==================
GeoDjango Tutorial
==================

Introduction
============

GeoDjango is an add-on for Django that turns it into a world-class geographic
Web framework.  GeoDjango strives to make it as simple as possible to create
geographic Web applications, like location-based services.  Some features
include:

* Django model fields for `OGC`_ geometries.
* Extensions to Django's ORM for the querying and manipulation of spatial data.
* Loosely-coupled, high-level Python interfaces for GIS geometry operations and
  data formats.
* Editing of geometry fields inside the admin.

This tutorial assumes a familiarity with Django; thus, if you're brand new to
Django please read through the :doc:`regular tutorial </intro/tutorial01>` to
introduce yourself with basic Django concepts.

.. note::

    GeoDjango has special prerequisites overwhat is required by Django --
    please consult the :ref:`installation documentation <ref-gis-install>`
    for more details.

This tutorial will guide you through the creation of a geographic Web
application for viewing the `world borders`_. [#]_ Some of the code
used in this tutorial is taken from and/or inspired by the `GeoDjango
basic apps`_ project. [#]_

.. note::

    Proceed through the tutorial sections sequentially for step-by-step
    instructions.

.. _OGC: http://www.opengeospatial.org/
.. _world borders: http://thematicmapping.org/downloads/world_borders.php
.. _GeoDjango basic apps: http://code.google.com/p/geodjango-basic-apps/

Setting Up
==========

Create a Spatial Database
-------------------------

.. note::

    MySQL and Oracle users can skip this section because spatial types
    are already built into the database.

First, a spatial database needs to be created for our project.  If using
PostgreSQL and PostGIS, then the following commands will
create the database from a :ref:`spatial database template
<spatialdb_template>`:

.. code-block:: bash

    $ createdb -T template_postgis geodjango

.. note::

    This command must be issued by a database user that has permissions to
    create a database.  Here is an example set of commands to create such
    a user:

    .. code-block:: bash

        $ sudo su - postgres
        $ createuser --createdb geo
        $ exit

    Replace ``geo`` with the system login user name that will be
    connecting to the database.  For example, ``johndoe`` if that is the
    system user that will be running GeoDjango.

Users of SQLite and SpatiaLite should consult the instructions on how
to create a :ref:`SpatiaLite database <create_spatialite_db>`.

Create GeoDjango Project
------------------------

Use the ``django-admin.py`` script like normal to create a ``geodjango``
project:

.. code-block:: bash

    $ django-admin.py startproject geodjango

With the project initialized, now create a ``world`` Django application within
the ``geodjango`` project:

.. code-block:: bash

    $ cd geodjango
    $ python manage.py startapp world

Configure ``settings.py``
-------------------------

The ``geodjango`` project settings are stored in the ``geodjango/settings.py``
file. Edit the database connection settings appropriately::

    DATABASES = {
        'default': {
             'ENGINE': 'django.contrib.gis.db.backends.postgis',
             'NAME': 'geodjango',
             'USER': 'geo',
         }
    }

In addition, modify the :setting:`INSTALLED_APPS` setting to include
:mod:`django.contrib.admin`, :mod:`django.contrib.gis`,
and ``world`` (our newly created application)::

    INSTALLED_APPS = (
        'django.contrib.auth',
        'django.contrib.contenttypes',
        'django.contrib.sessions',
        'django.contrib.sites',
        'django.contrib.messages',
        'django.contrib.staticfiles',
        'django.contrib.admin',
        'django.contrib.gis',
        'world'
    )

Geographic Data
===============

.. _worldborders:

World Borders
-------------

The world borders data is available in this `zip file`__.  Create a data
directory in the ``world`` application, download the world borders data, and
unzip. On GNU/Linux platforms the following commands should do it:

.. code-block:: bash

    $ mkdir world/data
    $ cd world/data
    $ wget http://thematicmapping.org/downloads/TM_WORLD_BORDERS-0.3.zip
    $ unzip TM_WORLD_BORDERS-0.3.zip
    $ cd ../..

