Developing a Practical Forecasting Screener for Domestic Violence Incidents

By Richard A. Berk, Yan He, Susan B. Sorenson. 2005

50 citations recorded by the source corpus.

Subfields: Demography, Global and Planetary Change, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. Jiaming Zeng
2. Berk Ustun
3. Cynthia Rudin
4. Sharad Goel
5. Justin M. Rao
6. Ravi Shroff
7. Richard A. Berk

In Global and Planetary Change

1. Jiaming Zeng
2. Berk Ustun
3. Cynthia Rudin
4. Justin Bleich
5. Sharad Goel
6. Justin M. Rao
7. Ravi Shroff
8. Richard A. Berk

In Statistics and Probability

1. Cynthia Rudin
2. Berk Ustun
3. Jiaming Zeng
4. Sharad Goel
5. Richard A. Berk
6. Ravi Shroff
7. Justin Bleich
8. G. Barnes

Citations in the corpus

• Interpretable classification models for recidivism prediction (2015, methodology): “Berk et al. (2005) conducted a study that helped the Los Angeles Sheriff’s Department develop a simple and practical screener for forecasting domestic violence, also showing that decision trees were more accurate than logistic regression.”
• Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning (2009, unknown): “We have had some success with this implementation of random forests in previous work (Berk et al., 2005, 2006; Lennert-Cody and Berk, 2007).”
• Forecasts of Violence to Inform Sentencing Decisions (2013, background): “One might ordinarily apply a tree-based form of machine learning already shown to be effective in related circumstances (Barnes et al. 2010; Berk et al. 2005, 2006, 2009).”
• Personalized risk assessments in the criminal justice system (2016, methodology): “This work builds on past risk assessment tools for aiding officers in the field (Berk, He, and Sorenson 2005).”
• Asymmetric Loss Functions for Forecasting in Criminal Justice Settings (2011, background): “, 2007), repeat domestic violence incidents (Berk et al., 2005) and others.”
• Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions (2016, background): “…forecasting applications in criminal justice have addressed intimate partner violence to support police decisions when they arrive at the scene (Berk et al. 2005), mis-conduct in prison to help prison administrators assign inmates to appropriate security levels (Berk et al. 2006), homicides…”