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CBS Wijken en Buurten (Districts and Neighborhoods) — Statistics Netherlands

What This Dataset Is

Geometry of all municipalities (gemeenten), districts (wijken), and neighborhoods (buurten) in the Netherlands with statistical key figures (kerncijfers) as attributes. Published annually by CBS (Centraal Bureau voor de Statistiek / Statistics Netherlands) via PDOK. Versions are available for 2021-2025.

The three geographic levels form a hierarchy:

Gemeente (municipality) — the top administrative level (~340 municipalities)
Wijk (district) — subdivisions of a municipality (~3,000 districts)
Buurt (neighborhood) — the finest level, subdivisions of a district (~14,000 neighborhoods)

Each feature has demographic, household, and area statistics as attributes. Boundaries are derived from the BRK cadastral registration (municipalities), municipality submissions (neighborhoods), and Bestand Bodemgebruik (water/land borders).

Source: https://www.pdok.nl/introductie/-/article/cbs-wijken-en-buurten CBS page: https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische-data/wijk-en-buurtkaart-2024 Provider: CBS (Centraal Bureau voor de Statistiek / Statistics Netherlands) License: CC0 (public domain) Update frequency: Annual Attribution: CBS and Kadaster

How to Access

Three GeoParquet files (one per geographic level) and a combined PMTiles file for web visualization are available from Source Cooperative.

File	Features	Size	Contents
`buurten.parquet`	14,823	58 MB	Neighborhoods with statistics
`wijken.parquet`	3,505	39 MB	Districts with statistics
`gemeenten.parquet`	424	26 MB	Municipalities with statistics
`wijken_en_buurten.pmtiles`	—	99 MB	All three layers as vector tiles

Base URL: https://data.source.coop/cholmes/portolan-nl/cbs/wijken_en_buurten/

Also available via WFS and WMS from PDOK:

WFS (2025): https://service.pdok.nl/cbs/wijkenbuurten/2025/wfs/v1_0
WMS (2025): https://service.pdok.nl/cbs/wijkenbuurten/2025/wms/v1_0

Schema — Field Meanings

Field	Type	Meaning
`buurtcode`	string	Neighborhood code (e.g. "BU09989999"). Unique ID for each buurt. Prefix "BU" + 8 digits.
`buurtnaam`	string	Neighborhood name (Dutch).
`wijkcode`	string	District code (e.g. "WK099899"). Unique ID for each wijk. Prefix "WK" + 6 digits.
`gemeentecode`	string	Municipality code (e.g. "GM0998"). Unique ID for each gemeente. Prefix "GM" + 4 digits.
`gemeentenaam`	string	Municipality name (Dutch).
`water`	string	Water classification. "B" indicates a water body area.
`meestVoorkomendePostcode`	string	Most common 4-digit postcode in the area.
`omgevingsadressendichtheid`	int	Address density: average number of addresses within 1 km radius per address.

Important Columns

The columns you'll most often use:

buurtcode / wijkcode / gemeentecode — the key identifiers at each level
buurtnaam / gemeentenaam — human-readable names
aantalInwoners — total population
bevolkingsdichtheidInwonersPerKm2 — population density
aantalHuishoudens — number of households
gemiddeldeHuishoudsgrootte — average household size
jaar — the reference year (for filtering when multiple years are combined)
geom — the area boundary polygon

Geographic Hierarchy

The code prefixes encode the hierarchy:

To aggregate neighborhoods to districts: group by wijkcode. To aggregate neighborhoods to municipalities: group by gemeentecode.

Statistical Categories

The dataset covers these statistical domains:

Demographics: population count, gender split, age distribution (5 brackets)
Marital status: unmarried, married, divorced, widowed percentages
Households: count, single-person %, with/without children %, average size
Urbanity: address density, urbanity classification
Area: total, land, and water surface in hectares
Population density: inhabitants per km²

Note: The WFS service contains additional fields for heritage/origin percentages and other socioeconomic indicators not listed here.

Useful Query Patterns

Load and explore neighborhoods

Most populated neighborhoods

Age distribution by municipality

Urbanity classification

CBS uses address density to classify urbanity into 5 levels:

Household composition analysis

Land vs water area

Spatial join with other datasets

Geometry Notes

CRS is EPSG:28992 (RD New / Amersfoort) — coordinates are in meters, NOT degrees. This is the Dutch national coordinate system.
To convert to WGS84 (lon/lat) for web mapping:
All geometries are MultiPolygon (some areas consist of multiple parts)
Bounding box in WGS84: [3.37, 50.73, 7.24, 53.55] (all of Netherlands)

Visualization Styles

Three Mapbox GL v8 styles are available for interactive map visualization via the PMTiles file, all rendering the buurten (neighborhood) layer.

