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 regular tutorial to familiarize yourself with Django first.
Note
GeoDjango has additional requirements beyond what Django requires – 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]
Note
Proceed through the tutorial sections sequentially for step-by-step instructions.
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:
- Installing PostGIS
- Installing SpatiaLite
Create a New Project
Use the standard django-admin
script to create a project called geodjango
:
$ django-admin 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.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.gis', 'world', ]
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, 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.
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) 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): # __unicode__ on Python 2 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.
Run migrate
After defining your model, you need to sync it with the database. First, create a database migration:
$ 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:
$ python manage.py sqlmigrate world 0001
This command should produce the following output:
BEGIN; -- -- Create model WorldBorder -- 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 "mpoly" geometry(MULTIPOLYGON,4326) NOT NULL ) ; CREATE INDEX "world_worldborder_mpoly_id" ON "world_worldborder" USING GIST ( "mpoly" ); COMMIT;
If this looks correct, run migrate
to create this table in the database:
$ 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 LayerMapping data import utility.
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.
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
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 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 theWorldBorder
model. The value is the name of the shapefile field that data will be loaded from. - The key
mpoly
for the geometry field isMULTIPOLYGON
, 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 (withdata
subdirectory) to a different location, the script will still work. - The
transform
keyword is set toFalse
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 tellsogrinspect
to also generate a mapping dictionary for use withLayerMapping
. - The
--multi
option is specified so that the geographic field is aMultiPolygonField
instead of just aPolygonField
.
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:
$ 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 >>> 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 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) >>> WorldBorder.objects.get(mpoly__intersects=pnt) <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 <QuerySet [<WorldBorder: United States>]>
Raw queries
When using raw queries, you should generally wrap your geometry fields with the asText()
SQL function (or ST_AsText
for PostGIS) so that the field value will be recognized by GEOS:
City.objects.raw('SELECT id, name, asText(point) from myapp_city')
This is not absolutely required by PostGIS, but generally you should only use raw queries when you know exactly what you are doing.
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
Geographic annotations
GeoDjango also offers a set of geographic annotations to compute distances and several other operations (intersection, difference, etc.). See the Geographic Database Functions documentation.
Putting your data on the map
Geographic Admin
GeoDjango extends Django’s admin application with 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 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.GeoModelAdmin)
Next, edit your urls.py
in the geodjango
application folder as follows:
from django.conf.urls import url, include from django.contrib.gis import admin urlpatterns = [ url(r'^admin/', admin.site.urls), ]
Create an admin user:
$ python manage.py createsuperuser
Next, start up the Django development server:
$ 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 vertexes to the desired position.
OSMGeoAdmin
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 dataset hosted at OSGeo).
First, there are some important requirements:
-
OSMGeoAdmin
requires that GDAL is installed. - The PROJ.4 datum shifting files must be installed (see the PROJ.4 installation instructions for more details).
If you meet this requirement, then just substitute the OSMGeoAdmin
option class in your admin.py
file:
admin.site.register(WorldBorder, admin.OSMGeoAdmin)
Footnotes
[1] | Special thanks to Bjørn Sandvik of thematicmapping.org for providing and maintaining this dataset. |
[2] | GeoDjango basic apps was written by Dane Springmeyer, Josh Livni, and Christopher Schmidt. |
[3] | This point is the University of Houston Law Center. |
[4] | Open Geospatial Consortium, Inc., OpenGIS Simple Feature Specification For SQL. |
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