How Location Data Became the Most Valuable On-Chain Asset Nobody Talks About

This article is part of daGama's weekly blog series exploring the intersection of physical-world experience, on-chain infrastructure, and the future of how people discover and interact with the places around them.

Every time you open a maps app, search for a restaurant, check in somewhere, or ask your phone where to go — you generate location data. And that data goes somewhere.

It doesn't go to you.

It goes to the platforms that built the infrastructure you used to generate it. Google. Apple. Meta. Foursquare. A constellation of data brokers you've never heard of, who package and resell your movement patterns to advertisers, insurers, retailers, governments, and anyone else willing to pay. You generated the data. You received nothing.

This arrangement has been so normalized that most people don't think about it at all. But the economics underneath it are becoming increasingly difficult to ignore — and the infrastructure to change them is now mature enough to actually work.

Location data is one of the most valuable assets in the world. It just isn't owned by the people who create it.

The Scale of What's Being Extracted

The numbers put the scale of this in perspective.

The global geospatial solutions market was valued at $502 billion in 2025 and is projected to reach $1.56 trillion by 2034. The location analytics market alone — just the analytics layer on top of raw location data — was valued at $23.87 billion in 2025 and is projected to reach $74.63 billion by 2034. The location intelligence market is valued at $28.72 billion in 2026, expected to reach $109 billion by 2035 at a 16% annual growth rate.

These are large numbers. But what they represent is more important than their size: they represent the commercial value that has been extracted from the location behavior of billions of people who never consented to being data products and never received compensation for generating the raw material.

By 2025, there were over 27 billion IoT devices globally — vehicles, shipping containers, smart shelves, wearables — creating a continuous mesh of location signals. Every navigation query, check-in, transaction, and movement is a data point. When aggregated and analyzed, this data produces insights that are worth hundreds of billions of dollars annually. The people generating those insights see none of that value.

Why Location Data Is Different From Other Data

Not all personal data is equally valuable. Location data occupies a special category — and understanding why explains why its concentration in the hands of a few platforms is so consequential.

Location data is behavioral. It doesn't just tell you what someone said or what they clicked. It tells you where they physically went, how long they stayed, what they did around it, and over time, what that pattern of movement reveals about their life — their income, their health, their relationships, their political views, their religion. A study by MIT researchers showed that just four location data points are enough to uniquely identify 95% of individuals in a dataset of 1.5 million people. Location data is not anonymous. It never really was.

Location data is also persistent. A search query is a moment. A purchase is a transaction. A movement pattern is a continuous, accumulating record of a life being lived. The longer it's collected, the more valuable it becomes — and the more powerful the entity holding it becomes relative to the person it describes.

And location data is predictive. It doesn't just tell you what happened. It tells AI systems what will happen — where someone will go next, what they'll want when they get there, how to reach them at the exact moment and place they're most susceptible to influence. This predictive quality is what makes location data so valuable to advertisers, and so dangerous when concentrated without accountability.

The data brokers who trade in this information have built billion-dollar businesses on the back of behavioral signals that billions of people generated without knowing they were doing so. The location-based services (LBS) market is projected to reach $100 billion by 2026. Almost none of that value flows back to the people who made it possible.

The Web3 Moment for Location Data

The argument for putting location data on-chain is not ideological. It's structural.

The problem with the current model isn't that data collection exists — it's that the architecture of data collection was designed to extract value from people rather than return it to them. This isn't a policy failure that can be fixed with better regulation, though regulation helps at the margins. It's a design failure that requires a different design.

What on-chain location data infrastructure enables is specific:

Verified presence. The difference between a location signal that's been cryptographically attested and one that's been reported through a centralized system is the difference between proof and claim. A check-in on a Web2 app can be fabricated, gamed, or manipulated at scale. A Zero-Knowledge proof of presence — which confirms that a person was within a geographic zone at a specific time without revealing their identity or exact coordinates — is verifiable in a way that no centralized database can match.

Ownership. When location data is generated as an on-chain attestation tied to a user's wallet, it belongs to that user. It can be shared selectively, revoked, or monetized — but only with the user's consent and in ways the user controls. This is architecturally different from the current model, where the moment you generate a location signal through someone else's infrastructure, you've already lost ownership of it.

Incentive alignment. The most accurate, current, and useful location intelligence comes from people who are actually present at places and who have genuine knowledge of them. The current model rewards platforms for aggregating this knowledge without compensating the people who generate it. An on-chain model can reward the person who was there — for checking in, for contributing a verified review, for sharing knowledge about a place that turns out to be genuinely useful to others.

This isn't speculative. DePIN networks are already demonstrating that physical-world data can be generated, verified, and rewarded through crypto-native incentive structures at scale. XYO, a DePIN location data solution, has already deployed over 430,000 data nodes across Africa alone. The infrastructure question has been answered. The question now is what gets built on top of it.

What Institutions Are Already Doing With Location Data

It's worth understanding who currently benefits from location data at scale — because it clarifies what's at stake in changing the model.

Retailers use geofencing data to trigger ads the moment someone walks near a competitor. Insurance companies use mobility patterns to price risk. Hedge funds use foot traffic data to predict quarterly earnings before they're reported. Political campaigns use location signals to identify persuadable voters and target them at home. Governments use location data for surveillance, contact tracing, and population monitoring.

None of these use cases required the consent of the people whose data was used. In most cases, the people don't know their data was collected, let alone sold. The adoption of location intelligence was expedited during COVID-19 as organizations leveraged location data gathered from individuals to track movement patterns — a use case that became standard practice without any consent mechanism or compensation structure.

The asymmetry is stark: institutions extract enormous value from location data, bear the cost of aggregating and analyzing it, and pay data brokers who themselves paid nothing to the original generators. The person who actually walked the streets, visited the places, and created the signal is at the end of the chain, receiving nothing.

The on-chain model inverts this. When location attestations are generated on-chain by the people who are actually present, those people hold the data. They can choose to share it — with apps, with platforms, with networks that need verified location signals — and receive compensation when they do. The institutions that currently pay data brokers would instead pay the people whose behavior they're analyzing.

The Data That Actually Matters

There's a second dimension to this that gets less attention: the quality problem.

The location data that corporations collect is behavioral — it tells you where people went. But the most valuable location intelligence is experiential — it tells you what people found when they got there. Whether the restaurant was worth it. Whether the neighborhood has changed. Whether the hotel lives up to its photos. Whether the local market that doesn't show up on any app is worth the detour.

This experiential knowledge is what review platforms have been trying to capture for twenty years, with increasingly poor results. As we've discussed in earlier pieces in this series, the fake review problem has made platform-aggregated experiential data increasingly unreliable. The signal-to-noise ratio is deteriorating as AI-generated content floods platforms that have no structural mechanism to distinguish genuine experience from manufactured content.

Verified on-chain presence changes this. A review attached to a cryptographic proof that the reviewer was there is categorically different from a review submitted by an anonymous account. It doesn't just tell you what someone said. It tells you that they were there, that they're a real person, and that what they're sharing comes from actual experience.

This is the location data that AI systems will need most as they take on greater roles in physical-world navigation and discovery. Not the behavioral trace of where billions of people went. The verified signal of what people who were actually somewhere found meaningful enough to record and share.

That data is the most valuable asset that doesn't yet exist on-chain. Building the infrastructure to create it — and returning the value to the people who generate it — is the infrastructure problem that matters most for Web3's next phase.

daGama is building the verified discovery layer for the physical world — where proof of presence, on-chain identity, and genuine community knowledge create a new model for location data ownership. Learn more at dagama.world

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