In this section we give you a quick overview of the most commonly used commands to interact with Data Hub Spaces from the HERE Data Hub CLI.

See Get a HERE Account for additional information regarding authentication and configuration procedures.


Configuration is required for HERE Data Hub CLI to work

If you have skipped the previous section now is the time to go back and make sure HERE Data Hub CLI is properly configured with your developer identity. In case you are not sure, you can run here configure verify to check if your credentials are valid.

Supported Commands

HERE Data Hub CLI is built to be modular and extensible, so it is entirely possible that when you use the tool it has already learned a couple of new tricks.

The general structure is that you call the here command followed by command which essentially corresponds to the API you want to interact with or get more information from.

Getting help

You can always call up help on the HERE Data Hub CLI either by not providing any parameter at all or by using the --help switch.

here --help

Log-in to your Data Hub account

As explained earlier, HERE Data Hub CLI needs to know you to interact with your Data Hub Spaces. You can use

here configure account

to configure with the e-mail address and password you use for your HERE account.

You can also run

here configure verify

to check if the credentials provided can be used to interact with HERE APIs.

If you have upgraded your HERE account to an Add-On plan, you can run

here configure refresh

to enable using Data Hub Add-on features. Learn more about Data Hub Add-on features here.

Click here to learn how to set up and use Data Hub Add-on features Using HERE Data Hub CLI.

Interact with Data Hub Spaces

The xyz command is used to interact with Data Hub Spaces.

You can find the supported subcommands with the --help switch:

here xyz --help

List Spaces

To list all Spaces you have access to, you can use

here xyz list

-r, --raw shows the raw space list definition in json

--token <token> an external token to access another user's spaces

Use this option to list another user's spaces using an access token they have shared with you.


You can use this option with many CLI commands to access another user's spaces. Check for --token option in command's help.

--filter <filter> a comma separated list of strings to filter spaces by, based on title or description text

Lists only the spaces where the title or description contain the matching string(s).

-p, --prop <prop> choose which space property to show when listing spaces: id, title or description.

Create a new Space

here xyz create -t "sample test xyz" -d "sample creation"

When you create a new space, the SpaceID will be automatically generated.


The upload command can also automatically generate a new space ID for you if it is not specified


-t <title> title for the space

-d <desc> description for the space


When you have many spaces, you will be glad you added meaningful titles and descriptions.

--token <token> an external token to create a space in another user's account (must have appropriate admin permissions)

-s <schema definition> set a json schema definition for your space

Applies a schema validation json file to the space to be applied to future uploads. The schema definition can be in the form of a url or a local schema json file. Features that do not match this schema will not be uploaded to the space.


This is an Add-on feature that requires a license. Learn more about Data Hub Add-on features here.

Upload/Update data to a Space

The CLI makes it easy to upload geospatial files to a Data Hub Space, and there are many options to enhance, optimize and speed up the upload process.

Upload GeoJSON

Upload a GeoJSON file to a new space. Data Hub will automatically generate a space ID and display it for you.

here xyz upload -f /Users/xyz/data.geojson

Upload a GeoJSON file to an existing space.

here xyz upload SPACE_ID -f /Users/xyz/data.geojson


Use streaming for faster uploads

Streaming with the upload -s option will significantly reduce the time required to upload GeoJSON files. The default non-streaming mode is useful for troubleshooting. If your features are small, you can also try increasing the chunk size using -c. Chunk size for many point datasets can be safely increased to 1000 or more (the default is 200).


Data Hub requires that every feature in a space has a unique ID. This helps you access individual features in a space using the API.

If a GeoJSON feature does not have an ID (a common occurrence), Data Hub's default upload behavior is to create one based on a hash of the feature's property — note that if you have records with duplicate IDs in a dataset at the same location with the same properties, only one will be uploaded. You can define a new feature ID using more than one property using -i.)

By default, the CLI will respect any IDs already in a feature. Note that if a new feature has the same ID as an existing feature, Data Hub will consider it an update and overwrite the existing feature. Note that some public datasets have a set of feature IDs that are simply incremental integers, which can lead to feature replacement that you probably didn't want when you are uploading multiple files to the same space. In this case, you may want to use -o to override the existing ID and create a unique feature ID based on a hash of the feature's properties. You can also select multiple properties with -i to generate something unique and human-readable.

