GIS, urban-planning, and data-science instructors use catalog.xmap.ai to give students a real, structured spatial dataset for coursework and capstone projects instead of a synthetic one.
Give a GIS or data-science class a real, structured dataset to analyze in coursework.
Support student capstone projects with a real business-location layer for their study area.
Give students a real-world feature set for classification or clustering exercises.
Use a standardized, citable schema so student work is reproducible across sections.
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 | Feeds directly into GIS software and mapping libraries |
| category_level_1–3 | string | A real categorical feature for classification exercises |
| rating / review_count / dwell_time | numeric | Real-valued features for clustering or regression exercises |
| poi_id | string (hash) | A stable identifier for reproducible joins across assignments |
An instructor exports every business location in a metro area with category and rating fields, and has students cluster commercial sub-districts by category mix as a semester-long GIS assignment.