surveillance_spending

A DISCOVERY INDEX of surveillance and police-technology dollar figures found in local government meeting records across 130+ named vendors — NOT a table of confirmed spending, and NOT safe to SUM. An audit on 2026-07-29 found aggregate views were adding amounts that must not be added: the same procurement recurs at committee, council, approval, amendment and renewal stages; "not to exceed" figures are authorization ceilings, not payments. Use the canned query "the CONSERVATIVE total" for any number you intend to publish, and "data-quality report" to see what is flagged. Columns added for this: `amount_type` (stated / ceiling / proposed / recommended / estimated), `dup_rank` (>1 = redundant copy), `geo_suspect` (city disagrees with the source link), `anomaly` (implausible figure), `state_norm` (normalized state). Vendors covered include ALPR (Flock, Vigilant, Genetec), body cameras/tasers (Axon, WatchGuard, Utility, Getac), gunshot detection (ShotSpotter/SoundThinking), face recognition (Clearview AI, DataWorks Plus), fingerprint biometrics (Idemia AFIS/Livescan, filed separately under `sv:biometrics`), data fusion and OSINT (Fusus, Peregrine, LexisNexis Accurint, Thomson Reuters CLEAR), phone forensics (Cellebrite, GrayKey, Magnet), drones (Skydio, BRINC, DJI), cameras/VMS (Verkada, Avigilon), cell-site simulators, CAD/RMS records systems, photo enforcement, jail-call and student monitoring. Every row carries a VERBATIM quote from the minutes and a link to the source document, and a row only publishes if that quote was found character-for-character in the source text. READ THE `ledger` COLUMN: `sv:<category>:<vendor>`. `radio_infra` (two-way radios), `rms` (records/CAD software), `biometrics` (fingerprints) and `not_surveillance` are NOT surveillance sensors — exclude them before quoting any surveillance figure.

Data license: CC BY 4.0 · Data source: MyTown

1 row where ledger = "sv:camera:avigilon", state = "NC" and vendor = "Avigilon" sorted by amount descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: spend_date (date)

vendor 1

  • Avigilon · 1 ✖

state 1

ledger 1

  • sv:camera:avigilon · 1 ✖
Link rowid vendor city state spend_date product amount ▲ quote source_document mytown_record ledger amount_type dup_rank geo_suspect anomaly state_norm
3249 3249 Avigilon Wayne County NC 2026-05-19 cameras (budget) 46890.0 Avigilon Cameras Animal Control - $46,890; https://wayneconc.api.civicclerk.com/v1/Meetings/GetMeetingFileStream(fileId=4655,plainText=false) https://mytown.theboringparts.com/city/wayneconc/#m602231 sv:camera:avigilon stated 1 0 0 NC

Advanced export

JSON shape: default, array, newline-delimited

CSV options: 

CREATE TABLE surveillance_spending (
  vendor TEXT, city TEXT, state TEXT, spend_date TEXT, product TEXT,
  amount REAL, quote TEXT, source_document TEXT, mytown_record TEXT, ledger TEXT, amount_type TEXT, dup_rank INTEGER, geo_suspect INTEGER, anomaly INTEGER, state_norm TEXT);
CREATE INDEX ix_surv_vendor ON surveillance_spending(vendor);
CREATE INDEX ix_surv_state ON surveillance_spending(state);
CREATE INDEX ix_surv_amount ON surveillance_spending(amount);
CREATE INDEX ix_surv_ledger ON surveillance_spending(ledger);