{"title":"GeoDjango Tutorial","version":"1.9","locale":"ja","docname":"ref/contrib/gis/tutorial","url":"/ja/1.9/ref/contrib/gis/tutorial/","canonical":"https://djangodocs.dev/ja/1.9/ref/contrib/gis/tutorial/","summary":"GeoDjango is an included contrib module for Django that turns it into a world-class geographic Web framework.","html":"

GeoDjango TutorialLink to this heading#

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IntroductionLink to this heading#

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GeoDjango is an included contrib module for Django that turns it into a\nworld-class geographic Web framework. GeoDjango strives to make it as simple\nas possible to create geographic Web applications, like location-based services.\nIts features include:

\n\n

This tutorial assumes familiarity with Django; thus, if you're brand new to\nDjango, please read through the regular tutorial to\nfamiliarize yourself with Django first.

\n\n

This tutorial will guide you through the creation of a geographic web\napplication for viewing the world borders. [1] Some of the code\nused in this tutorial is taken from and/or inspired by the GeoDjango\nbasic apps project. [2]

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Setting UpLink to this heading#

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Create a Spatial DatabaseLink to this heading#

\n

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

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Create a New ProjectLink to this heading#

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Use the standard django-admin script to create a project called\ngeodjango:

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Shell
$ django-admin startproject geodjango\n
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This will initialize a new project. Now, create a world Django application\nwithin the geodjango project:

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Shell
$ cd geodjango\n$ python manage.py startapp world\n
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Configure settings.pyLink to this heading#

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The geodjango project settings are stored in the geodjango/settings.py\nfile. Edit the database connection settings to match your setup:

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

In addition, modify the INSTALLED_APPS setting to include\ndjango.contrib.admin, django.contrib.gis,\nand world (your newly created application):

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Code
INSTALLED_APPS = [\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'django.contrib.gis',\n    'world',\n]\n
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Geographic DataLink to this heading#

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World BordersLink to this heading#

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The world borders data is available in this zip file. Create a data\ndirectory in the world application, download the world borders data, and\nunzip. On GNU/Linux platforms, use the following commands:

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Shell
$ mkdir world/data\n$ cd world/data\n$ wget http://thematicmapping.org/downloads/TM_WORLD_BORDERS-0.3.zip\n$ unzip TM_WORLD_BORDERS-0.3.zip\n$ cd ../..\n
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The world borders ZIP file contains a set of data files collectively known as\nan ESRI Shapefile, one of the most popular geospatial data formats. When\nunzipped, the world borders dataset includes files with the following\nextensions:

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Use ogrinfo to examine spatial dataLink to this heading#

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The GDAL ogrinfo utility allows examining the metadata of shapefiles or\nother vector data sources:

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Shell
$ ogrinfo world/data/TM_WORLD_BORDERS-0.3.shp\nINFO: Open of `world/data/TM_WORLD_BORDERS-0.3.shp'\n      using driver `ESRI Shapefile' successful.\n1: TM_WORLD_BORDERS-0.3 (Polygon)\n
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ogrinfo tells us that the shapefile has one layer, and that this\nlayer contains polygon data. To find out more, we'll specify the layer name\nand use the -so option to get only the important summary information:

