The SRS Geospatial Knowledge Graph

Data Governance and Spatial Risk
There are four critical factors in play - from a data governance perspective.
1. Issuers/Counterparties
2. Security Level
3. Listing Level
4. Point in Time
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
3.5 Million+ Asset Locations
Country | Asset ID | Asset Name | Asset Owner | Latitude | Longitude |
---|---|---|---|---|---|
CN | 3647726 | Baise Coal | China Huaneng Group Co., Ltd. | 23.788 | 106.81 |
IR | 2839733 | Tabriz Refinery | Government of Iran | 38.05 | 46.16 |
DE | 2705768 | Porsche Leipzig GmbH | Dr. Ing. h.c. F. Porsche AG | 51.40 | 12.29 |
US | 2966155 | Poinciana Medical Center | HCA Healthcare | 28.14 | -81.47 |
US | 2705085 | Peterbilt Motors | PACCAR, Inc. | 33.20 | -97.17 |
MX | 2732106 | Mexican Door Company | Grupo Antolin Irausa, S.A. | 28.99 | -110.90 |
Our analytic edge
Multiple Name Support for a Given Asset Location
Fully Support Entity Name Resolution and Disambiguation
Asset ID | Asset Name |
---|---|
2700586 | EXXONMOBIL REFINERY COMPLEX |
2700586 | Chalmette Refinery |
2700586 | CHALMETTE REFINERY LLC |
2700586 | MOBIL OIL CORP. - CHALMETTE REFINERY |
2700586 | EXXON - CHALMETTE REFINERY |
2700586 | CHALMETTE REFINING LLC - CHALMETTE REFINERY |
Our analytic edge
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
Code | Taxonomy Description | Attribute Types | Attribute Count |
---|---|---|---|
X001 | Crime | 11 | 485,515 |
X002 | Economic | 14,585 | 154,107,889 |
X003 | Environmental | 206 | 54,571,425 |
X004 | Health/Medical | 763 | 33,134,798 |
X005 | Energy | 36 | 810,720 |
X006 | Housing | 1,633 | 612,809,433 |
X007 | Population/Age/Sex | 648 | 194,019,046 |
X008 | Income | 368 | 139,160,672 |
X009 | Education | 2,094 | 343,969,116 |
X010 | Poverty | 1,702 | 294,072,552 |
X011 | Race | 58 | 15,493,772 |
X013 | Climate | 416 | 87,218,320 |
X014 | Elections/Voting | 14 | 81,219 |
X015 | Religious Affiliation | 562 | 1,970,424 |
X016 | Spatial Risk | 139 | 25,494,467 |
X017 | Mobility/Transportation | 135 | 53,715,404 |
X018 | Social Vulnerability/Community Resiliency | 18 | 2,967,311 |
Z001 | Reference/Taxonomic | 2 | 150,578 |
23,390 | 2,014,232,661 |
Sample Attribute Types | Value |
---|---|
Energy: Power Plant/Facility (MW: Megawatt Capacity) | 2,786 |
TRI (Toxic Release Inventory) Tot pounds per year transferred off-site. | 157,328 |
Total Facility Emissions in metric tons CO2e (Annual) | 2,726,517 |
Number of Beds | 676 |
Age - 60 Years and Over: Percent below poverty level | 28.40% |
Bedrooms - Total Housing Units | 51,213 |
Coastal Flooding - Exposure - Building Value | $776,827,996 |
Our analytic edge
Extensive Classification Taxonomy
1,700+ Categories
Sector | Sub-Sector |
---|---|
Chemical manufacturing | Industrial gas manufacturing plant |
Petroleum and coal products manufacturing | Petroleum refineries |
Machinery manufacturing | Industrial machinery mfg plant |
Mining | Coal Mine |
Food Manufacturing | Dairy product manufacturing plant |
Hospitals/Clinics | Children's Hospital |
Selected Categories
Code | Taxonomy Description | Asset Location Count |
---|---|---|
S030 | Beverage and tobacco product manufacturing | 1,554 |
S031 | Textile mills | 2,829 |
S032 | Textile product mills | 1,316 |
S033 | Apparel manufacturing | 1,356 |
S034 | Leather and allied product manufacturing | 768 |
S035 | Paper manufacturing | 4,789 |
S036 | Printing and related support activities | 12,301 |
S037 | Wood product manufacturing | 13,494 |
S038 | Petroleum and coal products manufacturing | 9,372 |
S039 | Chemical manufacturing | 22,777 |
S040 | Plastics and rubber products manufacturing | 13,102 |
S041 | Nonmetallic mineral product manufacturing | 22,094 |
S042 | Primary metal manufacturing | 7,587 |
S043 | Fabricated metal product manufacturing | 38,183 |
S044 | Machinery manufacturing | 20,525 |
S045 | Computer and electronic product manufacturing | 10,220 |
Our analytic edge
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.
