Henry Levesque walks down a Cincinnati sidewalk wearing a small camera on his hat, not to film a vlog, but to rethink how cities understand people. The device — built from off-the-shelf parts like a Raspberry Pi and a GPS module — snaps photos every few seconds, logs location, and can measure temperature, humidity, and air quality. It’s part of his doctoral research at the University of Cincinnati’s College of Design, Architecture, Art and Planning, where he’s building an open-source toolkit that lets anyone collect hyper-local urban data without needing a big budget or technical degree.
Most smart city tech relies on massive datasets from traffic cameras, satellites, or government sensors. But those often miss what it feels like to live in a neighborhood — the heat radiating off pavement on a summer day, the cracked sidewalk that makes walking hard, or how people react emotionally in public spaces. Levesque’s system fills that gap by putting data collection directly into community members’ hands. His wearable tool costs under $200, uses common software, and stores data in open formats anyone can analyze.
One student used the device during career coaching sessions, capturing facial expressions every 30 seconds with consent. Using Levesque’s AI-assisted analysis tool, they mapped shifts in emotion — like surprise or concentration — over time, revealing patterns hard to notice in real-time conversations. Another project tracked microclimate changes along shaded versus sunny streets, showing how small design choices affect comfort.
The system is flexible: mount it on a bike for mobile scans, fix it to a wall for long-term monitoring, or swap out sensors depending on the question. No GPS? Remove it. Need more power? Add a solar panel. And because everything is open-source, others can improve or adapt it freely.
Levesque tested the full process in a four-week master class with students who had little coding experience. They built their own sensor kits, chose a local issue to study, collected data, and analyzed results — all within a month. "I hope to contribute more accessible methods of understanding urban environments," he said. "It's cheaper, it's easier, and it doesn't require a lot of specialized equipment or expertise."
This isn’t just about better data — it’s about fairer knowledge. When communities control how information is gathered, they can advocate for changes based on their own lived experiences. From tracking pollution hotspots to measuring emotional well-being in public spaces, Levesque’s tools are helping people see their cities anew — one block, one moment, one open-source sensor at a time.