Upload a CSV file
here xyz upload -f /Users/xyz/data.csv

Data Hub will attempt to choose the columns containing the latitude and longitude fields based on well-known names including:

y, ycoord, ycoordinate, coordy, coordinatey, latitude, lat x, xcoord, xcoordinate, coordx, coordinatex, longitude, lon, lng, long, longitud

If your csv uses different names, you can specify the latitude field with -y and longitude with -x. (If you think this is a common field and should be automatically included, file an issue or better yet, create a pull request!)

here xyz upload -f /Users/xyz/data.csv -x the_lon -y the_lat

If the csv combines coordinates into a single field, such as




you can specify the name of that column with -z. (Thanks to user ToonvanStrijp for some nice regex.)

here xyz upload -f /Users/xyz/data.csv -z points

Rows that have 0,0 or null values in the designated latitude and longitude columns will be tagged with null_island. They will not be displayed on the map, but you can access them via the API (or in by appending &tags=null_island so you can inspect and repair the records.

If the lat/lon columns contain letters or other invalid characters, the features are tagged with invalid.

If you specify upload --noCoords, the CLI will upload the CSV rows as features with a null geometry object, and tag it with null_island. This is useful for working with data tables that you want to dynamically merge with geometries using Virtual Spaces.


Use streaming for faster uploads

Using streaming via the upload -s option will significantly reduce the time required to upload CSV files of any size. Standard, non-streaming mode is useful for troubleshooting.


Using properties as the feature ID

The CLI converts a CSV row into a GeoJSON feature before uploading it. If you want values in a CSV column to be the GeoJSON feature ID, use -i columnName. Note that you can choose more than one column to create the feature ID.

Grouping multiple CSV rows into a single unique feature ID

You can use --groupby with -i to consolidate multiple rows "belonging to" a unique ID as nested objects within a single feature. A good example of this would be a CSV of election results across many electoral precincts, where each candidate running in that precinct has their own row. Please read the Group By tutorial for more details.


You can adjust the "chunk" size when streaming an upload. This controls the number of features that the CLI sends to the API at a time. The default chunk size is 200 features.


Using -c 1 and -e can help troubleshoot problematic features in individual GeoJSON features.

Large features

You may see upload errors from the CLI if your features are large, complex geometries. By decreasing the chunk size, you may still be able to upload these large features. Try -c 100, or even -c 10 or -c 1.

As of v1.6, if the CLI receives a 413 Request Entity Too Large error, it will cut the chunk size in half in an attempt to upload the features.

If you continue to see errors -- probably because a single feature is larger than the limit of the API gateway (approximately 10 MB -- you may need to simplify the geometry before uploading them.

Small features

If your features are small, like you might see in a GeoJSON file containing points or a CSV, you will see faster uploads if you increase the chunk size. For example, -c 1000 will enable the CLI to upload 1000 features at a time. If the features are very simple, -c 10000 may also be appropriate. As long as the chunk size is below the size of the API gateway, this will speed up your upload. The CLI will notify you if there is an upload error. (You can see more detailed errors using -e.)

Note that if you need to restart your upload, existing features will not be duplicated.

Upload and stream large CSV and GeoJSON files

To upload very large CSV and GeoJSON files to your Data Hub space, you will need to use -s -- this will stream the file and avoid Node.js memory errors. It will also be considerably faster than the standard upload method. (Note that you cannot currently stream a shapefile.)

here xyz upload YOUR_SPACE_ID -f /Users/xyz/big_data.csv -s


When a file is streamed with -s it is not loaded into memory and -a is not available to preview and assign tags. You can specify tags using -p.


HERE Data Hub is a database. Databases trade off storage space for speed, and your data will always take up more storage space in Data Hub than it does in a static file. When a file is uploaded into a Data Hub Space, features, their properties, and the geometries are broken out into multiple tables, indexed and tagged. All of this lets you query your geospatial data on demand, and access it dynamically as vector tiles. You can check the size of your Data Hub Spaces in your account dashboard or the CLI.

Upload a shapefile

If a shapefile is unzipped, select the file with the .shp extension, and the CLI will look for the accompanying files in that directory

here xyz upload -f /Users/dhatb/data.shp

As of v1.6, the CLI can upload a zipped shapefile:

here xyz upload -f /Users/dhatb/


Shapefiles cannot be streamed with -s.

Upload shapefile data to a Space.