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Shell
$ ogrinfo -so world/data/TM_WORLD_BORDERS-0.3.shp TM_WORLD_BORDERS-0.3\nINFO: Open of `world/data/TM_WORLD_BORDERS-0.3.shp'\n      using driver `ESRI Shapefile' successful.\n\nLayer name: TM_WORLD_BORDERS-0.3\nGeometry: Polygon\nFeature Count: 246\nExtent: (-180.000000, -90.000000) - (180.000000, 83.623596)\nLayer SRS WKT:\nGEOGCS["GCS_WGS_1984",\n    DATUM["WGS_1984",\n        SPHEROID["WGS_1984",6378137.0,298.257223563]],\n    PRIMEM["Greenwich",0.0],\n    UNIT["Degree",0.0174532925199433]]\nFIPS: String (2.0)\nISO2: String (2.0)\nISO3: String (3.0)\nUN: Integer (3.0)\nNAME: String (50.0)\nAREA: Integer (7.0)\nPOP2005: Integer (10.0)\nREGION: Integer (3.0)\nSUBREGION: Integer (3.0)\nLON: Real (8.3)\nLAT: Real (7.3)\n
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This detailed summary information tells us the number of features in the layer\n(246), the geographic bounds of the data, the spatial reference system\n("SRS WKT"), as well as type information for each attribute field. For example,\nFIPS: String (2.0) indicates that the FIPS character field has\na maximum length of 2. Similarly, LON: Real (8.3) is a floating-point\nfield that holds a maximum of 8 digits up to three decimal places.

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Geographic ModelsLink to this heading#

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Defining a Geographic ModelLink to this heading#

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Now that you've examined your dataset using ogrinfo, create a GeoDjango\nmodel to represent this data:

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Code
from django.contrib.gis.db import models\n\nclass WorldBorder(models.Model):\n    # Regular Django fields corresponding to the attributes in the\n    # world borders shapefile.\n    name = models.CharField(max_length=50)\n    area = models.IntegerField()\n    pop2005 = models.IntegerField('Population 2005')\n    fips = models.CharField('FIPS Code', max_length=2)\n    iso2 = models.CharField('2 Digit ISO', max_length=2)\n    iso3 = models.CharField('3 Digit ISO', max_length=3)\n    un = models.IntegerField('United Nations Code')\n    region = models.IntegerField('Region Code')\n    subregion = models.IntegerField('Sub-Region Code')\n    lon = models.FloatField()\n    lat = models.FloatField()\n\n    # GeoDjango-specific: a geometry field (MultiPolygonField)\n    mpoly = models.MultiPolygonField()\n\n    # Returns the string representation of the model.\n    def __str__(self):              # __unicode__ on Python 2\n        return self.name\n
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Note that the models module is imported from django.contrib.gis.db.

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

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Run migrateLink to this heading#

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

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Shell
$ python manage.py makemigrations\nMigrations for 'world':\n  0001_initial.py:\n    - Create model WorldBorder\n
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Let's look at the SQL that will generate the table for the WorldBorder\nmodel:

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Shell
$ python manage.py sqlmigrate world 0001\n
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This command should produce the following output:

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SQL
BEGIN;\n--\n-- Create model WorldBorder\n--\nCREATE TABLE "world_worldborder" (\n    "id" serial NOT NULL PRIMARY KEY,\n    "name" varchar(50) NOT NULL,\n    "area" integer NOT NULL,\n    "pop2005" integer NOT NULL,\n    "fips" varchar(2) NOT NULL,\n    "iso2" varchar(2) NOT NULL,\n    "iso3" varchar(3) NOT NULL,\n    "un" integer NOT NULL,\n    "region" integer NOT NULL,\n    "subregion" integer NOT NULL,\n    "lon" double precision NOT NULL,\n    "lat" double precision NOT NULL\n    "mpoly" geometry(MULTIPOLYGON,4326) NOT NULL\n)\n;\nCREATE INDEX "world_worldborder_mpoly_id" ON "world_worldborder" USING GIST ( "mpoly" );\nCOMMIT;\n
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If this looks correct, run migrate to create this table in the\ndatabase:

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Shell
$ python manage.py migrate\nOperations to perform:\n  Apply all migrations: admin, world, contenttypes, auth, sessions\nRunning migrations:\n  ...\n  Applying world.0001_initial... OK\n
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Importing Spatial DataLink to this heading#

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This section will show you how to import the world borders shapefile into the\ndatabase via GeoDjango models using the LayerMapping data import utility.

