GIS platforms and spatial-analytics products use catalog.xmap.ai as a ready-made business-location layer (joinable to parcels, demographics, and boundaries) instead of building and maintaining a POI dataset internally.
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.
Sourcing, deduplicating, and refreshing business-location data is a full-time job most GIS teams shouldn't take on.
Analytics platforms increasingly need a business-location layer to answer client questions about a place.
A clean lat/lon and category schema joins straight onto parcels, boundaries, and demographic layers.
Offer clients a POI layer inside your own platform without building the sourcing pipeline yourself.
Join POI records onto parcel or administrative-boundary layers using latitude and longitude.
Power one-off site-selection, market-sizing, or trade-area projects for consulting clients.
Generate density heatmaps by business category for a client-facing analytics dashboard.
Replace an internal, hard-to-maintain POI scrape with a dataset delivered as clean CSV, refreshed every 30 days.
Generate client-facing commercial-activity reports using a consistent, licensable data source.
Relative time-to-launch for a client-facing business-location feature.
Every export carries 36 columns; these are the ones GIS platforms use most.
| Field | Type | Why it matters here |
|---|---|---|
| latitude / longitude | float | Joins directly onto parcels, boundaries, and other spatial layers |
| category_level_1–3 | string | A standardized taxonomy for category-based heatmaps and filters |
| address / city / district / zip | string | Aggregates cleanly to administrative-boundary levels |
| brand_name / chain_flag | string / boolean | Supports chain-share and brand-density analyses |
| traffic_score / dwell_time | numeric | Adds an activity-signal layer beyond a static point location |
| open_status | string | Keeps a licensed layer current for client-facing dashboards |
| poi_id | string (hash) | A stable join key for versioned, repeatable spatial analyses |
| rating / review_count | numeric | Adds a consumer-sentiment dimension to a spatial layer |
A spatial-analytics platform's clients keep asking for a business-density layer alongside its parcel and demographic data. Instead of scoping an internal POI-sourcing project, the team licenses the catalog, joins it onto existing parcel geometries by coordinate, and ships the feature in weeks instead of quarters.
We'll run a live query against your target category and geography.