Spatial.ai collects and categorizes geotagged social media posts into 72 segments of consumer attributes and interests at the census block level.
By ranking census blocks on each of the 72 segments, Spatial.ai provides geosocial insights on consumer attributes such as Hobbies & Interests, Lifestyles, and Trendy Eats
Spatial.ai geospatial data provides insights in relation to location performance prediction based on current submarket tenant mix. Real estate investors and owners can leverage this location intelligence to optimize portfolio performance.
Spatial.ai’s geotagged data will help retailers choose their next site that best aligns with the brand strategy. Its curated 72 segments of customer attributes and interests reveal diverse insights on consumer behaviors and retail affinity.
Leveraging Cherre’s expansive knowledge graph, Spatial.ai data is seamlessly integrated into existing workflows to enable comprehensive market analysis and better decision-making.
query { spatial_ai{ geometry index_fashion_affinity index_high_end_affinity } usa_demographics{ state_fips median_household_income average_household_income expenses_gifts expenses_apparel expenses_personal_care } }
query { spatial_ai{ geometry index_fashion_affinity index_high_end_affinity } compstak_leases(where:{property_type:{_eq: "Retail"}}){ zip_code property_type property_subtype asking_rent_per_sq_ft net_effective_rent } }