Restaurant Inspection Data Scraper - 7 US Cities

One restaurant inspection data scraper that unifies 7 US city and county health portals into a single normalized schema - grades, scores, violation detail, and geocodes included.

What it does

This restaurant health inspection API pulls food inspection data from 7 US city and county open-data portals - NYC, Chicago, Cincinnati, Austin, King County (Seattle), Boulder County (CO), and Montgomery County (MD) - and normalizes them into one consistent schema. Every competing actor is single-city (usually NYC-only), which forces you to run and reconcile multiple scrapers with mismatched fields. This food inspection data scraper does the reconciliation for you: one run returns restaurant health grades, numeric scores, risk levels, and violation detail wherever each city publishes them, ready to export as CSV or JSON.

Each city comes back at the level of detail its portal publishes - one row per violation or one row per inspection - and every row says which in its granularity field (see the table below). Filter by city, date range, business name, or result.

Who it's for

• Food-safety monitoring teams tracking new violations and failed inspections across multiple markets
• Journalists and investigators building restaurant inspection datasets for reporting
• Restaurant-tech and real estate platforms scoring locations by health history
• Insurers and risk analysts underwriting food-service businesses
• Consumer apps (food delivery, review, discovery) surfacing health grades and scores

What you get / Output

Each record is normalized to the same fields regardless of source city:

Field	Description
sourceCity	Which of the 7 portals the record came from
businessName	Name of the establishment
businessId	Source-portal identifier for the establishment (for Montgomery County it is the county's inspection number)
facilityType	Type of facility (e.g. restaurant, mobile vendor)
address	Street address
zip	ZIP code
inspectionDate	Date of the inspection
inspectionType	Type of inspection (routine, complaint, re-inspection, etc.)
result	Inspection outcome (e.g. pass, fail)
score	Numeric inspection score where the city reports one
grade	Letter/health grade where the city reports one
riskLevel	Risk category where the city reports one
violationCode	Code for the specific violation
violationDescription	Text description of the violation. On per-inspection rows: the city's list of violations (Chicago) or the items found out of compliance (Montgomery County)
critical	true when the source flags the violation as critical - NYC's "Critical" flag, King County's RED violations, or (on Montgomery County's per-inspection rows) any item marked (C) found out of compliance. Empty where the city publishes no such flag
latitude	Geocoded latitude
longitude	Geocoded longitude
granularity	Whether the row is a violation or inspection record

Not every city publishes every field (for example, letter grades and numeric scores vary by jurisdiction). Fields are populated where the source portal provides them and left empty otherwise - the schema stays constant so your downstream code never breaks.

One row per violation, or per inspection?

City	granularity	What one row is
New York	violation	One cited violation, with code, description and critical flag
Cincinnati	violation	One cited violation, with code and the inspector's notes
King County (Seattle)	violation	One cited violation, with code, description and critical (RED) flag
Boulder County	violation	One inspector comment on a cited violation
Chicago	inspection	One inspection; its violations are listed together in violationDescription
Montgomery County	inspection	One inspection; the items found out of compliance are listed in violationDescription
Austin	inspection	One inspection score; Austin publishes no violation detail

On violation rows, an inspection with several violations spans several rows that share businessId, inspectionDate, inspectionType, result and score; an inspection where nothing was cited, where the city publishes one, is a single row with empty violationCode and violationDescription. To count inspections rather than violations, group on those shared fields.

Input / How to query

Configure the run with these filters and modes:

• cities - pick any subset of the 7 supported portals, or all of them
• inspection date range - limit results to a start/end window
• business name - target a specific establishment or chain
• result contains - filter by outcome text (e.g. fail; each portal words it differently - King County says Unsatisfactory, Cincinnati Not In Compliance)
• max run seconds - wall-clock budget for the run (default 240). Cities are fetched one after another; when the budget is reached the Actor stops cleanly, finishes successfully with the records it already has, names the cities it did not reach, and records stoppedEarlyOnTimeLimit: true in the run's SUMMARY. Raise it, together with the run timeout, for a deliberately large pull

Granularity is not a setting: each city is returned at the level its portal publishes (see the table above).

