Spatial

  • POI Geospatial Analytics

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

How it works?

RE Developers, Investors and Brokers

Hospitality Real Estate Investment and Leasing Strategy

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.

Unlocked Insights

Retail Site Selection and Brand Management

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.

Spatial.ai + Cherre

Leveraging Cherre’s expansive knowledge graph, Spatial.ai data is seamlessly integrated into existing workflows to enable comprehensive market analysis and better decision-making.

Spatial

Sample Case Studies

  1. query {
  2.   spatial_ai{
  3.     geometry
  4.     index_fashion_affinity
  5.     index_high_end_affinity
  6.   }
  7.   usa_demographics{
  8.     state_fips
  9.     median_household_income
  10.     average_household_income
  11.     expenses_gifts
  12.     expenses_apparel
  13.     expenses_personal_care
  14.   }
  15. }
Case Study 1

Spatial.ai + Public Foundation Data Layer by Cherre

With every census block mapped to Cherre’s comprehensive foundation data layer, Cherre platform users will be able to tap into hundreds of additional data fields such as census demographics and surrounding points-of-interest, and gain a holistic view of the submarket and consumer insights.

Spatial Integrations 
  1. query {
  2.   spatial_ai{
  3.     geometry
  4.     index_fashion_affinity
  5.     index_high_end_affinity
  6.   }
  7.   compstak_leases(where:{property_type:{_eq: "Retail"}}){
  8.     zip_code
  9.     property_type
  10.     property_subtype
  11.     asking_rent_per_sq_ft
  12.     net_effective_rent
  13.   }
  14. }
Case Study 2

Spatial.ai + CompStak by Cherre Platform

Real estate investors can easily leverage Spatial.ai and Cherre Connections, such as market lease comps provider CompStak, to find the right tenants to fill their retail space based on submarket consumer interests and shopping habits. Correlation analysis between Spatial.ai’s 72 consumer segments and CompStak market lease rent data will reveal the driving factors behind the rent price and allow real estate investors to optimize their holding portfolios.

Spatial Integrations