Instead of passing the content as a file with -f option you can also pipe the output of another command directly into the input stream of the HERE Data Hub CLI like cmd | here xyz upload YOUR_SPACE_ID -- this can be useful when piping data from geospatial tools like mapshaper.

There are many, many more tips in the Working with Shapefiles tutorial.

Upload a CSV using a unique ID
here xyz upload -f data.csv -i unique_id

You can upload CSV to a Data Hub space and select a column to be the GeoJSON feature ID.

This feature should be used if your data has well-known and truly unique identifiers that you want to preserve. The Data Hub API can query individual features by feature ID, so this can be a valuable method of accessing and updating features.

By default, the CLI will generate a unique feature ID during CSV upload based on a hash of the properties and geometry.


Unique IDs are important for Data Hub Add-on features such as Virtual Spaces.


Many GIS systems will simply assign incrementing integers as feature IDs to every file. These can conflict across files.

Upload and assign tags

Tags are special properties that can be added to a feature that makes it easy to query them from the Data Hub API using the &tags= parameter.


Data Hub Tags should be used selectively, ideally using Rule-Based Tags. Tags are not meant to be a replacement for Property Search as you will be duplicating existing data in a record.

Assign tags interactively
here xyz upload -f file.geojson -a

Uploads data and allows users to select tags from a list of feature keynames in shapefiles, CSVs and GeoJSON files, with a preview of the first few values.


-a does not allow you to stream the upload.


The mapping of CSV columns to GeoJSON is as follows:

Type CSV column GeoJSON
e.g. ...;lat;long; ...
"geometry": {
"type": "Point",
"coordinates": {
non-spatial anyOtherName properties.anyOtherName
e.g. ...;name;amenity;...
...;Anfield;Football Stadium;...
properties {
"name": "Anfield",
"amenity": "Football Stadium",
Assign tags using property names
here xyz upload -f file.geojson -p treatment

Uploads data and adds the value of the selected feature property as tag. These tags can be used to filter data when querying the HERE Data Hub API. The tags will be stored as propertyname@value. This is most effective when the property consists of a limited number of qualitative values -- if you have many unique values, or quantitative values, you are better off using Property Search.

treatment@green_paint, treatment@sharrows, treatment@hit_post
Upload data with a timestamp and date properties

If you have a timestamp or date properties in your data, the CLI can help you create additional time and date specific properties and time-based tags. This will make it easier to use the Data Hub Property Search feature.

here xyz upload <SPACE_ID> -f <CSV|GEOJSON> --date <propertyname>

Convert a valid date/timestamp property into an ISO 8601 date and a unix timestamp and adds them as datahub_timestamp_propertyname and datahub_iso8601_propertyname properties to the features.

here xyz upload <SPACE_ID> -f <CSV|GEOJSON> --date <propertyname> --dateprops [year,month,week,weekday,year_month,year_week,hour]

Extract the date parameter(s) specified following --dateprops from the property and upload them as individual properties to the features.

So for an example property in a feature: "start_time": "2020-04-29T22:00:00+05:30", you will get the following properties added to the feature upon upload.

--dateprops property value
year date_start_time_year 2020
month date_start_time_month April
week date_start_time_week 18
weekday date_start_time_weekday Wednesday
year_month date_start_time_year_month 2020-04
year_week date_start_time_year_week 2020-18
hour date_start_time_hour 22
here xyz upload <SPACE_ID> -f <CSV|GEOJSON> --date <propertyname> --datetag [year,month,week,weekday,year_month,year_week,hour]

Along similar lines, --datetag will let you specify which date parameter tags need to be created out of a date property for the features.

--datetag tag
year date_start_time_year@2020
month date_start_time_month@april
week date_start_time_week@18
weekday date_start_time_weekday@wednesday
year_month date_start_time_year_month@2020-04
year_week date_start_time_year_week@2020-18
hour date_start_time_hour@22
Upload history of a space

The CLI saves the 3 most recent upload commands for a space within the space metadata. This allows you to re-use one of them later with the --history option. You can specify a command index which can range from 0(newest) to 2 (oldest), or you can use the --history option without an index, and you will be provided with an interactive list of historical upload commands.

here xyz upload <SPACE_ID> --history [0-2|blank]

You can also mark one of the history commands as a "favorite" so that you can later re-execute it.

here xyz upload <SPACE_ID> --history save

To execute the favorite upload command:

here xyz upload <SPACE_ID> --history fav

This is a convenient way to save upload commands with particularly complex options, especially when you revisit a space a few months after you last worked with it.

