US SNAP
Every US retailer authorized to accept SNAP (food-stamp) benefits - ~254k stores - in one clean, geocoded table. USDA's Food and Nutrition Service exposes it as a paginated ArcGIS service; we page the whole layer and ship one fat row per store: name, store type, full address, county and lat/lon. A clean, open food-access / retail-footprint POI set - the kind of location data usually sold by scrapers, here authoritative and public domain. Owner/grantee personal-name field dropped (no PII).
- Source
- USDA FNS
- Licence
- Redistributable open data
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- ~254k SNAP-authorized US retailers - one clean, geocoded row per store
- Store name, store type, full street address, city/state/ZIP, county, lat/lon
- Open food-access / retail-footprint POI - authoritative alternative to scraped store data
- Business locations only - owner/grantee personal name dropped (no PII)
- Audiences: grocery/retail site-selection, food-desert research, CPG distribution, proptech
- Public domain (USDA FNS) - free commercial reuse & resale
- Source: U.S. Department of Agriculture, Food and Nutrition Service - SNAP Retailer Locator
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| record_id | int | USDA SNAP retailer record id (primary key) | 100.0% | 254,048 |
| store_name | str | Store name | 100.0% | 242,981 |
| store_type | str | Store type (Supermarket, Convenience Store, Grocery Store, …) | 100.0% | 8 |
| street_address | str | Street address | 100.0% | 244,061 |
| address_2 | str | Additional address line | 6.5% | 3,502 |
| city | str | City | 100.0% | 16,338 |
| state | str | US state | 100.0% | 53 |
| zip | str | ZIP code | 100.0% | 24,011 |
| county | str | County | 100.0% | 1,839 |
| latitude | float | Latitude | 100.0% | 241,905 |
| longitude | float | Longitude | 100.0% | 240,201 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: USDA FNS (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("us-snap-retailers_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
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