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THE SRS SPATIAL KNOWLEDGE GRAPH

Open Datasets – Unified Entities & Taxonomy

A global map of complex data relationships connecting dozens of data sets into a highly precise and unified data structure – the knowledge graph.

SRS Spatial Knowledge Graph
Core Product Principles

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.

SRS | Accelerating Time to Value

3.5 Million
Asset Locations

1,700+
Industry Types

Selected Categories

966 Million
Attribute Values

8,600+
Unique Attribute Types

View the data

Inside the PPP

View a detailed SRS Report on the Paycheck Protection Program.

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Email: kzockoll@spatialrisksystems.com

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