Transportation and infrastructure planning teams use catalog.xmap.ai to weigh transit-stop and infrastructure investment against real commercial density instead of population estimates alone.
Weigh candidate stop locations by nearby commercial density and category mix.
Rank infrastructure projects by the commercial activity they'd serve.
Identify commercial corridors where curb demand likely outstrips supply.
Document the existing business base along a corridor before a construction project begins.
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 |
|---|---|---|
| latitude / longitude | float | Drives every corridor and stop-siting calculation |
| category_level_1–3 | string | Distinguishes retail, office, and service commercial activity |
| traffic_score / dwell_time | numeric | A proxy for existing pedestrian and vehicle activity |
| open_status | string | Keeps corridor assessments current as businesses change |
A transportation department exports commercial density around three candidate stop locations, finds one corridor with markedly higher business activity, and moves it to the top of the funding-priority list.