Note that this will immediately upload the command.

You can clear a space's history using clear

Upload multiple files with batch upload

You can upload a directory full of geospatial files to a space in one command using --batch. This is a convenient way to rejoin datasets that have been broken into multiple files, or to add regional files to one space in order to build a national dataset.

You must specify the directory with -f and the filetype after --batch.

here xyz upload <SPACE_ID> --batch [geojson|csv|shp|gpx] -f <PATH_TO_FOLDER>

While uploading shapefiles, --batch will inspect one level of sub-directories within that specified directory to look for .shp and all other relevant files from when uncompressing a zipped shapefile.


-f, --file <file> GeoJSON, GeoJSONL, CSV, GPX, XLS/XLSX, or Shapefile to upload

-c, --chunk [chunk] chunk size (adjusts the number of features uploaded at once)

-t, --tags [tags] tags for the Data Hub space (used to filter data from the API)

--token <token> an external token to upload data to another user's space

-x, --lon [lon] choose longitude CSV field name, if not well-known

-y, --lat [lat] latitude CSV field name, if not well-known

-z, --point [point] points field name, e.g. (lat,lon)

--lonlat parse a --point/-z csv field as (lon,lat) instead of (lat,lon)

-p, --ptag [ptag] property name(s) whose values will be used to generate tags

-i, --id [id] property name(s) to be used as the feature ID

-a, --assign lists a sample of properties, allowing you to assign fields to be selected as tags and Feature IDs

-o, --override override existing GeoJSON feature IDs and generate unique IDs using the property hash function

-d, --delimiter [,] an alternate delimiter used in CSV (default: ",")

-q, --quote ["] quote used in CSV (default: "\"")

-e, --errors print data upload errors

-s, --stream speed up CSV and GeoJSON uploads. Required to stream large geojson and csv files (> 200 MB) (-a unavailable with streaming, use -p)

--string-fields <stringFields> property name(s) of CSV string fields not to be automatically converted into numbers or booleans (e.g. number-like census geoids, postal codes with leading zeros)

--groupby <groupby> consolidate multiple rows of a CSV into a single feature based on a unique ID designated with -i; values of each row within the selected column will become top-level properties within the consolidated feature

--flatten stores the groupby operation output in a flattened format separated by colon (:)

--promote <promote> comma-separated column names which should not be nested in the groupby object

--date <date> date-related property name(s) of a feature to be normalized as an ISO 8601 datestring (xyz_iso8601_[propertyname]), and unix timestamp (xyz_timestamp_[propertyname])

--datetag [datetagString] comma-separated list of granular date tags to be added via --date. possible options - year, month, week, weekday, year_month, year_week

--dateprops [datepropsString] comma separated list of granular date properties to be added via --date. possible options - year, month, week, weekday, year_month, year_week

--noCoords upload CSV files with no coordinates, generates null geometry (best used with -i and virtual spaces)

--history [history] repeat commands previously used to upload data to a space; save and recall a specific command using "--history save" and "--history fav"

--batch [batch] select type of files to be uploaded in batch (select directory with -f)

-h, --help output usage information


You can pipe data to the upload command and an existing space -- atoolthatoutputsgeojson | here xyz upload spaceID


Some GeoJSON features may cross the international dateline. In older GeoJSON files, some coordinates may have longitudes greater than 180 or less than -180. You cannot upload these features to Data Hub – these features should be split into MultiPolygons or MultiLineStrings. Also, some coordinates that are supposed to be 180/-180 can end up looking like 180.0000008576 thanks to floating-point precision errors in whatever process created them. This is also something you can't upload to Data Hub.

Join data to another space

here xyz join YOUR_SPACE_ID -f data.csv --keys csv_column

join will upload a csv and create a new Virtual Space -- it simplifies the steps one does with here xyz upload -f my.csv --noCoords and here xyz vs -a csvspace,geometryspace.


It is not recommended you build scripts using join until v1.7 as the order of the command will be changing to operate more like upload spaceID, where the space ID in the command will contain the csv, and --target spaceID will contain the geometry space.

Show contents of a space

here xyz show YOUR_SPACE_ID

Show the objects of a space in a table, filter by tags or property values, or open the space in other visualization tools.

