Mapping Restaurant in Phoenix: signals hidden in local POI data

For location intelligence teams, the story is in the pattern rather than a single listing. Filtering, grouping, mapping, and refreshing records turns an unstructured search into a repeatable process.

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 9 Restaurant records associated with Phoenix, Arizona. Their average public rating was 4.39, with 5,636 reviews in total — roughly 626 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

• Mi Casa Jugos y Licuados LLC — 4.7 rating, 13 reviews
• The Wright Bar — 4.6 rating, 264 reviews
• Arizona American Italian Club — 4.5 rating, 493 reviews

These examples were selected from records crawled around September 10, 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

The same data can support site selection, territory planning, competitor mapping, local SEO research, and location-aware lead generation. Coordinates and timestamps make the work reproducible.

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.