The definitive spatial foundation for building world-class products and location intelligence
Understand the true shape of POIs and how places relate to each other using reliable geometry data.
Robust polygon geometry data includes:
You need data you can trust and rely on. We apply complex engineering and human verification to ensure accuracy of all POIs in our datasets.
Our precise polygons are built to be the foundation of your mobility analysis. By joining SafeGraph Geometry with telemetry data, you can move beyond simple ‘proximity’ and achieve true attribution – distinguishing a customer inside a store from a pedestrian on the sidewalk.
We give you metadata so you can easily distinguish the spatial hierarchy and relationship between different places. With clear polygon hierarchies that have parent-child relationships, you’ll gain detailed insights about the POIs you’re analyzing.
Access data specs and delivery information
Learn how the geometry datasets work and what it includes.
Understand every attribute available in the geometry polygon database.
Track latest product releases and stay updated about the datasets you use.
Get a quick overview of coverage, depth, and available polygon data.
Get your data easily in any of the following 3 ways:
Set up an S3 bucket to receive scheduled monthly deliveries of Geometry data. Ideal for teams that need full datasets for internal processing.
Query SafeGraph Geometry directly in Snowflake. Integrate data into existing workflows without managing file transfers.
Explore sample Geometry data before committing. Review available columns and assess how it fits your current datasets.
Building footprint polygon data represents the actual shape and boundaries of a place. Instead of a single coordinate, it maps the full area a location occupies. This helps you understand how places exist in real space and how they relate to nearby locations.
Point data uses one latitude and longitude to represent a place, which can miss the true boundaries. Polygon data captures the full geometry, making it easier to analyze proximity, overlaps, and spatial relationships, especially in dense areas.
The dataset includes polygons in Well-Known Text (WKT) format along with useful metadata. This covers spatial hierarchies between places, attributes for parking areas, and indicators that help you assess polygon quality and reliability.
By using precise boundaries, polygon data helps determine whether a device is actually inside a location rather than just nearby. This improves the accuracy of visit attribution and reduces confusion between closely located places.
Building a reliable polygon dataset requires constant updates, data cleaning, and validation. Real-world locations change often, and maintaining accuracy at scale demands both engineering effort and ongoing quality checks.
