Structured geocoded address data with precise coordinates. Built for analytics, routing, and geospatial applications.
Global address data is fragmented. Many regions lack structured, geocoded addresses, limiting the accuracy of geospatial analysis and logistics operations.
SafeGraph addresses these gaps with a structured geocoded address database designed for hard-to-source markets.
Every record is parsed into discrete address fields and paired with precise geographic coordinates, ready for ingestion into your data warehouse.
Power routing and spatial analysis with precise address coordinates.
Load address data directly into warehouses for large-scale geospatial analysis.
Identify missing addresses and perform bulk address validation across markets.
Handle international address formats with multi-script support.
SafeGraph’s geocoded address dataset covers 35+ countries, with particular depth in markets where reliable address data is traditionally sparse or fragmented.
Coverage includes:
Teams across logistics, mapping, and geospatial analytics use SafeGraph geocoded address data to improve operational accuracy and strengthen geocoding systems.
SafeGraph address data helps logistics teams:
Common uses include:
The dataset follows a consistent, flat schema designed for direct ingestion into analytical warehouses. Each field is typed and documented.
| Column Name | Description | Type | Example |
|---|---|---|---|
| primary_number | A JSON string with alphabet as key. Value: A primary numeric identifier for the building. | JSON | { "latin": "4700" } |
| sub_building | A JSON string with alphabet as key. Value: Combined secondary designators (Ste, Unit, Bldg, Block). | JSON | { "latin": "Unit 1105" } |
| building_name | A JSON string with alphabet as key. Value: The name of the building. | JSON | { "latin": "Eaton" } |
| street | A JSON string with alphabet as key. Value: All street components combined. | JSON | { "latin": "Main Street" } |
| intermediate_locality | Additional details associated with locality like subdivision, neighborhood, or village. | JSON | { "latin": "Hohenlimburg" } |
| locality | Subdivision or district within a city. | JSON | { "latin": "Repto Robles" } |
| city | The city of the point of interest. | JSON | { "latin": "Clearwater" } |
| sub_region | Second largest administrative division in a country. | JSON | { "latin": "Tom" } |
| region | State, province, or county of the location. | JSON | { "latin": "Pinellas" } |
| postal_code | The postal code of the location. | JSON | { "latin": "12235" } |
| full_address | The full unparsed address from the source. | JSON | { "latin": "1680 Campbell Ln Bowling Green KY 42104-1062" } |
| iso_country_code | 2-letter ISO country code. | String | US |
| latitude | Latitude coordinate of the address. | Float | 36.714767 |
| longitude | Longitude coordinate of the address. | Float | 121.662912 |
SafeGraph address data integrates easily with modern analytics and geospatial infrastructure.
A geocoded address dataset contains structured address records paired with geographic coordinates such as latitude and longitude.
Geocoding APIs resolve addresses one at a time through a live request. That works for individual lookups but can become expensive at scale. SafeGraph delivers a flat file you load directly into your warehouse, so you can run bulk address validation and analysis without rate limits or live dependencies.
SafeGraph’s flat file format makes it well-suited for batch workflows. You can validate large address lists, fill in missing coordinates, and run coverage analysis across entire service areas directly from your warehouse, without managing API calls or hitting rate limits.
Yes, the SafeGraph Address dataset does include latitude and longitude for every record. Unliked a standard geocoding API that processes lookups one by one, this dataset provides these coordinates in a structured flat-file format.
SafeGraph delivers its geocoded address data as a flat-file (typically CSV or Parquet) designed for direct ingestion into modern data infrastructure.
