Catalyzing Policing Reform with Data Policing Typology for Los Angeles Neighborhoods

By Ashlin Oglesby-Neal Alena Stern Kathryn L. S. Pettit

Public scrutiny of police in the US—especially regarding racial disparities—has increased in recent years, with many communities experiencing strained relations with their local police. Police departments have also increased transparency by making some data about their activity (primarily arrests and reported crimes) public. However, access to high-quality, disaggregated police data is insufficient—these data must also be analyzed to inform and empower people in communities most affected by crime and the justice system as well as to benefit law enforcement agencies and policymakers. When meaningfully analyzed and shared, these data can support conversations between communities and police and catalyze local reforms. Local organizations in the National Neighborhood Indicators Partnership (NNIP)—a learning network coordinated by the Urban Institute that connects independent partner organizations in 30 cities—regularly provide data and analysis to support discussions about key issues in their communities. The NNIP’s mission is to ensure all communities can access data and have the skills to use information to advance equity and well-being across neighborhoods. In 2018, NNIP and Microsoft partnered to use the network to spur data-driven and community-led criminal justice reforms with the goal of building police-community trust and improving public safety.1 As one of the project’s activities, we selected one city—Los Angeles—to explore how we could create a comprehensive measure of community-police engagement using publicly available police data. Our motivating research questions are the following: ◼ Are there different patterns of community-initiated and police-initiated engagement? ◼ How many distinct patterns are there, and what makes them distinct? ◼ How do the patterns of community-police engagement vary across neighborhoods? In collaboration with the Microsoft Criminal Justice Reform team, the Microsoft Data Science team, and the University of Southern California’s Sol Price Center for Social Innovation (Los Angeles’s local NNIP partner), we synthesized data sources (including information on calls for service, stops, arrests, and crime) to develop a typology that elucidates the relationship between resident-initiated and policeinitiated activity, as well as how that relationship varies across Los Angeles neighborhoods. Our typology of community-police interactions reveals patterns in how calls to police and police activity (which varies by the severity of crime and levels of economic hardship) differ across neighborhoods. We also discuss how this neighborhood-policing typology can inform conversations about police reform and support local movements for a more equitable criminal justice system. We hope this report informs conversations in Los Angeles and demonstrates how open data can be a powerful tool for local data organizations and criminal justice advocates nationwide

Washington, DC: The Urban Institute, 2020. 34p.