Place-Based Predictive Policing

Place-based predictive policing forecasts where and when crime is most likely to occur, usually as small boxes on a map, so patrols can be directed to those locations. It is distinguished from individual risk scoring, which forecasts what a named person will do; here the unit of prediction is a place.

The approach spread rapidly from around 2010 and has contracted sharply since. The leading product, PredPol — later Geolitica — ceased operations at the end of 2023 and is covered in detail on its own page.

HOW IT WORKS

Models ingest historical crime incident reports and identify spatial and temporal clustering. The best-known implementation drew on earthquake aftershock mathematics, on the theory that crime clusters in space and time in comparable ways: an offence in a location raises the near-term probability of another nearby.

The output is a patrol instruction, not an investigative lead. Officers are directed to be present in the predicted area during the predicted window.

THE EVIDENCE

Independent analysis has been unusually damaging. The Markup's examination of 23,631 predictions generated for one US department found a success rate below half a percent, and — more consequentially — that arrest rates in predicted areas were the same whether or not a prediction had been made. The full findings are set out on the PredPol page.

Acoustic gunshot detection, covered separately, is a related but distinct proposition: it reports where a shot has already been fired rather than forecasting where one will be.

THE CASE FOR IT

Patrol resources are finite and must be allocated somehow. The realistic alternative is officer intuition and habit, which carries its own well-documented biases and cannot be audited. A model at least applies consistent criteria and can be examined — as it was.

Place-based prediction is also a narrower intrusion in principle than scoring individuals. It directs attention to a location rather than attaching a risk label to a person, and nobody acquires a record because a model flagged their street.

THE CASE AGAINST

The feedback problem is structural rather than a defect of any particular product. A model trained on recorded crime learns where police have previously been, because recorded crime is a record of enforcement as much as of offending. Directing patrols there produces more recorded incidents there, which feeds back into the model. The scholar Ruha Benjamin characterised this as a crime production algorithm, and better engineering does not resolve it.

The distributional consequence follows: predictions concentrate in neighbourhoods already heavily policed, with the additional stops and interactions falling on those communities regardless of what is found.

And the accuracy evidence, where it exists, is poor. A defence of prediction over intuition requires the prediction to be better than intuition, and the strongest independent analysis available found no measurable difference in outcomes.

WHAT IS NOT ESTABLISHED

Whether successor products from other vendors reproduce the same weaknesses is untested; no independent evaluation of the replacements has been identified.

How many forces worldwide currently operate place-based prediction is not centrally recorded.

No study identified compares crime outcomes in areas using place-based prediction against comparable areas that do not.