Spatial Finance is a way to combine geospatial-climate, socio-economic, entity relationship and other data sources to use for risk management, operational assessment, portfolio/index construction and variety of other business decisions. The resulting highly connected and consistent data structure is known as a Knowledge Graph.
The 5 Tiers of the SRS Data Engine
There is a massive of amount of open data that ‘lives’ at the tier 4 level. Climate, Weather, Environmental, Health just to name a few. Most of these data sets report at a local, country or regional level and it is extremely challenging to connect these data sets to enforce a consistent global view.
Effectively evaluating any asset, company or portfolio from a risk management perspective requires a keen understanding of local risk factors as reported by tier 4 data sets. SRS has created and actively supports a global geo-hierarchy which allows for quick and efficient connections to open data sets.
Collecting Asset level data is an extremely challenging task as thousands of different sources must be evaluated. A single company can have hundreds of different locations with activities differing from location to location. SRS has constructed a comprehensive taxonomy of over 1,700 classification types. This plays a critical role in mapping out global asset locations. Mapping Sub-Asset data to Asset Locations is an important piece of the data puzzle to effectively aggregate risk exposures. Knowledge Graphs are the best data structures for performing these tasks.
Mapping and aligning the first two tiers with Issuers, Ultimate Parents and Joint Venture Partners is where value really starts to be realized for measuring or evaluating company level risk for multiple dimensions. Securities are also included here as large capital intensive projects can be linked to specific project loans and bond offerings. It is also at this level that the first three tiers can be seamlessly connected to existing fundamental, earnings estimates and pricing data bases. Exposing Spatial-Finance data at this level presents new opportunities for company analysis and quantitative investing.
Effectively evaluating a portfolio or driving security selection based upon ESG and other operational risk factors is quickly becoming an important factor for global investors. Finding ways to leveraging accurately and timely spatial-finance data to comply with investing guidelines, overall portfolio risk or uncover alpha is a global opportunity.
Whether aggregating the first four tiers at a global sector level or at a country level for all sectors, incorporating Spatial-Finance data presents emerging opportunities for Macro Investors, Companies, Academics, Regulators, and Governments to improve and extend their overall data science capabilities with access to these new data layers. Once again knowledge graphs are the optimal data structure for ‘playing’ this game.
SRS Value Proposition:
By offering precisely inter-connected geo-spatial, socio-economic, environmental, ownership and supply chain data we accelerate research, free analysts from expensive data wrangling exercises and facilitate the discovery of new relationships and insights.
Until now, no unified data fabric connected asset locations and corporate ownership with available open, third party and regulatory data sources.
Aggregating this data underpins SRS’ evaluation of corporate, sector, industry, and municipal risks.
This for the first time provides a solid foundation which, for the first time, allows global institutions to rely on ‘hard data sources’ to effectively measure Climate, Environmental, and Socio-Economic risks linked to individual asset locations.
The SRS Knowledge Graph fills this foundational gap, providing proprietary sourced asset locations connected with proofed, standardized, open data.
A Spatial Finance Look at Pfizer and NextEra
View detailed SRS Spatial Finance data on Pfizer, NextEra Energy Resources and Seabrook Nuclear Powerplant.