In Django 1.2, support for multiple databases was introduced. In order to support multiple databases, GeoDjango has segregated its functionality into full-fledged spatial database backends:
In Django 1.2, the way to specify databases in your settings was changed. The old database settings format (e.g., the DATABASE_* settings) is backwards compatible with GeoDjango, and will automatically use the appropriate spatial backend as long as django.contrib.gis is in your INSTALLED_APPS. For example, if you have the following in your settings:
DATABASE_ENGINE='postgresql_psycopg2'
...
INSTALLED_APPS = (
...
'django.contrib.gis',
...
)
Then, django.contrib.gis.db.backends.postgis is automatically used as your spatial backend.
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. [6] 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.
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.
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 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.
Availability: PostGIS, Oracle, SpatiaLite
The following distance lookups are available:
Note
For measuring, rather than querying on distances, use the GeoQuerySet.distance() method.
Distance lookups take a tuple parameter comprising:
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
For users of PostGIS 1.4 and below, the routine ST_Distance_Sphere is used by default for calculating distances on geographic coordinate systems (e.g., WGS84) -- which may only be called with point geometries [4]. Thus, geographic distance lookups on traditional PostGIS geometry columns are only allowed on PointField model fields using a point for the geometry parameter.
Note
In PostGIS 1.5, ST_Distance_Sphere does not limit the geometry types geographic distance queries are performed with. [5] 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 *
>>> 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)))
The following table provides a summary of what spatial lookups are available for each spatial database backend.
| Lookup Type | PostGIS | Oracle | MySQL [7] | 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 |
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.collect() | X | ||
| GeoQuerySet.difference() | X | X | X |
| GeoQuerySet.distance() | X | X | X |
| GeoQuerySet.envelope() | X | X | |
| GeoQuerySet.extent() | X | X | |
| GeoQuerySet.extent3d() | X | ||
| GeoQuerySet.force_rhr() | X | ||
| GeoQuerySet.geohash() | X | ||
| GeoQuerySet.geojson() | X | ||
| GeoQuerySet.gml() | X | X | |
| GeoQuerySet.intersection() | X | X | X |
| GeoQuerySet.kml() | X | ||
| GeoQuerySet.length() | X | X | X |
| GeoQuerySet.make_line() | 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 |
| GeoQuerySet.unionagg() | 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 1.4 documentation on ST_distance_sphere. |
| [5] | See PostGIS 1.5 documentation on ST_distance_sphere. |
| [6] | See Creating Spatial Indexes in the MySQL 5.1 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. |
| [7] | Refer MySQL Spatial Limitations section for more details. |
Jul 05, 2010