Style files are Mapbox GL v8 JSON with relative PMTiles source paths. They can be used with MapLibre GL JS, OpenLayers (via ol-mapbox-style), or any Mapbox GL v8-compatible renderer.

styles/default.json — Population density. Red sequential ramp on bevolkingsdichtheidInwonersPerKm2. White (sparse) to dark red (10,000+ per km²). Shows urban density gradients within cities.
styles/by-aging.json — Aging population analysis. Purple sequential ramp on percentagePersonen65JaarEnOuder (% aged 65+). Light lavender to deep purple. Reveals aging patterns — retirement communities, historic villages with older populations, vs. young family suburbs.
styles/by-household-size.json — Household composition. Diverging blue-to-red ramp on gemiddeldeHuishoudsgrootte (average household size). Blue = small households (singles, city apartments), red = large households (family suburbs). Shows the spatial sorting of household types across Dutch neighborhoods.

Style files are at: https://data.source.coop/cholmes/portolan-nl/cbs/wijken_en_buurten/styles/

Caveats

Suppressed values: The value -99997 means the statistic is suppressed or unavailable, typically for privacy reasons when the area has too few inhabitants. Always filter out -99997 before aggregating:
Annual updates: Each year has its own version. Municipality mergers and boundary changes mean codes and boundaries change between years. Do not assume buurtcodes are stable across years.
EPSG:28992 coordinates: Coordinates are in meters (RD New), not degrees. You must transform to WGS84 for web maps or combining with global data.
Three feature types: The WFS service provides buurten, wijken, and gemeenten as separate layers. Each has the same statistical fields but aggregated to different levels.
Attribution: Credit CBS (Centraal Bureau voor de Statistiek) and Kadaster as data sources when publishing derived work.
Water areas: Some "neighborhoods" are actually water bodies (e.g., parts of the IJsselmeer). Check the water field ("B") to identify these.

Bestuurlijke Gebieden (kadaster/bestuurlijke_gebieden/) — administrative boundaries from Kadaster (municipalities, provinces, water boards) without the statistical attributes
BAG Light (kadaster/bag_light/) — all 11.4M buildings, can be spatially joined with neighborhoods for building-level analysis
Bestand Bodemgebruik (cbs/bestand_bodemgebruik_2017/) — land use data from CBS, used to derive water/land borders in this dataset

Hand-maintained agent guide (ported from this collection's llms.txt in August 2026). Update it alongside collection.json.

CBS Wijken en Buurten (Districts and Neighborhoods) — Statistics Netherlands

What This Dataset Is

The three geographic levels form a hierarchy:

Gemeente (municipality) — the top administrative level (~340 municipalities)
Wijk (district) — subdivisions of a municipality (~3,000 districts)
Buurt (neighborhood) — the finest level, subdivisions of a district (~14,000 neighborhoods)

How to Access

Three GeoParquet files (one per geographic level) and a combined PMTiles file for web visualization are available from Source Cooperative.

File	Features	Size	Contents
`buurten.parquet`	14,823	58 MB	Neighborhoods with statistics
`wijken.parquet`	3,505	39 MB	Districts with statistics
`gemeenten.parquet`	424	26 MB	Municipalities with statistics
`wijken_en_buurten.pmtiles`	—	99 MB	All three layers as vector tiles

Base URL: https://data.source.coop/cholmes/portolan-nl/cbs/wijken_en_buurten/

Also available via WFS and WMS from PDOK:

WFS (2025): https://service.pdok.nl/cbs/wijkenbuurten/2025/wfs/v1_0
WMS (2025): https://service.pdok.nl/cbs/wijkenbuurten/2025/wms/v1_0

Schema — Field Meanings

Field	Type	Meaning
`buurtcode`	string	Neighborhood code (e.g. "BU09989999"). Unique ID for each buurt. Prefix "BU" + 8 digits.
`buurtnaam`	string	Neighborhood name (Dutch).
`wijkcode`	string	District code (e.g. "WK099899"). Unique ID for each wijk. Prefix "WK" + 6 digits.
`gemeentecode`	string	Municipality code (e.g. "GM0998"). Unique ID for each gemeente. Prefix "GM" + 4 digits.
`gemeentenaam`	string	Municipality name (Dutch).
`water`	string	Water classification. "B" indicates a water body area.
`meestVoorkomendePostcode`	string	Most common 4-digit postcode in the area.
`omgevingsadressendichtheid`	int	Address density: average number of addresses within 1 km radius per address.

