Developing a Practical Forecasting Screener for Domestic Violence Incidents

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domestic violence
data mining
spousal assault
family violence
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In this paper, we report on the development of a short screening tool that deputies in the Los Angeles Sheriff's Department could use in the field to help forecast domestic violence incidents in particular households. The data come from over 500 households to which sheriff's deputies were dispatched in the fall of 2003. Information on potential predictors was collected at the scene. Outcomes were measured during a three month follow-up. The data were analyzed with modern data mining procedures in which true forecasts were evaluated. A screening instrument was then developed based on a small fraction of the information collected. Making the screening instrument more complicated did not improve forecasting skill. Taking the relative costs of false positives and false negatives into account, the instrument correctly forecasted future calls for service about 60% of the time. Future calls involving domestic violence misdemeanors and felonies were correctly forecast about 50% of the time. The 50% figure is especially important because such calls require a law enforcement response and yet are a relatively small fraction of all domestic violence calls for service. A number of broader policy implications follow. It is feasible to construct a quick-response, domestic violence screener that is practical to deploy and that can forecast with useful skill. More informed decisions by police officers in the field can follow. Although the same kinds of predictors are likely to be effective in a wide variety of jurisdictions, the particular indicators selected will vary in response to local demographics and the local costs of forecasting errors. It is also feasible to evaluate such quick-response threat assessment tools for their forecasting accuracy. But, the costs of forecasting errors must be taken into account. Also, when the data used to build the forecasting instrument are also used to evaluate its accuracy, inflated estimates of forecasting skill are likely.

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© 2005 Sage Publications. Postprint version. Published in Evaluation Review, Volume 29, Issue 4, pages 358-383 Publisher URL: 10.1177/0193841X05275333. NOTE: At the time of publication, authors Susan B. Sorenson and Richard A. Berk were affiliated with the University of California. Currently (August 2007), she is a faculty member in the School of Social Policy and Practice, and he is a faculty member in the Department of Criminology at the University of Pennsylvania.
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