# Searching for Nearby Points of Interest with MongoDB and Mapbox

When it comes to location data, MongoDB's ability to work with GeoJSON through geospatial queries is often under-appreciated. Being able to query for intersecting or nearby coordinates while maintaining performance is functionality a lot of organizations are looking for.

Take the example of maintaining a list of business locations or even a fleet of vehicles. Knowing where these locations are, relative to a particular position isn't an easy task when doing it manually.

In this tutorial we're going to explore the `$near` operator within a MongoDB Realm application to find stored points of interest within a particular proximity to a position. These points of interest will be rendered on a map using the Mapbox service.

To get a better idea of what we're going to accomplish, take the following animated image for example:

We're going to pre-load our MongoDB database with a few points of interest that are formatted using the GeoJSON specification. When clicking around on the map, we're going to use the `$near` operator to find new points of interest that are within range of the marker.

## The Requirements

There are numerous components that must be accounted for to be successful with this tutorial:

-   A MongoDB Atlas free tier cluster or better to store the data.
-   A MongoDB Realm application to access the data from a client-facing application.
-   A Mapbox free tier account or better to render the data on a map.

The assumption is that MongoDB Atlas has been properly configured and that MongoDB Realm is using the MongoDB Atlas cluster.

>MongoDB Atlas can be used for FREE with a M0 sized cluster. Deploy MongoDB in minutes within the MongoDB Cloud.

In addition to Realm being pointed at the Atlas cluster, anonymous authentication for the Realm application should be enabled and an access rule should be defined for the collection. All users should be able to read all documents for this tutorial.

In this example, Mapbox is a third-party service for showing interactive map tiles. An account is necessary and an access token to be used for development should be obtained. You can learn how in the Mapbox documentation.

## MongoDB Geospatial Queries and the GeoJSON Data Model

Before diving into geospatial queries and creating an interactive client-facing application, a moment should be taken to understand the data and indexes that must be created within MongoDB.

Take the following example document:

``` json
{
    "_id": "5ec6fec2318d26b626d53c61",
    "name": "WorkVine209",
    "location": {
        "type": "Point",
        "coordinates": 
            -121.4123,
            37.7621
        ]
    }
}
```

Let's assume that documents that follow the above data model exist in a **location_services** database and a **points_of_interest** collection.

To be successful with our queries, we only need to store the location type and the coordinates. This `location` field makes up a [GeoJSON feature, which follows a specific format. The `name` field, while useful isn't an absolute requirement. Some other optional fields might include an `address` field, `hours_of_operation`, or similar.

Before being able to execute the geospatial queries that we want, we need to create a special index.

The following index should be created:

``` none
db.points_of_interest.createIndex({ location: "2dsphere" });
```

The above index can be created numerous ways, for example, you can create it using the MongoDB shell, Atlas, Compass, and a few other ways. Just note that the `location` field is being classified as a `2dsphere` for the index.

With the index created, we can execute a query like the following:

``` none
db.points_of_interest.find({
    "location": {
        "$near": {
            "$geometry": {
                "type": "Point",
                "coordinates": -121.4252, 37.7397]
            },
            "$maxDistance": 2500
        }
    }
});
```

Notice in the above example, we're looking for documents that have a `location` field within 2,500 meters of the point provided in the filter.

With an idea of how the data looks and how the data can be accessed, let's work towards creating a functional application.

## Interacting with Places using MongoDB Realm and Mapbox

Like previously mentioned, you should already have a Mapbox account and MongoDB Realm should already be configured.

On your computer, create an **index.html** file with the following boilerplate code:

``` xml

    
    
    

    
        
        
    

```

In the above code, we're including both the Mapbox library as well as the MongoDB Realm SDK. We're creating a `map` placeholder component which will show our map, and it is lightly styled with CSS.

You can run this file locally, serve it, or host it on [MongoDB Realm.

Within the `