Important Columns

The columns you'll most often use:

buurtcode / wijkcode / gemeentecode — the key identifiers at each level
buurtnaam / gemeentenaam — human-readable names
aantalInwoners — total population
bevolkingsdichtheidInwonersPerKm2 — population density
aantalHuishoudens — number of households
gemiddeldeHuishoudsgrootte — average household size
jaar — the reference year (for filtering when multiple years are combined)
geom — the area boundary polygon

Geographic Hierarchy

The code prefixes encode the hierarchy:

To aggregate neighborhoods to districts: group by wijkcode. To aggregate neighborhoods to municipalities: group by gemeentecode.

Statistical Categories

The dataset covers these statistical domains:

Demographics: population count, gender split, age distribution (5 brackets)
Marital status: unmarried, married, divorced, widowed percentages
Households: count, single-person %, with/without children %, average size
Urbanity: address density, urbanity classification
Area: total, land, and water surface in hectares
Population density: inhabitants per km²

Note: The WFS service contains additional fields for heritage/origin percentages and other socioeconomic indicators not listed here.

Useful Query Patterns

Load and explore neighborhoods

Most populated neighborhoods

Age distribution by municipality

Urbanity classification

CBS uses address density to classify urbanity into 5 levels:

Household composition analysis

Land vs water area

Spatial join with other datasets

Geometry Notes

CRS is EPSG:28992 (RD New / Amersfoort) — coordinates are in meters, NOT degrees. This is the Dutch national coordinate system.
To convert to WGS84 (lon/lat) for web mapping:
All geometries are MultiPolygon (some areas consist of multiple parts)
Bounding box in WGS84: [3.37, 50.73, 7.24, 53.55] (all of Netherlands)

Visualization Styles

Three Mapbox GL v8 styles are available for interactive map visualization via the PMTiles file, all rendering the buurten (neighborhood) layer.

Style files are Mapbox GL v8 JSON with relative PMTiles source paths. They can be used with MapLibre GL JS, OpenLayers (via ol-mapbox-style), or any Mapbox GL v8-compatible renderer.

styles/default.json — Population density. Red sequential ramp on bevolkingsdichtheidInwonersPerKm2. White (sparse) to dark red (10,000+ per km²). Shows urban density gradients within cities.
styles/by-aging.json — Aging population analysis. Purple sequential ramp on percentagePersonen65JaarEnOuder (% aged 65+). Light lavender to deep purple. Reveals aging patterns — retirement communities, historic villages with older populations, vs. young family suburbs.
styles/by-household-size.json — Household composition. Diverging blue-to-red ramp on gemiddeldeHuishoudsgrootte (average household size). Blue = small households (singles, city apartments), red = large households (family suburbs). Shows the spatial sorting of household types across Dutch neighborhoods.

Style files are at: https://data.source.coop/cholmes/portolan-nl/cbs/wijken_en_buurten/styles/

Caveats

Suppressed values: The value -99997 means the statistic is suppressed or unavailable, typically for privacy reasons when the area has too few inhabitants. Always filter out -99997 before aggregating:
Annual updates: Each year has its own version. Municipality mergers and boundary changes mean codes and boundaries change between years. Do not assume buurtcodes are stable across years.
EPSG:28992 coordinates: Coordinates are in meters (RD New), not degrees. You must transform to WGS84 for web maps or combining with global data.
Three feature types: The WFS service provides buurten, wijken, and gemeenten as separate layers. Each has the same statistical fields but aggregated to different levels.
Attribution: Credit CBS (Centraal Bureau voor de Statistiek) and Kadaster as data sources when publishing derived work.
Water areas: Some "neighborhoods" are actually water bodies (e.g., parts of the IJsselmeer). Check the water field ("B") to identify these.

Bestuurlijke Gebieden (kadaster/bestuurlijke_gebieden/) — administrative boundaries from Kadaster (municipalities, provinces, water boards) without the statistical attributes
BAG Light (kadaster/bag_light/) — all 11.4M buildings, can be spatially joined with neighborhoods for building-level analysis
Bestand Bodemgebruik (cbs/bestand_bodemgebruik_2017/) — land use data from CBS, used to derive water/land borders in this dataset

Hand-maintained agent guide (ported from this collection's llms.txt in August 2026). Update it alongside collection.json.

