New data fill long-standing gaps in the study of policing.

Data limitations have long stymied research on racial bias in policing. To persuasively demonstrate bias, scholars have sought to compare officer behavior toward minority versus white civilians while holding constant all other factors in the police-civilian encounter that might provide alternative explanations for enforcement disparities. These comparisons in "similar circumstances" are also critical in litigation concerning discriminatory policing, which can often lead to court-ordered remedies (1). Such "all-else-equal" scenarios are elusive in many realms of social science, but two challenges have made them particularly difficult to find in the study of policing. On page 1397 of this issue, Aggarwal et al. (2) report using data from the ridesharing service Lyft—having obtained vehicle location on more than 200,000 drivers using highfrequency GPS pings from their smartphones—to analyze speeding enforcement by the Florida Highway Patrol (FHP) and to show how such data offer a path forward for addressing both challenges. [ABSTRACT FROM AUTHOR]