Asset ID | Asset Name Parent | Ult Parent |
---|---|---|
3521905 | Huntersville LNG Facility Piedmont Natural Gas Co. | Duke Energy (NYSE: DUK) |
Source ID | Source Description | Source Value |
17 | US Census County FIPS Code | 37119 |
18 | US Congressional District Code (CD) | NC: Congressional District 12 |
20 | US Census Core Based Statistical Area (CBSA) | 16740 |
21 | US Census Combined Statistical Area (CSA) | 172 |
23 | US Census Metropolitan/Micropolitan Indicator (METMIC) | 1 |
26 | US EPA Reg ID | 110038855511 |
32 | US Census Region Code | 3 |
33 | US Census Division Code | 5 |
34 | US Census State FIPS Code | 37 |
42 | US Census Tract Code | 37119006215 |
Asset ID | Asset Name Parent | Ult Parent |
---|---|---|
15584 | Bartow Regional Med Ctr BayCare Health System | BayCare Health System |
Source ID | Source Description | Source Value |
1 | NGA GEOnet Names | 4146729 |
13 | Wikipedia | 42042432 |
14 | Wikidata | Q16164841 |
17 | US Census County FIPS Code | 12105 |
18 | US Congressional District Code (CD) | FL: Congressional District 17 |
20 | US Census Core Based Statistical Area (CBSA) | 29460 |
21 | US Census Combined Statistical Area (CSA) | 422 |
23 | US Census Metropolitan/Micropolitan Indicator (METMIC) | 1 |
26 | US EPA Reg ID | 110016724311 |
32 | US Census Region Code | 3 |
33 | US Census Division Code | 5 |
34 | US Census State FIPS Code | 12 |
42 | US Census Tract Code | 12105014803 |
44 | US HIFLD Object ID Hospitals | 5812 |
45 | US HIFLD ID Hospitals | 2333831 |
46 | US NPPES NPI Number | 1558734095 |
46 | US NPPES NPI Number | 1922052018 |
48 | US CMS Certification Number (CCN) | 100121 |
Our analytic edge
20.6 million+ Cross-Reference Relationships
From dozens of sources.
Source ID | Source Description | Source Count |
---|---|---|
67 | ISIN Number | 38,810 |
63 | Issuer CUSIP Number | 61,514 |
14 | Wikidata | 278,034 |
13 | Wikipedia | 248,728 |
16 | US State School District ID Number | 34,774 |
36 | US NCES Public School ID Number | 108,719 |
17 | US Census County FIPS Code | 1,824,229 |
18 | US Congressional District Code (CD) | 1,558,826 |
20 | US Census Core Based Statistical Area (CBSA) | 1,538,541 |
24 | US EIA Utility ID | 6,535 |
25 | US EIA Power Plant ID | 14,097 |
26 | US EPA Reg ID | 808,421 |
42 | US Census Tract Code | 1,786,545 |
46 | US NPPES NPI Number | 49,169 |
48 | US CMS Certification Number (CCN) | 77,739 |
50 | US VA VISN Region (Veterans Integrated Service Network) | 3,241 |
51 | US VA Market (Veterans Integrated Service Network) | 3,320 |
52 | US VA Sub-Market (Veterans Integrated Service Network) | 3,223 |
55 | UN Country/Region Codes | 241 |
Our analytic edge
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.
