Indiana License Plate Surveillance: A Living Statistical Report

Abstract

Automated license plate readers photograph every passing vehicle and log its plate, location, and timestamp in searchable databases. To measure the scale of this infrastructure, Eyes Off Indiana continuously analyzes every ALPR camera recorded in OpenStreetMap — the crowdsourced dataset behind the DeFlock mapping project — together with US Census boundary files and population estimates, and detection volumes that Indiana agencies publish on their own Flock Safety transparency portals.

As of July 11, 2026, 3,156 ALPR cameras are documented in Indiana (at least 2,696 of them Flock Safety hardware), in 84 of 92 counties. Indiana ranks #9 nationally by camera count and #6 per capita, holding 2.7% of the 110,198 cameras mapped nationwide. A net 612 cameras were documented in the past 30 days. Applying the per-camera detection rate Indiana agencies self-report (691 vehicles per camera per day), the documented network alone reads on the order of 2.2 million plates per day. Indiana has no statewide law governing retention, access, or oversight of this data.

Every count on this page is a floor, not a census: these are cameras volunteers have found and documented. Section 6 explains what these numbers can and cannot say.

1. Growth of the Documented Network, 2022–Present

Download: CSV · JSON · Series updated July 11, 2026

The first ALPR camera in Indiana was documented in OpenStreetMap in 2022-12. Each camera in the series is dated to the earliest OpenStreetMap version carrying its ALPR tags — not the node's creation date, because mappers occasionally retag an existing pole or signal node when a camera is mounted on it. Months after July 2026 use the maximum daily statewide total recorded by our monitoring, which queries the Overpass API every day; the maximum (rather than the latest value) smooths transient dips when a mirror returns partial results. The source column in the download flags which method produced each row.

2. All 50 States Ranked

Download: CSV · JSON · Updated July 01, 2026 (July 2026 baseline)

110,198 ALPR cameras are currently mapped in the United States. Each camera is assigned to a state with Census Bureau boundary polygons; rankings are provided by raw count, per 100,000 residents, and per 1,000 square miles of land area. Click any column header to re-sort — per-capita and density rankings often tell a different story than raw counts. Indiana's row is highlighted.

Table 2. ALPR cameras by state (click headers to sort)

3. All 92 Indiana Counties Ranked

Download: CSV · JSON · Updated July 11, 2026 (July 2026 baseline)

3,035 cameras are assigned to Indiana counties using Census cartographic county polygons. 84 counties have at least one documented camera; a county showing zero means none documented, not none installed. Each county name links to a local report with a camera map and the agencies involved.

Table 3. ALPR cameras by county (click headers to sort)

4. Individual Camera Coordinates

Download: CSV · JSON · Updated July 11, 2026

Latitude and longitude of every documented camera (3,035 points), with the county each point falls in and whether the camera carries a Flock Safety manufacturer tag in OpenStreetMap. Useful for mapping, spatial joins against school zones, clinics, or places of worship, and independent verification of every figure above. Points are © OpenStreetMap contributors and redistributable under the ODbL. Explore them interactively on any county page.

5. What Agencies Themselves Report: Detection Volumes

Download: CSV · JSON · Updated July 11, 2026 (July 2026 snapshot)

Some Indiana agencies operate public Flock Safety "transparency portals" that disclose how many cameras they run and how many vehicles those cameras detected in the last 30 days. 18 Indiana portals currently disclose both figures (aggregated by Eyes On Flock): 189 cameras detecting 3,920,631 vehicles per 30 days — a camera-weighted mean of 691 vehicles per camera per day (median agency: 694).

Table 4. Self-reported detections on Indiana Flock transparency portals

5.1 The statewide plates-scanned estimate

The homepage figure "plates scanned in Indiana today" is derived from this dataset in three steps, with every input exposed at /api/plates-scanned-today:

• Rate. The camera-weighted mean detection rate from Table 4: 3,920,631 vehicles ÷ 189 cameras ÷ 30 days = 691 per camera per day.
• Scale. Multiplied by the 3,156 cameras currently documented statewide: ≈ 2,180,796 plate reads per day.
• Time of day. Distributed across the day with a typical weekday hourly traffic profile in Indiana local time (roughly 1% of daily traffic falls in the 2–3 a.m. hour versus about 8% at the 4–5 p.m. peak), rather than assuming a uniform rate around the clock.

This estimate supersedes an earlier version of our homepage counter that assumed a uniform 8,640 reads per camera per day — an assumption roughly 12 times higher than what Indiana agencies themselves report. The current method still carries real uncertainty (Section 6.3, item 6), but every component now traces to a published source.

6. Reading These Numbers Correctly

6.1 Documented is not installed

Everything on this page counts cameras that volunteers have physically located and added to OpenStreetMap. No Indiana agency is required to disclose camera locations, and most do not; crowdsourced documentation is the only statewide accounting that exists. Three consequences follow. First, every count is a lower bound — the installed total is certainly higher. Second, coverage is uneven: areas with active mappers (cities, suburbs, interstate corridors) are documented more completely than rural areas, so low rural counts partly reflect fewer mappers, not just fewer cameras. Third, a county with zero documented cameras is a county where none have been found, which is not evidence of absence.

