GeoDjango Database API

Spatial Backends

GeoDjango currently provides the following spatial database backends:

  • django.contrib.gis.db.backends.postgis
  • django.contrib.gis.db.backends.mysql
  • django.contrib.gis.db.backends.spatialite

MySQL Spatial Limitations

MySQL’s spatial extensions only support bounding box operations (what MySQL calls minimum bounding rectangles, or MBR). Specifically, MySQL does not conform to the OGC standard:

Currently, MySQL does not implement these functions [Contains, Crosses, Disjoint, Intersects, Overlaps, Touches, Within] according to the specification. Those that are implemented return the same result as the corresponding MBR-based functions.

In other words, while spatial lookups such as contains are available in GeoDjango when using MySQL, the results returned are really equivalent to what would be returned when using bbcontains on a different spatial backend.


True spatial indexes (R-trees) are only supported with MyISAM tables on MySQL. [5] In other words, when using MySQL spatial extensions you have to choose between fast spatial lookups and the integrity of your data – MyISAM tables do not support transactions or foreign key constraints.

Raster Support

RasterField is currently only implemented for the PostGIS backend. Spatial queries (such as lookups and distance) are not yet available for raster fields.

Creating and Saving Models with Geometry Fields

Here is an example of how to create a geometry object (assuming the Zipcode model):

>>> from zipcode.models import Zipcode
>>> z = Zipcode(code=77096, poly='POLYGON(( 10 10, 10 20, 20 20, 20 15, 10 10))')

GEOSGeometry objects may also be used to save geometric models:

>>> from django.contrib.gis.geos import GEOSGeometry
>>> poly = GEOSGeometry('POLYGON(( 10 10, 10 20, 20 20, 20 15, 10 10))')
>>> z = Zipcode(code=77096, poly=poly)

Moreover, if the GEOSGeometry is in a different coordinate system (has a different SRID value) than that of the field, then it will be implicitly transformed into the SRID of the model’s field, using the spatial database’s transform procedure:

>>> poly_3084 = GEOSGeometry('POLYGON(( 10 10, 10 20, 20 20, 20 15, 10 10))', srid=3084)  # SRID 3084 is 'NAD83(HARN) / Texas Centric Lambert Conformal'
>>> z = Zipcode(code=78212, poly=poly_3084)
>>> from django.db import connection
>>> print(connection.queries[-1]['sql']) # printing the last SQL statement executed (requires DEBUG=True)
INSERT INTO "geoapp_zipcode" ("code", "poly") VALUES (78212, ST_Transform(ST_GeomFromWKB('\\001 ... ', 3084), 4326))

Thus, geometry parameters may be passed in using the GEOSGeometry object, WKT (Well Known Text [1]), HEXEWKB (PostGIS specific – a WKB geometry in hexadecimal [2]), and GeoJSON [3] (requires GDAL). Essentially, if the input is not a GEOSGeometry object, the geometry field will attempt to create a GEOSGeometry instance from the input.

For more information creating GEOSGeometry objects, refer to the GEOS tutorial.

Creating and Saving Models with Raster Fields

Добавлено в Django 1.9.

When creating raster models, the raster field will implicitly convert the input into a GDALRaster using lazy-evaluation. The raster field will therefore accept any input that is accepted by the GDALRaster constructor.

Here is an example of how to create a raster object from a raster file volcano.tif (assuming the Elevation model):

>>> from elevation.models import Elevation
>>> dem = Elevation(name='Volcano', rast='/path/to/raster/volcano.tif')

GDALRaster objects may also be used to save raster models:

>>> from django.contrib.gis.gdal import GDALRaster
>>> rast = GDALRaster({'width': 10, 'height': 10, 'name': 'Canyon', 'srid': 4326,
...                    'scale': [0.1, -0.1]'bands': [{"data": range(100)}]}
>>> dem = Elevation(name='Canyon', rast=rast)

Note that this equivalent to:

>>> dem = Elevation.objects.create(
...     name='Canyon',
...     rast={'width': 10, 'height': 10, 'name': 'Canyon', 'srid': 4326,
...           'scale': [0.1, -0.1]'bands': [{"data": range(100)}]}
... )

Spatial Lookups

GeoDjango’s lookup types may be used with any manager method like filter(), exclude(), etc. However, the lookup types unique to GeoDjango are only available on geometry fields. Filters on ‘normal’ fields (e.g. CharField) may be chained with those on geographic fields. Thus, geographic queries take the following general form (assuming the Zipcode model used in the GeoDjango Model API):

>>> qs = Zipcode.objects.filter(<field>__<lookup_type>=<parameter>)
>>> qs = Zipcode.objects.exclude(...)

For example:

>>> qs = Zipcode.objects.filter(poly__contains=pnt)

In this case, poly is the geographic field, contains is the spatial lookup type, and pnt is the parameter (which may be a GEOSGeometry object or a string of GeoJSON , WKT, or HEXEWKB).

A complete reference can be found in the spatial lookup reference.

Distance Queries


Distance calculations with spatial data is tricky because, unfortunately, the Earth is not flat. Some distance queries with fields in a geographic coordinate system may have to be expressed differently because of limitations in PostGIS. Please see the Selecting an SRID section in the GeoDjango Model API documentation for more details.