Using show on a large space will generate a long table. You can see the raw GeoJSON of the first 5000 features using -r. This can also be very long. You may want to direct this output to a file, or pipe to more.

here xyz show spaceID -r > my.geojson

If your space contains a few hundred to a few thousand features, you can open the space in, a data preview tool, using show -w.
Larger spaces can be previewed in Space Invader, a Tangram-based tool from Data Hub Labs, using show -v, and features like H3 hexbin and quadbin clustering can be used to visualize even larger spaces.


As of v1.6, tokens generated using -v and -w are for the that space only. Also, these tokens expire in 48 hours by default. To generate a permanent token for a space, use --permanent or -x.

Filter by Tags

You can filter tags from Data Hub using tags with -t:

here xyz show spaceID -t my_tag (records with my_tag will be printed in the console) here xyz show spaceID -w -t my_tag (records with my_tag will be opened in here xyz show spaceID -v -t my_tag (records with my_tag will be opened in Data Hub Space Invader)

If a property has been indexed by Data Hub, you can filter them with -s or --search. The property name must be prefixed by p.:

here xyz show spaceID -s "p.property_name>value"

here xyz show spaceID -s ",Tom+p.age<'9999999'+p.zipcode=123456" -w
  • Operators include =,!=,>,>=,<,<=
  • Search expressions must be enclosed in double quotes, e.g. "p.property_name>value"
  • Use comma separated values to search multiple values of a property, e.g. OR. Use + for AND.
  • Use single quotes to signify a string value, e.g. "p.property_name>value='100'" vs "p.property_name>value=100"
  • To access feature ID, timestamps, or tags, prefix them with f., e.g., f.updatedAt, f.tags f.createdAt
  • When accessing Property Search via the API, the URL-safe arguments are =, !=, =gt=, =gte=, =lt=, =lte=.


Property Search is available in spaces with fewer than 10,000 features by default. For spaces larger than 10,000 features, a limited number will be indexed. To access more, you'll need a Data Hub Add-on license, learn more about Data Hub Add-on features here.

Property Filters

You can use show -p or --prop to filter the properties that are returned by the Data Hub API. This is useful when your features have a large number of properties, and you only need to return some of them along with the geometry.

here xyz show -p p.property1,p.property2 -w

You can use --spatial to search for features in a Data Hub space that fall within the radius from a point, or within a polygon, or along a line.

You can specify a point and a radius, or a feature in another Data Hub space, or a GeoJSON file containing a feature.

  • --center: comma separated lon,lat values (x,y) that specify the center point for the search. While this might work unquoted in most shells, some shells are going to interpret the - as an argument, so it's safest to use double quotes.
  • --radius: the radius of the search, in meters, from the --center point, or a buffer around a geometry specified with --feature or --geometry
  • --feature: comma separated spaceid,featureid values that specify a reference geometry from another HERE Data Hub space -- this will return features from the first space that fall within or along a feature from the second space
  • --geometry: a single GeoJSON feature in a file to be uploaded for the spatial query (not a feature collection)


These results are most easily viewable using show -w.


-l, --limit <limit> Number of objects to be fetched

-o, --offset <offset> The offset / handle to continue the iteration

-t, --tags <tags> Tags to filter on

-r, --raw show raw Data Hub space content (this iterates through every feature, so you can direct this to a file using >

--all iterate over entire Data Hub space to get entire data of space, output will be shown on the console in geojson format (and can be directed to a file using >)

--geojsonl to print output of --all in geojsonl format

-c, --chunk [chunk] chunk size to use in --all option, default 5000

--token <token> an external token to access another user's space

-p, --prop <prop> selection of properties, use p.\ or f.

-w, --web display Data Hub space on

-v, --vector inspect and analyze using Data Hub Space Invader and tangram.js

-x, --permanent generate a permanent token for the space when using -v or -w instead of a temporary token that expires in 48 hours)

-s, --search <propfilter> search expression in "double quotes", use single quote to signify string value, use p.\ or f.\ (Use '+' for AND , Operators : >,<,<=,<=,=,!=) (use comma separated values to search multiple values of a property) {e.g. ",Tom+p.age<'9999999'+p.zipcode=123456"}

--spatial make a spatial search on the space

--radius <radius> indicate a radius for a spatial search, or to thicken the input linestring or polygon (in meters)

--center <center> comma separated lon,lat values to specify the center point for radius search (best to surround this with quotes)

--feature <feature> comma separated spaceid,featureid values to specify reference geometry (taken from feature) for spatial query

--geometry <geometry> geometry file to upload for a spatial query (single feature in geojson file)

-h, --help display help for command

Delete a Space

here xyz delete YOUR_SPACE_ID

Delete a space you have access to. By default, you will be shown information about the space for context, and a confirmation prompt.


