The landscape of local surveillance is shifting rapidly across Missouri, often moving faster than the public conversation surrounding it. A prime example can be found in St. Francois County, where the deployment of Automated License Plate Readers (ALPRs) has steadily expanded over the last few years.

I’ve done a separate post regarding Flock images and data as evidence – click here for that post.

Crowdsourced mapping data from the online tool DeFlock reveals a high density of active ALPR cameras concentrated heavily around municipal centers like Park Hills, Bonne Terre, and Farmington. The interactive map shows an overarching web of surveillance tracking vehicles along major county arteries and intersections, with dozens of active devices blanketing the local area. This link is directly to this view below and I assume Deflock will update it when it gets more information. I have

I have not confirmed this data, but it appears consistent with I see in driving in the area.

The Trend of Unannounced Deployment

A key takeaway from tracking these networks is the inconsistency in how their presence is communicated to the public. Sometimes local governments openly celebrate the acquisition of these systems; sometimes they go live without a formal press release or notice.

For instance, back in January 2023, Park Hills Mayor Stacey Easter posted a public announcement on Facebook expressing excitement about the police department’s acquisition of 8 Flock Safety Falcon ALPR cameras. At the time of her announcement, four were already active, and the remainder were quickly brought online.

However, because these systems are “Infrastructure-Free”—meaning they rely on solar power and LTE connectivity to stream real-time data directly to the cloud—they can be mounted discreetly on existing utility poles or streetlights in a matter of weeks without the community ever noticing a physical installation crew.

Beyond License Plates: “Vehicle Fingerprinting”

Marketing materials shared by local officials detail that these cameras do not merely read license plate numbers. The software leverages machine learning to build a “Vehicle Fingerprint,” which indexes data by:

• Vehicle type, make, and color
• State of the license plate
• Unique identifying features (e.g., bumper stickers, decals, roof racks)
• Missing or intentionally covered plates

This data integrates directly into law enforcement networks, pushing real-time alerts to dispatchers and active patrol cars when a flagged vehicle passes a sensor. As municipalities continue to expand their subscription networks secretly or overtly, the boundary between public roadways and total vehicular tracking continues to blur.