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

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

GeoDjango is an included contrib module 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.
Its features include:

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

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

.. note::

    GeoDjango has additional requirements beyond what Django requires --
    please consult the :doc:`installation documentation <install/index>`
    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: https://www.ogc.org/
.. _world borders: https://thematicmapping.org/downloads/world_borders.php
.. _GeoDjango basic apps: https://code.google.com/archive/p/geodjango-basic-apps

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

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

Typically no special setup is required, so you can create a database as you
would for any other project. We provide some tips for selected databases:

* :doc:`install/postgis`
* :doc:`install/spatialite`

Create a New Project
--------------------

Use the standard ``django-admin`` script to create a project called
``geodjango``:

.. console::

    $ django-admin startproject geodjango

This will initialize a new project. Now, create a ``world`` Django application
within the ``geodjango`` project:

.. console::

    $ 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 to match your setup::

    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`` (your newly created application)::

    INSTALLED_APPS = [
        "django.contrib.admin",
        "django.contrib.auth",
        "django.contrib.contenttypes",
        "django.contrib.sessions",
        "django.contrib.messages",
        "django.contrib.staticfiles",
        "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, use the following commands:

.. console::

    $ mkdir world/data
    $ cd world/data
    $ wget https://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 dataset 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.

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

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

The GDAL ``ogrinfo`` utility allows examining the metadata of shapefiles or
other vector data sources:

.. console::

    $ 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)

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

.. console::

    $ 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 geographic bounds of the data, the spatial reference system
("SRS WKT"), as well as type information for each attribute field. For example,
``FIPS: String (2.0)`` indicates that the ``FIPS`` character field has
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.

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

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

Now that you've examined your dataset using ``ogrinfo``, 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, null=True)
        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)
        mpoly = models.MultiPolygonField()

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

Note that the ``models`` module is imported from ``django.contrib.gis.db``.

The default spatial reference system for geometry fields is WGS84 (meaning
the `SRID`__ is 4326) -- in other words, the field coordinates are in
longitude, latitude pairs in units of degrees.  To use a different
coordinate system, set the SRID of the geometry field with the ``srid``
argument. Use an integer representing the coordinate system's EPSG code.

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

Run ``migrate``
---------------

After defining your model, you need to sync it with the database. First,
create a database migration:

.. console::

    $ python manage.py makemigrations
    Migrations for 'world':
      world/migrations/0001_initial.py:
        - Create model WorldBorder

Let's look at the SQL that will generate the table for the ``WorldBorder``
model:

.. console::

    $ python manage.py sqlmigrate world 0001

This command should produce the following output:

.. console::

    BEGIN;
    --
    -- Create model WorldBorder
    --
    CREATE TABLE "world_worldborder" (
        "id" bigint NOT NULL PRIMARY KEY GENERATED BY DEFAULT AS IDENTITY,
        "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
        "mpoly" geometry(MULTIPOLYGON,4326) NOT NULL
    )
    ;
    CREATE INDEX "world_worldborder_mpoly_id" ON "world_worldborder" USING GIST ("mpoly");
    COMMIT;

If this looks correct, run :djadmin:`migrate` to create this table in the
database:

.. console::

    $ python manage.py migrate
    Operations to perform:
      Apply all migrations: admin, auth, contenttypes, sessions, world
    Running migrations:
      ...
      Applying world.0001_initial... OK

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

This section will show you how to import the world borders shapefile into the
database via GeoDjango models using the :doc:`layermapping`.

There are many different ways to import data into a spatial database --
besides the tools included within GeoDjango, you may also use the following:

* `ogr2ogr`_: A command-line utility included with GDAL that
  can import many vector data formats into PostGIS, MySQL, and Oracle databases.
* `shp2pgsql`_: This utility included with PostGIS imports ESRI shapefiles into
  PostGIS.

.. _ogr2ogr: https://gdal.org/programs/ogr2ogr.html
.. _shp2pgsql: https://postgis.net/docs/using_postgis_dbmanagement.html#shp2pgsql_usage

.. _gdalinterface:

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

Earlier, you used ``ogrinfo`` to examine the contents of the world borders
shapefile.  GeoDjango also includes a Pythonic interface to GDAL's powerful OGR
library that can work with all the vector data sources that OGR supports.

First, invoke the Django shell:

.. console::

    $ python manage.py shell

If you downloaded the :ref:`worldborders` data earlier in the tutorial, then
you can determine its path using Python's :class:`pathlib.Path`:

.. code-block:: pycon

    >>> from pathlib import Path
    >>> import world
    >>> world_shp = Path(world.__file__).resolve().parent / "data" / "TM_WORLD_BORDERS-0.3.shp"

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

.. code-block:: pycon

    >>> 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:

.. code-block:: pycon

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

You can see the layer's geometry type and how many features it contains:

.. code-block:: pycon

    >>> 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, not Polygons.
    It's important to use a more general field type in models: a
    GeoDjango ``MultiPolygonField`` will accept a ``Polygon`` geometry, but a
    ``PolygonField`` will not accept a ``MultiPolygon`` type geometry.  This
    is why the ``WorldBorder`` model defined above uses a ``MultiPolygonField``.

