322 lines
14 KiB
Plaintext
322 lines
14 KiB
Plaintext
.. _ref-gis-db-api:
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======================
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GeoDjango Database API
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======================
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.. module:: django.contrib.gis.db.models
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:synopsis: GeoDjango's database API.
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.. _spatial-backends:
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Spatial Backends
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================
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GeoDjango currently provides the following spatial database backends:
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* :mod:`django.contrib.gis.db.backends.postgis`
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* :mod:`django.contrib.gis.db.backends.mysql`
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* :mod:`django.contrib.gis.db.backends.oracle`
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* :mod:`django.contrib.gis.db.backends.spatialite`
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.. _mysql-spatial-limitations:
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MySQL Spatial Limitations
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-------------------------
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MySQL's spatial extensions only support bounding box operations
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(what MySQL calls minimum bounding rectangles, or MBR). Specifically,
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`MySQL does not conform to the OGC standard <http://dev.mysql.com/doc/refman/5.1/en/functions-for-testing-spatial-relations-between-geometric-objects.html>`_:
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Currently, MySQL does not implement these functions
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[``Contains``, ``Crosses``, ``Disjoint``, ``Intersects``, ``Overlaps``,
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``Touches``, ``Within``]
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according to the specification. Those that are implemented return
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the same result as the corresponding MBR-based functions.
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In other words, while spatial lookups such as :lookup:`contains <gis-contains>`
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are available in GeoDjango when using MySQL, the results returned are really
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equivalent to what would be returned when using :lookup:`bbcontains`
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on a different spatial backend.
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.. warning::
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True spatial indexes (R-trees) are only supported with
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MyISAM tables on MySQL. [#fnmysqlidx]_ In other words, when using
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MySQL spatial extensions you have to choose between fast spatial
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lookups and the integrity of your data -- MyISAM tables do
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not support transactions or foreign key constraints.
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Creating and Saving Geographic Models
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=====================================
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Here is an example of how to create a geometry object (assuming the ``Zipcode``
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model)::
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>>> from zipcode.models import Zipcode
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>>> z = Zipcode(code=77096, poly='POLYGON(( 10 10, 10 20, 20 20, 20 15, 10 10))')
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>>> z.save()
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:class:`~django.contrib.gis.geos.GEOSGeometry` objects may also be used to save geometric models::
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>>> from django.contrib.gis.geos import GEOSGeometry
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>>> poly = GEOSGeometry('POLYGON(( 10 10, 10 20, 20 20, 20 15, 10 10))')
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>>> z = Zipcode(code=77096, poly=poly)
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>>> z.save()
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Moreover, if the ``GEOSGeometry`` is in a different coordinate system (has a
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different SRID value) than that of the field, then it will be implicitly
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transformed into the SRID of the model's field, using the spatial database's
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transform procedure::
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>>> 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'
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>>> z = Zipcode(code=78212, poly=poly_3084)
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>>> z.save()
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>>> from django.db import connection
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>>> print(connection.queries[-1]['sql']) # printing the last SQL statement executed (requires DEBUG=True)
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INSERT INTO "geoapp_zipcode" ("code", "poly") VALUES (78212, ST_Transform(ST_GeomFromWKB('\\001 ... ', 3084), 4326))
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Thus, geometry parameters may be passed in using the ``GEOSGeometry`` object, WKT
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(Well Known Text [#fnwkt]_), HEXEWKB (PostGIS specific -- a WKB geometry in
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hexadecimal [#fnewkb]_), and GeoJSON [#fngeojson]_ (requires GDAL). Essentially,
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if the input is not a ``GEOSGeometry`` object, the geometry field will attempt to
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create a ``GEOSGeometry`` instance from the input.
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For more information creating :class:`~django.contrib.gis.geos.GEOSGeometry`
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objects, refer to the :ref:`GEOS tutorial <geos-tutorial>`.
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.. _spatial-lookups-intro:
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Spatial Lookups
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===============
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GeoDjango's lookup types may be used with any manager method like
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``filter()``, ``exclude()``, etc. However, the lookup types unique to
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GeoDjango are only available on geometry fields.
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Filters on 'normal' fields (e.g. :class:`~django.db.models.CharField`)
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may be chained with those on geographic fields. Thus, geographic queries
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take the following general form (assuming the ``Zipcode`` model used in the
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:ref:`ref-gis-model-api`)::
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>>> qs = Zipcode.objects.filter(<field>__<lookup_type>=<parameter>)
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>>> qs = Zipcode.objects.exclude(...)
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For example::
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>>> qs = Zipcode.objects.filter(poly__contains=pnt)
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In this case, ``poly`` is the geographic field, :lookup:`contains <gis-contains>`
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is the spatial lookup type, and ``pnt`` is the parameter (which may be a
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:class:`~django.contrib.gis.geos.GEOSGeometry` object or a string of
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GeoJSON , WKT, or HEXEWKB).
