Global place intelligence with 23M+ U.S. POIs with coverage in 150+ countries: polygon boundaries, brand hierarchy, NAICS classification, and geofencing for foot traffic analytics and attribution.
Since 2018
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Overview
GSDSI's flagship location product: a global Points of Interest database with 23M+ U.S. POIs and coverage in 150+ countries. Each POI record includes precise polygon boundaries, brand-to-parent hierarchy mapping, NAICS classification codes, centroid coordinates, opening hours, and rich place attributes. Purpose-built for foot traffic measurement, trade area modeling, competitive benchmarking, site selection, and campaign attribution at any geographic scale.
Want the buying guide?
If you’re evaluating vendors or writing an RFP, this guide lays out what to ask for (coverage, refresh, governance) and links to the best references.
A representative slice of the Points of Interest (POI) & Geofencing schema. The data dictionary shared during evaluation lists every field with its type, nullability, join keys, and refresh cadence.
Field
What it contains
poi_id
Stable identifier for the point-of-interest record.
polygon
Geospatial boundary describing the physical footprint of the location.
brand_parent
Normalized parent brand in the location hierarchy.
naics_code
NAICS classification assigned to the place.
latitude
Latitude of the point-of-interest centroid.
longitude
Longitude of the point-of-interest centroid.
opening_hours
Published operating hours when available.
How the POI Database Is Built and Maintained
GSDSI's POI database is constructed through a multi-source aggregation pipeline that combines authoritative business registries, commercial data partnerships, satellite and aerial imagery verification, and crowdsourced validation. Each Point of Interest is geocoded with a precise polygon boundary (not just a centroid pin) that reflects the actual physical footprint of the location, enabling accurate visit attribution even in dense commercial environments like shopping malls, strip centers, and mixed-use developments. Brand hierarchy mapping links every franchise, subsidiary, and DBA location to its parent company, supporting rollup analytics from individual store to brand to corporate parent. NAICS classification codes are assigned and verified for every POI, enabling category-level analysis across retail, restaurant, healthcare, financial services, entertainment, and dozens of other verticals. Refresh cadence, change-detection scope and delivery frequency are defined per dataset in the order form rather than being uniform across the catalogue, with historical snapshots available for longitudinal analysis.
Common Applications for POI & Geofencing Data
Retailers and QSR brands use POI data for competitive benchmarking, comparing their locations' foot traffic against competitors at the brand, category, and trade area level. Real estate investment firms evaluate acquisition targets by analyzing foot traffic density, visitor origins, and co-tenancy effects at properties anchored by specific POI brands. Media agencies build geofence-based audience segments by defining custom boundaries around retail locations, events, or competitor sites, then targeting devices observed within those geofences with relevant advertising. Site selection teams use the POI database to identify whitespace markets where consumer demand exists but competitive supply is limited, overlaying foot traffic patterns with demographic and mobility data to score potential locations. Attribution platforms match ad-exposed devices against POI geofences to measure whether campaign impressions drove incremental store visits, providing closed-loop measurement from impression to visit.
Location buyers should inspect polygons, not just POI counts. Procurement usually starts with a sample of known malls, strip centers, drive-throughs, campuses, and same-address businesses to expose centroid and boundary errors.
Inspect polygon footprints against dense retail, mixed-use, and multi-tenant locations.
Validate brand hierarchy, DBA mapping, category taxonomy, and closure/opening freshness.
Test geofence attribution rules before using visits for measurement or site selection.
Confirm daily refresh expectations and historical snapshot availability.
Frequently asked questions about Points of Interest (POI) & Geofencing
What does GSDSI's POI dataset cover?▾
Global points-of-interest with deduped names, categories, and polygon-validated geometries. Coverage spans retail, restaurants, healthcare, government, transit, lodging, sports/entertainment, and parks. Polygon quality is validated nightly to remove overlapping or oversized geofences that would over-count visits.
How fresh is the POI inventory?▾
Refresh cadence is defined per dataset and contract rather than being uniform across the catalogue. New openings, closings and rebrand events are flagged in the change-delta file, and polygon edits propagate on the cadence set in your order form. Ask for the cadence attached to the specific dataset you are scoping.
What's the difference between a POI radius and a polygon geofence?▾
A radius geofence is a circle around the centroid; it over-counts neighboring buildings and undercounts large-format venues. A polygon geofence traces the actual building footprint, so visit attribution is accurate. GSDSI ships polygons by default and includes the centroid for legacy systems that still require radius input.
How is POI data delivered?▾
CSV, JSON, Parquet via S3, SFTP, or direct API. Customers building real-time geofencing applications typically use the API; analytics teams typically use Parquet on S3 with a daily change-delta. Snowflake direct-share is available on request.
Can the POI feed be combined with foot-traffic data?▾
Yes. The POI feed is the polygon-and-name layer; the Global Mobility / Location Data feed is the visit layer. Joining the two gives you visits-by-POI, dwell time, origin-destination by venue, and competitive share-of-visit. Both feeds use shared POI IDs so the join is one-to-one.
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