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======================
GeoDjango Database API
======================
.. _spatial-backends:
Spatial Backends
================
.. module:: django.contrib.gis.db.backends
:synopsis: GeoDjango's spatial database 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.oracle``
* ``django.contrib.gis.db.backends.spatialite``
.. module:: django.contrib.gis.db.models
:synopsis: GeoDjango's database API.
.. _mysql-spatial-limitations:
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
<http://dev.mysql.com/doc/refman/5.6/en/spatial-relation-functions.html>`_:
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 :lookup:`contains <gis-contains>`
are available in GeoDjango when using MySQL, the results returned are really
equivalent to what would be returned when using :lookup:`bbcontains`
on a different spatial backend.
.. warning::
True spatial indexes (R-trees) are only supported with
MyISAM tables on MySQL. [#fnmysqlidx]_ 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))')
>>> z.save()
:class:`~django.contrib.gis.geos.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)
>>> z.save()
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)
>>> z.save()
>>> 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 [#fnwkt]_), HEXEWKB (PostGIS specific -- a WKB geometry in
hexadecimal [#fnewkb]_), and GeoJSON [#fngeojson]_ (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 :class:`~django.contrib.gis.geos.GEOSGeometry`
objects, refer to the :ref:`GEOS tutorial <geos-tutorial>`.
.. _creating-and-saving-raster-models:
Creating and Saving Models with Raster Fields
=============================================
.. versionadded:: 1.9
When creating raster models, the raster field will implicitly convert the input
into a :class:`~django.contrib.gis.gdal.GDALRaster` using lazy-evaluation.
The raster field will therefore accept any input that is accepted by the
:class:`~django.contrib.gis.gdal.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')
>>> dem.save()
:class:`~django.contrib.gis.gdal.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)
>>> dem.save()
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-intro:
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. :class:`~django.db.models.CharField`)
may be chained with those on geographic fields. Thus, geographic queries
take the following general form (assuming the ``Zipcode`` model used in the
:doc:`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, :lookup:`contains <gis-contains>`
is the spatial lookup type, and ``pnt`` is the parameter (which may be a
:class:`~django.contrib.gis.geos.GEOSGeometry` object or a string of
GeoJSON , WKT, or HEXEWKB).
A complete reference can be found in the :ref:`spatial lookup reference
<spatial-lookups>`.
.. _distance-queries:
Distance Queries
================
Introduction
------------
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 :ref:`selecting-an-srid` section
in the :doc:`model-api` documentation for more details.
.. _distance-lookups-intro:
Distance Lookups
----------------
*Availability*: PostGIS, Oracle, SpatiaLite
The following distance lookups are available:
* :lookup:`distance_lt`
* :lookup:`distance_lte`
* :lookup:`distance_gt`
* :lookup:`distance_gte`
* :lookup:`dwithin`
.. note::
For *measuring*, rather than querying on distances, use the
:class:`~django.contrib.gis.db.models.functions.Distance` function.
Distance lookups take a tuple parameter comprising:
#. A geometry to base calculations from; and
#. A number or :class:`~django.contrib.gis.measure.Distance` object containing the distance.
If a :class:`~django.contrib.gis.measure.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.
.. note::
In PostGIS, ``ST_Distance_Sphere`` does *not* limit the geometry types
geographic distance queries are performed with. [#fndistsphere15]_ 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
:ref:`geography columns <geography-type>` 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 fromstr
>>> 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 = fromstr('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)))
__ https://github.com/django/django/blob/master/tests/gis_tests/distapp/models.py
.. _compatibility-table:
Compatibility Tables
====================
.. _spatial-lookup-compatibility:
Spatial Lookups
---------------
The following table provides a summary of what spatial lookups are available
for each spatial database backend.
================================= ========= ======== ============ ==========
Lookup Type PostGIS Oracle MySQL [#]_ SpatiaLite
================================= ========= ======== ============ ==========
:lookup:`bbcontains` X X X
:lookup:`bboverlaps` X X X
:lookup:`contained` X X X
:lookup:`contains <gis-contains>` X X X X
:lookup:`contains_properly` X
:lookup:`coveredby` X X
:lookup:`covers` X X
:lookup:`crosses` X X
:lookup:`disjoint` X X X X
:lookup:`distance_gt` X X X
:lookup:`distance_gte` X X X
:lookup:`distance_lt` X X X
:lookup:`distance_lte` X X X
:lookup:`dwithin` X X
:lookup:`equals` X X X X
:lookup:`exact` X X X X
:lookup:`intersects` X X X X
:lookup:`overlaps` X X X X
:lookup:`relate` X X X
:lookup:`same_as` X X X X
:lookup:`touches` X X X X
:lookup:`within` X X X X
:lookup:`left` X
:lookup:`right` X
:lookup:`overlaps_left` X
:lookup:`overlaps_right` X
:lookup:`overlaps_above` X
:lookup:`overlaps_below` X
:lookup:`strictly_above` X
:lookup:`strictly_below` X
================================= ========= ======== ============ ==========
.. _database-functions-compatibility:
Database functions
------------------
.. module:: django.contrib.gis.db.models.functions
:synopsis: GeoDjango's database functions.
The following table provides a summary of what geography-specific database
functions are available on each spatial backend.
==================================== ======= ====== ===== ==========
Method PostGIS Oracle MySQL SpatiaLite
==================================== ======= ====== ===== ==========
:class:`Area` X X X X
:class:`AsGeoJSON` X X
:class:`AsGML` X X X
:class:`AsKML` X X
:class:`AsSVG` X X
:class:`BoundingCircle` X
:class:`Centroid` X X X
:class:`Difference` X X X
:class:`Distance` X X X X
:class:`Envelope` X X
:class:`ForceRHR` X
:class:`GeoHash` X
:class:`Intersection` X X X
:class:`Length` X X X X
:class:`MemSize` X
:class:`NumGeometries` X X X
:class:`NumPoints` X X X
:class:`Perimeter` X X X
:class:`PointOnSurface` X X X
:class:`Reverse` X X X (≥ 4.0)
:class:`Scale` X X
:class:`SnapToGrid` X X
:class:`SymDifference` X X X
:class:`Transform` X X X
:class:`Translate` X X
:class:`Union` X X 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.
.. currentmodule:: django.contrib.gis.db.models
======================= ======= ====== ==========
Aggregate PostGIS Oracle SpatiaLite
======================= ======= ====== ==========
:class:`Collect` X (from v3.0)
:class:`Extent` X X (from v3.0)
:class:`Extent3D` X
:class:`MakeLine` X
:class:`Union` X X X
======================= ======= ====== ==========
.. rubric:: Footnotes
.. [#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).
.. [#fnewkb] *See* `PostGIS EWKB, EWKT and Canonical Forms <http://postgis.net/docs/manual-2.1/using_postgis_dbmanagement.html#EWKB_EWKT>`_, PostGIS documentation at Ch. 4.1.2.
.. [#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).
.. [#fndistsphere15] *See* `PostGIS documentation <http://postgis.net/docs/manual-2.1/ST_Distance_Sphere.html>`_ on ``ST_distance_sphere``.
.. [#fnmysqlidx] *See* `Creating Spatial Indexes <http://dev.mysql.com/doc/refman/5.6/en/creating-spatial-indexes.html>`_
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.
.. [#] Refer :ref:`mysql-spatial-limitations` section for more details.
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