A place is not a point

Two real facility records, one from OpenStreetMap and one from GRID3’s Nigeria health-facility data. One says Karfi Health Post, with coordinates just off the Kano to Zaria road. The other says Karfi Primary Health Centre, a little over two kilometres away.

Same facility, or neighbours?

If you trust the coordinates they are different. Two kilometres is not GPS jitter. If you trust the names they nearly agree, except one is a Health Post and the other a Primary Health Centre, and in the Nigerian health system those are different tiers.

Every signal you would match on is arguing with the others.

The answer took local knowledge, not better maths. The facility was upgraded. The health post became a PHC, the name changed with the tier, and the two coordinates were captured years apart by different field teams. One standing at the gate, one at the junction where the access road starts.

One place, two references, and the disagreement between them is not noise. It is history.

Space is what the GPS sees

Edward Relph drew the distinction in 1976, in Place and Placelessness. Yi-Fu Tuan sharpened it a year later.

Space is abstract. Coordinates, extents, distance.

Place is what happens when people claim a piece of space by naming it, using it, and staying long enough for it to accumulate meaning. Relph’s line is that engineers create localities, but time is needed to create place.

Read that as a data person and it lands with a thud, because our systems store space and pretend it is place.

A latitude and longitude is space. “Karfi”, the town whose name both records carry, is a place. It will keep being Karfi if every GPS unit in Kano is off by a kilometre, and it was Karfi before either coordinate was captured.

This is not romantic. It is a claim about which attributes are stable. Coordinates for one real facility routinely disagree, because someone measured at the gate, someone at the borehole, someone from a desk with a gazetteer.

The name, the type, and the named things that contain it drift far more slowly.

Space is precise and unreliable. Place is fuzzy and durable. Any system has to hold both.

Take Elephant and Castle in London. In conversation, it can mean the station, the junction, the former shopping centre, or the wider district. A coordinate can name one of those points exactly. It cannot decide which one a person meant without the rest of the reference.

The address was never about finding your way

Deirdre Mask’s The Address Book has a reversal every identity person should sit with. Street addresses were not invented so you could find places. They were invented so the state could find you.

House numbering arrives in Europe in the 1700s as an instrument of taxation, conscription and policing.

And here is the detail that connects it to everything else. Mask reports that in 1700s England, some ninety percent of men shared one of just eight first names. John, William, Henry and their five brothers had collided so completely that a name could not identify anyone.

States had tried fixing names before. Hereditary surnames, parish registers. The collisions kept coming.

House numbering attacked it from the other side. Pin each person to a numbered place, and an ambiguous name becomes resolvable.

That is the moment the address became an identity attribute. It was adopted because the name-collision problem had already broken identification centuries earlier. Frequency-weighted matching and the postal address are two answers to the same problem, three hundred years apart.

So how do you give a place an identity?

This is the question I actually want to think about, and there are only two families of answer.

Derive it. Compute an identifier from the attributes you already have. Same inputs, same key, and anyone can recompute it without asking permission.

Assign it. Mint an identifier, write it in a register, and maintain that register as the world changes.

These are the two main families of answer, and the choice determines what a system can and cannot do. It is worth looking at the best current example of each.

Placekey derives

Placekey is a free, open identifier for physical places, backed by a few hundred organisations including Esri and CARTO. Its structure is the interesting part.

It has two halves. The What encodes the address and, optionally, the point of interest at that address, so a coffee shop inside a supermarket is distinguishable from the supermarket. The Where is a hexagon from Uber’s H3 grid, at resolution 10, roughly fifteen thousand square metres.

The appeal is real. No registry to join, no permission to ask, no central authority to trust. Two organisations can compute the same key for the same place and join their data without ever talking to each other.

That is a genuinely good property, and it is why the thing has spread.

But look at what it derives from. The What half is built from a postal address. Placekey’s own documentation says it works for places with postal addresses, and full support currently covers the United States, Canada and the Netherlands.

Hold that thought.

Overture assigns

The Global Entity Reference System takes the other road. Overture mints an ID for an entity and commits to keeping it stable across releases.

To make that work it runs infrastructure: a monthly reference map of the current canonical state, a registry of every ID ever published, a changelog of what changed between releases, and bridge files connecting IDs back to the source records they came from.

That is a lot of machinery, and the machinery is the point. Because the identifier is not computed from the attributes, the attributes can change without the identity changing. A shop can be renamed, remeasured and recategorised, and it remains the same entity.

The cost is that somebody has to run it, forever, and you have to trust them.

The trade-off underneath

Put the two side by side and a general shape appears.

Neither is a bad design. They are answers to different fears. Derivation is afraid of dependence on an authority. Assignment is afraid of the world changing underneath a key.

And the deeper point is that this is not really a geospatial question. Identity is never in the data. So either you compute a proxy from data and inherit every weakness of your inputs, or you appoint someone to decide and inherit their politics.

Places just make the trade unusually visible.

What derivation costs

Back to that held thought. If your identifier is derived from a postal address, then people without postal addresses do not get identifiers.

The exclusion is not marginal. More than a billion people live in slums or informal settlements, where recognised addresses are often absent or do not describe how people actually find a home.

So the elegant, permissionless, no-authority-required design fails precisely where an address was never available to derive from. Not by oversight. By the choice of what to derive from.

This is not an argument against Placekey, which does what it says on the tin in the countries it covers. It is an argument about what “universal” means when the input is an artefact of state administration that most of the world never received.

