Forecasting for Police Officer Safety: A Demonstration of Concept

Abstract

Abstract Purpose Police officers in the USA are often put in harm’s way when responding to calls for service. This paper provides a demonstration of concept for how machine learning procedures combined with conformal prediction inference can be properly used to forecast the amount of risk associated with each dispatch. Accurate forecasts of risk can help improve officer safety. Methods The unit of analysis is each of 1928 911 calls involving weapons offenses. Using data from the calls and other information, we develop a machine learning algorithm to forecast the risk that responding officers will face. Uncertainty in those forecasts is captured by nested conformal prediction sets. Results For approximately a quarter of a holdout sample of 100 calls, a forecast of high risk was correct with the odds of at least 3 to 1. For approximately another quarter of the holdout sample, a forecast of low risk was correct with an odds of at least 3 to 1. For remaining cases, insufficiently reliable forecasts were identified. A result of “can’t tell” is an appropriate assessment when the data are deficient. Conclusions Compared to current practice at the study site, we are able to forecast with a useful level of accuracy the risk for police officers responding to calls for service. With better data, such forecasts could be substantially improved. We provide examples.