Saint Paul Restaurant data: what an 80,000-record POI sample shows

A useful local-market page begins with the places on the ground, not a generic national total. Structured POI data gives analysts a way to see where a category is present and how records compare.

The numbers behind this local view

This article uses a random 80,000-record sample from the operational listings in our US POI collection. After filtering for numeric ratings and review counts, the sample contained 3 Restaurant records associated with Saint Paul, Minnesota. Their average public rating was 4.50, with 4,339 reviews in total — roughly 1,446 reviews per record.

That is a sample statistic, not a census of every Restaurant in the city. It gives teams a consistent way to compare local categories without pretending that one snapshot is the whole market.

Representative records in the sample

• Cecil's — 4.7 rating, 3,025 reviews
• Crasqui Restaurant — 4.6 rating, 334 reviews
• Pillbox Tavern — 4.2 rating, 980 reviews

These examples were selected from records crawled around August 28, 2026. They show the name, category, rating, review, address, coordinate, and status fields available in a structured POI record. They are not endorsements, rankings, or promises about current availability.

Where the data becomes useful

GIS teams can layer these points with transit, demographics, competitor locations, or service areas. Marketing teams can use the same structure to prioritize local landing pages.

Read the numbers with care

Ratings, reviews, opening hours, and operating status change. City labels can also reflect a source-defined metro area rather than a strict municipal boundary. Validate current source information before a record is used in customer-facing content or operational decisions.

Explore US POI datasets to build location-data workflows around state, city, category, coordinates, and business signals.