• POI Geospatial Analytics

SafeGraph powers innovation through point of interest (POI) geospatial data, business footprints, and store visitor insights for consumer places (retail chains, mom-and-pop shops, airports, and more) in the US and Canada.

SafeGraph provides detailed data on over 6,200 brands and 6 million points of interest (POI). SafeGraph's unique technical approach includes ingesting data from thousands of sources, including monthly updates, and processing it using both machine learning and human feedback to account for store openings and closings.

SafeGraph’s store visitor analytics and foot traffic data provide a real time answer to questions such as: how often do people visit this location, where did they come from, and where else do they shop?

How it works?

RE Developers, Investors and Brokers

Real Estate Professionals

SafeGraph’s point of interest (POI) and foot-traffic data offers up-to-date market insights and enriches due diligence research through detailed industry and competitor analysis.



SafeGraph’s Places data provides insights into co-tenants, building geometries, and consumer behavior, enabling insurance providers to streamline underwriting, conduct better risk assessments, and adjust liability coverage.

Unlocked Insights

Retail Brand Management

SafeGraph’s point of interest (POI) data can be joined with internal store and CRM systems to understand shopper demographics, home location and visitation patterns, and strategize resource planning and allocation.

SafeGraph and Cherre

Leveraging Cherre’s expansive knowledge graph, SafeGraph data can be easily integrated into existing workflows, from spreadsheets to tenant management systems, to unlock unparalleled insights to stay ahead of the curve.


Sample Case Studies

  1. query Top_10_Weekday_Lunch_Spots {
  2. safegraph_core_poi_geometry_pattern (
  3. where: {
  4. safegraph_compstak_boundary__safegraph_place_id: {
  5. compstak_market_boundary__id: { name: { _eq: "Midtown Eastside" }}
  6. safegraph_popularity_by_day__safegraph_place_date_id: {
  7. day_of_week: { _nin: ["Saturday", "Sunday"] }
  8. }
  9. top_naics_category: { _eq: "Restaurants and Other Eating Places" }}
  10. ) {
  11. location name safegraph_popularity_by_hour__safegraph_place_date_id (
  12. where: { visit_hour: { _in: ["11", "12", "13", "14"] } }
  13. order_by: { visitor_count: desc }
  14. limit: 10
  15. ) {
  16. visit_hour
  17. visitor_count }}}
Case Study 1

Top 10 weekday lunch spots in Midtown Manhattan

Where do consumers working in Midtown Manhattan frequently visit during lunch hours? SafeGraph’s Patterns data details foot-traffic behavior down to the hour, and this bird’s-eye view of property-level information enables innovative analysis into submarkets and the competitive landscape.

Safegraph Integrations
  1. query Safegraph_POI_Compstak_Leases {
  2. safegraph_core_poi_geometry_pattern(
  3. where: {location_name:{_like: "Starbucks"}}
  4. ) {
  5. location_name
  6. safegraph_compstak_boundary__safegraph_place_id {
  7. compstak_market_boundary__id {
  8. market
  9. name
  10. compstak_leases_compstak_boundary__boundary_id {
  11. compstak_leases__compstak_id {
  12. property_type
  13. starting_rent_per_sq_ft
  14. }
  15. }
  16. }
  17. }
  18. }
Case Study 2

POI density vs. leasing prices

Is the prevalence of particular POI (i.e. Starbucks) in a submarket an indicator of higher rent? Joining forces with Cherre, SafeGraph’s comprehensive points of interest datasets can be seamlessly integrated into a sophisticated statistical correlation analysis. Looking for a leading indicator of a market turning point? Cherre + SafeGraph empowers unparalleled ideation of investment opportunities.

Safegraph Integrations