How We Built DMV Camera Watch

When we first started looking into automated traffic enforcement in the Washington, D.C. area, the lack of accessible public information was shocking. Local governments were deploying hundreds of cameras and collecting over $100 million in fines annually, yet trying to find a simple, accurate map of where these devices were located felt like trying to access classified military secrets.

Some jurisdictions published PDF lists buried deep on their websites. Others provided clunky, outdated web maps that crashed on mobile devices. Some counties refused to publish exact locations at all, hiding behind the vague excuse of "ongoing police operations."

We realized that if drivers wanted transparency, we were going to have to build it ourselves. This is the story of how DMV Camera Watch was built—and why data accessibility is a fundamental civil rights issue.

The Data Scavenger Hunt

The first hurdle was aggregating the data. Because the DMV region encompasses three distinct state-level systems (D.C., Maryland, and Virginia) and a dozen local counties and cities, there is no standardized format for automated enforcement data.

We started with Open Data portals. Washington, D.C., to its credit, has a robust open data initiative. We were able to pull initial coordinate data for hundreds of speed and red light cameras via APIs. But the data was noisy. Cameras marked "active" had actually been removed months prior. Cameras labeled "speed" were actually red light cameras.

In Maryland, the situation was worse. We had to scrape HTML tables, parse poorly formatted PDFs, and in some cases, submit Freedom of Information Act (FOIA) requests to force counties to hand over their camera deployment rosters.

Virginia presented its own unique challenges, as their school zone speed camera programs were brand new, and the data was scattered across local school board meeting minutes and municipal press releases.

Standardizing the Chaos

Once we had the raw data, we had to build a unified schema. A camera in Montgomery County needed to look the same in our database as a camera in Arlington.

We categorized every device:

• Type: Speed, Red Light, School Zone, Work Zone, Stop Sign.
• Status: Active, Proposed, Decommissioned.
• Speed Limit: What is the posted limit vs the enforcement threshold?

We then spent weeks manually geocoding missing locations. We used Google Street View to verify the physical existence of pole cameras, cross-referencing intersections to ensure our map pins were accurate to the exact corner.

The Revenue Equation

Mapping the cameras wasn't enough; we wanted to follow the money. We began scraping annual police department reports, municipal budgets, and state audit documents to find citation volumes and revenue figures.

By attaching estimated annual violation counts and revenue data directly to the camera profiles, we transformed the map from a simple location tool into a financial accountability dashboard. Suddenly, you could see that a single camera on a D.C. highway was generating more revenue than a small town’s entire municipal budget.

The Tech Stack

To make this data fast, reliable, and accessible, we built DMV Camera Watch on modern web infrastructure:

• Cloudflare Workers: The site is heavily reliant on serverless functions at the edge, ensuring lightning-fast load times regardless of traffic spikes.
• Cloudflare KV: We store our camera data and blog content in distributed key-value stores for instant retrieval.
• Vanilla JavaScript & CSS: We eschewed heavy front-end frameworks to ensure the site is highly performant on mobile devices—because drivers need this info on the go, not just at a desktop.

A Pro-Transparency Mission

Governments love to say that if you aren’t breaking the law, you have nothing to fear from cameras. We reject that premise entirely. When automated enforcement is tied directly to municipal revenue, the financial incentive for abuse is overwhelming.

Transparency forces accountability. If a jurisdiction wants to deploy a camera in the name of safety, they should have no problem with the public knowing exactly where it is, how it operates, and how much money it generates.

DMV Camera Watch is a living project. We update it as new cameras are installed, old ones are taken down, and new data is released. But our core mission remains the same: We watch the cameras, so you don’t have to.