GeoDjango Tutorial


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 regular tutorial to introduce yourself with basic Django concepts.


GeoDjango has special prerequisites overwhat is required by Django – please consult the installation documentation for more details.

This tutorial will guide you through the creation of a geographic Web application for viewing the world borders. [1] Some of the code used in this tutorial is taken from and/or inspired by the GeoDjango basic apps project. [2]


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

Setting Up

Create a Spatial Database


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 spatial database template:

$ createdb -T template_postgis geodjango


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:

$ 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 SpatiaLite database.

Create GeoDjango Project

Use the script like normal to create a geodjango project:

$ startproject geodjango

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

$ cd geodjango
$ python startapp world


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

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

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


Geographic Data

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:

$ mkdir world/data
$ cd world/data
$ wget
$ unzip
$ 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.

Use ogrinfo to examine spatial data

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

$ 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:

$ 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:
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):

Two important things to note:

  1. The models module is imported from django.contrib.gis.db.
  2. The model overrides its default manager with 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.

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 sqlall world

This management command should produce the following output:

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 );

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

$ python 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 LayerMapping data import utility. 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.

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:

$ python shell

If the World Borders 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 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)
>>> lyr = ds[0]
>>> print lyr

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

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


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 Layer may also have a spatial reference system associated with it – if it does, the srs attribute will return a SpatialReference object:

>>> srs = lyr.srs
>>> print srs
>>> 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

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 ], ...


We’re going to dive right in – create a file called 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'), 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:

$ python shell

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

>>> from world import load

Try ogrinspect

Now that you’ve seen how to define geographic models and import data with the LayerMapping data import utility, it’s possible to further automate this process with use of the ogrinspect management command. The 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:

$ python 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:

$ python 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 LayerMapping.
  • The --multi option is specified so that the geographic field is a MultiPolygonField instead of just a PolygonField.

The command produces the following output, which may be copied directly into the 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:

$ python shell

Now, define a point of interest [3]:

>>> 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). [4] 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 GEOS geometry object 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 GeoDjango Database 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>]

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)
>>> pnt.contains(sm.mpoly)

GeoQuerySet Methods

Putting your data on the map


Geographic Admin

GeoDjango extends Django’s admin application to enable support for editing geometry fields.


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 inside the world application, and insert the following:

from django.contrib.gis import admin
from models import WorldBorder, admin.GeoModelAdmin)

Next, edit your in the geodjango application folder to look as follows:

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


urlpatterns = patterns('',
    url(r'^admin/', include(,

Start up the Django development server:

$ python 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.


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

First, there are some important requirements and limitations:

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


[1]Special thanks to Bjørn Sandvik of for providing and maintaining this data set.
[2]GeoDjango basic apps was written by Dane Springmeyer, Josh Livni, and Christopher Schmidt.
[3]Here the point is for the University of Houston Law Center.
[4]Open Geospatial Consortium, Inc., OpenGIS Simple Feature Specification For SQL.