Using Body Worn Camera Footage to Investigate Predictors of Officer Behavior and the Outcomes of Police-Community Interactions

The objective of this study was to use interval-level metrics to code a random sample of body worn camera footage from a large (N similar to 700) municipal police department in 2019. Just over 1,100 videos were coded for (1) community member factors; (2) officer behaviors-including an overall "performance" score; and (3) encounter outcomes. Our goal was to answer the following: Do police receive higher overall performance scores when interacting with some types of community members compared to others? Which community member factors significantly predict specific officer behaviors? Which community member factors significantly predict encounter outcomes? We found that officers received higher performance scores when interacting with women, and with community members with mental illness. We found that socio-economic-status and gender were the most common predictors of officer behaviors, while race and ethnicity, socio-economic-status, gender, and armed status predicted encounter outcomes. The policy implications of these findings are discussed.