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Geospatial Queries

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  • Geospatial Data
  • Geospatial Indexes
  • Geospatial Queries
  • Geospatial Models
  • Example

MongoDB supports query operations on geospatial data. This section introduces MongoDB's geospatial features.

In MongoDB, you can store geospatial data as GeoJSON objects or as legacy coordinate pairs.

To calculate geometry over an Earth-like sphere, store your location data as GeoJSON objects.

To specify GeoJSON data, use an embedded document with:

  • a field named type that specifies the GeoJSON object type and
  • a field named coordinates that specifies the object's coordinates.

    If specifying latitude and longitude coordinates, list the longitude first and then latitude:

    • Valid longitude values are between -180 and 180, both inclusive.
    • Valid latitude values are between -90 and 90, both inclusive.
<field>: { type: <GeoJSON type> , coordinates: <coordinates> }

For example, to specify a GeoJSON Point:

location: {
type: "Point",
coordinates: [-73.856077, 40.848447]
}

For a list of the GeoJSON objects supported in MongoDB as well as examples, see GeoJSON objects.

MongoDB geospatial queries on GeoJSON objects calculate on a sphere; MongoDB uses the WGS84 reference system for geospatial queries on GeoJSON objects.

To calculate distances on a Euclidean plane, store your location data as legacy coordinate pairs and use a 2d index. MongoDB supports spherical surface calculations on legacy coordinate pairs via a 2dsphere index by converting the data to the GeoJSON Point type.

To specify data as legacy coordinate pairs, you can use either an array (preferred) or an embedded document.

Specify via an array (Preferred):
<field>: [ <x>, <y> ]

If specifying latitude and longitude coordinates, list the longitude first and then latitude; i.e.

<field>: [<longitude>, <latitude> ]
  • Valid longitude values are between -180 and 180, both inclusive.
  • Valid latitude values are between -90 and 90, both inclusive.
Specify via an embedded document:
<field>: { <field1>: <x>, <field2>: <y> }

If specifying latitude and longitude coordinates, the first field, regardless of the field name, must contains the longitude value and the second field, the latitude value ; i.e.

<field>: { <field1>: <longitude>, <field2>: <latitude> }
  • Valid longitude values are between -180 and 180, both inclusive.
  • Valid latitude values are between -90 and 90, both inclusive.

To specify legacy coordinate pairs, arrays are preferred over an embedded document as some languages do not guarantee associative map ordering.

MongoDB provides the following geospatial index types to support the geospatial queries.

2dsphere indexes support queries that calculate geometries on an earth-like sphere.

To create a 2dsphere index, use the db.collection.createIndex() method and specify the string literal "2dsphere" as the index type:

db.collection.createIndex( { <location field> : "2dsphere" } )

where the <location field> is a field whose value is either a GeoJSON object or a legacy coordinates pair.

For more information on the 2dsphere index, see 2dsphere Indexes.

2d indexes support queries that calculate geometries on a two-dimensional plane. Although the index can support $nearSphere queries that calculate on a sphere, if possible, use the 2dsphere index for spherical queries.

To create a 2d index, use the db.collection.createIndex() method, specifying the location field as the key and the string literal "2d" as the index type:

db.collection.createIndex( { <location field> : "2d" } )

where the <location field> is a field whose value is a legacy coordinates pair.

For more information on the 2d index, see 2d Indexes.

You cannot use a geospatial index as a shard key when sharding a collection. However, you can create a geospatial index on a sharded collection by using a different field as the shard key.

The following geospatial operations are supported on sharded collections:

Starting in MongoDB 4.0, $near and $nearSphere queries are supported for sharded collections.

In earlier MongoDB versions, $near and $nearSphere queries are not supported for sharded collections; instead, for sharded clusters, you must use the $geoNear aggregation stage or the geoNear command (available in MongoDB 4.0 and earlier).

You can also query for geospatial data for a sharded cluster using $geoWithin and $geoIntersects.

