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The SRS Geospatial Knowledge Graph

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Locations
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Location Attributes
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Mapping Relationships

Data Governance and Spatial Risk

There are four critical factors in play - from a data governance perspective.

1. Issuers/Counterparties

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

CountryAsset IDAsset NameAsset Owner LatitudeLongitude
CN3647726Baise CoalChina Huaneng Group Co., Ltd.23.788106.81
IR2839733Tabriz RefineryGovernment of Iran38.0546.16
DE2705768Porsche Leipzig GmbHDr. Ing. h.c. F. Porsche AG51.4012.29
US2966155Poinciana Medical Center HCA Healthcare28.14-81.47
US2705085Peterbilt MotorsPACCAR, Inc.33.20-97.17
MX2732106Mexican Door CompanyGrupo 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 IDAsset Name
2700586EXXONMOBIL REFINERY COMPLEX
2700586Chalmette Refinery
2700586CHALMETTE REFINERY LLC
2700586MOBIL OIL CORP. - CHALMETTE REFINERY
2700586EXXON - CHALMETTE REFINERY
2700586CHALMETTE 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

CodeTaxonomy DescriptionAttribute TypesAttribute Count
X001 Crime11485,515
X002 Economic14,585154,107,889
X003 Environmental20654,571,425
X004 Health/Medical76333,134,798
X005 Energy36810,720
X006 Housing1,633612,809,433
X007 Population/Age/Sex648194,019,046
X008 Income368139,160,672
X009 Education2,094343,969,116
X010 Poverty1,702294,072,552
X011 Race5815,493,772
X013 Climate41687,218,320
X014 Elections/Voting1481,219
X015 Religious Affiliation5621,970,424
X016 Spatial Risk13925,494,467
X017 Mobility/Transportation13553,715,404
X018 Social Vulnerability/Community Resiliency182,967,311
Z001 Reference/Taxonomic2150,578
23,3902,014,232,661
Sample Attribute TypesValue
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 Beds676
Age - 60 Years and Over: Percent below poverty level28.40%
Bedrooms - Total Housing Units51,213
Coastal Flooding - Exposure - Building Value$776,827,996

Our analytic edge

Extensive Classification Taxonomy

1,700+ Categories

SectorSub-Sector
Chemical manufacturingIndustrial gas manufacturing plant
Petroleum and coal products manufacturingPetroleum refineries
Machinery manufacturingIndustrial machinery mfg plant
MiningCoal Mine
Food ManufacturingDairy product manufacturing plant
Hospitals/ClinicsChildren's Hospital

Selected Categories

CodeTaxonomy DescriptionAsset Location Count
S030Beverage and tobacco product manufacturing1,554
S031Textile mills2,829
S032Textile product mills1,316
S033Apparel manufacturing1,356
S034Leather and allied product manufacturing768
S035Paper manufacturing4,789
S036Printing and related support activities12,301
S037Wood product manufacturing13,494
S038Petroleum and coal products manufacturing9,372
S039Chemical manufacturing22,777
S040Plastics and rubber products manufacturing13,102
S041Nonmetallic mineral product manufacturing22,094
S042Primary metal manufacturing7,587
S043Fabricated metal product manufacturing38,183
S044Machinery manufacturing20,525
S045Computer and electronic product manufacturing10,220

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 IDAsset Name Parent  Ult Parent
3521905Huntersville LNG Facility Piedmont Natural Gas Co. Duke Energy (NYSE: DUK)
Source IDSource DescriptionSource Value
17US Census County FIPS Code37119
18US Congressional District Code (CD)NC: Congressional District 12
20US Census Core Based Statistical Area (CBSA)16740
21US Census Combined Statistical Area (CSA)172
23US Census Metropolitan/Micropolitan Indicator (METMIC)1
26US EPA Reg ID110038855511
32US Census Region Code3
33US Census Division Code5
34US Census State FIPS Code37
42US Census Tract Code37119006215
Asset IDAsset Name Parent Ult Parent
15584Bartow Regional Med Ctr BayCare Health System BayCare Health System
Source IDSource DescriptionSource Value
1NGA GEOnet Names4146729
13Wikipedia42042432
14WikidataQ16164841
17US Census County FIPS Code12105
18US Congressional District Code (CD)FL: Congressional District 17
20US Census Core Based Statistical Area (CBSA)29460
21US Census Combined Statistical Area (CSA)422
23US Census Metropolitan/Micropolitan Indicator (METMIC)1
26US EPA Reg ID110016724311
32US Census Region Code3
33US Census Division Code5
34US Census State FIPS Code12
42US Census Tract Code12105014803
44US HIFLD Object ID Hospitals5812
45US HIFLD ID Hospitals2333831
46US NPPES NPI Number1558734095
46US NPPES NPI Number1922052018
48US CMS Certification Number (CCN)100121

Our analytic edge

20.6 million+ Cross-Reference Relationships

From dozens of sources.

