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Addresses

Overview​

Note: This theme is currently in Alpha and we anticipate significant changes over the next several releases. We invite the Overture community to test the addresses schema and data and offer feedback via the data and schema repos.

Overture maintains nearly 394 million address point entities. Data in the address theme is derived from over 60 sources, many distributed by OpenAddresses and AddressForAll, but also includes open data from the City of New York and the U.S. Department of Transportation.

For licensing information, please see the Attribution and Licensing page.

Address data can be used for a variety of purposes, which can include:

  • Mapping: Addresses may be displayed on the map for reference purposes.
  • Geocoding: Addresses are a primary component of high-accuracy geocoding services (i.e. converting text for an address to a complete address with a location).
  • Conflation: Addresses can be used to conflate to other data themes (e.g. places, buildings) where appropriate for mapping or other use cases (e.g. refining search).
  • Standardization: Parsing an input address into address components based on an existing schema or address model.
  • Normalization: Adhering to standard and consistent forms of address components.
  • Validation and Verification: Confirming an address exists within a known list of addresses.
Overture address coverage
Coverage of Overture address data, visualized using H3 cells.

Dataset description​

Feature type descriptions​

An address is a feature type that represents a physical place through a series of attributes: street number, street name, unit, address_levels, postalcode and/or country. They also have a Point geometry, which provides an approximate location of the position most commonly associated with the feature. We encourage you to consult the schema reference documentation for the address feature type.

Counts, per country, of the address feature type
CountryAddress Count
AT2,492,155
AU15,698,769
BE6,976,417
BR89,899,299
CA16,457,788
CH3,147,964
CL4,041,876
CO7,786,046
DE15,493,910
DK3,924,583
EE2,205,726
ES13,188,482
FI3,719,087
FR26,041,499
HR1,680,107
IS134,956
IT9,063,795
JP19,576,027
LT1,036,251
LU164,415
LV544,380
MX30,278,896
NL9,750,341
NO2,571,756
NZ2,361,858
PL7,669,619
PT5,676,331
SI574,930
SK1,630,271
TW8,502,220
US80,704,806

Data columns​

The addresses GeoParquet file contains the following properties:

Schema for the GeoParquet files in the addresses theme
Property NameTypeDescription
idstringA feature ID. This may be an ID associated with the Global Entity Reference System (GERS) if—and-only-if the feature represents an entity that is part of GERS.
geometryblobA WKB representation of the entity's geometry - a Point, Polygon, MultiPolygon, or LineString.
bboxarrayThe bounding box of an entity's geometry, represented with float values, in a xmin, xmax, ymin, ymax format.
countrystringISO 3166-1 alpha-2 country code of the country or country-like entity, that this address represents or belongs to.
postcodestringThe postcode for the address.
streetstringThe street name associated with this address. The street name can include the street "type" or street suffix, e.g., Main Street. Ideally this is fully spelled out and not abbreviated but we acknowledge that many address datasets abbreviate the street name so it is acceptable.
numberstringThe house number for this address. This field may not strictly be a number. Values such as "74B", "189 1/2", "208.5" are common as the number part of an address and they are not part of the "unit" of this address.
unitstringThe suite/unit/apartment/floor number.
address_levelsarrayThe administrative levels present in an address. The number of values in this list and their meaning is country-dependent. For example, in the United States we expect two values: the state and the municipality. In other countries there might be only one. Other countries could have three or more. The array is ordered with the highest levels first.
postal_citystringIn some countries or regions, a mailing address may need to specify a different city name than the city that actually contains the address coordinates. This optional field can be used to specify the alternate city name to use.
versionintegerVersion number of the feature, incremented in each Overture release where the geometry or attributes of this feature changed.
sourcesarrayThe array of source information for the properties of a given feature, with each entry being a source object which lists the property in JSON Pointer notation and the dataset that specific value came from. All features must have a root level source which is the default source if a specific property's source is not specified.
filenamestringName of the S3 file being queried.
themestringName of the Overture theme being queried.
typestringName of the Overture feature type being queried.

Data access and retrieval​

Overture's data themes, including addresses, are freely available on both Amazon S3 and Microsoft Azure Blob Storage at these locations:

ProviderLocation
Amazon S3s3://overturemaps-us-west-2/release/
Azure Blob Storagehttps://overturemapswestus2.blob.core.windows.net/release/

Overture distributes its datasets as GeoParquet, a column-oriented spatial data format that is a backwards-compatible extension of Apache Parquet. Parquet (and GeoParquet) is optimized for "cloud-native" queries, which means you can use many developer-friendly tools to efficiently fetch column "chunks" of cloud-hosted data. We encourage users who are new to GeoParquet to consult this guide.

