Concept
Reality Mapping
The discipline of mapping real-world objects, events, places, and responsibilities into trustworthy digital representations.
Position in the Trusted Data Framework
Reality Mapping
Living Graph View
Reality Mapping in the concept network
Drag nodes to adjust the view, double-click a node to expand its neighbourhood, and right-click a node to hide it while preserving the exploration context.
Relationship Matrix
Reality Mapping as a network node
Reality Mapping clarifies which real-world objects, events, places, and responsibilities must be represented digitally.
Identity depends on knowing which records refer to the same real-world thing and which represent different things.
Reality Mapping supplies the real-world distinctions that an ontology must encode as concepts and relationships.
A knowledge graph connects digital records; Reality Mapping keeps those records anchored to real-world meaning.
Mappings between reality and data change over time, so ownership, validation, and review are required.
Concept Relationships
How Reality Mapping works with other concepts
Reality Mapping clarifies which real-world objects, events, places, and responsibilities must be represented digitally.
A digital twin programme starts by deciding which assets, components, inspections, and operational events are in scope.
What real-world things must be represented before the data model is designed?
Identity depends on knowing which records refer to the same real-world thing and which represent different things.
Three source systems may refer to one pump using different identifiers; Reality Mapping defines the entity boundary.
Where do identifiers conflict, duplicate, or hide real-world ambiguity?
Reality Mapping supplies the real-world distinctions that an ontology must encode as concepts and relationships.
Field operations reveal the difference between Asset, Component, Observation, Inspection, and Intervention.
Which distinctions from operational reality must become formal concepts?
A knowledge graph connects digital records; Reality Mapping keeps those records anchored to real-world meaning.
A graph links an asset to defects and decisions, while Reality Mapping proves which physical asset the graph describes.
What evidence links graph nodes back to real objects and events?
Mappings between reality and data change over time, so ownership, validation, and review are required.
When an asset is replaced, merged, renamed, or decommissioned, representation governance decides how records and graph nodes change.
Who is accountable for keeping digital representations aligned with reality?
Definition
Reality Mapping links the physical, organisational, and operational world to its digital representations. It asks what exists, how it is observed, who is responsible, and how it becomes data.
Why It Matters
AI can only reason responsibly when the underlying representation of reality is clear. Weak reality mapping creates identity confusion, duplicated assets, missing context, and unreliable decisions.
Role in the Trusted Data Framework
Reality Mapping begins at the Reality and Representation layers. It is the foundation for trusted identity, data capture, semantic modelling, and governance.
Practical Examples
- Mapping physical infrastructure assets to digital twins and operational records.
- Clarifying which identifiers refer to the same real-world thing.
- Linking events, inspections, documents, sensor readings, and decisions to real objects.
Case Evidence
GBlocks shows Reality Mapping doing real work, not just naming things. EPC, Price Paid, and OpenStreetMap each describe "a property" differently -- one assesses it, one records a transaction on it, one attempts an address -- and none is complete alone. Reality Mapping is what makes it possible to state precisely which partial view each source offers, rather than treating them as interchangeable.
It also caught a live failure: a source returned geometrically valid GeoJSON that silently misrepresented every coordinate, because an API returned EPSG:4326 in a different axis order depending on request method. It was only caught because a data quality check expected thousands of matching rows and got zero -- proof that Reality Mapping needs continuous verification, not a one-time exercise.
A second, later addition to the same case shows the other side of that discipline: when a new dataset genuinely returned zero rows for the study area, the instinct to assume a bug was resisted. Instead, the investigation queried the dataset's own publisher field and found the real cause -- only 4 councils nationally currently publish to that feed, none in the target region. Reality Mapping isn't just catching false positives (data that looks right but isn't); it's also correctly reading a true negative (data that genuinely isn't there yet) instead of "fixing" a query until it returns something.
Consulting Questions
- What real-world objects and events are in scope?
- Which digital records represent them today?
- Where do representations conflict or duplicate each other?
- What evidence proves that a digital representation is current?
- Who governs the mapping between reality and data?