6.2 Why the statewide totals differ slightly across tables

Careful readers will notice the statewide total varies by up to a few percent depending on the table. These are boundary-resolution and method artifacts, not errors; each table is internally consistent. Compare within a table, not across tables.

Table 5. The same network, measured four ways

Figure	Measurement	Why it differs
3,156	Statewide total (daily Overpass query) Used in: Homepage counter; growth series (latest months)	Direct count of ALPR nodes inside Indiana's OpenStreetMap administrative boundary, refreshed daily.
3,035	Sum of county assignments Used in: County rankings; county pages	Each camera point assigned to a county with Census cartographic county polygons; cameras just outside the polygons (border roads, simplification gaps) are dropped.
2,983	Indiana row in the 50-state table Used in: State rankings	Assignment with generalized 1:20,000,000 state outlines, which clip more border cameras than county polygons do.
3,156	Growth-series endpoint Used in: Growth dataset	Survivor curve (cameras on today's map dated by first documentation) extended with the maximum daily Overpass total each month.

6.3 Known biases and limitations

1. Documentation growth is not installation growth. The growth curve in Section 1 dates each camera to when it was first documented. A surge in the curve can reflect a burst of volunteer mapping (for example, after DeFlock attracted national attention in 2025) as much as a burst of installation, and the curve's start in late 2022 marks the beginning of mapping, not of ALPR use in Indiana. Historical cross-checks against the OpenStreetMap time-machine database agree with the curve within about 4%, so deletions do not distort the trend — but the curve should be read as "documentation of the network," which converges toward the network itself only as mapping matures.
2. Survivor bias. The per-camera dating method cannot see cameras that were mapped and later removed from the map. The ~4% agreement with direct historical queries indicates this effect is small.
3. Geographic coverage bias. Mapping effort concentrates where mappers live and drive. Urban and suburban counts are more complete than rural ones, which compresses true differences between metro and rural counties by an unknown amount.
4. Vendor attribution is incomplete. The Flock Safety figure counts cameras whose OpenStreetMap entry carries a Flock manufacturer tag (2,696 of 3,035 at last refresh). Untagged cameras may also be Flock hardware, so the Flock share is itself a floor.
5. Per-capita denominators use residential population. Cameras scan travelers, not residents. Counties with heavy commuter or interstate traffic will scan far more non-residents than the per-capita rate implies.
6. The plates-scanned estimate inherits its inputs' limits. The per-camera rate comes from the 18 agencies that choose to publish portals, which may not represent all operators; the figures are self-reported and unaudited; detections of the same vehicle by multiple cameras count separately (the relevant measure for location tracking, but not unique vehicles); and the hourly profile is a typical weekday, not measured Indiana traffic.
7. Boundary artifacts. Cameras within a few meters of state or county lines can be clipped by simplified boundary polygons, producing the small discrepancies in Table 5 (at most about 1.5% of the statewide total).

7. Data and Methods

Camera locations. Every OpenStreetMap node tagged man_made=surveillance with surveillance:type=ALPR, retrieved via the Overpass API. The statewide total refreshes daily; the state and county rankings, camera coordinates, and growth series refresh nightly at midnight (US Eastern). This is the same community-maintained dataset that powers DeFlock.me. If a refresh fails, the page serves the last successful figures and flags the section with its baseline date rather than showing an error.

Geographic assignment. Each camera point is assigned to a state or county by ray-casting point-in-polygon tests against US Census Bureau cartographic boundary files (1:20,000,000 outlines for states; higher-resolution county polygons for Indiana). The District of Columbia and Puerto Rico are excluded from the 50-state ranking.

Growth series. For each camera on today's map we retrieve its OpenStreetMap edit history and date it to the earliest version carrying the ALPR tags, correcting for nodes that were retagged from existing poles or signals (skipping this correction would backdate several cameras by years). This survivor curve was cross-checked against the Overpass API's historical database at every quarter boundary since 2019, agreeing within about 4% throughout. From August 2026 onward, each month's value is the maximum daily statewide total recorded by our monitoring.

Denominators. US Census Bureau Vintage 2024 population estimates and 2024 gazetteer land areas (states and counties).

Detection volumes. "Vehicles detected in the last 30 days" and camera counts as published on agencies' Flock Safety transparency portals, collected by the independent aggregator Eyes On Flock and re-derived by us nightly from Indiana portals disclosing both figures.

Availability and license. All five datasets are downloadable above in CSV and JSON; the JSON downloads embed source, license, and citation metadata. Camera locations are © OpenStreetMap contributors under the ODbL; our compilations and analysis are CC BY 4.0. Analysis scripts and archival snapshots are available on request at info@eyesoffindiana.org. Downloads are free to republish and analyze with attribution; please do not hotlink these endpoints as a live data API for another site — download and host a copy, or contact us about automated access.

Take Action

Indiana has no statewide law limiting how long ALPR location data is kept, who can search it, or how it is audited. Sign the Eyes Off Indiana petition asking the General Assembly to set rules for retention, access, and oversight, and contact your state legislators.