# Django 1.8.18.dev20170123152445 documentation

# 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.oracle`
- `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.

Warning

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.

## 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()

`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 [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.

## 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.

Note

GeoDjango constructs spatial SQL with the `GeoQuerySet`, a subclass of `QuerySet`. The `GeoManager` instance attached to your model is what enables use of `GeoQuerySet`.

## 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 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:

- `distance_lt`
- `distance_lte`
- `distance_gt`
- `distance_gte`
- `dwithin`

Note

For measuring, rather than querying on distances, use the `GeoQuerySet.distance()` method.

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.

Note

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)
        objects = models.GeoManager()

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 |  |  |

### `GeoQuerySet` Methods

The following table provides a summary of what `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
--- | --- | --- | ---
`GeoQuerySet.area()` | X | X | X
`GeoQuerySet.centroid()` | X | X | X
`GeoQuerySet.difference()` | X | X | X
`GeoQuerySet.distance()` | X | X | X
`GeoQuerySet.envelope()` | X |  | X
`GeoQuerySet.force_rhr()` | X |  |
`GeoQuerySet.geohash()` | X |  |
`GeoQuerySet.geojson()` | X |  | X
`GeoQuerySet.gml()` | X | X | X
`GeoQuerySet.intersection()` | X | X | X
`GeoQuerySet.kml()` | X |  | X
`GeoQuerySet.length()` | X | X | X
`GeoQuerySet.mem_size()` | X |  |
`GeoQuerySet.num_geom()` | X | X | X
`GeoQuerySet.num_points()` | X | X | X
`GeoQuerySet.perimeter()` | X | X |
`GeoQuerySet.point_on_surface()` | X | X | X
`GeoQuerySet.reverse_geom()` | X | X |
`GeoQuerySet.scale()` | X |  | X
`GeoQuerySet.snap_to_grid()` | X |  |
`GeoQuerySet.svg()` | X |  | X
`GeoQuerySet.sym_difference()` | X | X | X
`GeoQuerySet.transform()` | X | X | X
`GeoQuerySet.translate()` | X |  | X
`GeoQuerySet.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.

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

Footnotes

[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`.

[5] | 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.