Food Safety Inspection Scanner — Restaurant Health Inspections (NYC + Socrata Cities)

Extract restaurant food-safety and health-inspection records from public government open-data portals as clean, normalized JSON — ready for spreadsheets, dashboards, databases, and automation pipelines.

Ships preconfigured for the NYC Department of Health (DOHMH) Restaurant Inspection Results dataset, which is updated daily and contains every inspection and violation cited at New York City food-service establishments. The backend is generic Socrata — point it at any Socrata open-data portal (Chicago, Los Angeles, Boston, Austin, and 200+ other cities) by changing two input fields.

What it does

• Queries the city's official open-data API (no scraping, no CAPTCHAs, no broken HTML parsers — the data comes straight from the government source)
• Filters by borough, cuisine, ZIP code, inspection grade (A/B/C), critical violations only, and recency (last N days)
• Normalizes every row into a stable record schema with deterministic IDs (safe for incremental syncs and deduplication)
• Handles throttling and transient errors with retry + exponential backoff
• Runs in seconds — a single API request per run, minimal compute cost

Input

Field	Type	Default	Description
days_back	integer	30	Only include inspections from the last N days
max_items	integer	100	Maximum records to return (cap: 5,000)
boro	select	Any	NYC borough filter
cuisine	string	—	Cuisine keyword, e.g. Pizza, Chinese, Bakery
zipcode	string	—	Exact ZIP code filter
grade	select	Any	Letter grade filter (A / B / C)
critical_only	boolean	false	Only critical violations
socrata_domain	string	data.cityofnewyork.us	Any Socrata open-data portal
dataset_id	string	43nn-pn8j	Socrata dataset ID

Output

One JSON record per inspection violation, e.g.:

{
  "id": "NTAxODA0NTJ8MjAyNi0wNy",
  "source": "socrata:data.cityofnewyork.us/43nn-pn8j",
  "restaurant_id": "50180452",
  "restaurant_name": "ALL LIN COMING INC",
  "boro": "Staten Island",
  "address": "1022 ROSSVILLE AVENUE",
  "zipcode": "10309",
  "phone": "3472799411",
  "cuisine_description": "Chinese",
  "inspection_date": "2026-07-18",
  "inspection_type": "Cycle Inspection / Initial Inspection",
  "action": "Violations were cited in the following area(s).",
  "violation_code": "04L",
  "violation_description": "Evidence of mice or live mice present in facility...",
  "critical_flag": "Critical",
  "score": 17,
  "grade": "B",
  "grade_date": "2026-07-18",
  "record_date": "2026-07-21",
  "latitude": 40.552129043699,
  "longitude": -74.212092661776,
  "url": "https://data.cityofnewyork.us/resource/43nn-pn8j.json?camis=50180452"
}

Export as JSON, CSV, or Excel from the Apify dataset, or consume via API.

Real-world examples

Example 1 — Weekly Manhattan pizza inspection roundup

A food-delivery platform wants to display health grades next to restaurant listings.

{
  "boro": "Manhattan",
  "cuisine": "Pizza",
  "days_back": 7,
  "max_items": 500
}

Example 2 — Critical violations only, last 14 days

A compliance auditor needs to flag restaurants with serious hygiene issues for follow-up.

{
  "critical_only": true,
  "days_back": 14,
  "max_items": 1000
}

Example 3 — Chicago food inspections

A market-research firm tracks inspection trends across multiple cities.

{
  "socrata_domain": "data.cityofchicago.org",
  "dataset_id": "4ijn-s7e5",
  "days_back": 30,
  "max_items": 2000
}

Use cases

• Delivery platforms — enrich restaurant listings with official health grades and violation histories
• Restaurant chains & franchise auditors — monitor your own locations for new violations and grade changes (schedule daily runs + webhooks)
• Insurance underwriting — food-safety risk signals for hospitality clients
• Commercial real estate — due-diligence on food-service tenants
• Journalism & research — track enforcement patterns by borough, cuisine, or ZIP code
• Competitive intelligence — monitor compliance trends across a market

Other cities

Any city on a Socrata open-data portal works. Examples:

City	socrata_domain	Example dataset
NYC (default)	data.cityofnewyork.us	43nn-pn8j (restaurant inspections)
Chicago	data.cityofchicago.org	4ijn-s7e5 (food inspections)
Austin	data.austintexas.gov	ecmv-9xxi (food establishment inspections)

Note: filter fields (boro, grade, cuisine, zipcode) are NYC-specific column names; for other datasets use days_back / max_items and check the dataset's column names. Date, limit, and ordering filters work everywhere.

Scheduling & integrations

Run on a schedule (daily/weekly) directly in Apify, or trigger via API, Zapier, Make, or webhooks. Use deterministic record IDs for incremental syncs into your own database.

Cost & reliability

• Flat pricing: $0.00036 per result + $0.001 run start fee — 1,000 records costs $0.36
• Deterministic: no headless browser, no LLM, no paid third-party APIs
• Cheap: one HTTP request per run, typically <10 seconds of compute
• Data license: public government open data, free to reuse
• Source stability: Socrata (Tyler Technologies) powers 200+ government portals; schemas are stable and changes are announced