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:

.. code-block:: pycon

    >>> srs = lyr.srs
    >>> print(srs)
    GEOGCS["WGS 84",
    DATUM["WGS_1984",
        SPHEROID["WGS 84",6378137,298.257223563,
            AUTHORITY["EPSG","7030"]],
        AUTHORITY["EPSG","6326"]],
    PRIMEM["Greenwich",0,
        AUTHORITY["EPSG","8901"]],
    UNIT["degree",0.0174532925199433,
        AUTHORITY["EPSG","9122"]],
    AXIS["Latitude",NORTH],
    AXIS["Longitude",EAST],
    AUTHORITY["EPSG","4326"]]
    >>> srs.proj  # PROJ representation
    '+proj=longlat +datum=WGS84 +no_defs'

This shapefile is in the popular WGS84 spatial reference
system -- in other words, the data uses longitude, latitude pairs in
units of degrees.

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']

The following code will let you examine the OGR types (e.g. integer or
string) associated with each of the fields:

    >>> [fld.__name__ for fld in lyr.field_types]
    ['OFTString', 'OFTString', 'OFTString', 'OFTInteger', 'OFTString', 'OFTInteger', 'OFTInteger64', '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):

.. code-block:: pycon

    >>> 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:

.. code-block:: pycon

    >>> 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:

.. code-block:: pycon

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

Boundary geometries may be exported as WKT and GeoJSON:

.. code-block:: pycon

    >>> 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``
----------------

To import the data, use a ``LayerMapping`` in a Python script.
Create a file called ``load.py`` inside the ``world`` application,
with the following code::

    from pathlib import Path
    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 = Path(__file__).resolve().parent / "data" / "TM_WORLD_BORDERS-0.3.shp"


    def run(verbose=True):
        lm = LayerMapping(WorldBorder, world_shp, world_mapping, transform=False)
        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.  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 GeoDjango will import the field as.  Even simple polygons in
  the shapefile 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,
  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).

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

.. console::

    $ python manage.py shell

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

.. code-block:: pycon

    >>> 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
:doc:`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:

.. console::

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

``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:

.. console::

    $ 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)


    # 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 adds spatial lookups to the Django ORM.  For example, you
can find the country in the ``WorldBorder`` table that contains
a particular point.  First, fire up the management shell:

.. console::

    $ python manage.py shell

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

.. code-block:: pycon

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

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

.. code-block:: pycon

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

Here, you retrieved a ``QuerySet`` with only one model: the border of the
United States (exactly what you would expect).

Similarly, you may also use a :doc:`GEOS geometry object <geos>`.
Here, you can combine the ``intersects`` spatial lookup with the ``get``
method to retrieve only the ``WorldBorder`` instance for San Marino instead
of a queryset:

.. code-block:: pycon

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

The ``contains`` and ``intersects`` lookups are just a subset of the
available queries -- the :doc:`db-api` documentation has more.

.. _automatic-spatial-transformations:

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

.. code-block:: pycon

    >>> from django.contrib.gis.geos import GEOSGeometry, Point
    >>> 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:

.. code-block:: pycon

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

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

.. code-block:: pycon

    >>> 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
    <QuerySet [<WorldBorder: United States>]>

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

.. _gis-raw-sql:

.. admonition:: Raw queries

    When using :doc:`raw queries </topics/db/sql>`, you must wrap your geometry
    fields so that the field value can be recognized by GEOS:

    .. code-block:: pycon

        from django.db import connection
        # or if you're querying a non-default database:
        from django.db import connections
        connection = connections['your_gis_db_alias']

        City.objects.raw('SELECT id, name, %s as point from myapp_city' % (connection.ops.select % 'point'))

    You should only use raw queries when you know exactly what you're doing.

Lazy Geometries
---------------
GeoDjango loads geometries in a standardized textual representation.  When the
geometry field is first accessed, GeoDjango creates a
:class:`~django.contrib.gis.geos.GEOSGeometry` object, exposing powerful
functionality, such as serialization properties for popular geospatial
formats:

.. code-block:: pycon

    >>> 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
    '{ "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:

.. code-block:: pycon

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

Geographic annotations
----------------------

GeoDjango also offers a set of geographic annotations to compute distances and
several other operations (intersection, difference, etc.). See the
:doc:`functions` documentation.

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

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

:doc:`Django's admin application </ref/contrib/admin/index>` supports editing
geometry fields.

Basics
~~~~~~

The Django admin allows users to create and modify geometries on a JavaScript
slippy map (powered by `OpenLayers`_).

Let's dive right in. Create a file called ``admin.py`` inside the ``world``
application with the following code::

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

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

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

    from django.contrib import admin
    from django.urls import include, path

    urlpatterns = [
        path("admin/", admin.site.urls),
    ]

Create an admin user:

.. console::

    $ python manage.py createsuperuser

Next, start up the Django development server:

.. console::

    $ python manage.py runserver

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

.. _OpenLayers: https://openlayers.org/
.. _OpenStreetMap: https://www.openstreetmap.org/
.. _Vector Map Level 0: http://web.archive.org/web/20201024202709/https://earth-info.nga.mil/publications/vmap0.html
.. _OSGeo: https://www.osgeo.org/

``GISModelAdmin``
~~~~~~~~~~~~~~~~~

With the :class:`~django.contrib.gis.admin.GISModelAdmin`, GeoDjango uses an
`OpenStreetMap`_ layer in the admin.
This provides more context (including street and thoroughfare details) than
available with the :class:`~django.contrib.admin.ModelAdmin` (which uses the
`Vector Map Level 0`_ WMS dataset hosted at `OSGeo`_).

The PROJ datum shifting files must be installed (see the :ref:`PROJ
installation instructions <proj4>` for more details).

If you meet this requirement, then use the ``GISModelAdmin`` option class
in your ``admin.py`` file::

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

.. rubric:: Footnotes

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