Source IDSource DescriptionSource Count
67ISIN Number38,810
63Issuer CUSIP Number61,514
14Wikidata278,034
13Wikipedia248,728
16US State School District ID Number34,774
36US NCES Public School ID Number108,719
17US Census County FIPS Code1,824,229
18US Congressional District Code (CD)1,558,826
20US Census Core Based Statistical Area (CBSA)1,538,541
24US EIA Utility ID6,535
25US EIA Power Plant ID14,097
26US EPA Reg ID808,421
42US Census Tract Code1,786,545
46US NPPES NPI Number49,169
48US CMS Certification Number (CCN)77,739
50US VA VISN Region (Veterans Integrated Service Network)3,241
51US VA Market (Veterans Integrated Service Network)3,320
52US VA Sub-Market (Veterans Integrated Service Network)3,223
55UN Country/Region Codes241

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 LevelAddress Level
Country1
Census Region2
Census Division3
State/Province4
Metropolitan Area5
Voting District6
County/Regional7
District/Regional8
City9
City Sub-Section 10
Postal/Zip Code11
Census Tract12
City-Parish/Ward/Neighborhood13
Census Block Group14
Census Block15
Street Address16

Our analytic edge

Simple Data Aggregation Path

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

Geo LevelStateCountyCityPostal CodeCensus Tract
Census TractTexasBexar CountySan Antonio48029110100
Census TractTexasBexar CountySan Antonio48029110300
Census TractTexasBexar CountySan Antonio48029110500
Postal CodeTexasBexar CountyAdkins78101
Postal CodeTexasBexar CountyAtascosa78002
Postal CodeTexasBexar CountyConverse78109
CityTexasBexar CountyConverse
CityTexasBexar CountyLive Oak
CityTexasBexar CountySan Antonio
CountyTexasBee County
CountyTexasBell County
CountyTexasBexar County
Congr DistrictTX: Congressional District 21
Congr DistrictTX: Congressional District 22
Congr DistrictTX: Congressional District 23
StateArkansas
StateNew Mexico
StateTexas
CBSASan Angelo, TX Metro Area
CBSASanta Fe, NM Metro Area
CBSASearcy, 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 IDAttribute Type Description
10307Percentile for Air toxics cancer risk
10306Percentile for Diesel particulate matter level in air
10304Percentile for % pre-1960 housing (lead paint indicator)
10300Percentile for % less than high school
10301Percentile for % of households (interpreted as individuals) in linguistic isolation
10299Percentile for % low-income
10298Percentile for % people of color
10303Percentile for % over age 64
10314Percentile for Ozone level in air
10315Percentile for PM2.5 level in air
10311Percentile for Proximity to National Priorities List (NPL) sites
10312Percentile for Proximity to Risk Management Plan (RMP) facilities
10309Percentile for Traffic proximity and volume
10313Percentile for Proximity to Treatment Storage and Disposal (TSDF) facilities
10310Percentile for Indicator for major direct dischargers to water
8411Indicates the most recent inspection of the facility by EPA.
8412Indicates the most recent inspection of the facility by the state environmental agency.
8403TRI (Toxic Release Inventory) Total pounds per year transferred off-site.
1352Commuting To Work - Workers 16 Years and Over - Car Truck or Van -- Drove Alone
1356Commuting To Work - Workers 16 Years and Over - Car Truck or Van -- Carpooled

Our Method

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.

Data Precision

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

Standardized Taxonomies

A consistent approach to classifying and relating data points. ML/AI does not like inconsistent or multiple data schemas.

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.

Data Aggregation

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

Point in Time

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

Data Visualization

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

Data Integration

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

Single Data Catalog

Simple way to drive data discovery and data access.