A “near me” microservice for exposing your geocoded tabular data

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Orbitron is a microservice that you can deploy into your environment to expose a location finder service within your own applications. The service can be deployed in minutes to a cloud provider with minimal dependencies. A simple script is provided to help you upload your data to a PorgresSQL server instance.

Usage

To perform a search against your endpoint, simply use the following fluent URL: /nearest/{limit}/{source-name}/to/{zip-code}. This will perform a distance search against stored geocoded tabular data using functionality exposed through PostgreSQL.

Example

A query for the nearest pharmacies: /nearest/100/pharmacies/to/75001

{
        "name": "Mom & Pop's Local Pharmacy",
        "latitude": 33.435921,
        "longitude": -111.720686,
        ...
}

Custom Data Models

Additional data fields may be returned depending upon the contents of "rowdata" field in the database. It may vary depending on the data source type, i.e. if the geopoint represents a store or office, it may be convenient to include a contact phone number, but for something like a national monument, it might be helpful to include a construction date or other historical details of interest.

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Dependencies

This software requires access to a postgresql server with PostGIS 2.5.1 (or compatible version), with credentials set in a configuration file named ".env". A sample.env file is included.

Building the postal code geodata also requires the file US.txt from geoname.org's free data at http://download.geonames.org/export/zip/

Installation

Edit the file sample.env to reflect your postgresql credentials, and save the updated file as .env in the root folder of the project.

Place US.txt in the root folder and execute python install.py to perform initial database setup.

Configuring Location Data

Your location data needs to be tied to a sourceType defined in the Orbitron.Sources table. The Name field will correspond to the {source-type} parameter of the request URL. The script load.py can be used to load the neighbor data, it takes a csv file as a parameter, i.e. python load.py ./example.csv. The csv file should have columns in the following order:

[ "SourceId", "Name", "Latitude", "Longitude", "RowData" ]

• SourceId is the appropriate Id of a source from the Orbitron.Sources table
• Name is the name of the location
• Latitude and Longitude are geocoordinates of each location
• RowData is any additional data that is to be associated with the record, formatted as a JSON string

For example, the csv file might be formatted as follows, and should not contain a header row:

1,Test Location,42.872004,-87.952139,"{""phone"": ""555-555-5555""}"

Geocoding Your Dataset

If you do not have the latitude & longitude information for the locations in your dataset, but you do have address information, then you'll need to run the addresses through a geocoding service, such as https://www.geocod.io/.

Running

Using Python>=3.8, install dependencies with pip install -r requirements.txt. To start a development server, run uvicorn main:app --reload.

See http://localhost:8000/docs for auto-generated Swagger API documentation.

Quick start using open pharamacy location data on local development server

1. Set up a postgresql instance with PostGIS

2. Perform installation as indicated in "Installation" section.

3. Download Rx Open's pharacy location database from https://rxopen.org/api/v1/map/download/facility and place the resulting facility.csv in the project's root folder.

4. Execute python import-pharmacies.py

5. Run development server, as indicated in "Running" section.

You should now be able to issue http requests using the API endpoints against the local server, such as http://localhost:8000/nearest/100/pharmacies/to/75001

Running in Azure

This software can run in an Azure App Service with the custom startup command python -m uvicorn main:app --host 0.0.0.0

Credits

A special thanks goes to Matt McInerney for freely sharing his futuristic Orbitron font.

License

This is an open source project licensed under MIT.