The world borders ZIP file contains a set of data files collectively known as
an `ESRI Shapefile`__, one of the most popular geospatial data formats.  When
unzipped the world borders data set includes files with the following
extensions:

* ``.shp``: Holds the vector data for the world borders geometries.
* ``.shx``: Spatial index file for geometries stored in the ``.shp``.
* ``.dbf``: Database file for holding non-geometric attribute data
  (e.g., integer and character fields).
* ``.prj``: Contains the spatial reference information for the geographic
  data stored in the shapefile.

__ http://thematicmapping.org/downloads/TM_WORLD_BORDERS-0.3.zip
__ http://en.wikipedia.org/wiki/Shapefile

Use ``ogrinfo`` to examine spatial data
---------------------------------------

The GDAL ``ogrinfo`` utility is excellent for examining metadata about
shapefiles (or other vector data sources):

.. code-block:: bash

    $ ogrinfo world/data/TM_WORLD_BORDERS-0.3.shp
    INFO: Open of `world/data/TM_WORLD_BORDERS-0.3.shp'
          using driver `ESRI Shapefile' successful.
    1: TM_WORLD_BORDERS-0.3 (Polygon)

Here ``ogrinfo`` is telling us that the shapefile has one layer, and that such
layer contains polygon data.  To find out more we'll specify the layer name
and use the ``-so`` option to get only important summary information:

.. code-block:: bash

    $ ogrinfo -so world/data/TM_WORLD_BORDERS-0.3.shp TM_WORLD_BORDERS-0.3
    INFO: Open of `world/data/TM_WORLD_BORDERS-0.3.shp'
          using driver `ESRI Shapefile' successful.

    Layer name: TM_WORLD_BORDERS-0.3
    Geometry: Polygon
    Feature Count: 246
    Extent: (-180.000000, -90.000000) - (180.000000, 83.623596)
    Layer SRS WKT:
    GEOGCS["GCS_WGS_1984",
        DATUM["WGS_1984",
            SPHEROID["WGS_1984",6378137.0,298.257223563]],
        PRIMEM["Greenwich",0.0],
        UNIT["Degree",0.0174532925199433]]
    FIPS: String (2.0)
    ISO2: String (2.0)
    ISO3: String (3.0)
    UN: Integer (3.0)
    NAME: String (50.0)
    AREA: Integer (7.0)
    POP2005: Integer (10.0)
    REGION: Integer (3.0)
    SUBREGION: Integer (3.0)
    LON: Real (8.3)
    LAT: Real (7.3)

This detailed summary information tells us the number of features in the layer
(246), the geographical extent, the spatial reference system ("SRS WKT"),
as well as detailed information for each attribute field.  For example,
``FIPS: String (2.0)`` indicates that there's a ``FIPS`` character field
with a maximum length of 2; similarly, ``LON: Real (8.3)`` is a floating-point
field that holds a maximum of 8 digits up to three decimal places.  Although
this information may be found right on the `world borders`_ Web site, this
shows you how to determine this information yourself when such metadata is not
provided.

Geographic Models
=================

Defining a Geographic Model
---------------------------

Now that we've examined our world borders data set using ``ogrinfo``, we can
create a GeoDjango model to represent this data::

    from django.contrib.gis.db import models

    class WorldBorder(models.Model):
        # Regular Django fields corresponding to the attributes in the
	# world borders shapefile.
        name = models.CharField(max_length=50)
        area = models.IntegerField()
        pop2005 = models.IntegerField('Population 2005')
        fips = models.CharField('FIPS Code', max_length=2)
        iso2 = models.CharField('2 Digit ISO', max_length=2)
        iso3 = models.CharField('3 Digit ISO', max_length=3)
        un = models.IntegerField('United Nations Code')
        region = models.IntegerField('Region Code')
        subregion = models.IntegerField('Sub-Region Code')
    	lon = models.FloatField()
    	lat = models.FloatField()

	# GeoDjango-specific: a geometry field (MultiPolygonField), and
        # overriding the default manager with a GeoManager instance.
	mpoly = models.MultiPolygonField()
	objects = models.GeoManager()

        # Returns the string representation of the model.
        def __unicode__(self):
            return self.name

Two important things to note:

1. The ``models`` module is imported from :mod:`django.contrib.gis.db`.
2. The model overrides its default manager with
   :class:`~django.contrib.gis.db.models.GeoManager`; this is *required*
   to perform spatial queries.