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A complete reference can be found in the :ref:`spatial lookup reference
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<spatial-lookups>`.
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.. note::
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GeoDjango constructs spatial SQL with the :class:`GeoQuerySet`, a
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subclass of :class:`~django.db.models.query.QuerySet`. The
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:class:`GeoManager` instance attached to your model is what
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enables use of :class:`GeoQuerySet`.
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.. _distance-queries:
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Distance Queries
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================
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Introduction
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------------
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Distance calculations with spatial data is tricky because, unfortunately,
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the Earth is not flat. Some distance queries with fields in a geographic
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coordinate system may have to be expressed differently because of
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limitations in PostGIS. Please see the :ref:`selecting-an-srid` section
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in the :ref:`ref-gis-model-api` documentation for more details.
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.. _distance-lookups-intro:
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Distance Lookups
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----------------
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*Availability*: PostGIS, Oracle, SpatiaLite
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The following distance lookups are available:
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* :lookup:`distance_lt`
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* :lookup:`distance_lte`
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* :lookup:`distance_gt`
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* :lookup:`distance_gte`
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* :lookup:`dwithin`
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.. note::
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For *measuring*, rather than querying on distances, use the
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:meth:`GeoQuerySet.distance` method.
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Distance lookups take a tuple parameter comprising:
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#. A geometry to base calculations from; and
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#. A number or :class:`~django.contrib.gis.measure.Distance` object containing the distance.
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If a :class:`~django.contrib.gis.measure.Distance` object is used,
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it may be expressed in any units (the SQL generated will use units
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converted to those of the field); otherwise, numeric parameters are assumed
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to be in the units of the field.
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.. note::
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For users of PostGIS 1.4 and below, the routine ``ST_Distance_Sphere``
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is used by default for calculating distances on geographic coordinate systems
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(e.g., WGS84) -- which may only be called with point geometries [#fndistsphere14]_.
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Thus, geographic distance lookups on traditional PostGIS geometry columns are
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only allowed on :class:`PointField` model fields using a point for the
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geometry parameter.
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.. note::
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In PostGIS 1.5, ``ST_Distance_Sphere`` does *not* limit the geometry types
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geographic distance queries are performed with. [#fndistsphere15]_ However,
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these queries may take a long time, as great-circle distances must be
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calculated on the fly for *every* row in the query. This is because the
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spatial index on traditional geometry fields cannot be used.
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For much better performance on WGS84 distance queries, consider using
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:ref:`geography columns <geography-type>` in your database instead because
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they are able to use their spatial index in distance queries.
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You can tell GeoDjango to use a geography column by setting ``geography=True``
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in your field definition.
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For example, let's say we have a ``SouthTexasCity`` model (from the
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`GeoDjango distance tests`__ ) on a *projected* coordinate system valid for cities
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in southern Texas::
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from django.contrib.gis.db import models
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class SouthTexasCity(models.Model):
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name = models.CharField(max_length=30)
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# A projected coordinate system (only valid for South Texas!)
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# is used, units are in meters.
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point = models.PointField(srid=32140)
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objects = models.GeoManager()
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Then distance queries may be performed as follows::
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>>> from django.contrib.gis.geos import *
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>>> from django.contrib.gis.measure import D # ``D`` is a shortcut for ``Distance``
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>>> from geoapp import SouthTexasCity
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# Distances will be calculated from this point, which does not have to be projected.
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>>> pnt = fromstr('POINT(-96.876369 29.905320)', srid=4326)
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# If numeric parameter, units of field (meters in this case) are assumed.
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>>> qs = SouthTexasCity.objects.filter(point__distance_lte=(pnt, 7000))
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# Find all Cities within 7 km, > 20 miles away, and > 100 chains away (an obscure unit)
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>>> qs = SouthTexasCity.objects.filter(point__distance_lte=(pnt, D(km=7)))
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>>> qs = SouthTexasCity.objects.filter(point__distance_gte=(pnt, D(mi=20)))
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>>> qs = SouthTexasCity.objects.filter(point__distance_gte=(pnt, D(chain=100)))
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__ https://github.com/django/django/blob/master/django/contrib/gis/tests/distapp/models.py
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.. _compatibility-table:
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Compatibility Tables
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====================
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.. _spatial-lookup-compatibility:
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Spatial Lookups
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---------------
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The following table provides a summary of what spatial lookups are available
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for each spatial database backend.
