Introduction

Duration is 3 min

Last Updated: January 31, 2020

We recently partnered with the Greater Chicago Food Depository (GCFD) to build a location-aware chatbot that helps those in need find nearby food pantries.

The bot works as such:

A user of the chatbot texts the bot with their current location.
The chatbot returns the 3 closest food pantries to the provided location. The message includes information such as the phone number, address, and operating hours of the pantry.

Although the Greater Chicago Food Depository is geared at a very specific use case of helping food insecure individuals find nearby food pantries, this type of chatbot concept can be applied in many different situations and industries. For example, a gas station brand could build a similar service directing drivers to nearby gas stations in areas with poor data service. In this situation, a SMS-based chatbot would be appropriate.

What you’ll need

Duration is 5 min

This tutorial assumes some familiarity with JavaScript and Node.js.

In this tutorial, we’ll be using a few different services:

HERE Geocoding API: a service to translate coordinates to addresses and vice-versa.
The Data Hub API provided by HERE: a cloud-based location storage service.
Twilio SMS API: a service to send SMS messages programmatically.

Navigate to the HERE Developer Portal to create a new project. If you don’t already have an account, now would be a good time to make one.

Additionally, navigate to the Twilio Developer Console and create a new Programmable SMS project.

Architecture

Set up the Node.js skeleton

Duration is 5 min

We’ll get started by preparing our Node.js code skeleton and installing the required dependencies.

Create a new file called server.js and paste the following:

const http = require('http');
const express = require('express');
const MessagingResponse = require('twilio').twiml.MessagingResponse;
const bodyParser = require('body-parser');
const request = require('request');
const turf = require('@turf/turf');

const app = express();
app.use(bodyParser.json());
app.use(bodyParser.urlencoded({
   extended: true
}));

//HERE credentials
const here = {
   apiKey: 'HERE_API_KEY',
   token: 'HERE_Studio_TOKEN'
}

//Twilio credentials:
const twilio = {
   sid: 'TWILIO_SID',
   token: 'TWILIO_TOKEN'
}

const client = require('twilio')(twilio.sid, twilio.token);

Be sure to swap out your HERE Developer Account app id/code and Twilio sid/token. Leave the HERE token value as empty for now; we'll be obtaining the HERE token in a later step.

Additionally, run the command npm install express twilio body-parser @turf/turf request in the directory of the server.js file to ensure the correct dependencies are installed.

We're also going to need to install ngrok to run a tunnel providing an externally available URL to our server.

You can install ngrok globally with the following command:

npm install -g ngrok

Send messages with Twilio

Duration is 8 min

Now that our skeleton is complete, we can start writing some logic to send messages with the Twilio Programmable SMS API.

Once Twilio is configured correctly, the following code will enable our bot to reply with 'Hello, there!' anytime you text the Twilio phone number.

We'll want to configure our server to reply to messages sent to our Twilio phone number. In the server.js file, add the following code:

app.post('/sms', (req, res) => {
  const twiml = new MessagingResponse();
  twiml.message('Hello, there!');
  res.writeHead(200, {'Content-Type': 'text/xml'});
  res.end(twiml.toString());
});

http.createServer(app).listen(1337, () => {
   console.log('Express server listening on port 1337');
});

To get the server up and running, you're going to want to run node server.js (alternatively nodemon server.js if you have it installed. This eliminates the need to restart the server) in the command line. In order to make your server publicly accessible, you'll want to run the command ngrok http 1337. Port 1337 is now exposed to the public internet.

Copy the forwarding URL (highlighted in the below screenshot) provided by the ngrok console.

Configure a Twilio phone number. Take the copied URL and configure it as a webhook for your phone number. Under the Messaging header, paste this URL followed by /sms in the message comes in field.

Once you've got that configured, try sending a message to your Twilio phone number. You should get a friendly reply saying 'Hello, there!'

Upload data to a Data Hub space

Duration is 20 min

We've set up the infrastructure to send messages via Twilio, now let's upload our data to a Data Hub space for storage.

Data Hub Spaces are powerful geospatial databases that can store millions of rows. For the simplicity of this tutorial, we will only be uploading a small amount of features into our Data Hub space.

For more information about Data Hub spaces, take a look at the documentation.

There are many ways to upload data to an Data Hub space:

via REST API
via Studio
via the HERE Command Line Interface (CLI)

I prefer the CLI because of its ease of use. To install the CLI, run the command:

npm install -g @here/cli

Next, configure your credentials by running:

here configure set

When prompted, enter your app id/code from the HERE Developer Portal.

The next step is to create a new Data Hub space with the following command:

here xyz create -t "food pantries" -d "locations for GCFD food pantries"

The -t and -d options are for the space's title and description.