When declaring a geometry field on your model the default spatial reference
system is WGS84 (meaning the `SRID`__ is 4326) -- in other words, the field
coordinates are in longitude/latitude pairs in units of degrees.  If you want
the coordinate system to be different, then SRID of the geometry field may be
customized by setting the ``srid`` with an integer corresponding to the
coordinate system of your choice.

__ http://en.wikipedia.org/wiki/SRID

Run ``syncdb``
--------------

After you've defined your model, it needs to be synced with the spatial
database. First, let's look at the SQL that will generate the table for the
``WorldBorder`` model::

    $ python manage.py sqlall world

This management command should produce the following output:

.. code-block:: sql

    BEGIN;
    CREATE TABLE "world_worldborder" (
        "id" serial NOT NULL PRIMARY KEY,
        "name" varchar(50) NOT NULL,
        "area" integer NOT NULL,
        "pop2005" integer NOT NULL,
        "fips" varchar(2) NOT NULL,
        "iso2" varchar(2) NOT NULL,
        "iso3" varchar(3) NOT NULL,
        "un" integer NOT NULL,
        "region" integer NOT NULL,
        "subregion" integer NOT NULL,
        "lon" double precision NOT NULL,
        "lat" double precision NOT NULL
    )
    ;
    SELECT AddGeometryColumn('world_worldborder', 'mpoly', 4326, 'MULTIPOLYGON', 2);
    ALTER TABLE "world_worldborder" ALTER "mpoly" SET NOT NULL;
    CREATE INDEX "world_worldborder_mpoly_id" ON "world_worldborder" USING GIST ( "mpoly" GIST_GEOMETRY_OPS );
    COMMIT;

If satisfied, you may then create this table in the database by running the
``syncdb`` management command::

    $ python manage.py syncdb
    Creating table world_worldborder
    Installing custom SQL for world.WorldBorder model

The ``syncdb`` command may also prompt you to create an admin user; go ahead
and do so (not required now, may be done at any point in the future using the
``createsuperuser`` management command).

Importing Spatial Data
======================

This section will show you how to take the data from the world borders
shapefile and import it into GeoDjango models using the
:ref:`ref-layermapping`.
There are many different ways to import data in to a spatial database --
besides the tools included within GeoDjango, you may also use the following to
populate your spatial database:

* `ogr2ogr`_: Command-line utility, included with GDAL, that
  supports loading a multitude of vector data formats into
  the PostGIS, MySQL, and Oracle spatial databases.
* `shp2pgsql`_: This utility is included with PostGIS and only supports
  ESRI shapefiles.

.. _ogr2ogr: http://www.gdal.org/ogr2ogr.html
.. _shp2pgsql: http://postgis.refractions.net/documentation/manual-1.5/ch04.html#shp2pgsql_usage

.. _gdalinterface:

GDAL Interface
--------------

Earlier we used the ``ogrinfo`` to explore the contents of the world borders
shapefile.  Included within GeoDjango is an interface to GDAL's powerful OGR
library -- in other words, you'll be able explore all the vector data sources
that OGR supports via a Pythonic API.

First, invoke the Django shell:

.. code-block:: bash

    $ python manage.py shell

If the :ref:`worldborders` data was downloaded like earlier in the
tutorial, then we can determine the path using Python's built-in
``os`` module::

    >>> import os
    >>> import world
    >>> world_shp = os.path.abspath(os.path.join(os.path.dirname(world.__file__),
    ...                             'data/TM_WORLD_BORDERS-0.3.shp'))

Now, the world borders shapefile may be opened using GeoDjango's
:class:`~django.contrib.gis.gdal.DataSource` interface::

    >>> from django.contrib.gis.gdal import DataSource
    >>> ds = DataSource(world_shp)
    >>> print(ds)
    / ... /geodjango/world/data/TM_WORLD_BORDERS-0.3.shp (ESRI Shapefile)

