28 posts tagged with "Māori GIS"

The public discussion around artificial intelligence often treats American and Chinese models as if they come from completely different technological worlds. Politically and legally, they do operate under different jurisdictions. Their companies have different ownership structures, regulatory obligations, cloud environments and terms of service. Those differences matter.

But when we look at how the models themselves are trained, the broad pattern is strikingly similar. OpenAI, Anthropic, Google, Meta, Alibaba and DeepSeek all rely on some mixture of large public datasets, licensed or partner material, human-created examples, synthetic data and extensive post-training. The details vary, but the core industrial process is recognisable across borders.

For Māori organisations, this means “American model” versus “Chinese model” is not a sufficient risk framework. The better questions are what data sources were used, how transparent the developer is, what product is being used now, where current prompts are processed, what jurisdiction applies, and whether the model can be run locally without sending new Māori information back to the developer.

A common warning about generative AI says that anything typed into a chatbot becomes part of the model. That is too crude to be useful. What actually happens depends on the provider, the product, whether the account is consumer or enterprise, the settings in force, whether feedback is submitted and whether the organisation has negotiated different retention terms.

For Māori organisations and GIS teams, this matters because the difference between a public chatbot and an enterprise or local system can be substantial. A planning report, shapefile attribute table, field note, Treaty research document or set of candidate place names may be handled very differently depending on the service used.

The first technical point is simple: entering a prompt normally causes inference. The already-trained model processes the current context and produces a response. The model is not usually changing its weights while the user types. After that inference, however, the service may retain the interaction, and in some consumer products the retained material may be eligible for later model improvement. Those are separate stages.

This article looks at current published policies as at 4 September 2026. They change, so the exact product terms should always be checked before important Māori or organisational data are used.

Local AI has become much more useful in 2026. A normal desktop computer can now run a capable language model, search a collection of PDFs, extract structured information, help write Python or QGIS expressions and keep the working material on the machine. That makes it particularly interesting for Māori GIS, where the question is often not only whether AI can do the work, but where the information goes while it is doing it.

The most useful local workflow is not a private version of ChatGPT that somehow knows everything about Māori GIS. It is more modest and, for research, more useful. Give the system documents you are allowed to work with. Ask it to find candidate information. Keep the original wording and source reference. Have a person review the result. Check any geographic match against an appropriate source. Only then let the reviewed information become GIS data.

That distinction matters. A language model is quite capable of reading a reference to an old kāinga, deciding what modern place it probably means and supplying coordinates that look perfectly respectable. A GIS will then plot those coordinates without showing the slightest concern about whether they were invented. Local AI changes where the mistake was made. It does not make the mistake authoritative.

The practical question is therefore: what can we now run locally, what is genuinely offline, what appears promising for te reo Māori, and where should AI stop before the map begins?

A few years ago the AI part of a GIS workflow was simple enough. Open ChatGPT, type a question, see what comes back. Sometimes it was useful. Sometimes it confidently invented a Python function. At least the administrative overhead was low.

That has changed. A Māori GIS practitioner wanting help with an ordinary piece of work can now face a small set of decisions before the work starts. ChatGPT, Claude, Gemini, Copilot or something more specialised? A fast model or the one that thinks for longer? Ordinary chat, a project, a research mode, a coding agent or a persistent workspace? Is the task worth using a limited premium allowance? Does the system need three source documents or the whole project folder? Can it use the web? Can it run code? Will it remember anything later? Most importantly for Māori GIS, what information are we about to give it, and should that information leave the environment it is already in?

This is starting to look like a professional skill in its own right. Knowing AI no longer means being good at typing questions into one chatbot. It increasingly means knowing how to select and manage several different kinds of machine assistance, give them the right amount of context, use expensive capability where it matters, keep data under appropriate control, and check the result before it quietly becomes part of the GIS.

There is now a small new addition to Māori GIS Mapping: Kupu Matawhenua, a weekday five-letter word game made for the Ngā Poutama Matawhenua community and anyone else who spends too much time thinking about maps, whenua, taiao and GIS.

