Problem:
ArcGIS excels at storing and analyzing spatial data, but relationships between entities (people, assets, locations, events) are often only modeled indirectly. While ArcGIS Knowledge supports knowledge graphs, there is limited native integration with open graph databases (Neo4j, TigerGraph, JanusGraph, etc.). This makes it difficult to combine spatial context with rich relational analysis (e.g., which assets are connected through shared contractors, or which water mains are related through maintenance history?).
Enhancement:
Introduce a native ArcGIS–Graph DB connector that allows:
Storage: Persist feature attributes and IDs into a graph database alongside geometries in the geodatabase.
Relationships: Define edges between GIS features (e.g., parcel → owner → utility connection → service incident).
Query: Run combined Cypher/Gremlin + spatial queries (e.g., “Find all substations within 5 miles of this outage that share contractors with another substation”).
Visualization: Expose results in ArcGIS Pro Knowledge Graph views and as map overlays.
Use Cases:
Utilities: Map water mains as nodes, maintenance events as nodes, and connect them to contractors. Answer: “Which contractor worked on the highest-failure mains within 2 miles of schools?”
Public Safety: Link suspects, locations, and events in a graph. Query: “Show all people connected through shared addresses within this neighborhood buffer.”
Transportation: Model logistics supply chains: warehouses → suppliers → shipments → delivery routes, all linked and spatially analyzed.
Value:
Deeper insights: Graph + GIS reveals connections beyond maps alone.
Open ecosystem: Supports Neo4j, TigerGraph, or ArcGIS Knowledge interchange.
Scalability: Graph queries can handle complex relational data efficiently.