The Getting Data section of this documentation offers instructions for using several tools to access Overture data, including DuckDB and Overture's Python command-line tool. See examples below for addresses.

We recommend querying and downloading only the Overture data you need. If you have a particular geographic area of interest, there are several options for using a simple bounding box to extract address data.

First, follow the setup guide for DuckDB.

DuckDB allows you to pass a bounding box in your query to select features in a specified geogrpahic area.

This example returns address data for Calgary, CA and the surrounding area:

LOAD spatial; --noqa
LOAD httpfs;  --noqa
-- Access the data on AWS in this example
SET s3_region='us-west-2';

SELECT
  *
FROM
  read_parquet('s3://overturemaps-us-west-2/release/2024-12-18.0/theme=addresses/type=*/*', filename=true, hive_partitioning=1)
WHERE
  bbox.xmin > -114.305
  AND bbox.xmax < -113.784
  AND bbox.ymin > 50.854
  AND bbox.ymax < 51.219;

Data manipulation and analysis​

Using this query, you can get a count of addresses per country:

Query
LOAD spatial; --noqa
LOAD httpfs;  --noqa
-- Access the data on AWS in this example
SET s3_region='us-west-2';

SELECT
  count(*),
  country
FROM
  read_parquet('s3://overturemaps-us-west-2/release/2024-12-18.0/theme=addresses/type=*/*', filename=true, hive_partitioning=1)
GROUP BY country;

This query will create a shapefile of address data in New Zealand, with limited attributes:

Query
LOAD spatial; --noqa
LOAD httpfs;  --noqa
-- Access the data on AWS in this example
SET s3_region='us-west-2';

COPY (
    SELECT
      id,
      number,
      street,
      unit,
      postcode,
      geometry  -- DuckDB v.1.1.0 will autoload this as a `geometry` type
    FROM
      read_parquet('s3://overturemaps-us-west-2/release/2024-12-18.0/theme=addresses/type=*/*', filename=true, hive_partitioning=1)
    WHERE
    	country = 'NZ'
  )
  TO
    'NZaddresses.shp'
  WITH (
    FORMAT GDAL,
    DRIVER 'ESRI Shapefile',
    SRS 'EPSG:4326'
  );

This query will create a CSV file of address within the State of Utah, using the divisions theme data in a spatial query:

Query
INSTALL spatial;                  -- noqa
LOAD spatial;                     -- noqa

-- Access the data on AWS in this example
SET s3_region='us-west-2';

COPY (
  -- Create a temp table with the state of Utah
  WITH utah AS (
    SELECT
      id AS utah_id,
      geometry AS utah_geom -- DuckDB v.1.1.0 will autoload this as a `geometry` type
    FROM
      read_parquet('s3://overturemaps-us-west-2/release/2024-12-18.0/theme=divisions/type=division_area/*', filename=true, hive_partitioning=1)
    WHERE
      id = '085022383fffffff0167572d4665d6f9'
  ),

  -- Use the geometry of Utah to filter addresses within the state's boundary
  addresses AS (
    SELECT
      *,
      geometry -- DuckDB v.1.1.0 will autoload this as a `geometry` type
    FROM
      read_parquet('s3://overturemaps-us-west-2/release/__OVERTURE_RELEASE/theme=addresses/type=*/*', filename=true, hive_partitioning=1)
    INNER JOIN
      utah
    ON ST_WITHIN(geometry, utah.utah_geom)
    WHERE
      country = 'US'
  )

  -- Export the places selection to a CSV file
  SELECT
    id,
    street,
    number,
    unit
  FROM
    addresses
)
TO
  'utah_addresses.csv';

Revision history​

Version info​

You can find the most recent release notes here.

Support​

Feedback​

You can find a list of Overture repositories here.

Discussions are generally reserved for broader conversations around the addresses project as a whole (supporting a new workflow, adding a dataset, null attributes).

Issues are generally reserved for more specific concerns with specific entities in the dataset (geometry validation, missing entities, duplicate entities) or country-specific concerns.

Discussions​

You can start and add to discussions in each of the public Overture repositories. Some examples:

Discussions around Overture's address data should be filed in the Data repository.

Issues​

You can start and add to issues in each of the public Overture repositories, too. Some examples:

Issues around Overture's address data should be filed in the data repository.

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