Exploring Officer Patrol Behaviors Using Automated Vehicle Locator and Body-Worn Camera Data in Kansas City, Missouri

Policing is largely defined by low-levels of in-field supervision and high levels of officer autonomy. These aspects of policing present significant challenges to the accurate measurement of police officer activity in the field. Sherman (2013) argued the measurement of police officer activity could drastically expand and improve due to the emergence of two technologies: automated vehicle locators (AVLs) and body-worn cameras (BWCs). However, the manner by which AVLs and BWCs have been incorporated in policing—and used by police researchers—has not accelerated the tracking of police activity. AVL data has mostly been used for the singular purpose of measuring officer presence during patrol-based crime prevention interventions, providing little insight on the contextual aspects of police presence or how certain patrol behaviors concentrate in time and space. BWC research predominately focuses on measuring whether BWCs impact outcomes of interest (e.g., citizen complaints against officers, police use of force, etc.), with analysis of BWC video focusing on a narrow range of topics. Both AVL and BWC data have largely been considered in isolation rather than triangulated with additional data sources to generate a more holistic understanding of police activity.

We propose a mixed-methods research project that leverages AVL and BWC data to analyze police patrol activity and police-citizen interactions in Kansas City, MO over a one-year period (1/1/24-12/31/24). Seven total research questions will be explored through our analysis. We will first operationalize daily patrol officer travel patterns from AVL data, specifically focusing on committed vs. uncommitted time, proactive vs. reactive deployment, driving speed, single vs. multiple-vehicle responses, and stationary vs. in-motion presence. We will then select a stratified random sample of incidents for further analysis through systematic social observation (SSO) of associated BWC video. The SSO will measure the degree to which police officers adhere to principles of procedural justice, deploy de-escalation tactics in contentious citizen encounters, and use their discretion to minimize punitive responses. Data from the AVL and BWC video will further be triangulated with a wide range of KCPD and public data sources to identify contextual factors that are significantly related to a range of police patrol behaviors and response outcomes. This study’s novel methodology provides a blueprint for leveraging AVL and BWC data in support of evidence-based policing and police reform. CA/NCF