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

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GDAL InterfaceLink to this heading#

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

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First, invoke the Django shell:

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Shell
$ python manage.py shell\n
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If you downloaded the World Borders data earlier in the\ntutorial, then you can determine its path using Python's built-in\nos module:

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Code
>>> import os\n>>> import world\n>>> world_shp = os.path.abspath(os.path.join(os.path.dirname(world.__file__),\n...                             'data', 'TM_WORLD_BORDERS-0.3.shp'))\n
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Now, open the world borders shapefile using GeoDjango's\nDataSource interface:

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Code
>>> from django.contrib.gis.gdal import DataSource\n>>> ds = DataSource(world_shp)\n>>> print(ds)\n/ ... /geodjango/world/data/TM_WORLD_BORDERS-0.3.shp (ESRI Shapefile)\n
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Data source objects can have different layers of geospatial features; however,\nshapefiles are only allowed to have one layer:

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Code
>>> print(len(ds))\n1\n>>> lyr = ds[0]\n>>> print(lyr)\nTM_WORLD_BORDERS-0.3\n
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You can see the layer's geometry type and how many features it contains:

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Code
>>> print(lyr.geom_type)\nPolygon\n>>> print(len(lyr))\n246\n
\n\n

The Layer may also have a spatial reference\nsystem associated with it. If it does, the srs attribute will return a\nSpatialReference object:

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Code
>>> srs = lyr.srs\n>>> print(srs)\nGEOGCS["GCS_WGS_1984",\n    DATUM["WGS_1984",\n        SPHEROID["WGS_1984",6378137.0,298.257223563]],\n    PRIMEM["Greenwich",0.0],\n    UNIT["Degree",0.0174532925199433]]\n>>> srs.proj4 # PROJ.4 representation\n'+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs '\n
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This shapefile is in the popular WGS84 spatial reference\nsystem -- in other words, the data uses longitude, latitude pairs in\nunits of degrees.

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

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Code
>>> print(lyr.fields)\n['FIPS', 'ISO2', 'ISO3', 'UN', 'NAME', 'AREA', 'POP2005', 'REGION', 'SUBREGION', 'LON', 'LAT']\n
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The following code will let you examine the OGR types (e.g. integer or\nstring) associated with each of the fields:

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Code
>>> [fld.__name__ for fld in lyr.field_types]\n['OFTString', 'OFTString', 'OFTString', 'OFTInteger', 'OFTString', 'OFTInteger', 'OFTInteger', 'OFTInteger', 'OFTInteger', 'OFTReal', 'OFTReal']\n
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You can iterate over each feature in the layer and extract information from both\nthe feature's geometry (accessed via the geom attribute) as well as the\nfeature's attribute fields (whose values are accessed via get()\nmethod):

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Code
>>> for feat in lyr:\n...    print(feat.get('NAME'), feat.geom.num_points)\n...\nGuernsey 18\nJersey 26\nSouth Georgia South Sandwich Islands 338\nTaiwan 363\n
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Layer objects may be sliced:

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Code
>>> lyr[0:2]\n[<django.contrib.gis.gdal.feature.Feature object at 0x2f47690>, <django.contrib.gis.gdal.feature.Feature object at 0x2f47650>]\n
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And individual features may be retrieved by their feature ID:

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Code
>>> feat = lyr[234]\n>>> print(feat.get('NAME'))\nSan Marino\n
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Boundary geometries may be exported as WKT and GeoJSON:

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Code
>>> geom = feat.geom\n>>> print(geom.wkt)\nPOLYGON ((12.415798 43.957954,12.450554 ...\n>>> print(geom.json)\n{ "type": "Polygon", "coordinates": [ [ [ 12.415798, 43.957954 ], [ 12.450554, 43.979721 ], ...\n
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LayerMappingLink to this heading#

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To import the data, use a LayerMapping in a Python script.\nCreate a file called load.py inside the world application,\nwith the following code:

