GeoDjango Tutorial¶

Create a Spatial Database¶

First, create a spatial database for your project.

If you are using PostGIS, create the database from the spatial database template:

$ createdb -T template_postgis geodjango

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

If you are using SQLite and SpatiaLite, consult the instructions on how to create a SpatiaLite database.

Create a New Project¶

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

$ django-admin.py startproject geodjango

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

$ 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 INSTALLED_APPS setting to include django.contrib.admin, django.contrib.gis, and world (your 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'
)

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:

$ 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 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.

Use ogrinfo to examine spatial data¶

The GDAL ogrinfo utility allows examining the metadata of 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)

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:

$ 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.

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)
    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

Please note two important things:

1. The models module is imported from django.contrib.gis.db.
2. You must override the model’s default manager with GeoManager to perform spatial queries.

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.

Run syncdb¶

After defining your model, you need to sync it with the database. First, let’s look at the SQL that will generate the table for the WorldBorder model:

$ python manage.py sqlall world

This command should produce the following output:

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 this looks correct, run syncdb to create this table in the database:

$ 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. Either do so now, or later by running django-admin.py createsuperuser.

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:

$ python manage.py shell

If you downloaded the World Borders data earlier in the tutorial, then you can determine its 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, open the world borders shapefile 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))
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:

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

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)
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 '

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', '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

Boundary geometries may be exported as 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¶

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:

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. 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).
• The encoding keyword is set to the character encoding of the 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 manage.py shell

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

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

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 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:

$ 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 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 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 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:

$ python manage.py 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, 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). [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, you retrieved a GeoQuerySet with only one model: the border of the United States (exactly what you would expect).

Similarly, you may also use a GEOS geometry object. 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:

>>> 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 the available queries – the GeoDjango Database API documentation has more.

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:

>>> 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)')

GeoDjango’s ORM 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>]

City.objects.raw('SELECT id, name, asText(point) from myapp_city')

Lazy Geometries¶

GeoDjango loads geometries in a standardized textual representation. When the geometry field is first accessed, 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¶

Geographic Admin¶

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

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

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

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)),
)

$ python manage.py runserver

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