1GM0363        = Amsterdam (gemeente)
2  WK036300    = Wijk 00 Centrum (district within Amsterdam)
3    BU03630000 = Buurt Burgwallen-Oude Zijde (neighborhood within Centrum)
4    BU03630001 = Buurt Burgwallen-Nieuwe Zijde
5  WK036301    = Wijk 01 Westelijk havengebied
6    BU03630100 = Buurt Westelijk havengebied

1GM0363        = Amsterdam (gemeente)
2  WK036300    = Wijk 00 Centrum (district within Amsterdam)
3    BU03630000 = Buurt Burgwallen-Oude Zijde (neighborhood within Centrum)
4    BU03630001 = Buurt Burgwallen-Nieuwe Zijde
5  WK036301    = Wijk 01 Westelijk havengebied
6    BU03630100 = Buurt Westelijk havengebied

1import duckdb
2
3con = duckdb.connect()
4con.execute("INSTALL spatial; LOAD spatial;")
5
6URL = 'https://data.source.coop/cholmes/portolan-nl/cbs/wijken_en_buurten/buurten.parquet'
7df = con.execute(f"SELECT * FROM read_parquet('{URL}') LIMIT 5").df()

1import duckdb
2
3con = duckdb.connect()
4con.execute("INSTALL spatial; LOAD spatial;")
5
6URL = 'https://data.source.coop/cholmes/portolan-nl/cbs/wijken_en_buurten/buurten.parquet'
7df = con.execute(f"SELECT * FROM read_parquet('{URL}') LIMIT 5").df()

1INSTALL spatial; LOAD spatial;
2
3SELECT buurtcode, buurtnaam, gemeentenaam, aantalInwoners, bevolkingsdichtheidInwonersPerKm2
4FROM read_parquet('https://data.source.coop/cholmes/portolan-nl/cbs/wijken_en_buurten/buurten.parquet')
5ORDER BY aantalInwoners DESC
6LIMIT 20

1INSTALL spatial; LOAD spatial;
2
3SELECT buurtcode, buurtnaam, gemeentenaam, aantalInwoners, bevolkingsdichtheidInwonersPerKm2
4FROM read_parquet('https://data.source.coop/cholmes/portolan-nl/cbs/wijken_en_buurten/buurten.parquet')
5ORDER BY aantalInwoners DESC
6LIMIT 20

1SELECT buurtnaam, gemeentenaam, aantalInwoners, bevolkingsdichtheidInwonersPerKm2
2FROM buurten
3WHERE aantalInwoners > 0
4ORDER BY aantalInwoners DESC
5LIMIT 20

1SELECT buurtnaam, gemeentenaam, aantalInwoners, bevolkingsdichtheidInwonersPerKm2
2FROM buurten
3WHERE aantalInwoners > 0
4ORDER BY aantalInwoners DESC
5LIMIT 20

1SELECT gemeentenaam,
2       SUM(aantalInwoners) AS totaal,
3       AVG(percentagePersonen0Tot15Jaar) AS avg_pct_0_15,
4       AVG(percentagePersonen65JaarEnOuder) AS avg_pct_65plus
5FROM buurten
6WHERE aantalInwoners > 0
7GROUP BY gemeentenaam
8ORDER BY avg_pct_65plus DESC
9LIMIT 20

1SELECT gemeentenaam,
2       SUM(aantalInwoners) AS totaal,
3       AVG(percentagePersonen0Tot15Jaar) AS avg_pct_0_15,
4       AVG(percentagePersonen65JaarEnOuder) AS avg_pct_65plus
5FROM buurten
6WHERE aantalInwoners > 0
7GROUP BY gemeentenaam
8ORDER BY avg_pct_65plus DESC
9LIMIT 20

1SELECT
2    CASE
3        WHEN omgevingsadressendichtheid >= 2500 THEN '1 - Zeer sterk stedelijk'
4        WHEN omgevingsadressendichtheid >= 1500 THEN '2 - Sterk stedelijk'
5        WHEN omgevingsadressendichtheid >= 1000 THEN '3 - Matig stedelijk'
6        WHEN omgevingsadressendichtheid >= 500  THEN '4 - Weinig stedelijk'
7        ELSE '5 - Niet stedelijk'
8    END AS stedelijkheid,
9    COUNT(*) AS buurten,
10    SUM(aantalInwoners) AS inwoners
11FROM buurten
12WHERE omgevingsadressendichtheid >= 0
13GROUP BY 1
14ORDER BY 1

1SELECT
2    CASE
3        WHEN omgevingsadressendichtheid >= 2500 THEN '1 - Zeer sterk stedelijk'
4        WHEN omgevingsadressendichtheid >= 1500 THEN '2 - Sterk stedelijk'
5        WHEN omgevingsadressendichtheid >= 1000 THEN '3 - Matig stedelijk'
6        WHEN omgevingsadressendichtheid >= 500  THEN '4 - Weinig stedelijk'
7        ELSE '5 - Niet stedelijk'
8    END AS stedelijkheid,
9    COUNT(*) AS buurten,
10    SUM(aantalInwoners) AS inwoners
11FROM buurten
12WHERE omgevingsadressendichtheid >= 0
13GROUP BY 1
14ORDER BY 1

