The Earth is Changing

The earth is changing under our feet as you read this. Human impact and changing climates are all impacting how we interact within our global biosphere. Measuring and understanding the nature and severity of these changes on a location-by-location basis is an emerging field called Spatial Finance.

Governments, Regulators, and Companies are all taking a keen interest in measuring the impact of climate and environmental changes and their links to commercial activities and corporate and government ownership. Until now, no authoritative data set focuses on this need.

SRS is an innovative data and analytics company focused on building a playing field level data base enabling institutions to accurately assess risks and opportunities at the underlying asset locations of their investment, transaction, and operating activities.

Founded by data leaders from FactSet, Bridgewater and Moodys, SRS has engineered the first ‘spatial knowledge graph’ to quantify these risks and opportunities at the playing field level unifying hundreds of open data and proprietary data sets under a single set of entity identifiers, and a comprehensive business taxonomy.

Unifying hundreds of spatial level data sets into a single fact or attribute table creates unprecedented opportunities for risk assessment and data science.

Extracting value out of the vast amounts of open data available today is difficult and expensive. One of the main reasons is that open data sets do not connect easily with each other nor with the data sets owned by the user. To connect these datasets, one needs to understand the various data identification schemas used by the government agencies and industry bodies that publish open data and figure out how to interconnect entities and locations of interest to the information available in open datasets. In addition, given the ever-changing nature of the world, maintaining the interconnects between data sources is a continuous effort of change monitoring and adjustments. These problems make the construction and keeping of fit-for-purpose data to support data science needs expensive and time-consuming effectively.


SRS has engineered the first ‘spatial knowledge graph’ unifying hundreds of open data and proprietary data sets under a single set of entity identifiers and a comprehensive business taxonomy.

The SRS Spatial Graph enables location Intelligence insight and dramatically redcues the cost and time in preparing data for analysis, and at the same time, offers a simple way for data scientists, analysts, and compliance professionals to discover and extract relevant data via SRS’s innovative single factor and attribute tables. 

The Component Scores

The Dimensions of Spatial Risk

Climate Impact

Asset Exposure | Historic Loss | Hazard Frequency | 18 Factors (Flood, etc.)

Environmental Risk

Greenhouse Gas emissions | Air Pollution | Proximity to Toxic Sites

Social Vulnerability

age | gender | Wealth | poverty | Race | Special Needs | Service Sector

Social Resilience

Human well being | Economic Financial Outlook | Infrastructure

The SRS Geospatial Portfolio Risk Score

SRS Risk Scores provide investment professionals the ability, for the first time, to quantify spatial risks associated with portfolios, investments and transactions.

SRS Risk Score Components

Our Products

The Dimensions of Spatial risk Systems

The SRS Risk Scores

GeoSpatial Analytics

Facility Report

Geo | Parent | EPA...More

Data Science

966 Million Attributes
Single FacT Table

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Founded by data science leaders from the financial sector, SRS quantifies risk by unifying, standardizing, and analyzing empirical data sources, helping investors to better understand ESG and sustainable investing outcomes, from a facility to a large-scale geographic perspective.

SRS has engineered a massive spatial-level knowledge graph, unifying hundreds of open and proprietary- source data sets, under a single set of enterprise standards. This spatial graph exposes thousands of potential factors that can be incorporated in quantifying spatial-related risks.

Building and connecting these data layers to traditional content sources is the next evolutionary step for the financial sector data ecosystem.