Data source objects can have different layers of geospatial features; however,
shapefiles are only allowed to have one layer::

    >>> print(len(ds))
    1
    >>> lyr = ds[0]
    >>> print(lyr)
    TM_WORLD_BORDERS-0.3

You can see what the geometry type of the layer is and how many features it
contains::

    >>> print(lyr.geom_type)
    Polygon
    >>> print(len(lyr))
    246

.. note::

    Unfortunately the shapefile data format does not allow for greater
    specificity with regards to geometry types.  This shapefile, like
    many others, actually includes ``MultiPolygon`` geometries in its
    features.  You need to watch out for this when creating your models
    as a GeoDjango ``PolygonField`` will not accept a ``MultiPolygon``
    type geometry -- thus a ``MultiPolygonField`` is used in our model's
    definition instead.

The :class:`~django.contrib.gis.gdal.Layer` may also have a spatial reference
system associated with it -- if it does, the ``srs`` attribute will return a
:class:`~django.contrib.gis.gdal.SpatialReference` object::

    >>> srs = lyr.srs
    >>> print(srs)
    GEOGCS["GCS_WGS_1984",
        DATUM["WGS_1984",
            SPHEROID["WGS_1984",6378137.0,298.257223563]],
        PRIMEM["Greenwich",0.0],
        UNIT["Degree",0.0174532925199433]]
    >>> srs.proj4 # PROJ.4 representation
    '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs '

Here we've noticed that the shapefile is in the popular WGS84 spatial reference
system -- in other words, the data uses units of degrees longitude and
latitude.

In addition, shapefiles also support attribute fields that may contain
additional data.  Here are the fields on the World Borders layer:

    >>> print(lyr.fields)
    ['FIPS', 'ISO2', 'ISO3', 'UN', 'NAME', 'AREA', 'POP2005', 'REGION', 'SUBREGION', 'LON', 'LAT']

Here we are examining the OGR types (e.g., whether a field is an integer or
a string) associated with each of the fields:

    >>> [fld.__name__ for fld in lyr.field_types]
    ['OFTString', 'OFTString', 'OFTString', 'OFTInteger', 'OFTString', 'OFTInteger', 'OFTInteger', 'OFTInteger', 'OFTInteger', 'OFTReal', 'OFTReal']

You can iterate over each feature in the layer and extract information from both
the feature's geometry (accessed via the ``geom`` attribute) as well as the
feature's attribute fields (whose **values** are accessed via ``get()``
method)::

    >>> for feat in lyr:
    ...    print(feat.get('NAME'), feat.geom.num_points)
    ...
    Guernsey 18
    Jersey 26
    South Georgia South Sandwich Islands 338
    Taiwan 363

:class:`~django.contrib.gis.gdal.Layer` objects may be sliced::

    >>> lyr[0:2]
    [<django.contrib.gis.gdal.feature.Feature object at 0x2f47690>, <django.contrib.gis.gdal.feature.Feature object at 0x2f47650>]

And individual features may be retrieved by their feature ID::

    >>> feat = lyr[234]
    >>> print(feat.get('NAME'))
    San Marino

Here the boundary geometry for San Marino is extracted and looking
exported to WKT and GeoJSON::

    >>> geom = feat.geom
    >>> print(geom.wkt)
    POLYGON ((12.415798 43.957954,12.450554 ...
    >>> print(geom.json)
    { "type": "Polygon", "coordinates": [ [ [ 12.415798, 43.957954 ], [ 12.450554, 43.979721 ], ...