1SELECT gemeentenaam,
2       SUM(aantalHuishoudens) AS huishoudens,
3       AVG(percentageEenpersoonshuishoudens) AS avg_pct_eenpersoons,
4       AVG(percentageHuishoudensMetKinderen) AS avg_pct_met_kinderen,
5       AVG(gemiddeldeHuishoudsgrootte) AS avg_grootte
6FROM buurten
7WHERE aantalHuishoudens > 0
8GROUP BY gemeentenaam
9ORDER BY huishoudens DESC
10LIMIT 20

1SELECT gemeentenaam,
2       SUM(aantalHuishoudens) AS huishoudens,
3       AVG(percentageEenpersoonshuishoudens) AS avg_pct_eenpersoons,
4       AVG(percentageHuishoudensMetKinderen) AS avg_pct_met_kinderen,
5       AVG(gemiddeldeHuishoudsgrootte) AS avg_grootte
6FROM buurten
7WHERE aantalHuishoudens > 0
8GROUP BY gemeentenaam
9ORDER BY huishoudens DESC
10LIMIT 20

1SELECT gemeentenaam,
2       SUM(oppervlakteTotaalInHa) AS totaal_ha,
3       SUM(oppervlakteLandInHa) AS land_ha,
4       SUM(oppervlakteWaterInHa) AS water_ha,
5       ROUND(100.0 * SUM(oppervlakteWaterInHa) / NULLIF(SUM(oppervlakteTotaalInHa), 0), 1) AS pct_water
6FROM buurten
7WHERE oppervlakteTotaalInHa > 0
8GROUP BY gemeentenaam
9ORDER BY pct_water DESC
10LIMIT 20

1SELECT gemeentenaam,
2       SUM(oppervlakteTotaalInHa) AS totaal_ha,
3       SUM(oppervlakteLandInHa) AS land_ha,
4       SUM(oppervlakteWaterInHa) AS water_ha,
5       ROUND(100.0 * SUM(oppervlakteWaterInHa) / NULLIF(SUM(oppervlakteTotaalInHa), 0), 1) AS pct_water
6FROM buurten
7WHERE oppervlakteTotaalInHa > 0
8GROUP BY gemeentenaam
9ORDER BY pct_water DESC
10LIMIT 20

1INSTALL spatial; LOAD spatial;
2
3-- Find which neighborhood a point falls in (convert WGS84 to RD New)
4WITH target AS (
5    SELECT ST_Transform(ST_Point(4.9003, 52.3792), 'EPSG:4326', 'EPSG:28992') AS pt
6)
7SELECT buurtcode, buurtnaam, gemeentenaam, aantalInwoners
8FROM buurten, target
9WHERE ST_Intersects(geom, pt)

1INSTALL spatial; LOAD spatial;
2
3-- Find which neighborhood a point falls in (convert WGS84 to RD New)
4WITH target AS (
5    SELECT ST_Transform(ST_Point(4.9003, 52.3792), 'EPSG:4326', 'EPSG:28992') AS pt
6)
7SELECT buurtcode, buurtnaam, gemeentenaam, aantalInwoners
8FROM buurten, target
9WHERE ST_Intersects(geom, pt)

1ST_Transform(geom, 'EPSG:28992', 'EPSG:4326')

1ST_Transform(geom, 'EPSG:28992', 'EPSG:4326')

1WHERE aantalInwoners >= 0

1WHERE aantalInwoners >= 0

Dutch Portolan Demo

Dutch Portolan Demo

CBS Wijken en Buurten (Districts and Neighborhoods) — Statistics Netherlands

What This Dataset Is

How to Access

Schema — Field Meanings

Important Columns

Geographic Hierarchy

Statistical Categories

Useful Query Patterns

Load and explore neighborhoods

Most populated neighborhoods

Age distribution by municipality

Urbanity classification

Household composition analysis

Land vs water area

Spatial join with other datasets

Geometry Notes

Visualization Styles

Caveats

Related Datasets in This Catalog

CBS Wijken en Buurten (Districts and Neighborhoods) — Statistics Netherlands

What This Dataset Is

How to Access

Schema — Field Meanings

Important Columns

Geographic Hierarchy

Statistical Categories

Useful Query Patterns

Load and explore neighborhoods

Most populated neighborhoods

Age distribution by municipality

Urbanity classification

Household composition analysis

Land vs water area

Spatial join with other datasets

Geometry Notes

Visualization Styles

Caveats

Related Datasets in This Catalog