Pelias is a geocoder powered completely by open data, available freely to everyone.
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What is Pelias?
Pelias is a search engine for places worldwide, powered by open data. It turns addresses and place names into geographic coordinates, and turns geographic coordinates into places and addresses. With Pelias, you’re able to turn your users’ place searches into actionable geodata and transform your geodata into real places.
We think open data, open source, and open strategy win over proprietary solutions at any part of the stack and we want to ensure the services we offer are in line with that vision. We believe that an open geocoder improves over the long-term only if the community can incorporate truly representative local knowledge.
This importer is designed to bring data into Pelias from a properly formatted CSV file.
It's originally based off of the OpenAddresses importer, which also uses a CSV format.
This importer will process any CSV, attempting to create a Pelias document for each row.
In order to be useful, each row needs to define a source, a latitude, a longitude, and a name. Address components can optionally be specified.
This importer will accept any column name as uppercase or lowercase. Lowercase has priority if both are present.
Latitude can come from a column called lat. It should be a
WGS84 value
between -90.0 and 90.0.
Longitude can come from a column called lon. It should be a
WGS84 value
between -180.0 and 180.0.
A valid address consists of at least a street, and possibly a housenumber and postalcode.
Valid column names for street are: street
Valid column names for housenumber are: housenumber, number
Valid column names for postalcode are: postalcode, postcode, zipcode
Valid column names for intersections are: cross_street (note: street is also required!)
A free-form string that represents the name of a record. It might be the name of a venue which also has an address, or the name of a city, mountain, or other interesting feature.
Valid column names for name are: name.
Pelias allows sorting records into different layers, representing different classes of data.
The most common layers are address, street, and venue. Address and street
have special meaning to Pelias: when Pelias looks for an address, it can also
attempt to use its interpolation engine
to fill in missing addresses. If no addresses (exact or interpolated) are
found, Pelias will try to find a street record matching the street from the
original address in the query.
Another type of layer is "administrative" layers such as city and country.
Layers do not have to fall into these categories. Any layer that doesn't have
special meaning to Pelias can still be use to filter with the layers
parameter to the Pelias API.
Valid column names for the layer value are: layer and layer_id
Pelias understands that different data records come from different sources, and allows filtering based on source. Common data projects that represent sources in Pelias include OpenStreetMap, OpenAddresses, and Who's on First.
Custom data with arbitrary sources are supported by this importer and can be used for user filtering. The source value won't have any other effect on how Pelias treats a record when querying.
Valid column names for the source value are : source
An ID is a unique identifier for each record. Pelias IDs are strings, so they can contain text. Pelias records must have a unique source, layer, and ID. Attempting to create multiple records with the same source, layer and ID will cause all but the most recent record to be overwritten.
If an ID is not specified for a row in a CSV, the row number will be used.
Multiple names in different languages can be assigned by using the name_$lang fields, where $lang is an ISO 639-1 language code.
For example, to create a record for London in English and French, use the following CSV:
| id | name | name_fr | source | layer | lat | lon |
|---|---|---|---|---|---|---|
| 1 | London | Londres | custom | locality | 5 | 6 |
A record can have multiple aliases, or alternative names, specified as an array using the name_json field.
The following CSV will create a record for John F Kennedy International Airport, with common aliases including JFK and JFK airport.
| id | name | name_json | source | layer | lat | lon |
|---|---|---|---|---|---|---|
| 1 | John F Kennedy International Airport | "[""JFK"", ""JFK Airport""]" | custom | venue | 40.639722 | -73.778889 |
The contents of the name_json field must be a JSON array. As a reminder, in CSV files, records that contain commas must be quoted using double quotes, and records with a double quote in the value itself must be double-double-quoted, as shown above.
Aliases and languages can both be specified. For example, the name_json_es field allows setting multiple aliases in Spanish.
Popularity values can be specified to mark records as more important than others. This value should be an integer greater than zero, in the popularity column.
Category values can be added to a record. For a single category, use the category field. For multiple categories, use category_json, with the same formatting as for alias names.
Arbitrary custom data that does not fit into the standard Pelias schema can be stored for later retrieval under the addendum property.
Currently, custom data is supported when encoded as any valid JSON object. In the future, support for adding individual values via CSV columns will be supported.
Custom data entires are namespaced, so this importer supports any column starting with addendum_json_. The rest of the column name will determine the namespace.
For example, to store a WikiData and Geonames concordance ID, the following CSV format might be used:
| id | name | source | layer | lat | lon | addendum_json_geonames | addendum_json_wikidata |
|---|---|---|---|---|---|---|---|
| 1 | test | custom | venue | 5 | 6 | "{ ""id"": 600 } | { ""id"": ""Q47"" }" |
The Pelias API will then return a GeoJSON Feature like the following:
{
"properties": {
"id": "1",
"gid": "custom:venue:1",
"layer": "venue",
"source": "custom",
"source_id": "1",
"name": "test",
"confidence": 1,
"match_type": "exact",
"accuracy": "centroid",
"label": "London, England, United Kingdom",
"addendum": {
"geonames": {
"id": 600
},
"wikidata": {
"id": "Q47"
}
}
}
}
Node.js is required.
See Pelias software requirements for supported versions.
git clone https://github.com/pelias/csv-importer
cd csv-importer
npm install# download files, if desired
./bin/download
# run an import
./bin/startThis importer includes a downloader that supports downloading any uncompressed CSV files over HTTP/HTTPS.
This importer can be configured in pelias-config, in the imports.csv
hash. A sample configuration file might look like:
{
"esclient": {
"hosts": [
{
"env": "development",
"protocol": "http",
"host": "localhost",
"port": 9200
}
]
},
"api": {
"targets": {
"yoursource": ["yourlayers"]
}
},
"logger": {
"level": "debug"
},
"imports": {
"whosonfirst": {
"datapath": "/mnt/data/whosonfirst/",
"importPostalcodes": false,
"importVenues": false
},
"csv": {
"datapath": "/path/to/your/csv/files",
"files": [],
"download": [
"https://example.com/csv-to-download.csv"
]
}
}
}Important: You must put any custom source and layers imported by your data in pelias.json as explained in the relevant API configuration documentation. For a reasonably common use case for the source csv with only records in the address layer, the following configuration is a good starting point:
{
"api": {
"targets": {
"csv": ["address"]
}
}
}
The following properties are recognized:
This importer is configured using the pelias-config module.
The following configuration options are supported by this importer.
| key | required | default | description |
|---|---|---|---|
datapath |
yes | The absolute path of the directory containing data files, or where downloaded files will be stored. | |
files |
no | [] |
An array of the names of the files to import. If specified, only these files will be imported. If not specified, or empty, all .csv files in the given directory will be imported. |
download |
no | [] |
An array of URLs of CSV files that can be downloaded. Files must be plain-text (uncompressed) CSV files |