Example use cases

• Multi-city violation monitoring - pull NYC and King County (Seattle), which both publish one row per violation with a critical flag, on a nightly schedule and alert when new critical violations appear.
• Health-grade enrichment for a delivery app - fetch grade, score, and result per businessName and address to display trust signals next to listings.
• Investigative reporting - export failed inspections for a date range (result contains: fail for Chicago and Montgomery County, unsatisfactory for King County), then map them with latitude/longitude where the city geocodes them.
• Real-estate site scoring - join zip, facilityType, and riskLevel to evaluate the food-service risk profile of a commercial location.
• Insurance underwriting - build a per-establishment inspection history keyed on businessId to price food-service policies.

Recurring use / scheduling

Set up a schedule in Apify (for example, daily or weekly) to keep an inspection dataset current for monitoring. Because each record carries businessId, inspectionDate, violationCode and violationDescription, you can dedupe incremental runs on that combination and flag only genuinely new inspections or violations - ideal for a restaurant inspection monitoring API that surfaces fresh failures and health-grade changes.

FAQ

How do I get restaurant health inspection data for multiple US cities?

Run this actor with the cities you need selected. It returns normalized inspection records - grades, scores, and violations - for any subset of NYC, Chicago, Cincinnati, Austin, King County (Seattle), Boulder County (CO), and Montgomery County (MD) in a single dataset.

Is there an API for restaurant inspection scores and grades?

Yes. This actor exposes inspection score, grade, result, and riskLevel fields through the Apify API and dataset exports, so you can pull them programmatically wherever the source city publishes them.

How do I download NYC and Chicago restaurant inspection data as CSV?

Select NYC and Chicago as your cities, run the actor, and export the resulting dataset as CSV (JSON, Excel, and other formats are also available). Both cities come back in the same normalized schema, so there's nothing to reconcile.

How can I monitor restaurant health inspection results for new violations?

Schedule the actor to run on an interval for the cities you track, then dedupe on businessId + inspectionDate + violationCode + violationDescription to detect records you haven't seen before - especially those flagged critical (NYC, King County and Montgomery County publish that flag).

Where can I get restaurant food safety violation data in bulk?

Run with a broad date range. NYC, Cincinnati, King County (Seattle) and Boulder County come back one row per violation, with violationCode (NYC, Cincinnati, King County), violationDescription, and the critical flag (NYC, King County); Chicago and Montgomery County list each inspection's violations in violationDescription - all in one export.

How do I normalize restaurant inspection grades across different cities?

That's the core job of this actor. Each of the 7 portals uses different field names and formats; the actor maps them all to one schema (grade, score, result, riskLevel, and more) so grades are comparable across cities.

What is the best restaurant inspection data API for a food delivery or review app?

For multi-city coverage in a single schema, this actor is purpose-built for it: pull grade, score, result, address, and geocodes per establishment and drop them straight into your app's listings without stitching together separate single-city scrapers.

How do journalists get restaurant inspection records for investigations?

Filter by date range, result contains (fail, or the word the city uses), and the cities in scope, then export the full dataset with violation detail and latitude/longitude for mapping and analysis - all from public health-department records.

Data source & notes

Data comes from the official public open-data portals operated by each city or county (NYC, Chicago, Cincinnati, Austin, King County/Seattle, Boulder County CO, and Montgomery County MD), served via their Socrata open-data APIs. These are public government records published by the respective health departments.

Notes and limitations:

• Field coverage varies by city - jurisdictions that don't issue letter grades or numeric scores will leave grade/score empty, while the rest of the schema stays consistent.
• Data freshness reflects whatever each portal has published; the actor does not add records beyond what the source provides.
• Montgomery County's portal re-publishes its whole inspection list (roughly the last two years) every day. The actor reads only the newest daily copy, so each inspection appears once, in its latest version, and Montgomery County results reach back roughly two years.
• Boulder County's portal republishes some inspections, so the same row can appear there twice, identical in every column; the actor returns such a copy once (the run summary counts how many were skipped). Other cities' rows are returned as published. Two rows can still look alike when they differ only in a column this schema does not carry or in formatting it normalizes (for example a 9-digit vs 5-digit ZIP).
• Coverage is limited to the 7 supported portals listed above.

Related actors

Part of a set of US public-records tools that work well together:

• New Business Leads Scraper - catch restaurants when they first register
• US Building Permits Scraper - build-out and remodel permits for the same venues

See all fifteen at apify.com/maydit.

Free guide: Where US cities publish restaurant inspection data - the undocumented traps in this data source, measured against the live API.

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