The idea is deliberately simple. You get six attempts to find a five-letter word. Some answers are familiar GIS terms such as LAYER, POINT or QUERY. Others are kupu Māori connected with the places, relationships, environmental themes and kinds of mahi that regularly appear in Māori GIS. The aim is not to turn te reo Māori into decoration around an English GIS game. The word set includes concepts such as marae, taiao, ingoa, rāhui, urupā, tūnga, pānga, kūrae, mauri and pūaha because they belong naturally in conversations about place and spatial work.

Tākaro ināianei / Play Kupu Matawhenua →

GIS people are naturally rewarded for adding information. Another layer appears, another dataset is joined, another set of points is mapped and the system becomes richer. That is usually presented as progress. Māori GIS taught me that a good map is sometimes defined just as much by what it deliberately leaves out.

That is not the same as hiding an error or producing an incomplete map through carelessness. It is a decision about kaupapa, authority and responsibility. Some information is too sensitive for a public map. Some locations should be generalised. Some details belong in an internal system rather than a website. Some knowledge may not need to become digital at all. The fact that GIS can map something does not mean it should.

A good GIS workshop can make everything look easy. The trainer has the right software, the example data is clean, the projector works and somebody has already solved every problem the participants are about to encounter. For a few hours the whole thing feels manageable. Then Monday morning arrives and the participant opens their own computer to discover a different version of the software, messy data, missing permissions, six spellings of the same place and an organisation already asking when the finished map will be ready.

That gap between a successful workshop and useful capability has shaped a great deal of the Māori GIS work I have been involved with. Over time it also changed how I thought about what a wānanga was for. The aim was not simply to transfer a set of technical instructions from the expert to the participant. It was to create enough understanding, confidence and connection that people could carry the mahi forward themselves.

The 2017 Indigenous Mapping Wānanga at Claudelands in Hamilton was built around modern mapping tools, practical training and a large collaborative programme. It also provided a very effective demonstration of something much less glamorous: the cloud only works if you can actually reach it. A large room full of participants and trainers opened web mapping applications, imagery and online services at roughly the same time. The venue Wi-Fi struggled, connections slowed and trainers began improvising.

It was frustrating at the time, but it remains one of the better lessons I have taken from years of GIS capability work. The problem was not simply technical. It exposed the difference between a technology being theoretically available and people having a realistic, reliable way to use it for their own kaupapa.

There was a period in Māori GIS when one of our answers to the problem of access was a box containing ten DVDs. At the time this did not seem particularly ridiculous. It seemed innovative. Professional GIS software was expensive, the data was large, internet connections were limited and many of the people who needed access to claimant mapping did not have a full GIS environment sitting on their desk. We wanted people to be able to explore more than a few finished maps, so we developed resource packages using ArcReader and prepared GIS content that could be distributed physically.

At one point the complete package occupied ten discs. The digital future therefore arrived in a cardboard box. What interests me now is not only how old the technology looks, but what problem we were actually trying to solve. The aim was to move more mapping capability closer to the people whose whenua, histories and claims were being represented, rather than leaving the useful GIS locked inside specialist offices.

One sentence eventually became one of the clearest principles in Ngā Poutama Matawhenua: we will never ask you for your data. It sounds like a small operational rule for a training programme. In reality it contains much of what twenty years of Māori GIS work taught me about authority, trust, rangatiratanga and the limits of technical access.

GIS people are trained to acquire data. We search for layers, download services, request files, convert formats and build databases. The professional instinct is often that more information is better. In many ordinary projects that instinct is useful. In Māori GIS it can become dangerous if the technical desire to collect information gets ahead of the questions that should come first: whose information is this, what relationships does it sit within, who has authority over it, why is it being collected and what responsibilities come with holding it?

I am not Māori and do not claim to define tikanga for other people's information. What I can describe is the change in my own practice after years of working with claimant information, iwi and hapū projects, Māori GIS practitioners and training programmes. The more I learnt, the less comfortable I became with the ordinary technology assumption that the person who possesses the file is therefore entitled to organise, copy, analyse and publish it. Technical custody and legitimate authority are not the same thing.

That distinction sits close to rangatiratanga. In a GIS context, I have come to think about it practically: who can decide what is collected, who can see it, where it lives, what leaves the organisation and whether a map should be made public at all? A platform can give people access while quietly shifting those decisions elsewhere. A community can be invited into a modern digital system while losing control over where the information goes. The interface may look empowering while the architecture does the opposite.