This dataset defines AI-derived neighborhood areas in the United States and Canada that group locations with similar demographic, retail, and sales characteristics. It integrates retail point-of-sale sales data, Census Bureau demographic inputs, and geospatial boundaries to create roughly 2,200 U.S. and 364 Canadian precision areas. Boundaries align to the smallest available geographic units and are periodically refreshed to balance spatial accuracy with year-over-year comparability. Areas reconcile back to official retail measurement totals when aggregated. The dataset supports local market performance assessment, site selection, regional segmentation, spatial clustering, and comparative analysis between neighborhoods and stores.

Key attributes include:

• AI-driven neighborhood delineations mapped to small-area geography

• Integrated retail point-of-sale sales metrics linked to areas

• Census-based demographic and socioeconomic profiles

• Area boundaries refreshed for temporal comparability and aggregation

• Reconciliation to official retail measurement totals for accuracy

Retailers, manufacturers, and planners can evaluate market performance at a neighborhood scale to bridge gaps between national trends and individual store data, improving site selection, segmentation, and local strategy. Public sector analysts and planners can also use the areas for targeted service delivery and local market monitoring.