Converts big, complex and hard to read XML Dutch addresses database (BAG or Basisregistratie Adressen en Gebouwen) into a user-friendly, file based, blazingly fast SQLite database by running a single Python script. No need to install any dependencies or a database server.

Additional scripts will convert this SQLite database to other formats, like CSV.

If you don't want to run the script yourself, download the latest BAG in SQLite or CSV format from our releases section.

The Dutch public addresses and buildings database (BAG or Basisregistratie Adressen en Gebouwen) is freely downloadable from the Dutch cadastre agency named Kadaster. Hooray 🙂.

The bad news is: The original BAG comes in a complex and hard to read XML format using thousands of zipped XML files, which will quickly temper your initial enthusiasm to quite low levels. It also does not include municipalities or provinces and provides coordinates using a system that non-experts won't expect named Rijksdriehoekscoördinaten😲.

This Python utility parses the BAG database and converts it into a clean, easy to read & use SQLite database. Municipalities (gemeenten) and provinces (provincies) are added. Rijksdriehoekscoördinaten coordinates are converted to standard WGS84 latitude and longitude coordinates. A few BAG bugs are fixed. Year of construction, floor area and intended use of buildings are also provided. Several tables (nummers, verblijfsobjecten, panden, ligplaatsen and standplaatsen) are merged into a general 'adressen' table. The SQLite database can be used directly, or as a source to generate a *.csv file or update your own addresses databases. There are a couple of options available in the config.py.

• Python 3.10 or 3.11. Python 3.8 probably works, but is 20% slower.

• Download, or better (because it allows easy updates) use git to download the BAG parser. Install parser:
  git clone https://github.com/digitaldutch/BAG_parser
  Update parser: git pull https://github.com/digitaldutch/BAG_parser
• Download the BAG (2.8 GB) and save the file as bag.zip in the input folder.
• The gemeenten.csv file is already included in the input folder, but you can download the latest version from the CBS website. Save it as gemeenten.csv in the input folder.
• Set your options in config.py
• Run import_bag.py
• Drink cocktails for 40 minutes 🌴🍹😎 while watching the progress bar.
• Open the SQLite database with your favorite adminstration tool. I like DBeaver. Here's an example query on SQLite database to get information about postcode 2514GL, huisnummer 78 (Paleis Noordeinde):

SELECT
  a.postcode,
  a.huisnummer,
  a.huisletter || a.toevoeging AS toevoeging,
  o.naam                       AS straat,
  g.naam                       AS gemeente,
  w.naam                       AS woonplaats,
  p.naam                       AS provincie,
  a.bouwjaar,
  a.latitude,
  a.longitude,
  a.rd_x,
  a.rd_y,
  a.oppervlakte                AS vloeroppervlakte,
  a.gebruiksdoel
FROM adressen a
  LEFT JOIN openbare_ruimten o ON a.openbare_ruimte_id = o.id
  LEFT JOIN gemeenten g        ON a.gemeente_id        = g.id
  LEFT JOIN woonplaatsen w     ON a.woonplaats_id      = w.id
  LEFT JOIN provincies p       ON g.provincie_id       = p.id
WHERE postcode = '2514GL'
  AND huisnummer = 68;

• When done parsing, use the export_to_csv_postcodes.py or export_to_csv.py to create a *.csv file. These functions are easy to customize. I myself use one (not on GitHub yet, as it is customized) to pump the SQLite data into a live Firebird database.

Parses the original BAG file and transforms it into a SQLite database. Takes about 50 minutes to complete, or double that if you switch on the parse_geometries option in the config.py.

Exports the addresses in SQLite database to a *.csv file with address info only. Takes about 15 seconds.

Exports the addresses in SQLite database to a *.csv file including additional information, like year of construction, latitude, longitude, floor area and intended use of buildings. Takes about 40 seconds.

Exports statistics of postal code groups (4, 5 or all 6 characters) in SQLite database to a *.csv file including number of addresses and average lat/lon of addresses in that zone. Takes several seconds.

Checks de SQLite database for info and errors. import_bag.py also performs these tests after parsing.

Reduces the SQlite database size by removing BAG tables (nummers, verblijfsobjecten, panden, ligplaatsen and standplaatsen) that are no longer needed due to the new 'adressen' table. The parser also does this as a final step if delete_no_longer_needed_bag_tables is set to True in config.py.

• Only active addresses are included. History data is left out.
• Residence info (verblijfsobjecten) is only added to the main address (hoofdadres). Other addresses (nevenadres) are ignored.
• Probably several more things that I forgot. Let me know by filing a GitHub issue.
• The WGS84 coordinates are calculated using approximation equations by F.H. Schreutelkamp and G.L. Strang van Hees. This conversion has an error of a few decimeters. Don't use the WGS84 coordinates if you need higher accuracy.

Just a limited amount of data is parsed. If you need more data or professional support, buy it from nlextract, who have their own, much more complete parser.

This software is made available under the MIT license.