AI ready state

Spatial-Level data fully optimized for emerging AI Technologies – LLM (Large Language Models), Generative AI

For data to be considered in an AI ready state, the following ‘data’ conditions need to be in place.

Data Connectivity

All the individual data points need to be directly connected or indirectly connected to one another.

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Data Precision

Data must be high quality. 'Dirty' data will invalidate ML/AI results every time.

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Standardized Taxonomies

Connecting and standardizing dozens of data sets into a single knowledge graph, SRS provides a turn-key data solution

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Hierarchical Relationships

Hierarchies play a critical factor in mapping data relationships, data networks, and building decision trees. ML/AI 'shines' when hierarchical relationships are fully supported.

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Data Aggregation

Knowledge Graphs support simple methods for data aggregation and data drill down.

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Point in Time

All nodes or entities in a Knowledge Graph can support attribute values over time.

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Data Visualization

Data connectivity greatly expands and enhances data visualization and data reporting.

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Data Integration

The ability to provide multiple ways to easily connect to other third party and open data sources.

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Single Data Catalog

Simple way to drive data discovery and data access.

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Data Governance and Spatial Risk

Critical factors in play

from a data governance perspective.

1. Issuers

2. Security Level

ISIN, CUSIP, Loan Identifiers

3. Listing Level

Exchange Code/Ticker Symbol, SEDOL

4. Point in Time

Time Series - Climate, Environmental, Emissions, Electricity Generation.

SRS Knowledge Graph now adds a spatial layer to the existing data ecosystem

Seamlessly connecting asset locations to public, private and governmental entities, their financial identifiers (i.e., ISIN, Ticker, etc..) and relevant climate, environmental and socioeconomic factors.

5. Point in Space

Location-specific factors impacting investment and operational outcomes.

3.5 Million+ Asset Locations

Multiple Name Support for a Given Asset Location

Fully Support Entity Name Resolution and Disambiguation

wdt_ID Asset ID Asset Name
2 2700586 Chalmette Refinery
wdt_ID Code Taxonomy Description Attribute Types Attribute Count
1 X001 Crime 11 485.515
2 X002 Economic 14.585 154.107.889
3 X003 Environmental 206 54.571.425
4 X004 Health/Medical 763 33.134.798
5 X005 Energy 36 810.720
6 X006 Housing 1.633 612.809.433
7 X007 Population/Age/Sex 648 194.019.046
8 X008 Income 368 139.160.672
9 X009 Education 2.094 343.969.116
10 X010 Poverty 1.702 294.072.552

Single Attribute File

Unifying hundreds of spatial level data sets under a single set of standards
23,000+ Unique Attribute Types
2 Billion+ Attribute Values

wdt_ID Sample Attribute Types Value
1 Energy: Power Plant/Facility (MW: Megawatt Capacity) 2.786
2 TRI (Toxic Release Inventory) Tot pounds per year transferred off-site. 157.328
3 Total Facility Emissions in metric tons CO2e (Annual) 2.726.517
4 Number of Beds 676
5 Age - 60 Years and Over: Percent below poverty level 28
6 Bedrooms - Total Housing Units 51.213
7 Coastal Flooding - Exposure - Building Value 0

Extensive Classification Taxonomy

1,700+ Categories

wdt_ID Sector Sub-Sector
1 Chemical manufacturing Industrial gas manufacturing plant
2 Petroleum and coal products manufacturing Petroleum refineries
3 Machinery manufacturing Industrial machinery mfg plant
4 Mining Coal Mine
5 Food Manufacturing Dairy product manufacturing plant
6 Hospitals/Clinics Children's Hospital
wdt_ID Code Taxonomy Description Asset Location Count
1 S030 Beverage and tobacco product manufacturing 1.554
2 S031 Textile mills 2.829
3 S032 Textile product mills 1.316
4 S033 Apparel manufacturing 1.356
5 S034 Leather and allied product manufacturing 768
6 S035 Paper manufacturing 4.789
7 S036 Printing and related support activities 12.301
8 S037 Wood product manufacturing 13.494
9 S038 Petroleum and coal products manufacturing 9.372
10 S039 Chemical manufacturing 22.777

Selected Categories

Map relevant facility-level or geographic-level data to an asset location

Enables thousands of data points (Points in Space) to be quickly aggregated and exposed at an issuer level for risk analytics, reporting, and data science purposes.

