Street-Level Surveillance
US cities have deployed camera networks, facial recognition, and license plate readers unevenly — and the geography of that unevenness tells a story. An interactive investigation.
"Surveillance infrastructure concentrates in specific neighborhoods — patterns invisible at street level but stark when mapped at scale."
Investigation
An interactive investigation of surveillance technology deployment across US cities. The project maps the spatial distribution of camera networks, facial recognition systems, and automated license plate readers, examining how surveillance infrastructure concentrates in specific neighborhoods and communities.
Built as part of the Conflict Urbanism studio, the project combines data journalism methods with spatial analysis to surface patterns that are invisible at street level but stark when mapped at scale.
Findings
- Surveillance doesn't distribute evenly. Camera density correlates with income and racial demographics — not crime rates alone.
- Facial recognition adoption accelerated post-2020. Cities that deployed protest-monitoring tools rarely rolled them back after the moment passed.
- License plate readers create movement histories. ALPRs passively log every vehicle that passes — building retroactive location databases without warrants.