that were used as inputs to the models, it is important to note that HunchLab used a different model for each crime type. Log files that identified which variables were relevant in predicting each crime type were no longer available at the time this manuscript was drafted due to the transfer of ownership from Azavea to ShotSpotter; therefore, it is unknown how important each of these variables were in generating the forecast for each crime type.

  7. 7.

    At the request of the police department, we have anonymized the district location information. Therefore, A–E may, or may not, include districts also identified as F–J.

  8. 8.

    The expected odds = exp((log odds for control district (constant)) + (coefficient for marked condition)), then calculating the expected proportion. The expected proportion is derived as [odds ratio / (1 + odds ratio)]

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Funding

This research was made possible by award number 2014-R2-CX-0002 from the National Institute of Justice.

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Correspondence to Jerry H. Ratcliffe.

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Conflict of interest

Any views or opinions expressed herein do not necessarily reflect the official policies of the Department of Justice, the National Institute of Justice, the Philadelphia Police Department, or the City of Philadelphia. Any mention of trade names, commercial practices, or organizations does not imply endorsement by the Department of Justice, the National Institute of Justice, the Philadelphia Police Department, or the City of Philadelphia. None of the authors has any financial investment in Azavea, Inc. or ShotSpotter Inc., and none of the authors has received any financial or other incentive from either company related to the HunchLab product.

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Ratcliffe, J.H., Taylor, R.B., Askey, A.P. et al. The Philadelphia predictive policing experiment. J Exp Criminol 17, 15–41 (2021). https://doi.org/10.1007/s11292-019-09400-2

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Keywords

  • Predictive policing
  • Randomized controlled trial
  • Police patrol
  • Philadelphia
  • Police