Upon completion, the here xyz create command will a success message like:

xyzspace 'zgsrj7y4' created successfully

Be sure, to copy the id in quotations--we'll be using this in a future step.

Here is a peak at the data we'll be uploading to the Data Hub space:

{
   "type": "FeatureCollection",
   "features": [{
      "id": "a2c91f4bfa199b3eaa1da6b138",
      "type": "Feature",
      "properties": {
         "Zip": "60160",
         "City": "Melrose Park",
         "Name": "Lighthouse of Hope",
         "State": "IL",
         "Phone number": "(708) 831-5370",
         "Operating hours": "Saturday: 9:30 am - 11:30 am",
         "Distribution address": "1719 W North Ave"
      },
      "geometry": {
         "type": "Point",
         "coordinates": [-87.85532,
            41.9080299
         ]
      }
   }]
}

You can download the whole file here.

To upload the data to the space, use the following command:

here xyz upload YOUR_SPACE_ID -f food_pantries.geojson

Be sure to replace YOUR_SPACE_ID with the space id that was outputted when you created the space with the here xyz create command.

Now is also a good time to grab your HERE Developer token. To see your available tokens, run the following command:

here xyz token

Go ahead and copy one of these (one with reading and writing permissions), and paste it into the the server.js code, inside the const here variable.

Congrats! We've now created a new Data Hub space and uploaded data. To verify you've successfully created the space, you can run the following command:

here xyz show YOUR_SPACE_ID -w

This command will open up the space in the HERE Studio GeoJSON viewer.

Location Services

Duration is 5 min

So far, we've configured our SMS infrastructure (Twilio) and our database (Data Hub). Now it's time to write some code that will handle our search logic.

We'll want to perform the following tasks:

Transform our user received input (addresses like 425 W. Randolph Street Chicago) into coordinates using the Geocoder API.
Search for the closest food pantries in our Data Hub space by performing a distance request from the starting coordinates to the coordinates of each food pantry. This is by no means a computationally efficient method, but it is adequate for such a small dataset.
Return the top 3 closest food pantries back to the user.

For distance calculations, we will be using the as the crow flies distance. This means we will be calculating distance as the closest path between points A and B on a spherical earth, regardless of road networks. This is also known as the haversine formula.

Geocoder API

Duration is 8 min

The first task we'd like to accomplish is geocoding the user's address input. Using the HERE Geocoding API, we can do this. Modify the app.post('/sms') to do perform the geocoding:

app.post('/sms', (req, res) => {
   const twiml = new MessagingResponse();
   const query = req.body.Body;
   const geocodeURL = `https://geocoder.ls.hereapi.com/6.2/geocode.json
?apiKey=${here.apiKey}&searchtext=${query}`;

   request.get(geocodeURL, (error, response, body) => {
      const geocodeJson = JSON.parse(body);
      const coordinates = {
         lat: geocodeJson.Response.View[0].Result[0].Location.DisplayPosition.Latitude,
         long: geocodeJson.Response.View[0].Result[0].Location.DisplayPosition.Longitude
      };
      console.log(coordinates)
   });

   twiml.message('Hello, there!');
   res.writeHead(200, {
      'Content-Type': 'text/xml'
   });
   res.end(twiml.toString());
});

This results in us extracting the query's coordinates and storing them in the coordinates variable.

Query a Data Hub space

Duration is 8 min

Our next step is to query our Data Hub space for the nearby food banks. We can do so with a helpful REST API.

For more information about the possible Data Hub space API requests, take a look at the Swagger playground.

Staying inside of the geocode request.get() block, we can add the following code:

const xyzSpaceId = 'SPACE_ID';
const xyzURL = `https://xyz.api.here.com/hub/spaces/${xyzSpaceId}/search?access_token=${here.token}`;
request.get(xyzURL, (error, response, body) => {
   const xyzJson = JSON.parse(body);
});

What we've done here is query all the food banks inside of the Data Hub space. So far, we're just confirming we can access the data. In the next step, we'll be doing some calculations with the data.

Calculate distance from origin to destination

Duration is 8 min

Since we'll be doing a haversine formula distance calculation, we'll employ the help of the handy turf module. turf is an open source geospatial engine for JavaScript. We'll be using the distance function.

Inside of our second request.get() block, the xyz one, let's insert the following code:

let features = xyzJson.features;
for (let i = 0; i < features.length; i++) {
   const pantryCoordinates = features[i].geometry.coordinates;
   features[i].properties.distanceFromOrigin = turf.distance(turf.point([coordinates.long,coordinates.lat]), turf.point(pantryCoordinates), {units: 'miles'});
}

features = features.sort((a,b) => a.properties.distanceFromOrigin - b.properties.distanceFromOrigin).slice(0, 3);

In the above code block, we:

loop through all the food pantries (the Data Hub space features) and calculate the distance from the origin (the coordinates of the user's query) to the destination (the coordinates of each food pantry). The distance is outputted in miles.
sort the features based on the shortest distance.
and keep only the first 3 elements of the array (the 3 pantries with the shortest distance from origin to destination).

