Grocery and supermarket chains use catalog.xmap.ai to see trade-area overlap between formats, catch a competitor's new store before it opens, and choose the right format (hypermarket, midsize, or convenience) for the gap that's actually there.
catalog.xmap.ai is a self-serve database of 140M+ real business locations across 40+ countries. Every record is an actual, physically-located business (not an estimate) with up to 36 structured fields: name, full address, latitude/longitude, category (three levels deep), brand and chain affiliation, hours of operation, phone and website, star rating and review count, open/closed status, and more.
You filter the catalog by category, brand, or geography in a live web interface, watch the matching record count update as you narrow the search, and export the results as a clean CSV file, ready to open in a spreadsheet, load into GIS software, or join against your own data.
The dataset refreshes every 30 days, so an export reflects recent openings, closures, and changes rather than a static snapshot from years ago.
The right format for a trade area depends on what's already serving it, at what size.
A new store too close to an existing one can shrink both instead of growing the network.
Delivery-only grocery competitors can serve a trade area with no visible storefront.
See how far a proposed store's catchment overlaps with your own network before opening it.
Catch a competitor's new location as soon as it appears in a monthly refresh.
Match hypermarket, midsize, or convenience format to what a trade area is actually missing.
Score a proposed site against your own existing stores before approving a new one.
Choose a fulfillment-only location based on demand density rather than storefront visibility.
Find neighborhoods with population but no full-format grocer within a reasonable radius.
Store count by format within a 5-mile radius.
Every export carries 36 columns; these are the ones grocery teams use most.
| Field | Type | Why it matters here |
|---|---|---|
| category_level_3 | string | Distinguishes hypermarket, midsize grocer, convenience, and dark store formats |
| brand_name / chain_flag | string / boolean | Separates national chains from independent grocers |
| latitude / longitude | float | Drives every trade-area overlap and cannibalization calculation |
| traffic_score / dwell_time | numeric | Signals how much of a destination trip a location already captures |
| open_status | string | Flags new store openings and closures for monthly monitoring |
| rating / review_count | numeric | Signals which nearby grocers are winning customer loyalty |
| hours_of_operation | string | Reveals 24-hour or extended-hours competitive gaps |
| venue_hierarchy | string | Ties a grocery unit to the shopping center it anchors |
A grocery chain is expanding into a growing suburb. The team exports every existing grocery format in the trade area, sees hypermarkets are saturated but midsize grocers are scarce, and greenlights a midsize-format store instead of the hypermarket originally proposed.
We'll pull a live sample for your category and market.