django/docs/ref/contrib/gis/db-api.txt

======================
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.

Creating and Saving Geographic Models
=====================================

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>`.

.. _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>`.

.. note::

    GeoDjango constructs spatial SQL with the :class:`GeoQuerySet`, a
    subclass of :class:`~django.db.models.query.QuerySet`.  The
    :class:`GeoManager` instance attached to your model is what
    enables use of :class:`GeoQuerySet`.

.. _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
    :meth:`GeoQuerySet.distance` method.

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 1.5, ``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)
        objects = models.GeoManager()

Then distance queries may be performed as follows::

    >>> from django.contrib.gis.geos import *
    >>> from django.contrib.gis.measure import D # ``D`` is a shortcut for ``Distance``
    >>> from geoapp 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/django/contrib/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
=================================  =========  ========  ============ ==========

.. _geoqueryset-method-compatibility:

``GeoQuerySet`` Methods
-----------------------
The following table provides a summary of what :class:`GeoQuerySet` methods
are available on each spatial backend.  Please note that MySQL does not
support any of these methods, and is thus excluded from the table.

====================================  =======  ======  ==========
Method                                PostGIS  Oracle  SpatiaLite
====================================  =======  ======  ==========
:meth:`GeoQuerySet.area`              X        X       X
:meth:`GeoQuerySet.centroid`          X        X       X
:meth:`GeoQuerySet.collect`           X                (from v3.0)
:meth:`GeoQuerySet.difference`        X        X       X
:meth:`GeoQuerySet.distance`          X        X       X
:meth:`GeoQuerySet.envelope`          X                X
:meth:`GeoQuerySet.extent`            X        X       (from v3.0)
:meth:`GeoQuerySet.extent3d`          X
:meth:`GeoQuerySet.force_rhr`         X
:meth:`GeoQuerySet.geohash`           X
:meth:`GeoQuerySet.geojson`           X                X
:meth:`GeoQuerySet.gml`               X        X       X
:meth:`GeoQuerySet.intersection`      X        X       X
:meth:`GeoQuerySet.kml`               X                X
:meth:`GeoQuerySet.length`            X        X       X
:meth:`GeoQuerySet.make_line`         X
:meth:`GeoQuerySet.mem_size`          X
:meth:`GeoQuerySet.num_geom`          X        X       X
:meth:`GeoQuerySet.num_points`        X        X       X
:meth:`GeoQuerySet.perimeter`         X        X
:meth:`GeoQuerySet.point_on_surface`  X        X       X
:meth:`GeoQuerySet.reverse_geom`      X        X
:meth:`GeoQuerySet.scale`             X                X
:meth:`GeoQuerySet.snap_to_grid`      X
:meth:`GeoQuerySet.svg`               X                X
:meth:`GeoQuerySet.sym_difference`    X        X       X
:meth:`GeoQuerySet.transform`         X        X       X
:meth:`GeoQuerySet.translate`         X                X
:meth:`GeoQuerySet.union`             X        X       X
:meth:`GeoQuerySet.unionagg`          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-1.5/ch04.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 1.5 documentation <http://postgis.net/docs/manual-1.5/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.