Geometry data

Reduce Attribution Errors With High Quality Building Footprint Data

Incredibly accurate POI footprint data for a clear picture on the boundary of a place.

Hone In On Location Accuracy

Our Geometry dataset helps data leaders to better understand the shapes of points of interest (POIs) and their relationships with other places.

Geometry is a robust dataset that includes
The shape of the place as Well-Known Text (WKT)
Spatial hierarchy metadata showing relationships between polygons
Additional attributes related to parking lots and polygon quality

Explore premium rows

Parking Lots

A premium set of Geometry rows depicting the shape and size of surface parking lots and their relationship to surrounding POIs.

High Quality

Maintaining Your Own Database of Polygons is Challenging

We apply complex engineering and have human verification for accuracy so you can have data you trust.

Learn more about SafeGraph Geometry
Polygon data is complex to create and maintain. SafeGraph Geometry data provides reliable and accurate building footprints verified by actual people, so you can be confident in their veracity.

Inaccurate Data is Costing You Time and Money

Our accurate polygons allow you to reduce errors and more precisely attribute consumer visits to a specific place.

Read our guide to visit attribution
Building footprints are the only true representations of place boundaries; a centroid radius or an isochrone is not granular enough for precision analytics.

Our World is Complex - We Make Analyzing it Easy

We give you metadata so you can easily distinguish the spatial hierarchy and relationship between different places.

Explore the data schema
Spatial hierarchy metadata reveals how places relate to each other, such as a store within a shopping mall or a suite within an office complex.

Solve strategic problems with high quality data

Download a free sample of Geometry Data

Explore precision polygons for global places.


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How much does the Geometry dataset cost?

Like all of our data, the cost of Geometry depends on the amount of rows, columns, and frequency of delivery you request. You can buy data directly from the shop or contact our sales team to learn about enterprise pricing.

How often is the dataset updated?

SafeGraph issues updates to Geometry once per month, which is much more frequently than other POI vendors, who may update once every 3-6 months. We can do this because we work with more sources of data and are much more efficient at combining those sources. During each month, some subset of our sources will send us their updates, and we ensure that we onboard and integrate those changes quickly and easily.

This enables us to quickly reflect store openings and closings in our POI database. The time between a store opening/closing and being reflected in our POI database is approximately equal to the time that the store update is seen by one of our sources and the time it takes SafeGraph to reflect this in our data. The latter of these two is typically within the month, which is very fast compared to other providers, which might be within 3 months. The former of these two is hard to predict - but we do work with sources that generally receive updates very quickly.

How do you determine spatial hierarchy or overlapping polygons?

We identify spatial relationships (what we refer to as “spatial hierarchy”) by measuring polygon overlap. For each pair of overlapping polygons, if the larger polygon contains at least 80% of the smaller polygon, and if the larger polygon is also of a particular POI category, then we mark it as the “parent” of the smaller polygon. It’s important to restrict parent POI candidates to a specific set of categories or brands so that we’re not solely reliant on polygon precision to determine spatial hierarchy. For example, we want airports to be parents when overlapping other POIs, but we generally don’t want cafes to be parents if overlap exists and the cafe happens to be the larger of the two polygons. See our Geometry docs for a complete list of POI categories that are eligible parents. We flag these relationships in our geometry data by setting the “parent_placekey” of the smaller POI equal to the “placekey” of the larger, encompassing POI. We colloquially refer to the larger, containing POI as the "parent" and the smaller POI as the "child."

How do you deal with POIs that are completely enclosed by others?

Similar to determining eligible parent POIs, we rely on categories to distinguish enclosing vs. non-enclosing spatial hierarchy relationships. See our Geometry docs for a complete breakdown of the spatial hierarchy relationships we treat as “enclosing.”

Although simple, the enclosed column is super important fuel for our visits algorithm. Due to GPS drift within major structures, when 'enclosed' = “true,” we exclude visits to that POI and only attribute visits to the parent POI. We pride ourselves in archiving facts, so we would rather not attribute visits at all than make rash assumptions.


Learn more about accurate and precise global POI data