Stratified Surveillance: Policing in the Age of Big Data

Abstract

In the wake of 9/11, federal agencies provided considerable funding to state and local law enforcement agencies to collect, analyze, share and deploy a wide range of new data. Increasingly, local law enforcement agencies recognized these data could be useful for their own surveillance activities. The rise of “big data” raises a host of sociological questions about implications for surveillance and inequality. In this dissertation, I analyze the use of big data within the Los Angeles Police Department (LAPD). I draw on observational and interview data collected from fieldwork with various area and specialized divisions, a crime analysis center, a multi-agency intelligence center, and a software company in order to offer an on the ground account of how the police use big data.

In the first chapter, I describe the surveillance landscape in the United States today, highlighting the influx of federal funds going to local law enforcement agencies in the wake of 9/11. In the second chapter, I outline my research design and method. In the third chapter, I describe data use practices within the LAPD. In the fourth chapter, I analyze to what extent the adoption of new analytic technologies transforms police patrol, investigative, and analytic practices. Based on my fieldwork, I highlight seven key shifts associated with the adoption of big data analytics in law enforcement. In the fifth chapter, I study how the police themselves respond to changes associated with big data analytics. In the discussion, I highlight the social side of big data. In the conclusion, I discuss the implications of this research, offering suggestions for law, regulation and policy. Finally, I explain how the transformations I identify in law enforcement can be applied to other institutional contexts and highlight implications for social inequality.