``LayerMapping``
----------------

We're going to dive right in -- create a file called ``load.py`` inside the
``world`` application, and insert the following::

    import os
    from django.contrib.gis.utils import LayerMapping
    from models import WorldBorder

    world_mapping = {
        'fips' : 'FIPS',
        'iso2' : 'ISO2',
        'iso3' : 'ISO3',
        'un' : 'UN',
        'name' : 'NAME',
        'area' : 'AREA',
        'pop2005' : 'POP2005',
        'region' : 'REGION',
        'subregion' : 'SUBREGION',
        'lon' : 'LON',
        'lat' : 'LAT',
        'mpoly' : 'MULTIPOLYGON',
    }

    world_shp = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data/TM_WORLD_BORDERS-0.3.shp'))

    def run(verbose=True):
        lm = LayerMapping(WorldBorder, world_shp, world_mapping,
                          transform=False, encoding='iso-8859-1')

        lm.save(strict=True, verbose=verbose)

A few notes about what's going on:

* Each key in the ``world_mapping`` dictionary corresponds to a field in the
  ``WorldBorder`` model, and the value is the name of the shapefile field
  that data will be loaded from.
* The key ``mpoly`` for the geometry field is ``MULTIPOLYGON``, the
  geometry type we wish to import as.  Even if simple polygons are encountered
  in the shapefile they will automatically be converted into collections prior
  to insertion into the database.
* The path to the shapefile is not absolute -- in other words, if you move the
  ``world`` application (with ``data`` subdirectory) to a different location,
  then the script will still work.
* The ``transform`` keyword is set to ``False`` because the data in the
  shapefile does not need to be converted -- it's already in WGS84 (SRID=4326).
* The ``encoding`` keyword is set to the character encoding of string values in
  the shapefile. This ensures that string values are read and saved correctly
  from their original encoding system.

Afterwards, invoke the Django shell from the ``geodjango`` project directory:

.. code-block:: bash

   $ python manage.py shell

Next, import the ``load`` module, call the ``run`` routine, and watch ``LayerMapping``
do the work::

   >>> from world import load
   >>> load.run()


.. _ogrinspect-intro:

Try ``ogrinspect``
------------------
Now that you've seen how to define geographic models and import data with the
:ref:`ref-layermapping`, it's possible to further automate this process with
use of the :djadmin:`ogrinspect` management command.  The :djadmin:`ogrinspect`
command  introspects a GDAL-supported vector data source (e.g., a shapefile)
and generates a model definition and ``LayerMapping`` dictionary automatically.

The general usage of the command goes as follows:

.. code-block:: bash

    $ python manage.py ogrinspect [options] <data_source> <model_name> [options]

Where ``data_source`` is the path to the GDAL-supported data source and
``model_name`` is the name to use for the model.  Command-line options may
be used to further define how the model is generated.

For example, the following command nearly reproduces the ``WorldBorder`` model
and mapping dictionary created above, automatically:

.. code-block:: bash

    $ python manage.py ogrinspect world/data/TM_WORLD_BORDERS-0.3.shp WorldBorder \
        --srid=4326 --mapping --multi

A few notes about the command-line options given above:

* The ``--srid=4326`` option sets the SRID for the geographic field.
* The ``--mapping`` option tells ``ogrinspect`` to also generate a
  mapping dictionary for use with
  :class:`~django.contrib.gis.utils.LayerMapping`.
* The ``--multi`` option is specified so that the geographic field is a
  :class:`~django.contrib.gis.db.models.MultiPolygonField` instead of just a
  :class:`~django.contrib.gis.db.models.PolygonField`.

The command produces the following output, which may be copied
directly into the ``models.py`` of a GeoDjango application::

    # This is an auto-generated Django model module created by ogrinspect.
    from django.contrib.gis.db import models

    class WorldBorder(models.Model):
        fips = models.CharField(max_length=2)
        iso2 = models.CharField(max_length=2)
        iso3 = models.CharField(max_length=3)
        un = models.IntegerField()
        name = models.CharField(max_length=50)
        area = models.IntegerField()
        pop2005 = models.IntegerField()
        region = models.IntegerField()
        subregion = models.IntegerField()
        lon = models.FloatField()
        lat = models.FloatField()
        geom = models.MultiPolygonField(srid=4326)
        objects = models.GeoManager()

