Build a store locator that includes online sellers

Let’s say you’ve been tasked to build an application to help consumers find agencies providing a specific service. Some of these agencies are local brick-and-mortar storefronts and others are online-only agencies that service the same local area.

This problem was initially posed by Alejo Arias in the Algolia forum:

I have a series of providers in my index:

• some are national providers (they have a national: true attribute)
• some are state-wide providers (they have a state attribute with the list of states they service, e.g. [“NY”, “FL”])
• some are local (they have specific lat/lng in their _geoloc attribute)

I want in my results anything that matches local providers by location close to my users, but also provide (in the same results) state and national providers.

Adding a aroundLatLng filter automatically removes other results, no matter what facets or filters I try.

How can I achieve this?
Basically I want to have something like: aroundLatLng: x,y OR state: NY OR national: true

So how do you combine results from a geographic search for brick-and-mortar stores and with other results from boolean or text-based queries? And how do you build an interface to display them in a unified way?

Geographic data and Algolia Search

As Alejo mentions, you can use Algolia for geographic searches by adding a special _geoloc attribute on your records. You put one or more sets of latitude/longitude tuples into this attribute to indicate geographic locations linked to the record.

Then you use the Algolia client libraries to query against these geocoded records — filtering either within a radius around a fixed point (aroundLatLong) or the area within a shape (insideBoundingBox or insidePolygon). The documentation goes into more detail about the differences between these methods. You can also read through these posts that walk you through building a pure-geographic store locator.

However, you cannot extract geographic and non-geographic results from the same query. If you are searching for proximity, records lacking _geoloc attributes will not show up in the result set.

So how do you perform this search when not all records have geographic coordinates?

A single index solution

You could do the whole thing via geographic search. By adding _geoloc data to the state and national records, you could search for everything using a geographic query. For instance, placing the statewide agencies at coordinates at the center of each state. This was the initial solution I added to the forum post, but there are several problems with this solution:

1. Alejo specifically mentions that some providers span multiple states
2. Placing providers at the center of the state will cause inaccurate results for consumers who live near state borders
3. National providers would need records in every state

A multi-index solution

As an alternative you can build a multi-index solution with one index for the brick-and-mortar storefronts that include geographic data, and another for the state and national providers. You can then search the two data sources independently and blend the result sets. This approach requires two Algolia queries per search, but it will allow us to guarantee results from both types of providers.

Preparing your indices

First, you’ll need an agency data set. You can build one from scratch using a couple of sources. You can start with anonymized address data from this repo containing about 3000 addresses across the United State. Then, run these addresses through a small script to add fictional agency names and randomly flag some of the agencies as “preferred”.

def transform_records(addresses):
  address_records = []
  for address in addresses:
    record = {}
    record_geocode = {}
    # One in ten chance agency is preferred 
    record['preferred'] = 10 == random.randint(1,10)

    record['objectID'] = random_name.generate_name().title()
    if record['preferred']:
      record['name'] = f"{record['objectID']} Agency (Preferred)"
    else:
      record['name'] = f"{record['objectID']} Agency"
    record['address'] = address.get('address1')
    record['city'] = address.get('city')
    record['state'] = address.get('state')
    record['zip'] = address.get('postalCode')
    record_geocode['lat'] = address['coordinates']['lat']
    record_geocode['lng'] = address['coordinates']['lng']
    record['_geoloc'] = record_geocode
    address_records.append(record)
  return address_records

You can use another script to generate statewide and multi-state agencies for the second index. Both data sets reside in this repo. You can create indices from these data sets under your existing Algolia account or sign up for a free account and set up a new agency_finder application.

Building the front end

Now that you’ve populated your indices, it’s time to build the front end. Algolia’s geoSearch component in the InstantSearch library includes a helper component to initialize the Google Maps API, render a Map, and tie that map to geolocation queries in your Algolia index. It’s the same component I used previously to build a COVID-19 case visualizer. However, for this project, you want the user to type in an address and resolve the geolocation information for them using the Google Places API. This proves challenging using the out-of-the-box components in InstantSearch, so you’ll build your own interface from scratch.

This blog post gives us a solid model for building an address autocomplete form in React. You’ll use this as the foundation for your AgencyFinderForm component to render the address autocomplete input field as well as read-only fields to display the resulting address. The latitude/longitude are stored in state, but not shown on the form

You can modernize the code from the blog by using the Google Wrapper around your React components to initialize the google object and add the Places API.

   renderForm = (status) => {
    switch (status) {
      case Status.SUCCESS:
        return ;
      default:
        return <h3>{status} ...</h3>;
      };
  }

  render() {
    return (
      <div>
        <h1>Find an Agency</h1>
        <p className='instructions'>🔍 Search for your address to find the closest agencies.</p>
        <div className='left-panel'>
          <Wrapper apiKey={process.env.REACT_APP_GOOGLE_API_KEY} render={this.renderForm} libraries={["places"]} />
        </div>
        <div className='right-panel'>
          <AgencyFinderResults hits={this.state.results} />
        </div>
      </div>
    )
  }
}

Next you add a clear button to the basic form.

  handleClear() {
    this.setState(this.initialState);
    var input = document.getElementById('autocomplete');
    input.value = '';
    google.maps.event.removeListener(this.autocompleteListener);
    this.initAutocomplete();
  }

Finally, you’ll clean up processing the address_components from the Places API with following code:

  handlePlaceSelect() {
    const addressObject = this.autocomplete.getPlace();
    const address = addressObject.address_components.reduce((seed, { short_name, types }) => {
      types.forEach(t => {
        seed[t] = short_name;
      });
      return seed;
    }, {});
    [this setState](this.setState)({
      streetAddress: `${address.street_number} ${address.route}`,
      city: address.locality ? address.locality : address.sublocality_level_1,
      state: address.administrative_area_level_1,
      zipCode: address.postal_code,
      geoCode: addressObject.geometry.location.lat() + ', ' + addressObject.geometry.location.lng(),
    });
  }

Querying for results

After the user has selected a location and you have the latitude, longitude, and address information stored in the component state, you’re ready to query the indices. You use the multipleQueries method from the Javascript API client to batch together the two queries and combine the results. This will still count as two queries against your Algolia limit, but it reduces the number of round trips to the API.

handleSubmit(event) {
    const queries = [{
      indexName: statesIndex,
      query: this.state.state,
      params: {
        hitsPerPage: 10
      }
    }, {
      indexName: geoIndex,
      query: '',
      params: {
        aroundLatLng: this.state.geoCode,
        facetFilters: [ this.state.preferred ? 'preferred:true' : '' ],
        hitsPerPage: 10,
      }
    }];
    
    this.searchClient.multipleQueries(queries).then(({ results }) => {
      let allHits = [];
      results.map((result) => {
        return allHits.push(...result.hits);
      });
      this.props.handleCallback(allHits);
    });
  }

First, you initialize the two queries. Notice how the multipleQueries method allows us to mix geographic and string-based queries, and even layer in an
optional facetFilter for your “Preferred” agencies. You then pass the array of queries to the client. The response includes the individual results from each
query, but you can just smash the hits from the two result sets into a single array and pass them to the AgencyFinderResults component.

Putting it all together

You now have a solid proof-of-concept React component for layering geographic and non-geographic results into a single result set. At this point you could improve the example by adding a Google Map to display the geographic results. You could also pivot back to a single index, using multipleQueries ability to query the same index multiple times with different parameters.

The complete example is available in this Github repo or you can try out a live demo.