Surveillance Inequality: Race, Poverty, and the Geography of Automated License Plate Reader Deployment

In November 2025, a federal judge in the Eastern District of Virginia unsealed a spreadsheet containing the locations of 614 automatic license plate reader (ALPR) cameras currently in use in Hampton Roads, Virginia. ALPR cameras are an emergent form of networked surveillance infrastructure that capture images of every vehicle that passes by, generate a “vehicle fingerprint,” and store those data in databases searchable by law enforcement, typically without warrants or court orders for access. The release of these locational data provides a rare opportunity to examine the opaque geography of contemporary surveillance and to assess whether ALPR camera deployment reproduces the same racialized and classed patterns long associated with policing and state surveillance in the United States. In this article, we use geographic information systems (GIS) and descriptive statistical analysis to map the distribution of 614 Flock Safety ALPR cameras in relation to racial and poverty profiles of the neighborhoods where the cameras are located. Our findings show that ALPR camera deployment is deeply and systematically racialized and economically stratified, with predominantly Black and high-poverty neighborhoods bearing a disproportionate share of ALPR surveillance infrastructure across Hampton Roads. We argue that these patterns do not reflect isolated siting decisions, but rather are the result of broader structural dynamics, including the privatization of surveillance infrastructure, weak democratic oversight, and the normalization of seemingly objective, tech-washed policing. We conclude by discussing the implications of these findings for public policy, civil liberties, democratic accountability, and Fourth Amendment protections.