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This service is essentially an ElasticSearch instance containing Geocoding data that can be used with Pelias Geocoder.

\n

General Pelias information

\n

Start by reading (Note that it might not be up-to-date):

\n
\n

https://mapzen.com/blog/pelias-setup-tutorial

\n
\n

How is data built?

\n

On build time the data is fetched from multiple sources and processed and loaded into ElasticSearch using\nPelias tools. At high level this is what happens:

\n
    \n
  1. Download and extract Finland related shapefiles of administrational areas and regions from WhosOnFirst (or use embedded data of pelias-data-container source)
    2. \n
  2. Download Open Street Map Finland data
    3. \n
  3. Download DVV data (building locations with street addresses)
    4. \n
  4. Download NLS places (an extensive list of venues and place names from the National Lands Survey)
    5. \n
  5. Download national GTFS data, including GTFS stops
    6. \n
  6. Start ElasticSearch
    7. \n
  7. Create pelias schema
    8. \n
  8. Run GTFS stop import
    9. \n
  9. Run NLS places import
    10. \n
  10. Run OpenStreetMap import
    11. \n
  11. Run DVV data import
    12. \n
  12. Run bike station import from an OpenTripPlanner service endpoint
    13. \n
\n

Exploring data

\n

For exploring ElasticSearch data, you can install the extension \"elasticsearch-head\" to Chrome browser and use it as follows:

\n
    \n
  1. Wait until cluster health changes to \"green\".
    2. \n
  2. Click on \"Browser\"-tab
    3. \n
  3. Write queries to \"name.default\" field.
    4. \n
  4. Browse results
    5. \n
\n

For Gis data exploration you can use e.g. QGis

\n
\n

http://www.qgis.org/en/site/

\n
\n

Datasources

\n

DVV (formerly VRK)

\n\n

Open addresses is a open data collaborative to produce global address data around the world. We use addresses from Open addresses as primary data.

\n

OpenStreetMap

\n\n

OpenStreetMap import reads all items which are tagged with a name and one or more tags/tag pairs from the following list:

\n\n

You can improve digitransit geocoding by contributing new or corrected data to OpenStreetMap. Please always include a Swedish name version, if available, to your data contributions.\nBelow is a simple example node with proper names:

\n
{\n    \"id\":26430225,\n    \"type\":\"node\",\n    \"lat\":60.2070123, \"lon\":24.7022998,\n    \"tags\":{\n        \"name\":\"Koivuhovi\",\n        \"name:sv\":\"Björkgård\",\n        \"railway\":\"station\"\n    }\n}
\n

For more information, check out:

\n
\n

http://wiki.openstreetmap.org/wiki/Beginners%27_guide

\n
\n

Our goal is to use as much data from OSM as possible. Unfortunately, at the moment it doesn't contain everything that we need so we have to use other sources also.

\n

NLS Paikat

\n\n

National Land survey Nimistö (\"places\") contains place names in Finland. It provides places like \"Takalammi\".

\n

Key service delivery activities

\n
    \n
  1. Keep up with our development on GitHub
    \nhttps://github.com/HSLdevcom/pelias-data-container
    2. \n
  2. Keep up with Pelias importer projects
    \nhttps://github.com/HSLdevcom/pelias-schema
    \nhttps://github.com/HSLdevcom/pelias-gtfs
    \nhttps://github.com/HSLdevcom/pelias-vrk
    \nhttps://github.com/HSLdevcom/pelias-nlsfi-places-importer
    \nhttps://github.com/HSLdevcom/openstreetmap
    \nhttps://github.com/HSLdevcom/bikes-pelias
    3. \n
  3. Keep up with Who's on First development
    \nhttps://github.com/whosonfirst/whosonfirst-data/
    4. \n
  4. Keep up with Geospatial Data Abstraction Library development
    \nhttp://www.gdal.org/
    5. \n
  5. Keep up with ElasticSearch docker image and it's changes
    \nhttps://hub.docker.com/_/elasticsearch/
    6. \n
","tableOfContents":"","excerpt":"This service is essentially an ElasticSearch instance containing Geocoding data that can be used with Pelias Geocoder. General Pelias information Start by reading (Note that it might not be up-to-date…"}},"pageContext":{"slug":"/en/developers/architecture/x-apis/3-geocoding-api/data-container/"}},"staticQueryHashes":["3675773199","4050193485"]}