OpenStreetMap Data is a service offered by ThinkGeo which provides data sourced from OpenStreetMap in a variety of formats with regular updates.

When using the OpenStreetMap Data provided by ThinkGeo, it is useful to understand how features are categorized in OpenStreetMap and how we present them in our OpenStreetMap Data.

All features in OSM are simply stored as geometries and any number of key-value pairs referred to in OSM as “tags”. A sample tag could be name=“Lake Superior” where name is the key and “Lake Superior” is the value.

In addition to descriptive tags like “name”, tags are also used for categorization of features. Many features contain tags that associate them with one or more feature types which are themselves organized into more general feature categories. For example, “amenity” and “highway” are two common feature categories. Feature types are more specific classifications within feature categories and are represented by tags in OSM. A feature representing an ATM would have the tag amenity=“ATM”. This feature belongs to the “amenity” feature category, and more specifically belongs to the “ATM” feature type.

Each feature's tags in our OpenStreetMap Data are represented using the tag key as the column name and the tag value as the value.

For more details and a breakdown of the feature categories and feature types, see the OSM wiki.

For the following database formats, you will receive a database with up to three tables for each of the different feature categories (amenity,highway,boundary, etc.). The number of tables for each feature category depends on what geometry types are appropriate for that category. For example, for the amenity category, there is a linestring table, a point table, and a polygon table. For the highway category, however, polygon is not appropriate so for highway, there is only a point and linestring table.

In addition to the tables, the datasets include a number of useful SQL scripts to create views. The Feature Type scripts create views for the individual feature types within each feature category. These are useful for when you are frequently querying a specific feature type. The amenity_atm_point view would be defined as SELECT * FROM “amenity_point” WHERE “amenity” = 'atm'.

The PostgreSQL database is provided as a compressed dump using the pg_dump utility. It can be restored with the pg_restore command line utility or with the pgAdminIII GUI.

The additional scripts are simple .sql text files and can be easily run using psql on the command line or by opening and executing the scripts in pgAdminIII.

psql example: >psql -f script.sql

The Microsoft SQL Server database is provided as a SQL Server full database backup. It can easily be restored using SQL Server Management Studio or with a simple RESTORE DATABASE T-SQL statement. More details for both options can be found in the SQL Server documentation.

The additional scripts are simple .sql text files and can be easily run by opening and executing the scripts in SQL Server Management Studio or with the command line via sqlcmd or Invoke-Sqlcmd from the “SQLPS” Powershell module.

The Oracle database is provided as a detached database.

The additional scripts are simple .sql text files and can be easily run using sqlplus on the command line or by opening and executing the scripts in SQL Developer.

Tab separated values files are plain text files with each feature represented by one line of text containing tag information and geometry data. The first line of each TSV file contains the column names. Geometries are stored in the Well Known Text (WKT) format.

Many of the TSV files are 1GB or larger and may be difficult to edit non-programmatically. However, you can use programs that read large files like Large Text File Viewer or file pagers such as less for spot-checking.

The full listing of feature categories is shown below, along with the included columns and their data types. Every feature category contains id, osm_id, and geometry columns in addition to their tag columns.