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Last reviewed: 16 August 2026

Digital twins and Māori

A digital twin is often described as a dynamic digital representation of a physical asset or system that is updated with data and used to support analysis and decisions.

That sounds neutral. It is not.

Every digital twin decides:

  • what counts as the system
  • which data is included
  • what is left out
  • who can update it
  • which models are trusted
  • what outcomes are optimised
  • whose knowledge is required
  • who can see the result

For Māori GIS, the question is not simply whether Māori information can be added to the twin.

It is whether the twin's design recognises Māori authority and whether Māori knowledge needs to be inside it at all.

A digital twin is a model, not the place​

A city twin may combine:

  • buildings
  • roads
  • pipes
  • terrain
  • hazards
  • sensors
  • population models
  • planning data
  • transport data
  • environmental models

That can be extremely useful.

It is still an institutional model built for particular decisions.

It does not become the whenua, awa or community because it contains more layers or updates in real time.

Keep the distinction visible in governance and communication.

Ask whose twin it is​

Before Māori data is connected to a digital twin, ask:

  • Who owns or operates the twin?
  • What decisions will it influence?
  • Who defines success?
  • Who can add or remove data?
  • Who can see restricted layers?
  • Who controls derived models and outputs?
  • Can Māori data be disconnected later?
  • Does the twin create a new permanent dependency on an external platform?

A council digital twin can be useful to iwi without becoming an iwi-controlled representation of place.

Completeness can be a dangerous goal​

Digital twin projects often value integration and completeness.

That creates pressure to fill the gaps with cultural information, local knowledge or Māori-held datasets.

A gap in the twin does not necessarily need filling.

It may represent:

  • information the twin has no authority to hold
  • knowledge that should remain oral or relational
  • a restricted location
  • information held by another system
  • a deliberate choice not to digitise

The correct Māori data governance response may be to leave the twin incomplete.

Mātauranga should not become a sensor feed​

Mātauranga Māori can inform planning, resilience and environmental work. That does not mean it should be converted into another continuously ingested data stream.

If a digital twin project proposes to include mātauranga, ask:

  • What exactly is being represented?
  • Is the knowledge static, seasonal or relational?
  • Who has authority to supply it?
  • Can it be separated from the people and context that give it meaning?
  • Will the twin expose it through APIs, dashboards or derived models?
  • Can it be copied into future versions or other twins?

Sometimes the better design is for the twin to reference a Māori-governed source or decision process rather than ingest the knowledge itself.

Dynamic systems create ongoing governance​

A one-off map can be approved and archived.

A digital twin may update continuously.

That means governance must cover:

  • incoming data feeds
  • model changes
  • new integrations
  • user permissions
  • derived outputs
  • API access
  • retention
  • historic snapshots
  • supplier changes

One approval at project launch is not enough for a system that keeps changing.

Inference grows as layers accumulate​

A digital twin can combine layers that were individually low risk and produce new knowledge through analysis.

For example, a model might infer:

  • occupancy patterns
  • likely access routes
  • vulnerable infrastructure
  • household movement
  • culturally significant areas through repeated activity
  • environmental relationships not explicitly published

Review the combined system, not only each source dataset.

See Inference risk.

Scenario models are not predictions from the future​

Digital twins can run powerful scenarios for flooding, transport, climate adaptation or infrastructure.

The output depends on assumptions.

A good public or governance map should make clear:

  • which scenario is being shown
  • what inputs were used
  • where uncertainty is high
  • what the model does not include
  • whether Māori values or decisions were incorporated and how

Do not let a sophisticated 3D visualisation make one scenario look inevitable.

Māori involvement should be more than data contribution​

A project can say it has included Māori because iwi supplied cultural data.

That may still leave all meaningful authority with the twin operator.

Stronger involvement can include Māori participation in:

  • defining the problem
  • choosing what is not collected
  • scenario design
  • governance
  • access decisions
  • interpretation
  • model review
  • benefit-sharing
  • long-term stewardship

MBIE-funded digital-twin research in Aotearoa now includes projects working with iwi leaders and Māori partners. That makes governance design increasingly relevant, not theoretical.

Keep public and restricted twins apart​

Do not assume one digital twin needs every level of detail.

A safer architecture may have:

  • public twin or public view with open data
  • organisational operational views
  • restricted Māori-governed datasets outside the twin
  • approved summaries or indicators supplied to the twin where needed

This is similar to publishing safe derivatives from a GIS rather than exposing the source database.

Plan the exit before integration​

Ask early:

  • What happens if the supplier changes?
  • Can data be exported in usable formats?
  • Can derived models be taken with you?
  • Can Māori data be deleted or repatriated?
  • Are historic copies retained?
  • What happens to data in backups?
  • What skills remain locally after the project?

A twin that cannot be left without losing access to your own data creates a long-term governance problem.

A practical assessment​

Before connecting Māori data to a digital twin:

  1. Define the decision the twin supports.
  2. Identify Māori rights, relationships and responsibilities affected.
  3. List the data the twin proposes to ingest.
  4. Remove anything not genuinely required.
  5. Identify inference risks created by combinations.
  6. Decide which Māori-held information remains outside the twin.
  7. Establish ongoing governance for model and data changes.
  8. Define public, partner and restricted outputs separately.
  9. Test export, deletion and exit arrangements.
  10. Review whether the twin strengthens Māori capability or mainly centralises data elsewhere.

Sources​

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