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‎source/geo.txt‎

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@@ -39,10 +39,10 @@ Geospatial Data Formats
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All geospatial data in MongoDB is stored in one of the following formats:
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- GeoJSON, a format that represents geospatial data on an earth-like
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sphere.
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sphere
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- Legacy coordinate pairs, a format that represents geospatial data
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on a Euclidean plane.
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on a Euclidean plane
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GeoJSON
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~~~~~~~
@@ -88,8 +88,8 @@ Here are some common GeoJSON types and how you can specify them with positions:
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.. code-block:: python
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{
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"type": "Point",
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"coordinates": [-73.856077, 40.848447]
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"type": "Point",
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"coordinates": [-73.856077, 40.848447]
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}
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- ``LineString``: an array of two or more positions that forms a series of line
@@ -100,12 +100,12 @@ Here are some common GeoJSON types and how you can specify them with positions:
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.. code-block:: python
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{
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"type": "LineString",
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"coordinates":
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[[116.572, 40.430],
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[116.570, 40.434],
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[116.567, 40.436],
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[116.566, 40.441]]
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"type": "LineString",
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"coordinates":
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[[116.572, 40.430],
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[116.570, 40.434],
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[116.567, 40.436],
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[116.566, 40.441]]
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}
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- ``Polygon``: an array of positions in which the first and last
@@ -115,13 +115,13 @@ Here are some common GeoJSON types and how you can specify them with positions:
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.. code-block:: python
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{
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"type": "Polygon",
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"coordinates":
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[[[12.446086, 41.901977],
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[12.457952, 41.901559],
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[12.455375, 41.907351],
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[12.449863, 41.905186],
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[12.446086, 41.901977]]]
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"type": "Polygon",
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"coordinates":
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[[[12.446086, 41.901977],
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[12.457952, 41.901559],
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[12.455375, 41.907351],
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[12.449863, 41.905186],
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[12.446086, 41.901977]]]
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}
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To learn more about the GeoJSON types you can use in MongoDB, see the
@@ -136,7 +136,7 @@ Legacy Coordinate Pairs
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Use legacy coordinate pairs to store data that represents geospatial information
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on a two-dimensional plane.
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Legacy coordinate pairs are represented by an array of two values, in which the first
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Legacy coordinate pairs are represented by an array of two values, in which the first value
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represents the ``x`` axis value and the second represents the ``y`` axis value.
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For more information on legacy coordinate pairs, see the
@@ -200,88 +200,94 @@ The following example queries for documents with a ``location.geo`` field value
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within 1000 meters of the MongoDB Headquarters in New York City, NY. It returns documents
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from nearest to farthest.
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.. code-block:: python
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# set query with point at MongoDB headquarters and a maxDistance of 1000 meters
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query = {
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"location.geo": {
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"$near": {
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"$geometry": {
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"type": "Point",
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"coordinates": [-73.986805, 40.7620853] # Search around this location
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},
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"$maxDistance": 1000 # Distance in meters (1 km)
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}
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}
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}
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# fetches the _id and theaterId fields
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projection = { "theaterId": 1 }
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.. io-code-block::
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:copyable: true
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.. input::
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:language: python
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# set query with point at MongoDB headquarters and a maxDistance of 1000 meters
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query = {
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"location.geo": {
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"$near": {
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"$geometry": {
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"type": "Point",
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"coordinates": [-73.986805, 40.7620853] # Search around this location
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},
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"$maxDistance": 1000 # Distance in meters (1 km)
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}
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}
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}
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nearby_places = location.find(query, projection)
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# fetches the _id and theaterId fields
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projection = { "theaterId": 1 }
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for i in nearby_places:
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print(i)
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nearby_places = location.find(query, projection)
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The results of the preceding example contain the following documents:
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for i in nearby_places:
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print(i)
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.. code-block:: json
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:copyable: False
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.. output::
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:language: json
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:visible: false
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{ "_id" : ObjectId("59a47287cfa9a3a73e51e8e2"), "theaterId" : 1908 }
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{ "_id" : ObjectId("59a47286cfa9a3a73e51e838"), "theaterId" : 1448 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51e8e2"), "theaterId" : 1908 }
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{ "_id" : ObjectId("59a47286cfa9a3a73e51e838"), "theaterId" : 1448 }
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Query by Polygon
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~~~~~~~~~~~~~~~~
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The following example queries for documents with a ``location.geo`` field value that exists
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within the boundaries of Manhattan.
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.. code-block:: python
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# Polygon representation of Manhattan
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query = {
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"location.geo": {
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"$geoWithin": {
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"$geometry": {
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# Search around this location
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"type": "Polygon",
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"coordinates":
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[[[-73.925492, 40.877410],
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[-73.910372, 40.872366],
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[-73.935127, 40.834020],
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[-73.929049, 40.798569],
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[-73.976485, 40.711432],
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[-74.015747, 40.701229],
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[-74.018859, 40.708367],
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[-74.008007, 40.754307],
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[-73.925492, 40.877410]]]
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}
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}
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}
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}
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# fetches the _id and theaterId fields
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projection = { "theaterId": 1 }
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nearby_places = location.find(query, projection)
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for i in nearby_places:
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print(i)
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The results of the preceding example contain the following documents:
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.. code-block:: json
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:copyable: False
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{ "_id" : ObjectId("59a47287cfa9a3a73e51e8e2"), "theaterId" : 1908 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51eccb"), "theaterId" : 835 }
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{ "_id" : ObjectId("59a47286cfa9a3a73e51e838"), "theaterId" : 1448 }
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{ "_id" : ObjectId("59a47286cfa9a3a73e51e744"), "theaterId" : 1028 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51ebe1"), "theaterId" : 609 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51e8ed"), "theaterId" : 1906 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51e87d"), "theaterId" : 1531 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51eb63"), "theaterId" : 482 }
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.. io-code-block::
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:copyable: true
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.. input::
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:language: python
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# Polygon representation of Manhattan
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query = {
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"location.geo": {
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"$geoWithin": {
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"$geometry": {
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# Search around this location
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"type": "Polygon",
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"coordinates":
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[[[-73.925492, 40.877410],
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[-73.910372, 40.872366],
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[-73.935127, 40.834020],
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[-73.929049, 40.798569],
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[-73.976485, 40.711432],
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[-74.015747, 40.701229],
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[-74.018859, 40.708367],
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[-74.008007, 40.754307],
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[-73.925492, 40.877410]]]
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}
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}
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}
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}
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# fetches the _id and theaterId fields
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projection = { "theaterId": 1 }
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nearby_places = location.find(query, projection)
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for i in nearby_places:
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print(i)
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.. output::
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:language: json
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:visible: false
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{ "_id" : ObjectId("59a47287cfa9a3a73e51e8e2"), "theaterId" : 1908 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51eccb"), "theaterId" : 835 }
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{ "_id" : ObjectId("59a47286cfa9a3a73e51e838"), "theaterId" : 1448 }
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{ "_id" : ObjectId("59a47286cfa9a3a73e51e744"), "theaterId" : 1028 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51ebe1"), "theaterId" : 609 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51e8ed"), "theaterId" : 1906 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51e87d"), "theaterId" : 1531 }
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{ "_id" : ObjectId("59a47287cfa9a3a73e51eb63"), "theaterId" : 482 }
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Additional Resources
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--------------------

‎source/indexes.txt‎

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@@ -154,6 +154,8 @@ Text Index
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To learn more about text indexes, see the :ref:`pymongo-text-index`
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guide.
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.. _geospatial-indexes:
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Geospatial Index
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----------------
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