Geospatial indexes cannot cover a query.

Note

For spherical queries, use the 2dsphere index result.

The use of 2d index for spherical queries may lead to incorrect results, such as the use of the 2d index for spherical queries that wrap around the poles.

MongoDB provides the following geospatial query operators:

Name
Description
Selects geometries that intersect with a GeoJSON geometry. The 2dsphere index supports $geoIntersects.
Selects geometries within a bounding GeoJSON geometry. The 2dsphere and 2d indexes support $geoWithin.
Returns geospatial objects in proximity to a point. Requires a geospatial index. The 2dsphere and 2d indexes support $near.
Returns geospatial objects in proximity to a point on a sphere. Requires a geospatial index. The 2dsphere and 2d indexes support $nearSphere.

For more details, including examples, see the individual reference page.

MongoDB provides the following geospatial aggregation pipeline stage:

Stage
Description

Returns an ordered stream of documents based on the proximity to a geospatial point. Incorporates the functionality of $match, $sort, and $limit for geospatial data. The output documents include an additional distance field and can include a location identifier field.

$geoNear requires a geospatial index.

For more details, including examples, see $geoNear reference page.

MongoDB geospatial queries can interpret geometry on a flat surface or a sphere.

2dsphere indexes support only spherical queries (i.e. queries that interpret geometries on a spherical surface).

2d indexes support flat queries (i.e. queries that interpret geometries on a flat surface) and some spherical queries. While 2d indexes support some spherical queries, the use of 2d indexes for these spherical queries can result in error. If possible, use 2dsphere indexes for spherical queries.

The following table lists the geospatial query operators, supported query, used by each geospatial operations:

Operation
Spherical/Flat Query
Notes
$near (GeoJSON centroid point in this line and the following line, 2dsphere index)
Spherical
See also the $nearSphere operator, which provides the same functionality when used with GeoJSON and a 2dsphere index.
Flat
Spherical

Provides the same functionality as $near operation that uses GeoJSON point and a 2dsphere index.

For spherical queries, it may be preferable to use $nearSphere which explicitly specifies the spherical queries in the name rather than $near operator.

Spherical
Use GeoJSON points instead.
Spherical
$geoWithin : { $box: ... }
Flat
$geoWithin : { $polygon: ... }
Flat
$geoWithin : { $center: ... }
Flat
Spherical
Spherical
$geoNear aggregation stage (2dsphere index)
Spherical
$geoNear aggregation stage (2d index)
Flat

Create a collection places with the following documents:

db.places.insert( {
name: "Central Park",
location: { type: "Point", coordinates: [ -73.97, 40.77 ] },
category: "Parks"
} );
db.places.insert( {
name: "Sara D. Roosevelt Park",
location: { type: "Point", coordinates: [ -73.9928, 40.7193 ] },
category: "Parks"
} );
db.places.insert( {
name: "Polo Grounds",
location: { type: "Point", coordinates: [ -73.9375, 40.8303 ] },
category: "Stadiums"
} );

The following operation creates a 2dsphere index on the location field:

db.places.createIndex( { location: "2dsphere" } )

The following query uses the $near operator to return documents that are at least 1000 meters from and at most 5000 meters from the specified GeoJSON point, sorted in order from nearest to farthest:

db.places.find(
{
location:
{ $near:
{
$geometry: { type: "Point", coordinates: [ -73.9667, 40.78 ] },
$minDistance: 1000,
$maxDistance: 5000
}
}
}
)

The following operation uses the $geoNear aggregation operation to return documents that match the query filter { category: "Parks" }, sorted in order of nearest to farthest to the specified GeoJSON point:

db.places.aggregate( [
{
$geoNear: {
near: { type: "Point", coordinates: [ -73.9667, 40.78 ] },
spherical: true,
query: { category: "Parks" },
distanceField: "calcDistance"
}
}
] )
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