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Public Health Research & Academia

Restaurant inspection data, research-ready

Cross-jurisdiction, normalized, longitudinal — the dataset public health researchers actually need.

Health department portals are built for one-at-a-time consumer lookups, not for bulk research. We aggregate inspection records across dozens of jurisdictions, normalize them to a consistent schema, and deliver them in machine-readable formats. The result: a clean, citable dataset spanning years of inspection history across major U.S. metros — ready for regression analysis, outbreak modeling, and peer-reviewed publication.

The problem today

Sound familiar?

Portal lookups are one at a time — research requires thousands

Pulling inspection records for a city study means clicking through hundreds of portal pages, each with its own format. There's no bulk export, no API, no standardized schema across jurisdictions.

Cross-city comparison is nearly impossible without normalization

Boston calls them "critical violations," Chicago calls them "serious violations," LA uses a letter grade system. Before you can analyze anything, you're spending weeks on data cleaning.

FOIA timelines blow past grant cycles and publication deadlines

Formal public records requests for bulk data can take 90–180 days — if they're fulfilled at all. We've already aggregated and normalized what you need.

Bulk inspection data, normalized and ready to analyze

Years of restaurant health inspection records across major U.S. cities, standardized to a consistent schema and delivered in CSV, JSON, or Parquet. Citable as a data source in publications.

✓Cross-jurisdiction normalization: violation severity, type, and score mapped to a consistent schema
✓Longitudinal data going back 3–5 years depending on jurisdiction
✓Establishment-level records: name, address, permit number, inspection date, inspector ID (where available)
✓Violation-level records: description, severity classification, corrected-on-site flag, repeat violation flag
✓Chain-level linkage: franchise and corporate parent identifiers across locations
✓Outbreak-ready export: filter by date range, violation type, and geography for retrospective case-control studies
✓Coverage across 20+ U.S. metros including Boston, Chicago, NYC, LA, San Diego, Seattle, and more
✓Machine-readable formats: CSV, JSON, Parquet (on request)
✓Monthly refresh included with annual plans — track compliance trends over time
✓Data use agreement suitable for IRB submission and journal data availability statements

Pricing

Transparent pricing. No hidden fees. Cancel anytime.

Dataset License

One city, current year — good for a single study

$149one-time
  • ✓Full inspection + violation records for one metro
  • ✓Current calendar year
  • ✓CSV or JSON delivery within 48 hours
  • ✓Data use agreement included
  • ✓Citable as a named data source
  • ✓One revision request if schema questions arise
Request Dataset
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Longitudinal Access

One city, 3–5 year history + monthly refresh

$399/yr
  • ✓Everything in Dataset License
  • ✓Full historical archive (3–5 years)
  • ✓Monthly data refresh for ongoing studies
  • ✓Multi-year panel format for trend analysis
  • ✓Violation trajectory tracking per establishment
  • ✓Priority response to schema and coverage questions
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Institutional License

Unlimited cities, all years, team access

$999/yr
  • ✓All covered U.S. metros (20+)
  • ✓Full historical archive across all markets
  • ✓Up to 5 researchers on the same license
  • ✓Direct API access for automated pipeline ingestion
  • ✓Custom geographic extracts (county, zip, census tract)
  • ✓Dedicated data contact for methodology questions
Contact Us

“We were spending two weeks per city scraping and cleaning inspection data before we could even start analysis. This saved us that step entirely — and the schema documentation made the methods section of our paper much easier to write.”

Request a sample dataset for your research

Tell us your city, date range, and what you're studying and we'll pull a representative sample — including the schema and variable documentation — so you can see exactly what's in the data before committing.