Distance Lookups

Availability: PostGIS, Oracle, SpatiaLite

The following distance lookups are available:


For measuring, rather than querying on distances, use the Distance function.

Distance lookups take a tuple parameter comprising:

  1. A geometry to base calculations from; and
  2. A number or Distance object containing the distance.

If a Distance object is used, it may be expressed in any units (the SQL generated will use units converted to those of the field); otherwise, numeric parameters are assumed to be in the units of the field.


In PostGIS, ST_Distance_Sphere does not limit the geometry types geographic distance queries are performed with. [4] However, these queries may take a long time, as great-circle distances must be calculated on the fly for every row in the query. This is because the spatial index on traditional geometry fields cannot be used.

For much better performance on WGS84 distance queries, consider using geography columns in your database instead because they are able to use their spatial index in distance queries. You can tell GeoDjango to use a geography column by setting geography=True in your field definition.

For example, let’s say we have a SouthTexasCity model (from the GeoDjango distance tests ) on a projected coordinate system valid for cities in southern Texas:

from django.contrib.gis.db import models

class SouthTexasCity(models.Model):
    name = models.CharField(max_length=30)
    # A projected coordinate system (only valid for South Texas!)
    # is used, units are in meters.
    point = models.PointField(srid=32140)

Then distance queries may be performed as follows:

>>> from django.contrib.gis.geos import GEOSGeometry
>>> from django.contrib.gis.measure import D # ``D`` is a shortcut for ``Distance``
>>> from geoapp.models import SouthTexasCity
# Distances will be calculated from this point, which does not have to be projected.
>>> pnt = GEOSGeometry('POINT(-96.876369 29.905320)', srid=4326)
# If numeric parameter, units of field (meters in this case) are assumed.
>>> qs = SouthTexasCity.objects.filter(point__distance_lte=(pnt, 7000))
# Find all Cities within 7 km, > 20 miles away, and > 100 chains away (an obscure unit)
>>> qs = SouthTexasCity.objects.filter(point__distance_lte=(pnt, D(km=7)))
>>> qs = SouthTexasCity.objects.filter(point__distance_gte=(pnt, D(mi=20)))
>>> qs = SouthTexasCity.objects.filter(point__distance_gte=(pnt, D(chain=100)))

Compatibility Tables

Spatial Lookups

The following table provides a summary of what spatial lookups are available for each spatial database backend.

Lookup Type PostGIS Oracle MySQL [6] SpatiaLite
bbcontains X   X X
bboverlaps X   X X
contained X   X X
contains X X X X
contains_properly X      
coveredby X X    
covers X X    
crosses X     X
disjoint X X X X
distance_gt X X   X
distance_gte X X   X
distance_lt X X   X
distance_lte X X   X
dwithin X X    
equals X X X X
exact X X X X
intersects X X X X
overlaps X X X X
relate X X   X
same_as X X X X
touches X X X X
within X X X X
left X      
right X      
overlaps_left X      
overlaps_right X      
overlaps_above X      
overlaps_below X      
strictly_above X      
strictly_below X      

Database functions

The following table provides a summary of what geography-specific database functions are available on each spatial backend.

Function PostGIS Oracle MySQL SpatiaLite
Area X X X X
AsGeoJSON X     X
AsKML X     X
AsSVG X     X
BoundingCircle X      
Centroid X X X X
Difference X X   X
Distance X X X (≥ 5.6.1) X
Envelope X   X X
ForceRHR X      
GeoHash X      
Intersection X X   X
Length X X X X
MemSize X      
NumGeometries X X X X
NumPoints X X X X
Perimeter X X   X (≥ 4.0)
PointOnSurface X X   X
Reverse X X   X (≥ 4.0)
Scale X     X
SnapToGrid X     X (≥ 3.1)
SymDifference X X   X
Transform X X   X
Translate X     X
Union X X X (≥ 5.6.1) X

Aggregate Functions

The following table provides a summary of what GIS-specific aggregate functions are available on each spatial backend. Please note that MySQL does not support any of these aggregates, and is thus excluded from the table.

Aggregate PostGIS Oracle SpatiaLite
Collect X   (from v3.0)
Extent X X (from v3.0)
Extent3D X    
MakeLine X    
Union X X X


[1]See Open Geospatial Consortium, Inc., OpenGIS Simple Feature Specification For SQL, Document 99-049 (May 5, 1999), at Ch. 3.2.5, p. 3-11 (SQL Textual Representation of Geometry).
[2]See PostGIS EWKB, EWKT and Canonical Forms, PostGIS documentation at Ch. 4.1.2.
[3]See Howard Butler, Martin Daly, Allan Doyle, Tim Schaub, & Christopher Schmidt, The GeoJSON Format Specification, Revision 1.0 (June 16, 2008).
[4]See PostGIS documentation on ST_distance_sphere.

See Creating Spatial Indexes in the MySQL Reference Manual:

For MyISAM tables, SPATIAL INDEX creates an R-tree index. For storage engines that support nonspatial indexing of spatial columns, the engine creates a B-tree index. A B-tree index on spatial values will be useful for exact-value lookups, but not for range scans.
[6]Refer MySQL Spatial Limitations section for more details.