Walk Score, Green Score, Transit Score: Urban Environment Quality Assessment
How do you objectively compare two city neighborhoods? Not by feelings, but by data. Walk Score, Green Score, and Transit Score are numerical indices from 0 to 100 that transform the subjective "nice neighborhood" into measurable metrics. On osm2cdr.ru, 10 such scoring engines work based on OpenStreetMap data.
Walk Score: Walkability
Walk Score evaluates how many everyday amenities are accessible on foot from a given point. The original Walk Score (walkscore.com) analyzes distances to 13 POI categories.
On osm2cdr.ru, Walk Score is calculated from OpenStreetMap data:
- Groceries — supermarkets, convenience stores (shop=supermarket, shop=convenience).
- Restaurants/cafes — amenity=restaurant, amenity=cafe, amenity=fast_food.
- Shopping — shop=clothes, shop=electronics, shop=mall.
- Education — amenity=school, amenity=kindergarten, amenity=university.
- Health — amenity=pharmacy, amenity=clinic, amenity=hospital.
- Banks/post — amenity=bank, amenity=post_office.
- Entertainment — leisure=park, amenity=cinema, amenity=theatre.
Algorithm: for each category, the nearest objects within a 1.5 km radius are found. The closer they are, the higher the score. Linear decay: an object at 400m gives maximum score, at 1500m — zero. Category scores are weighted and normalized.
Interpretation:
| Score | Description | Example |
|---|---|---|
| 90-100 | "Walker's Paradise" | Moscow city center, Arbat |
| 70-89 | "Very Walkable" | Residential areas near metro |
| 50-69 | "Somewhat Walkable" | Bedroom communities |
| 25-49 | "Car-Dependent" | Industrial zones, outskirts |
| 0-24 | "Almost All Errands by Car" | Suburban settlements |
Green Score: Greenery
Green Score evaluates access to green spaces:
- Parks and gardens — leisure=park, leisure=garden.
- Forests — natural=wood, landuse=forest.
- Waterfronts — leisure=nature_reserve, natural=water.
- Playgrounds — leisure=playground.
- Sports facilities — leisure=pitch, leisure=sports_centre.
The algorithm considers not only distance but also green area size. A small garden (0.5 ha) near the house gives fewer points than a park (50 ha) 500 meters away.
High Green Score (80+) correlates with: - Higher property values (+7-15% according to studies). - Better resident health (physical activity, clean air). - Lower temperatures in summer (urban heat island effect). - Stormwater management (green infrastructure).
Transit Score: Public Transportation
Transit Score evaluates public transport quality:
- Metro — railway=station, station=subway. Maximum weight.
- Tram/LRT — railway=tram_stop. High weight.
- Buses — highway=bus_stop, public_transport=platform. Medium weight.
- Commuter rail — railway=halt. Medium weight.
Algorithm: for each stop, accessibility is calculated (distance + transport type). A metro station 300m away gives more points than a bus stop 100m away, because metro is faster and more reliable.
Extended Scoring Engines
Beyond the classic trio (Walk, Green, Transit), osm2cdr.ru calculates 7 more indices:
| Index | What It Evaluates | OSM Data |
|---|---|---|
| Safety Score | Safety | amenity=police, highway=street_lamp, man_made=surveillance |
| Education Score | Education | amenity=school, amenity=university, amenity=library |
| Health Score | Healthcare | amenity=hospital, amenity=clinic, amenity=pharmacy |
| Commerce Score | Commerce | shop=*, amenity=marketplace, shop=mall |
| Culture Score | Culture | tourism=museum, amenity=theatre, amenity=cinema |
| Connectivity Score | Connectivity | Road graph, intersection density |
| Livability Index | Composite | Weighted sum of 9 components |
Analysis Example: Presnensky District vs Maryino
Comparing two Moscow districts:
| Index | Presnensky District | Maryino |
|---|---|---|
| Walk Score | 95 | 72 |
| Green Score | 55 | 68 |
| Transit Score | 92 | 78 |
| Safety Score | 70 | 65 |
| Education Score | 88 | 82 |
| Health Score | 85 | 70 |
| Commerce Score | 93 | 75 |
| Culture Score | 90 | 45 |
| Livability Index | 85 | 68 |
Presnensky District wins on most metrics (city center, high POI density), but Maryino leads in Green Score — more parks and green areas.
Who Needs Scoring Data
Developers
Objective location assessment for residential complex marketing. "Walk Score 87 — everything within walking distance" is more convincing than "convenient location."
Urban Planners
Identifying areas with infrastructure deficits. If Education Score < 40 — a school is needed. If Green Score < 30 — a park is needed.
Homebuyers
Comparing neighborhoods by objective metrics. Scoring data is available on osm2cdr.ru city pages (/map/ section).
Investors
Predicting value growth. Areas with improving Transit Score (new metro station) are promising for investment.
Data Access
Scoring data is available in three ways:
- Web interface — on /map/ pages for 300+ cities.
- REST API — GET /api/v2/score/green?lat=55.75&lon=37.62 (JSON response).
- Export — download a neighborhood map in GeoJSON with scoring attributes.
Assess urban environment quality objectively at osm2cdr.ru.