Geographic Level | Address Level |
---|---|
Country | 1 |
Census Region | 2 |
Census Division | 3 |
State/Province | 4 |
Metropolitan Area | 5 |
Voting District | 6 |
County/Regional | 7 |
District/Regional | 8 |
City | 9 |
City Sub-Section | 10 |
Postal/Zip Code | 11 |
Census Tract | 12 |
City-Parish/Ward/Neighborhood | 13 |
Census Block Group | 14 |
Census Block | 15 |
Street Address | 16 |
Our analytic edge
Simple Data Aggregation Path
Data can be quickly aggregated at any one of the following geographic levels:
Geo Level | State | County | City | Postal Code | Census Tract |
---|---|---|---|---|---|
Census Tract | Texas | Bexar County | San Antonio | 48029110100 | |
Census Tract | Texas | Bexar County | San Antonio | 48029110300 | |
Census Tract | Texas | Bexar County | San Antonio | 48029110500 | |
Postal Code | Texas | Bexar County | Adkins | 78101 | |
Postal Code | Texas | Bexar County | Atascosa | 78002 | |
Postal Code | Texas | Bexar County | Converse | 78109 | |
City | Texas | Bexar County | Converse | ||
City | Texas | Bexar County | Live Oak | ||
City | Texas | Bexar County | San Antonio | ||
County | Texas | Bee County | |||
County | Texas | Bell County | |||
County | Texas | Bexar County | |||
Congr District | TX: Congressional District 21 | ||||
Congr District | TX: Congressional District 22 | ||||
Congr District | TX: Congressional District 23 | ||||
State | Arkansas | ||||
State | New Mexico | ||||
State | Texas | ||||
CBSA | San Angelo, TX Metro Area | ||||
CBSA | Santa Fe, NM Metro Area | ||||
CBSA | Searcy, AR Micro Area |
Our analytic edge
Asset Locations: By facility, company, industry, sector, portfolio
a. Where is it?
b. What happens there?
c. Who owns it?
d. Spatial Risks.

US Transportation Equipment Sector
Global Car and Truck Manufacturing

Our analytic edge
Demographic: hundreds of sources, multiple geographies
Organizing and Standardizing thousands of factors and 2 Billion+ attribute values into a single fact table.
Attribute Type ID | Attribute Type Description |
---|---|
10307 | Percentile for Air toxics cancer risk |
10306 | Percentile for Diesel particulate matter level in air |
10304 | Percentile for % pre-1960 housing (lead paint indicator) |
10300 | Percentile for % less than high school |
10301 | Percentile for % of households (interpreted as individuals) in linguistic isolation |
10299 | Percentile for % low-income |
10298 | Percentile for % people of color |
10303 | Percentile for % over age 64 |
10314 | Percentile for Ozone level in air |
10315 | Percentile for PM2.5 level in air |
10311 | Percentile for Proximity to National Priorities List (NPL) sites |
10312 | Percentile for Proximity to Risk Management Plan (RMP) facilities |
10309 | Percentile for Traffic proximity and volume |
10313 | Percentile for Proximity to Treatment Storage and Disposal (TSDF) facilities |
10310 | Percentile for Indicator for major direct dischargers to water |
8411 | Indicates the most recent inspection of the facility by EPA. |
8412 | Indicates the most recent inspection of the facility by the state environmental agency. |
8403 | TRI (Toxic Release Inventory) Total pounds per year transferred off-site. |
1352 | Commuting To Work - Workers 16 Years and Over - Car Truck or Van -- Drove Alone |
1356 | Commuting To Work - Workers 16 Years and Over - Car Truck or Van -- Carpooled |