Points of Interest, Building Footprints, & Mobility Data for Canada

Power geospatial analysis with accurate, up-to-date, and reliable POI, geometry, and foot traffic data.

Points of Interest, Building Footprints, & Mobility Data for Canada

Accurate, Fresh, and Comprehensive Geospatial Data for Canada

SafeGraph's leading POI, geometry, and mobility datasets power analytics and research across industries.

Compared to other Canadian places data providers, SafeGraph has:

  • More frequent data updates
  • Higher precision in polygon spatial hierarchy
  • Greater brand coverage

SafeGraph Places

Points of Interest

Leverage millions of POIs for thousands of brands across Canada to level-up your trade area analysis, site selection, investment research, and urban planning workflows.

Explore SafeGraph Places >

Points of Interest

SafeGraph Geometry

Building Footprints

Precisely map locations with machine-generated, human-verified polygon data. Understand the relationships between places with detailed spatial hierarchy metadata.

Learn more about SafeGraph Geometry >

Building Footprints

SafeGraph Patterns

Foot Traffic

Analyze consumer behavior with aggregated and anonymized mobility data for specific POIs and Dissemination Areas.

See SafeGraph Patterns in action >

Foot Traffic

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How often is SafeGraph data updated?

SafeGraph Places and Geometry datasets are updated monthly. Canadian Patterns data is updated weekly to ensure freshness and accuracy.

Where does the device data in Patterns come from?

We partner with mobile applications that obtain opt-in consent from its users to collect anonymous location data. This data is not associated with any name or email address. This data includes the latitude and longitude of a device at a given point in time. We take this latitude/longitude information and determine visits to points of interest. We then aggregate these anonymous visits to create our Patterns product.

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."


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