--force skip the confirmation prompt

--token <token> an external token to delete another user's space

-h, --help display help for command

Clear a Space

here xyz clear YOUR_SPACE_ID

Clear data from your space. You clear the entire space, or clear by tag or feature ID. By default, you will be shown information about the space for context, and a confirmation prompt.


-t, --tags <tags> tags for the Data Hub space

-i, --ids <ids> ids for the Data Hub space

--token <token> an external token to clear another user's space data

--force skip the confirmation prompt

-h, --help display help for command

List all tokens

here xyz token

Lists all the Data Hub tokens you have available:

id             | type    | lat      | description
--------------- --------- ---------- ------------------------------------------------------------------------------
YOUR_TOKEN_NR_1 PERMANENT 1534451767 xyz-hub=readFeatures,createFeatures,updateFeatures,deleteFeatures,manageSpaces
YOUR_TOKEN_NR_2 PERMANENT 1534516620 xyz-hub=readFeatures

--console opens the web console for Data Hub

-h, --help display help for command

Get or update more information about your spaces

You can use the config command to get and update information about your spaces.

Get information about a space
here xyz config SPACE_ID

This will print a formatted table with the title, description, and other high-level information about the space.

You can see the raw json response from the /statistics endpoint using -r:

here xyz config SPACE_ID -r
Get a list of tags and properties used in a space

You can get more details about a space by using the --stats option. This will return the number of features, the size of the space, the bbox, geometry types, names and counts of tags, as well as the names of properties (and if they can be accessed via Property Search).

here xyz config SPACE_ID --stats


Use here xyz analyze to get a count and list of values of a property in a space. Note that this is a client-side operation and is best suited for qualitative values. Only the first 500,000 features in a space will be analyzed.

Update the title and description of a space

To update the title and/or description of a space:

here xyz config -t "A meaningful title for a space" -d "additional details about this space that future you will appreciate 6 months from now"
Share a space

You can share a space with other users using the --shared option. If they have a Data Hub account, they will be able to read from that space using their own tokens (and any data transfer will be charged to their Data Hub account).

here xyz config spaceID --shared true

You can disable sharing by passing a false parameter:

here xyz config spaceID --shared false

Note that any Data Hub user will be able to read a space you have shared, and will be able to view a list all shared spaces.

If you want to selectively share a space, you should generate a token for just that space using the Data Hub Console.

Basic Options

--shared <flag> set your space as shared / public (default is false)

-t,--title [title] set title for the space

-d,--message [message] set description for the space

-c,--copyright [copyright] set copyright text for the space

--cacheTTL <cacheTTL> set cacheTTL value for the space with valid number

--stats see detailed space statistics

--token <token> an external token to access another user's space config and stats information

-r, --raw show raw json output

--console opens web console for Data Hub

-h, --help display help for command

Add-on Options


To use these features, your account needs access to the Data Hub Add-on Services. Learn more about Data Hub Add-on features here.

-s,--schema [schemadef] view or set schema definition (local filepath / http link) for your space, applicable on future data, use with add/delete/update

--searchable view or configure searchable properties of a Data Hub space, use with add/delete/update

--tagrules add, remove, view the conditional rules to tag your features automatically, use with add/delete/update -- at present all tag rules will be applied synchronously before features are stored ( mode : sync )

--delete use with schema/searchable/tagrules options to remove the respective configurations

--add use with schema/searchable/tagrules options to add/set the respective configurations

--update use with tagrules options to update the respective configurations

--view use with schema/searchable/tagrules options to view the respective configurations

--activitylog configure activity logs for your space interactively

Transform csv, shp and gpx to geojson

The here transform command converts CSVs, shapefiles, XLS/XLSX, and GPX files to GeoJSON. Note this will generate raw GeoJSON and not save it to a space. The upload command uses transform.

Geocode locations

The here geocode command takes an address string and uses the HERE Geocoder to return a GeoJSON feature containing the coordinates. This returns raw GeoJSON and does not save it to a space.

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