    # Auto-generated `LayerMapping` dictionary for WorldBorder model
    worldborders_mapping = {
        'fips' : 'FIPS',
        'iso2' : 'ISO2',
        'iso3' : 'ISO3',
        'un' : 'UN',
        'name' : 'NAME',
        'area' : 'AREA',
        'pop2005' : 'POP2005',
        'region' : 'REGION',
        'subregion' : 'SUBREGION',
        'lon' : 'LON',
        'lat' : 'LAT',
        'geom' : 'MULTIPOLYGON',
    }

Spatial Queries
===============

Spatial Lookups
---------------
GeoDjango extends the Django ORM and allows the use of spatial lookups.
Let's do an example where we find the ``WorldBorder`` model that contains
a point.  First, fire up the management shell:

.. code-block:: bash

    $ python manage.py shell

Now, define a point of interest [#]_::

    >>> pnt_wkt = 'POINT(-95.3385 29.7245)'

The ``pnt_wkt`` string represents the point at -95.3385 degrees longitude,
and 29.7245 degrees latitude.  The geometry is in a format known as
Well Known Text (WKT), an open standard issued by the Open Geospatial
Consortium (OGC). [#]_  Import the ``WorldBorder`` model, and perform
a ``contains`` lookup using the ``pnt_wkt`` as the parameter::

    >>> from world.models import WorldBorder
    >>> qs = WorldBorder.objects.filter(mpoly__contains=pnt_wkt)
    >>> qs
    [<WorldBorder: United States>]

Here we retrieved a ``GeoQuerySet`` that has only one model: the one
for the United States (which is what we would expect).  Similarly,
a :ref:`GEOS geometry object <ref-geos>` may also be used -- here the
``intersects`` spatial lookup is combined with the ``get`` method to retrieve
only the ``WorldBorder`` instance for San Marino instead of a queryset::

    >>> from django.contrib.gis.geos import Point
    >>> pnt = Point(12.4604, 43.9420)
    >>> sm = WorldBorder.objects.get(mpoly__intersects=pnt)
    >>> sm
    <WorldBorder: San Marino>

The ``contains`` and ``intersects`` lookups are just a subset of what's
available -- the :ref:`ref-gis-db-api` documentation has more.

Automatic Spatial Transformations
---------------------------------
When querying the spatial database GeoDjango automatically transforms
geometries if they're in a different coordinate system.  In the following
example, the coordinate will be expressed in terms of `EPSG SRID 32140`__,
a coordinate system specific to south Texas **only** and in units of
**meters** and not degrees::

    >>> from django.contrib.gis.geos import Point, GEOSGeometry
    >>> pnt = Point(954158.1, 4215137.1, srid=32140)

Note that ``pnt`` may also be constructed with EWKT, an "extended" form of
WKT that includes the SRID::

    >>> pnt = GEOSGeometry('SRID=32140;POINT(954158.1 4215137.1)')

When using GeoDjango's ORM, it will automatically wrap geometry values
in transformation SQL, allowing the developer to work at a higher level
of abstraction::

    >>> qs = WorldBorder.objects.filter(mpoly__intersects=pnt)
    >>> print(qs.query) # Generating the SQL
    SELECT "world_worldborder"."id", "world_worldborder"."name", "world_worldborder"."area",
    "world_worldborder"."pop2005", "world_worldborder"."fips", "world_worldborder"."iso2",
    "world_worldborder"."iso3", "world_worldborder"."un", "world_worldborder"."region",
    "world_worldborder"."subregion", "world_worldborder"."lon", "world_worldborder"."lat",
    "world_worldborder"."mpoly" FROM "world_worldborder"
    WHERE ST_Intersects("world_worldborder"."mpoly", ST_Transform(%s, 4326))
    >>> qs # printing evaluates the queryset
    [<WorldBorder: United States>]

__ http://spatialreference.org/ref/epsg/32140/

Lazy Geometries
---------------
Geometries come to GeoDjango in a standardized textual representation.  Upon
access of the geometry field, GeoDjango creates a `GEOS geometry object
<ref-geos>`, exposing powerful functionality, such as serialization properties
for popular geospatial formats::

