Urban-planning and geography researchers use catalog.xmap.ai to study land-use intensity, retail vacancy, and commercial turnover across neighborhoods and cities, on a grant-friendly budget.
Quantify how intensively different neighborhoods are commercially used, block by block.
Follow openings and closures across successive exports to measure commercial vitality.
Study how commercial land use shifts alongside transit or zoning changes.
Compare commercial structure across cities on one standardized schema.
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.
| Field | Type | Why it matters here |
|---|---|---|
| category_level_1–3 | string | A standardized taxonomy for coding land use across studies |
| latitude / longitude | float | Feeds directly into GIS software for spatial analysis |
| open_status | string | Tracks turnover and vacancy across successive monthly pulls |
| poi_id | string (hash) | A stable join key for tracking the same location over time |
A research team exports the same corridor every semester, tracks which locations disappear and which appear, and publishes a turnover rate for the district's commercial vitality.