Data Products: Ready-Made Geodata Extracts for Analysis

Exporting an entire map isn't always what you need. Sometimes you only want schools in a district, or a city's road network, or all water bodies in a region. For such tasks, osm2cdr provides Data Products — ready-made thematic geodata extracts from OpenStreetMap.

What Are Data Products

Data Products are pre-filtered and structured geographic datasets. Instead of downloading an entire map and filtering objects yourself, you get a ready-made dataset with a specific theme.

osm2cdr offers 288 products: - 274 extracts — thematic datasets - 8 visual products — art maps and posters - 6 widgets — embeddable components

This article focuses on extracts — the main type of Data Products.

Extract Categories

Points of Interest (POI)

POI is the largest category. It includes everything mapped in OpenStreetMap as point or polygon objects:

Education: - Schools (amenity=school) - Kindergartens (amenity=kindergarten) - Universities and colleges (amenity=university, amenity=college) - Libraries (amenity=library)

Healthcare: - Hospitals (amenity=hospital) - Clinics (amenity=clinic) - Pharmacies (amenity=pharmacy) - Dentists (amenity=dentist)

Retail: - Supermarkets (shop=supermarket) - Shopping malls (shop=mall) - Restaurants and cafes (amenity=restaurant, amenity=cafe) - Gas stations (amenity=fuel)

Transport: - Bus stops (highway=bus_stop) - Metro stations (station=subway) - Parking lots (amenity=parking) - Bicycle parking (amenity=bicycle_parking)

Culture and Recreation: - Museums (tourism=museum) - Theaters (amenity=theatre) - Parks (leisure=park) - Sports facilities (leisure=pitch)

Buildings

The buildings extract contains polygons of all structures in the selected area:

• All buildings — full set with type, floors, material
• Residential — building=residential, building=apartments
• Commercial — building=commercial, building=retail
• Industrial — building=industrial, building=warehouse

Each building includes OSM attributes: building:levels (floors), building:material, roof:shape, addr:* (address).

Road Network

• All roads — complete road network (highway=*)
• Highways — highway=motorway, highway=trunk
• City streets — highway=primary, secondary, tertiary, residential
• Pedestrian — highway=footway, highway=pedestrian, highway=path
• Cycling — highway=cycleway, cycleway=*

Water Bodies

• Rivers and streams — waterway=river, waterway=stream
• Lakes and reservoirs — natural=water, water=lake
• Canals — waterway=canal
• Coastline — natural=coastline

Natural Areas

• Forests — natural=wood, landuse=forest
• Meadows — landuse=meadow, natural=grassland
• Wetlands — natural=wetland
• Beaches — natural=beach

Administrative Boundaries

• Countries — admin_level=2
• Regions — admin_level=4
• Districts — admin_level=6
• Municipalities — admin_level=8

Export Formats

Each extract is available in several formats:

How to Download an Extract

Via Web Interface

1. Open osm2cdr.ru
2. Select an area on the map (rectangle or polygon)
3. Go to the "Data Products" section
4. Choose a category and specific extract
5. Select format (GeoJSON, CSV, XLSX)
6. Click "Download"

Via API

import requests

API_URL = "https://osm2cdr.ru/api"
headers = {"X-API-Key": "ocd_your_key"}

# Download all schools in an area
payload = {
    "product_id": "education_schools",
    "bbox": {"xmin": 37.58, "ymin": 55.74, "xmax": 37.65, "ymax": 55.76},
    "format": "geojson",
    "limit": 10000
}

response = requests.post(f"{API_URL}/data/extract", json=payload, headers=headers)
data = response.json()
print(data["feature_count"], "features")

The product id comes from the catalog: GET /api/data/catalog lists products by category and GET /api/data/search?q=school searches by name and tags. Small selections arrive inline in the data field, large ones as a link in download_url.

Practical Use Cases

Walkability Analysis

Task: determine whether all residents of a neighborhood have a school within 800 meters.

1. Download the "Schools" extract as GeoJSON
2. Download the "Residential Buildings" extract as GeoJSON
3. In QGIS, create an 800m buffer around each school
4. Intersect buffers with residential buildings
5. Buildings outside buffers are "underserved" areas

Transport Infrastructure Assessment

Task: calculate the density of public transport stops.

import geopandas as gpd

stops = gpd.read_file("transit_stops.geojson")
area_km2 = 25  # area size

density = len(stops) / area_km2
print(f"Density: {density:.1f} stops/km2")
# Urban standard: > 4 stops/km2

Green Space Inventory

Task: calculate the percentage of green areas in a city.

1. Download "Parks and Green Areas" extract (GeoJSON)
2. Load into QGIS
3. Calculate total area
4. Divide by total territory area

import geopandas as gpd

green = gpd.read_file("green_areas.geojson")
green = green.to_crs("EPSG:32637")  # UTM for meters
total_green_m2 = green.geometry.area.sum()
total_green_km2 = total_green_m2 / 1_000_000
print(f"Green areas: {total_green_km2:.2f} km2")

Scoring: Automated Area Assessment

Besides extracts, osm2cdr offers 10 scoring engines for automated area assessment:

These indices are calculated automatically from OSM data and are available for any point worldwide.

Data Quality

Data comes directly from OpenStreetMap. Quality depends on mapper activity in each region:

• Europe, Russia (major cities) — high coverage, up-to-date data
• Asia, Latin America — uneven coverage
• Africa — coverage mainly in major cities

Remember: OSM is a crowdsourced project. Data may be incomplete or outdated. For critical tasks, verification is recommended.

Conclusion

Data Products let you quickly obtain structured geodata on a specific theme without spending time filtering raw OSM data. The 274 extracts cover major categories of urban infrastructure, natural features, and administrative boundaries. Combined with 10 scoring indices, they provide a powerful tool for territorial analytics.