Walkability Score: How It Is Calculated and How to Use It

A walkability score answers one question: how much of daily life sits within a walk of this front door? Groceries, a pharmacy, a school, a park, somewhere to eat. It compresses all of that into a single number on a 0 to 100 scale, which is why property portals, relocation sites, and AI assistants have all adopted it.

The number is easy to display and worth getting right, because two addresses in the same city can sit in completely different neighbourhoods. This guide walks through the method, then applies it to real data. We computed a walkability index for 40 addresses across 20 European cities using the MapAtlas POI API, and the results show why the score belongs to an address rather than to a city.

What a walkability score measures

A walkability score is a proximity measure. It counts the everyday destinations reachable on foot from one coordinate, then weights them by how essential they are and how far away they sit.

Three consequences follow from that definition, and all three are worth stating wherever you publish the number.

It is a property of an address, not a city. Two flats three kilometres apart routinely differ by 30 points or more, which we can now show with data rather than assert.

It measures presence, not quality. The score knows that a supermarket sits 300 metres away. It does not know whether that supermarket is well stocked, expensive, or closed on Sundays.

It measures distance, not effort. A flat kilometre and a kilometre uphill both count as a kilometre. Terrain, crossings, and pavement quality sit outside the calculation entirely.

These are the boundaries of what a single number can carry, rather than flaws in the method.

How to calculate a walkability score

Any implementation comes down to the same three steps.

Step 1: Resolve the address to a coordinate

Start by geocoding the address into a latitude and longitude. The precision of that match sets the ceiling on everything downstream. A rooftop level match puts the origin on the building itself, while a postcode level match can land a few hundred metres away, which at walking scale changes the answer.

Use the match precision field if your geocoder returns one, and label any score computed from something coarser than street level as approximate.

Step 2: Collect what is inside the walking catchment

Next, decide what counts as walking distance. There are two options, and the choice matters more than it looks.

A radius takes everything within 1 km of the coordinate. It is fast, cheap, and reproducible, but it ignores the street network completely. A shop 400 metres away across a river with no bridge still counts as if it were next door.

An isochrone takes everything inside the polygon that is genuinely reachable in a 15 minute walk along real paths. It costs more per request, and in exchange it handles rivers, railways, motorways, and dead ends correctly.

The index below uses a 1 km radius, because a published benchmark needs to be reproducible by anyone who wants to check it. For a live listing feature, use the isochrone.

Step 3: Score each category on a curve, then weight

Finally, turn the counts into a score. Raw counts do not work here: a neighbourhood with 40 restaurants is not four times as walkable as one with 10, and the first supermarket matters far more than the twentieth. Each category is therefore scored on a curve with diminishing returns.

// One category, scored 0..1. `target` is the count that earns full marks.
const componentScore = (count, target) =>
  Math.min(1, Math.log(1 + count) / Math.log(1 + target));

// Weighted sum across categories gives the headline 0..100 number.
const score = components.reduce(
  (total, c) => total + componentScore(counts[c.key], c.target) * c.weight,
  0,
);

The logarithm does the work. Going from zero to one supermarket moves the component score a long way, while going from 30 to 31 barely registers, which is roughly how people experience a neighbourhood.

Then the weights are applied. These are ours, with the reasoning behind each one:

Weights are a judgement call rather than a fact, so we publish ours for you to check and adjust. A family portal might raise the weight on schools, and a student rental site might raise transport and food instead. Published weights are what make the number reproducible inside someone else's product.

What counts as a good score

A score is easier to act on when you read it against bands. These bands are calibrated on the European sample below, so a number in the middle of the range describes a real European neighbourhood.

Keep in mind that the bands describe the supply of amenities around an address, not the experience of walking to them.

The MapAtlas Walk Index: 20 European cities

We ran the method against two addresses in each of 20 European cities: the central square, and an outer residential district roughly 5 to 8 km out. Every count came from the MapAtlas POI API inside a 1 km radius, captured on 28 July 2026.

The gap between the two dots matters far more than the ranking. Central addresses averaged 94, while outer residential addresses in the same cities averaged 73. The blue dots cluster against the ceiling because European city centres are dense almost by construction, so it is the green dots that carry the real variation.

Three findings stand out from the table.

Rome has the widest internal spread in the sample, at 53 points. Piazza Venezia scores 93, while Ponte Mammolo, still well inside the city, scores 40. Within 1 km of that outer coordinate the API found 4 supermarkets, 19 food venues, and 6 green spaces, against 85, 1129, and 149 in the centre.