This example looks at the two asset locations and their associated identifiers. Behind these identifiers are thousands of factors and attributes associated with the facility and the local area.

wdt_ID Asset ID Asset Name Parent  Ult Parent
1 3521905 Huntersville LNG Facility Piedmont Natural Gas Co.  Duke Energy (NYSE: DUK)
2 Source ID Source Description Source Value
3 17 US Census County FIPS Code 37119
4 18 US Congressional District Code (CD) NC: Congressional District 12
5 20 US Census Core Based Statistical Area (CBSA) 16740
6 21 US Census Combined Statistical Area (CSA) 172
7 23 US Census Metropolitan/Micropolitan Indicator (METMIC) 1
8 26 US EPA Reg ID 110038855511
9 32 US Census Region Code 3
10 33 US Census Division Code 5
wdt_ID Asset ID Asset Name Parent  Ult Parent
1 15584 Bartow Regional Med Ctr BayCare Health System  BayCare Health System
2 Source ID Source Description Source Value
3 1 NGA GEOnet Names 4146729
4 13 Wikipedia 42042432
5 14 Wikidata Q16164841
6 17 US Census County FIPS Code 12105
7 18 US Congressional District Code (CD) FL: Congressional District 17
8 20 US Census Core Based Statistical Area (CBSA) 29460
9 21 US Census Combined Statistical Area (CSA) 422
10 23 US Census Metropolitan/Micropolitan Indicator (METMIC) 1

20.6 million+ Cross-Reference Relationships

From dozens of sources.

wdt_ID Source ID Source Description Source Count
1 67 ISIN Number 38.810
2 63 Issuer CUSIP Number 61.514
3 14 Wikidata 278.034
4 13 Wikipedia 248.728
5 16 US State School District ID Number 34.774
6 36 US NCES Public School ID Number 108.719
7 17 US Census County FIPS Code 1.824.229
8 18 US Congressional District Code (CD) 1.558.826
9 20 US Census Core Based Statistical Area (CBSA) 1.538.541
10 24 US EIA Utility ID 6.535
wdt_ID Geo-Spatial Lavel ID
1 County 1
2 Census Region 2
3 Census Division 3
4 State/Province 4
5 Metropolitan Area 5
6 Voting District 6
7 County/Regional 7
8 District/Regional 8
9 City 9
10 City Sub-Section 10

The SRS geo-hierarchy allows new data sets to be quickly onboarded in a matter of hours and not weeks...

…and have these factors quickly exposed via SRS’ single fact table. Most available data sets are published at a geographic or geopolitical level.

Simple Data Aggregation Path

Data can be quickly aggregated at any one of the following geographic levels:

wdt_ID Geo Level State County City Postal Code Census Tract
1 Census Tract Texas Bexar County San Antonio 2.147.483.647
2 Census Tract Texas Bexar County San Antonio 2.147.483.647
3 Census Tract Texas Bexar County San Antonio 2.147.483.647
4 Postal Code Texas Bexar County Adkins 78101
5 Postal Code Texas Bexar County Atascosa 78002
6 Postal Code Texas Bexar County Converse 78109
7 City Texas Bexar County Converse
8 City Texas Bexar County Live Oak
9 City Texas Bexar County San Antonio
10 County Texas Bee County


Asset Locations: By facility, company, industry, sector, portfolio

a. Where is it?

b. What happens there?

c. Who owns it?

d. Spatial Risks.

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US Transportation Equipment Sector

Car & Truck

Global Car and Truck Manufacturing


Demographic: hundreds of sources, multiple geographies

Organizing and Standardizing thousands of factors and 2 Billion+ attribute values into a single fact table.

wdt_ID Attribute Type ID Attribute Type Description
1 10307 Percentile for Air toxics cancer risk
2 10306 Percentile for Diesel particulate matter level in air
3 10304 Percentile for % pre-1960 housing (lead paint indicator)
4 10300 Percentile for % less than high school
5 10301 Percentile for % of households (interpreted as individuals) in linguistic isolation
6 10299 Percentile for % low-income
7 10298 Percentile for % people of color
8 10303 Percentile for % over age 64
9 10314 Percentile for Ozone level in air
10 10315 Percentile for PM2.5 level in air

Download Data Dictionary

Download SRS Data Dictionary