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================================= ========= ======== ============ ==========
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Lookup Type PostGIS Oracle MySQL [#]_ SpatiaLite
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================================= ========= ======== ============ ==========
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:lookup:`bbcontains` X X X
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:lookup:`bboverlaps` X X X
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:lookup:`contained` X X X
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:lookup:`contains <gis-contains>` X X X X
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:lookup:`contains_properly` X
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:lookup:`coveredby` X X
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:lookup:`covers` X X
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:lookup:`crosses` X X
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:lookup:`disjoint` X X X X
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:lookup:`distance_gt` X X X
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:lookup:`distance_gte` X X X
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:lookup:`distance_lt` X X X
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:lookup:`distance_lte` X X X
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:lookup:`dwithin` X X
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:lookup:`equals` X X X X
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:lookup:`exact` X X X X
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:lookup:`intersects` X X X X
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:lookup:`overlaps` X X X X
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:lookup:`relate` X X X
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:lookup:`same_as` X X X X
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:lookup:`touches` X X X X
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:lookup:`within` X X X X
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:lookup:`left` X
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:lookup:`right` X
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:lookup:`overlaps_left` X
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:lookup:`overlaps_right` X
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:lookup:`overlaps_above` X
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:lookup:`overlaps_below` X
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:lookup:`strictly_above` X
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:lookup:`strictly_below` X
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================================= ========= ======== ============ ==========
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.. _geoqueryset-method-compatibility:
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``GeoQuerySet`` Methods
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-----------------------
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The following table provides a summary of what :class:`GeoQuerySet` methods
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are available on each spatial backend. Please note that MySQL does not
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support any of these methods, and is thus excluded from the table.
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==================================== ======= ====== ==========
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Method PostGIS Oracle SpatiaLite
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==================================== ======= ====== ==========
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:meth:`GeoQuerySet.area` X X X
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:meth:`GeoQuerySet.centroid` X X X
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:meth:`GeoQuerySet.collect` X
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:meth:`GeoQuerySet.difference` X X X
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:meth:`GeoQuerySet.distance` X X X
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:meth:`GeoQuerySet.envelope` X X
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:meth:`GeoQuerySet.extent` X X
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:meth:`GeoQuerySet.extent3d` X
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:meth:`GeoQuerySet.force_rhr` X
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:meth:`GeoQuerySet.geohash` X
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:meth:`GeoQuerySet.geojson` X X
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:meth:`GeoQuerySet.gml` X X X
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:meth:`GeoQuerySet.intersection` X X X
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:meth:`GeoQuerySet.kml` X X
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:meth:`GeoQuerySet.length` X X X
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:meth:`GeoQuerySet.make_line` X
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:meth:`GeoQuerySet.mem_size` X
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:meth:`GeoQuerySet.num_geom` X X X
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:meth:`GeoQuerySet.num_points` X X X
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:meth:`GeoQuerySet.perimeter` X X
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:meth:`GeoQuerySet.point_on_surface` X X X
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:meth:`GeoQuerySet.reverse_geom` X X
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:meth:`GeoQuerySet.scale` X X
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:meth:`GeoQuerySet.snap_to_grid` X
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:meth:`GeoQuerySet.svg` X X
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:meth:`GeoQuerySet.sym_difference` X X X
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:meth:`GeoQuerySet.transform` X X X
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:meth:`GeoQuerySet.translate` X X
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:meth:`GeoQuerySet.union` X X X
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:meth:`GeoQuerySet.unionagg` X X X
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==================================== ======= ====== ==========
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.. rubric:: Footnotes
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.. [#fnwkt] *See* Open Geospatial Consortium, Inc., `OpenGIS Simple Feature Specification For SQL <http://www.opengis.org/docs/99-049.pdf>`_, Document 99-049 (May 5, 1999), at Ch. 3.2.5, p. 3-11 (SQL Textual Representation of Geometry).
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.. [#fnewkb] *See* `PostGIS EWKB, EWKT and Canonical Forms <http://postgis.refractions.net/documentation/manual-1.5/ch04.html#EWKB_EWKT>`_, PostGIS documentation at Ch. 4.1.2.
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.. [#fngeojson] *See* Howard Butler, Martin Daly, Allan Doyle, Tim Schaub, & Christopher Schmidt, `The GeoJSON Format Specification <http://geojson.org/geojson-spec.html>`_, Revision 1.0 (June 16, 2008).
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.. [#fndistsphere14] *See* `PostGIS 1.4 documentation <http://postgis.refractions.net/documentation/manual-1.4/ST_Distance_Sphere.html>`_ on ``ST_distance_sphere``.
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.. [#fndistsphere15] *See* `PostGIS 1.5 documentation <http://postgis.refractions.net/documentation/manual-1.5/ST_Distance_Sphere.html>`_ on ``ST_distance_sphere``.
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.. [#fnmysqlidx] *See* `Creating Spatial Indexes <http://dev.mysql.com/doc/refman/5.1/en/creating-spatial-indexes.html>`_
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in the MySQL 5.1 Reference Manual:
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For MyISAM tables, ``SPATIAL INDEX`` creates an R-tree index. For storage
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engines that support nonspatial indexing of spatial columns, the engine
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creates a B-tree index. A B-tree index on spatial values will be useful
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for exact-value lookups, but not for range scans.
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.. [#] Refer :ref:`mysql-spatial-limitations` section for more details.
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