Spatial Risk Systems (SRS) Launches First-of-Its-Kind Spatial Risk Scores designed to measure risk to assets and communities at the location level

NEW YORK, Oct. 20, 2021 /PRNewswire/ — Spatial Risk Systems today launched a first-of-its-kind tool to measure risk at the location…

NEW YORK, Oct. 20, 2021 /PRNewswire/ — Spatial Risk Systems today launched a first-of-its-kind tool to measure risk at the location level. The SRS Risk Scores are calculated and available at the census tract, county, city, and postal code level, and they can also be calculated on a user-defined geographic area. The SRS Spatial Risk Scores are designed to measure exposure to location-specific factors that have a long-term effect on asset value, operational effectiveness, environmental impact, and social sustainability. 

Spatial Risk Systems first-of-its-kind location-based Risk Scores communicate the long-term risk to an asset if demographic, environmental, and social risk factors are not addressed.

«To better understand the risk to assets and population, each SRS Risk Score carries within it the interaction of location, climate, environmental, demographics, vulnerability, and resilience,» said Raymond Clarke, Chief Product Officer of Spatial Risk Systems. «Alongside traditional aspects of portfolio risk assessment, we believe location-based risk should be at the forefront of every Portfolio Manager’s and ESG Compliance Officer’s toolset.»

Understanding the financials of an investment without coming to terms with location-based risks leaves a considerable blindside in asset and portfolio selection.

To learn more about Spatial Risk Systems and the SRS Spatial Risk Scores, visit:

An accompanying paper and methodology are available here.

About Spatial Risk Systems (SRS):  Founded by data science leaders from the financial sector, SRS is an innovative data and analytics company that has engineered the first global ‘spatial knowledge graph’ to quantify corporate and government location-based risks.  We’ve unified hundreds of open data and proprietary sets into a single fact table.  An optimized data state for Machine Learning and AI. 

  • 3.5 million Asset Locations
  • 19 million Open Data Set Mapping Relationships
  • 966 million Asset Location Relationships

For more information visit


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SOURCE Spatial Risk Systems