Big Data Policing Capacity Measurement

Big data, algorithms, and computing technologies are revolutionizing policing. Cell phone data. Transportation data. Purchasing data.

Social media and internet data. Facial recognition and biometric data. Use of these and other forms of data to investigate, and even predict, criminal activity is law enforcement’s presumptive future. Indeed, law enforcement in several major cities have already begun to develop a big data policing mindset, and new forms of data have played a central role in high-profile matters featured in the Serial and To Live and Die in LA podcasts, as well as in the Supreme Court’s leading privacy and criminal procedure case of Carpenter v. United States.

Although the ascendancy of big data policing appears inevitable, important empirical questions on local law enforcement agency capacity remain insufficiently answered. For example, do agencies have adequate capacity to facilitate big data policing? If not, how can policymakers best target resources to address capacity shortfalls? Are certain categories of agencies in a comparatively stronger position in terms of capacity?

Answering questions such as these requires empirical measurement of phenomena that are notoriously difficult to measure. This Article presents a novel, multidimensional measure of big data policing capacity in U.S. local law enforcement agencies: the Big Data Policing Capacity Index (“BDPCI”). Analysis of the BDPCI provides three principal contributions. First, it offers an overall summary of more than 2,000 local agencies’ inadequacy in big data policing capacity using a large-N dataset.

Second, it identifies factors that are driving lack of capacity in agencies. Third, it illustrates how differences between groups of Agencies might be analyzed based on size and location, including an illustrative ranking of the fifty U.S. states. This Article is meant to inform stakeholders on agencies’ current positions, advise on how best to improve such positions, and drive further research into empirical measurement and big data policing.