Market research and insights firms use catalog.xmap.ai to ground syndicated and custom market-sizing studies in a real, countable universe of business locations instead of a modeled estimate.
Count real locations by category and geography instead of extrapolating from a sample.
Use a standardized category hierarchy to compare markets on a consistent basis.
Anchor a published industry report in a transparently sourced, refreshed dataset.
Turn a one-off client research question into a filtered export the same day.
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.
Location count by category across the studied region.
| Field | Type | Why it matters here |
|---|---|---|
| category_level_1–3 | string | A standardized taxonomy for consistent market-sizing across studies |
| brand_name / chain_flag | string / boolean | Separates chain share from independent-operator share |
| address / city / zip | string | Aggregates cleanly to any geographic level a report needs |
| open_status | string | Refreshes market-size estimates as businesses open and close |
An insights firm exports every location in a category across a country, breaks it down by chain versus independent, and publishes a market-size figure grounded in an actual count rather than a survey extrapolation.