Warsaw is the one city where the outer address wins. Ursynów scores 84 against the Old Town's 82, because Ursynów is a planned residential district with groceries, schools, clinics, and parks designed into it. A historic centre and a good place to run a week are not the same thing.

Barcelona and Athens barely drop off at all, at 2 and 7 points. Continuous mid rise density carries amenity provision well beyond the tourist core in both cities.

Why the city average is the wrong number

Breaking one city apart shows the mechanism clearly.

Every component falls at once. Groceries drop from 93 to 34, food from 96 to 41, and schools from 93 to 39. This is not one missing category dragging an otherwise comparable neighbourhood down, it is a different kind of place.

Consider what happens when a portal labels both of those addresses "Rome, walkability 93". The buyer viewing the outer flat arrives expecting the centre, and the viewing corrects the listing. Computing the score from each property's own coordinate avoids that gap, and gives the buyer a reason to trust the rest of the page.

What the score does not tell you

Four limits are worth publishing alongside the number.

Presence is not quality. Six restaurants within 1 km score the same whether they are excellent or dire, because the score measures availability and nothing else.

Flat and steep score identically. Terrain is invisible to a proximity calculation, so in cities like Lisbon or Zurich the score reads optimistically.

Data coverage varies by country. Our dataset reports transit nodes but does not score them, because stop level tagging density differs too much between countries to compare fairly. Zurich shows why: the query returned a single transit node within 1 km of Paradeplatz, which is a tagging artefact rather than a fact about Swiss trams. Scoring it would have produced a confidently wrong number, so we left it out and explained why.

A snapshot is not a trend. These figures are from 28 July 2026, and amenities open and close, so publish the date alongside any score you compute.

How to add a walkability score to a property listing

Buyers ask neighbourhood questions before they ask property questions. A listing that answers those questions on the page keeps the visitor, instead of sending them to a search engine to find the answer somewhere else.

The implementation comes down to four steps.

1. Geocode the listing address once, at ingest, and store the coordinate along with its match precision. Do not geocode on every page view.
2. Request the walking catchment for that coordinate: a 15 minute pedestrian isochrone, then the amenity counts inside it.
3. Apply your weights and cache the result. Neighbourhoods change over months rather than days, so a monthly or quarterly refresh is enough.
4. Render the number and the evidence. Show the score, the component breakdown, and the named places behind it.

That last step has a second payoff. AI search engines can quote a block that lists real, named places when someone asks what is around an address, and a bare number gives them nothing to quote. Marking the block up with structured data makes it machine readable as well:

{
  "@context": "https://schema.org",
  "@type": "Residence",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "Via dei Fori Imperiali 1",
    "addressLocality": "Rome",
    "addressCountry": "IT"
  },
  "additionalProperty": {
    "@type": "PropertyValue",
    "name": "Walkability score",
    "value": 93,
    "maxValue": 100,
    "measurementTechnique": "Weighted amenity density within a 15 minute walking isochrone",
    "valueReference": "Computed 2026-07-28"
  },
  "amenityFeature": [
    { "@type": "LocationFeatureSpecification", "name": "Supermarkets within a 15 minute walk", "value": 12 },
    { "@type": "LocationFeatureSpecification", "name": "Primary schools within a 15 minute walk", "value": 4 }
  ]
}

Two fields carry the provenance there. measurementTechnique states how the number was produced, and the computation date travels with the value, so an answer engine can repeat both without hedging.

Where these scores get used

There are three surfaces, in rough order of commercial value. Property listings, where the score answers the neighbourhood question directly on the page. Relocation and rental search, where users filter by lifestyle before they filter by building. And AI assistants, which increasingly answer "is this a walkable area?" without sending anyone to a listing at all.

The third surface is changing fastest. A dated score, published with its method and the named places behind it, gives an answer engine something concrete to cite.

For the underlying data, the GeoEnrich API returns amenity counts and named places around a coordinate in a single call, and the Isochrone API produces the walking catchment those counts are measured inside. Keep the scoring weights in your own product, where they can reflect what your particular audience cares about.

Summary

A walkability score is a weighted, distance decayed count of everyday destinations near one coordinate. Geocode the address, collect what is inside its walking catchment, then score each category on a curve and apply your weights.

Compute it per address rather than per city. Our 20 city sample averaged 94 in the centre against 73 on the outskirts, with a 53 point spread inside Rome alone.

Then publish the method with the number: the weights, the catchment, the capture date, and the things the score deliberately does not measure.