A field guide to Restaurant data in Charlotte, North Carolina

A map can show nearby businesses in seconds, but a market view needs more context. Category labels, review signals, coordinates, and timestamps make the pattern easier to evaluate.

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 4 Restaurant records associated with Charlotte, North Carolina. Their average public rating was 4.60, with 1,570 reviews in total — roughly 392 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

• Mochi's Deli — 5.0 rating, 41 reviews
• Tipsy Pickle — 4.8 rating, 1,119 reviews
• Retaj Restaurant and Cafe — 4.4 rating, 242 reviews

These examples were selected from records crawled around August 29, 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

For directory operators, this is a starting point for deduplication, address validation, and category normalization. Analysts can join it with polygons, drive-time areas, or first-party data.

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.