Geospatial Indexing Example

This example shows how to create and use a GEO2D index in PyMongo.

See also

The MongoDB documentation on

geo

Creating a Geospatial Index

Creating a geospatial index in pymongo is easy:

>>> from pymongo import MongoClient, GEO2D
>>> db = MongoClient().geo_example
>>> db.places.create_index([("loc", GEO2D)])
u'loc_2d'

Inserting Places

Locations in MongoDB are represented using either embedded documents or lists where the first two elements are coordinates. Here, we’ll insert a couple of example locations:

>>> result = db.places.insert_many([{"loc": [2, 5]},
...                                 {"loc": [30, 5]},
...                                 {"loc": [1, 2]},
...                                 {"loc": [4, 4]}])  
>>> result.inserted_ids
[ObjectId('...'), ObjectId('...'), ObjectId('...'), ObjectId('...')]

Querying

Using the geospatial index we can find documents near another point:

>>> import pprint
>>> for doc in db.places.find({"loc": {"$near": [3, 6]}}).limit(3):
...   pprint.pprint(doc)
...
{u'_id': ObjectId('...'), u'loc': [2, 5]}
{u'_id': ObjectId('...'), u'loc': [4, 4]}
{u'_id': ObjectId('...'), u'loc': [1, 2]}

The $maxDistance operator requires the use of SON:

>>> from bson.son import SON
>>> query = {"loc": SON([("$near", [3, 6]), ("$maxDistance", 100)])}
>>> for doc in db.places.find(query).limit(3):
...   pprint.pprint(doc)
...
{u'_id': ObjectId('...'), u'loc': [2, 5]}
{u'_id': ObjectId('...'), u'loc': [4, 4]}
{u'_id': ObjectId('...'), u'loc': [1, 2]}

It’s also possible to query for all items within a given rectangle (specified by lower-left and upper-right coordinates):

>>> query = {"loc": {"$within": {"$box": [[2, 2], [5, 6]]}}}
>>> for doc in db.places.find(query).sort('_id'):
...     pprint.pprint(doc)
{u'_id': ObjectId('...'), u'loc': [2, 5]}
{u'_id': ObjectId('...'), u'loc': [4, 4]}

Or circle (specified by center point and radius):

>>> query = {"loc": {"$within": {"$center": [[0, 0], 6]}}}
>>> for doc in db.places.find(query).sort('_id'):
...   pprint.pprint(doc)
...
{u'_id': ObjectId('...'), u'loc': [2, 5]}
{u'_id': ObjectId('...'), u'loc': [1, 2]}
{u'_id': ObjectId('...'), u'loc': [4, 4]}

geoNear queries are also supported using SON:

>>> from bson.son import SON
>>> db.command(SON([('geoNear', 'places'), ('near', [1, 2])]))
{u'ok': 1.0, u'stats': ...}