Construct and send the SMS message

Duration is 5 min

Now for the fun part! Sending the message back to the user. We've successfully calculated distances and sorted the three shortest distances to the user, but we still need to construct a nice message to send back to the user.

const output = `Here are the 3 closest food pantries near ${query}: \n\n ${features.map(x =>
  `${x.properties.Name}\n${x.properties.distanceFromOrigin.toFixed(2)} miles away\n${x.properties['Phone number']}\n\n`
).join('')}`

twiml.message(output);
res.writeHead(200, {
  'Content-Type': 'text/xml'
});
res.end(twiml.toString());

output is the message we've constructed. When rendered properly, it will look something like:

Here are the 3 closest food pantries near 425 w randolph street chicago:

Pine Avenue United Church
6.50 miles away
(773) 287-4777

Project Care
7.23 miles away
(773) 881-8296

Sanad Organization
8.06 miles away
(773) 436-7989

Finally, we send the message to the user using the Twilio code.

Review

Duration is 1 min

Congratulations! You've completed this tutorial!

The end result should look something like:

In this tutorial, you've learned how to:

create Data Hub spaces and upload data with the HERE command line interface.
transform addresses to coordinates with the HERE Geocoding API.
send and receive messages with Twilio.

This is just a basic example of what can be done with HERE Location Services and the Data Hub API, take a look at the HERE Developer blog to see more examples of creative and useful applications of HERE Location Services and the Data Hub API.

Appendix

const http = require('http');
const express = require('express');
const MessagingResponse = require('twilio').twiml.MessagingResponse;
const bodyParser = require('body-parser');
const request = require('request');
const turf = require('@turf/turf');

const app = express();
app.use(bodyParser.json());
app.use(bodyParser.urlencoded({
   extended: true
}));

//HERE credentials
const here = {
   apiKey: 'HERE_API_KEY',
   token: 'HERE_Studio_TOKEN'
}

//Twilio credentials:
const twilio = {
   sid: 'TWILIO_SID',
   token: 'TWILIO_TOKEN'
}

const client = require('twilio')(twilio.sid, twilio.token);

app.post('/sms', (req, res) => {
   const twiml = new MessagingResponse();
   const query = req.body.Body; //Extract the location
   const geocodeURL = `https://geocoder.ls.hereapi.com/6.2/geocode.json
?apiKey=${here.apiKey}&searchtext=${query}`;

   request.get(geocodeURL, (error, response, body) => {
      const geocodeJson = JSON.parse(body);
      const coordinates = {
         lat: geocodeJson.Response.View[0].Result[0].Location.DisplayPosition.Latitude,
         long: geocodeJson.Response.View[0].Result[0].Location.DisplayPosition.Longitude
      };
      const xyzSpaceId = 'SPACE_ID';
      const xyzURL = `https://xyz.api.here.com/hub/spaces/${xyzSpaceId}/search?access_token=${here.token}`;
      request.get(xyzURL, (error, response, body) => {
         const xyzJson = JSON.parse(body);

         let features = xyzJson.features;
         for (let i = 0; i < features.length; i++) {
            const pantryCoordinates = features[i].geometry.coordinates;
            features[i].properties.distanceFromOrigin = turf.distance(
               turf.point([coordinates.long,coordinates.lat]),
               turf.point(pantryCoordinates),
               {units: 'miles'});
         }

         features = features.sort((a,b) => a.properties.distanceFromOrigin - b.properties.distanceFromOrigin).slice(0, 3);

         const output = `Here are the 3 closest food pantries near ${query}: \n\n ${features.map(x =>
            `${x.properties.Name}\n${x.properties.distanceFromOrigin.toFixed(2)} miles away\n${x.properties['Phone number']}\n\n`
         ).join('')}`;

         twiml.message(output);
         res.writeHead(200, {
            'Content-Type': 'text/xml'
         });
         res.end(twiml.toString());
      });
   });
});

http.createServer(app).listen(1337, () => {
   console.log('Express server listening on port 1337');
});

Create a location-aware chatbot with Twilio and HERE Location Services.

Introduction

Introduction

What you’ll need

Set up the Node.js skeleton

Send messages with Twilio

Upload data to a Data Hub space

Location Services

Geocoder API

Query a Data Hub space

Calculate distance from origin to destination

Construct and send the SMS message

Review

Appendix

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