How the score is computed
Outflock scores how exposed an address is to license plate readers: not how many cameras sit nearby, but whether a normal trip out can avoid them. Here is what the number means, how it is built, and what it does not yet see.
What the score measures
The Outflock score answers one question: if you live at this address and take a normal short trip, say three to five miles to a grocery store, a school, or a friend, what is the probability that an Automated License Plate Reader logs your plate at least once? And when it does, who sees that data?
Proximity to cameras is not the same thing as surveillance exposure. An address surrounded by cameras that all face a single highway carries less exposure than an address with fewer cameras positioned at every neighborhood egress point. What matters is whether you can leave without being captured, not how many cameras sit near your front door. That distinction is the whole point: it separates a meaningful score from a dot-counting exercise.
A score of 100 means you can leave in any direction without passing an ALPR. A score of 0 means every plausible exit route is covered. In practice, no real address sits at either extreme. The scale is calibrated so that an average American suburb reads in the C range and a heavily instrumented urban grid reads D or F.
Scoring scale
The score is a number from 0 to 100, displayed with a letter grade. Grades are calibrated against a set of reference addresses spanning the full range of US surveillance density, from rural areas with no mapped cameras to dense urban grids with hundreds.
| Grade | Score | What it means |
|---|---|---|
| A | 85 – 100 | Low exposure. Most short trips go unlogged. Camera coverage is sparse or positioned away from common egress routes. |
| B | 70 – 84 | Light exposure. Some routes are covered; others are not. You can avoid cameras with minor route adjustments. |
| C | 50 – 69 | Moderate exposure. Most short trips will cross at least one camera. Data sharing is limited. |
| D | 30 – 49 | High exposure. Capture is routine on common routes. Cameras are likely networked and data is shared broadly. |
| F | 0 – 29 | Very high exposure. Near-certain capture on short trips. Cameras belong to large sharing networks. |
How it works
Three things have to be true for the score to mean anything: the cameras have to be real, the routes have to be plausible, and the data has to be current. Here is what each of those means in practice.
Camera locations are sourced from OpenStreetMap, where volunteers, including the DeFlock community, tag ALPR cameras as they are spotted in the field. The data is open, citable, and improves over time as more people contribute. Coverage is best in cities with active contributors and weaker in places where no one has mapped yet.
Starting from the address, a routing engine models a representative set of short trips heading in different directions, at distances that match a typical errand. Each modeled route is checked against the camera inventory to see whether it crosses one. The score is the share of those routes that finish without a single capture. The result reflects how easy it is to leave, not how many cameras happen to sit nearby.
The camera inventory is refreshed on a regular cadence so new installations show up shortly after they are mapped. Counts are preserved over time, so every address can show whether local coverage is growing, shrinking, or stable. A town going from zero cameras to three in a month is a different signal than a neighborhood with a stable long-term count.
What it doesn't capture (yet)
The score is only as complete as the open data behind it, and some kinds of surveillance are not in it yet. Here is where it stops short today.
- •Cameras have to be mapped to count. Locations come from open community data, so a reader nobody has tagged is invisible to the score. Coverage is strongest where contributors are active and thinner in rural areas.
- •Only license plate readers, so far. Fixed CCTV, Ring and Neighbors cameras, drones, and police-mounted mobile readers are not modeled yet. They are next.
- •Real routines vary. The score models short trips in several directions; if your life runs mostly one way, your real exposure may differ from the number.
- •Every camera weighs the same, for now. Network weighting, scoring a camera by how widely it shares data, is coming but not yet live.
- •It reflects the latest mapped data. A camera installed last week will not appear until someone tags it and the next refresh runs.
Network weighting, more surveillance types, and change alerts for a saved address are on the way. Each one shows up here as it ships.