Grid codes are the obvious response, and they are genuinely useful. Plus Codes have been painted on doorways in Kolkata by a non-profit, giving hundreds of thousands of people something a delivery driver can use. what3words divides the earth into fifty-seven trillion three-metre squares and is used by emergency services in a hundred organisations.

But read them through Relph and something is missing. A grid code is pure space. No community speaks it, no history attaches to it, and it lives in an app rather than in neighbours’ heads. what3words is also owned, which is the exact inverse of the shared, checkable knowledge that made addresses work in the first place.

They solve delivery. They do not create place.

Places drift, so the identity has to date

Relph’s deepest observation is temporal. Time makes place, and time keeps remaking it.

Places do not change at the same rate. A restaurant can close, reopen, and change hands in a year. A hospital or university may hold its name and role for decades.

There is also category drift, which is worse because it is quiet. A business stays at the same address with the same pin, and changes what it does. The coordinate stays right while everything else goes wrong.

So any single refresh cadence across a place database is wrong for some large part of it. The identifier can stay stable. The resolution claim that attaches it to a reference needs a date.

Karfi Health Post genuinely was a different reference from Karfi PHC. The facility persisted while its tier, name, and recorded location changed. A system that returns a bare yes has thrown away the only honest part of the answer, which is when.

What the people who do this for a living found out

If the geographers are right that place is name plus containment plus meaning rather than coordinates, you would expect professional matchers to have discovered it painfully. There is a public record.

In 2022 Foursquare ran a point-of-interest matching competition on over one and a half million place entries, deliberately noised.

Nobody won on distance. The top solutions all share one shape: generate candidates from proximity and text similarity, then decide with learned rankers fed comparison features. The features doing the work are string similarity on names, token overlap, and category agreement.

Distance stays in the feature set and matters there. It cannot decide, because in dense areas everything is close. Two coffee shops thirty metres apart are two entities. One hospital measured twice at two kilometres is one.

Space proposes. Place decides.

For a sense of how unsolved this remains with real money behind it: when Foursquare conflated around two million Overture records in the United Kingdom, they matched roughly seven hundred thousand places with high confidence and the exercise generated more than seven hundred thousand suggested edits from attribute mismatches.

Two of the best-resourced open place datasets in the world, one country, and joining them still produced hundreds of thousands of claims that needed checking.

The sensitive half

Everything above treats places as public. A clinic, a junction, a charging point. Resolving them well helps everyone.

Make the place a home and the same machinery turns dangerous.

Golle and Partridge showed in 2009 that the pair of your home and work locations, even coarsened, narrows most people to a handful of candidates. De Montjoye and colleagues showed in 2013 that four rough spatio-temporal points uniquely identify ninety-five percent of people in a mobility dataset of one and a half million.

Location is not an attribute about you. Past a certain precision it simply is you.

Which means a place identifier for a residence is a person identifier wearing a different coat, and should be governed like one.

Madina junction

“Behind the Total filling station, Madina junction” gets used as an example of an address that breaks parsers. I want to read it the other way.

It is a rich place description. A landmark anchor, an implied containment, a spatial relation, and a verification community. Thousands of people can confirm it, correct it, and navigate by it.

It is more place-like than “Flat 2, 12 High Street”, not less. What it lacks is not information. What it lacks is a system willing to represent it.

And the cost of that is not inconvenience. The order gets placed, the payment clears, the digital half runs perfectly, and then the last hundred metres has to be improvised over the phone by two people with no shared way to name the same gate.

Whatever cannot be improvised over the phone does not arrive. Not the parcel, and not the ambulance, the meter reader, the enumerator, or the ballot.

Where I run out

I do not know how you build the assign-side registry for places that no state ever registered.

Derivation fails there because there is no address to derive from. Assignment fails there because assignment needs an institution, and the absence of an institution is precisely the condition being described. Grid codes fill the gap functionally and create nothing that behaves like a place.

The one thing I am fairly sure of is that the delivery platforms are slowly solving it, per-company, out of successful drop-offs. Each of them is learning an excellent representation of your gate and each of them owns it privately.

Which means your address gets solved inside one logistics stack and stays unsolved everywhere else. The ambulance still cannot find you.

That looks less like a technical problem than a question about who is allowed to hold the answer.

References

• Edward Relph, Place and Placelessness, Pion, 1976.
• Yi-Fu Tuan, Space and Place: The Perspective of Experience, University of Minnesota Press, 1977.
• Doreen Massey, “A Global Sense of Place,” Marxism Today, 1991. The relational rejoinder.
• Deirdre Mask, The Address Book, St. Martin’s Press, 2020.
• Placekey, on the What/Where structure and H3 resolution 10.
• GRID3 Nigeria health facilities v2.0, one side of the Karfi discrepancy, updated with Nigeria’s Health Facility Registry records.
• Overture Maps, Global Entity Reference System.
• Foursquare Location Matching, Kaggle, 2022.
• Foursquare OS Places, open sourced 2024, Apache 2.0.
• Plus Codes, addressing for Kolkata citizens.
• World Bank, urban development, on the global scale of informal settlements.
• Golle and Partridge, “On the Anonymity of Home/Work Location Pairs,” Pervasive Computing, 2009.
• de Montjoye et al., “Unique in the Crowd,” Scientific Reports 3:1376, 2013.
• Peter Christen, Data Matching, Springer, 2012.
• Christen, Ranbaduge and Schnell, Linking Sensitive Data, Springer, 2020.
• My reading notes on spatial identity.