    >>> sm = WorldBorder.objects.get(name='San Marino')
    >>> sm.mpoly
    <MultiPolygon object at 0x24c6798>
    >>> sm.mpoly.wkt # WKT
    MULTIPOLYGON (((12.4157980000000006 43.9579540000000009, 12.4505540000000003 43.9797209999999978, ...
    >>> sm.mpoly.wkb # WKB (as Python binary buffer)
    <read-only buffer for 0x1fe2c70, size -1, offset 0 at 0x2564c40>
    >>> sm.mpoly.geojson # GeoJSON (requires GDAL)
    '{ "type": "MultiPolygon", "coordinates": [ [ [ [ 12.415798, 43.957954 ], [ 12.450554, 43.979721 ], ...

This includes access to all of the advanced geometric operations provided by
the GEOS library::

    >>> pnt = Point(12.4604, 43.9420)
    >>> sm.mpoly.contains(pnt)
    True
    >>> pnt.contains(sm.mpoly)
    False

``GeoQuerySet`` Methods
-----------------------


Putting your data on the map
============================

Google
------

Geographic Admin
----------------

GeoDjango extends :doc:`Django's admin application </ref/contrib/admin/index>`
to enable support for editing geometry fields.

Basics
^^^^^^

GeoDjango also supplements the Django admin by allowing users to create
and modify geometries on a JavaScript slippy map (powered by `OpenLayers`_).

Let's dive in again -- create a file called ``admin.py`` inside the
``world`` application, and insert the following::

    from django.contrib.gis import admin
    from models import WorldBorder

    admin.site.register(WorldBorder, admin.GeoModelAdmin)

Next, edit your ``urls.py`` in the ``geodjango`` application folder to look
as follows::

    from django.conf.urls import patterns, url, include
    from django.contrib.gis import admin

    admin.autodiscover()

    urlpatterns = patterns('',
        url(r'^admin/', include(admin.site.urls)),
    )

Start up the Django development server:

.. code-block:: bash

    $ python manage.py runserver

Finally, browse to ``http://localhost:8000/admin/``, and log in with the admin
user created after running ``syncdb``.  Browse to any of the ``WorldBorder``
entries -- the borders may be edited by clicking on a polygon and dragging
the vertexes to the desired position.

.. _OpenLayers: http://openlayers.org/
.. _Open Street Map: http://openstreetmap.org/
.. _Vector Map Level 0: http://earth-info.nga.mil/publications/vmap0.html
.. _OSGeo: http://www.osgeo.org

.. _osmgeoadmin-intro:

``OSMGeoAdmin``
^^^^^^^^^^^^^^^

With the :class:`~django.contrib.gis.admin.OSMGeoAdmin`, GeoDjango uses
a `Open Street Map`_ layer in the admin.
This provides more context (including street and thoroughfare details) than
available with the :class:`~django.contrib.gis.admin.GeoModelAdmin`
(which uses the `Vector Map Level 0`_ WMS data set hosted at `OSGeo`_).

First, there are some important requirements and limitations:

* :class:`~django.contrib.gis.admin.OSMGeoAdmin` requires that the
  :ref:`spherical mercator projection be added <addgoogleprojection>`
  to the ``spatial_ref_sys`` table (PostGIS 1.3 and below, only).
* The PROJ.4 datum shifting files must be installed (see the
  :ref:`PROJ.4 installation instructions <proj4>` for more details).

If you meet these requirements, then just substitute in the ``OSMGeoAdmin``
option class in your ``admin.py`` file::

    admin.site.register(WorldBorder, admin.OSMGeoAdmin)

.. rubric:: Footnotes

.. [#] Special thanks to Bjørn Sandvik of `thematicmapping.org <http://thematicmapping.org>`_ for providing and maintaining this data set.
.. [#] GeoDjango basic apps was written by Dane Springmeyer, Josh Livni, and Christopher Schmidt.
.. [#] Here the point is for the `University of Houston Law Center <http://www.law.uh.edu/>`_.
.. [#] Open Geospatial Consortium, Inc., `OpenGIS Simple Feature Specification For SQL <http://www.opengeospatial.org/standards/sfs>`_.