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Code
import os\nfrom django.contrib.gis.utils import LayerMapping\nfrom .models import WorldBorder\n\nworld_mapping = {\n    'fips' : 'FIPS',\n    'iso2' : 'ISO2',\n    'iso3' : 'ISO3',\n    'un' : 'UN',\n    'name' : 'NAME',\n    'area' : 'AREA',\n    'pop2005' : 'POP2005',\n    'region' : 'REGION',\n    'subregion' : 'SUBREGION',\n    'lon' : 'LON',\n    'lat' : 'LAT',\n    'mpoly' : 'MULTIPOLYGON',\n}\n\nworld_shp = os.path.abspath(\n    os.path.join(os.path.dirname(__file__), 'data', 'TM_WORLD_BORDERS-0.3.shp'),\n)\n\ndef run(verbose=True):\n    lm = LayerMapping(\n        WorldBorder, world_shp, world_mapping,\n        transform=False, encoding='iso-8859-1',\n    )\n    lm.save(strict=True, verbose=verbose)\n
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A few notes about what's going on:

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

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Shell
$ python manage.py shell\n
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Next, import the load module, call the run routine, and watch\nLayerMapping do the work:

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Code
>>> from world import load\n>>> load.run()\n
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Try ogrinspectLink to this heading#

\n

Now that you've seen how to define geographic models and import data with the\nLayerMapping data import utility, it's possible to further automate this process with\nuse of the ogrinspect management command. The ogrinspect\ncommand introspects a GDAL-supported vector data source (e.g., a shapefile)\nand generates a model definition and LayerMapping dictionary automatically.

\n

The general usage of the command goes as follows:

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

data_source is the path to the GDAL-supported data source and\nmodel_name is the name to use for the model. Command-line options may\nbe used to further define how the model is generated.

\n

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

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Shell
$ python manage.py ogrinspect world/data/TM_WORLD_BORDERS-0.3.shp WorldBorder \\\n    --srid=4326 --mapping --multi\n
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A few notes about the command-line options given above:

\n\n

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

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Code
# This is an auto-generated Django model module created by ogrinspect.\nfrom django.contrib.gis.db import models\n\nclass WorldBorder(models.Model):\n    fips = models.CharField(max_length=2)\n    iso2 = models.CharField(max_length=2)\n    iso3 = models.CharField(max_length=3)\n    un = models.IntegerField()\n    name = models.CharField(max_length=50)\n    area = models.IntegerField()\n    pop2005 = models.IntegerField()\n    region = models.IntegerField()\n    subregion = models.IntegerField()\n    lon = models.FloatField()\n    lat = models.FloatField()\n    geom = models.MultiPolygonField(srid=4326)\n\n# Auto-generated `LayerMapping` dictionary for WorldBorder model\nworldborders_mapping = {\n    'fips' : 'FIPS',\n    'iso2' : 'ISO2',\n    'iso3' : 'ISO3',\n    'un' : 'UN',\n    'name' : 'NAME',\n    'area' : 'AREA',\n    'pop2005' : 'POP2005',\n    'region' : 'REGION',\n    'subregion' : 'SUBREGION',\n    'lon' : 'LON',\n    'lat' : 'LAT',\n    'geom' : 'MULTIPOLYGON',\n}\n
\n
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\n
\n

Spatial QueriesLink to this heading#

\n
\n

Spatial LookupsLink to this heading#

\n

GeoDjango adds spatial lookups to the Django ORM. For example, you\ncan find the country in the WorldBorder table that contains\na particular point. First, fire up the management shell:

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Shell
$ python manage.py shell\n
\n

Now, define a point of interest [3]:

\n
Code
>>> pnt_wkt = 'POINT(-95.3385 29.7245)'\n
\n

The pnt_wkt string represents the point at -95.3385 degrees longitude,\n29.7245 degrees latitude. The geometry is in a format known as\nWell Known Text (WKT), a standard issued by the Open Geospatial\nConsortium (OGC). [4] Import the WorldBorder model, and perform\na contains lookup using the pnt_wkt as the parameter:

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

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

\n

Similarly, you may also use a GEOS geometry object.\nHere, you can combine the intersects spatial lookup with the get\nmethod to retrieve only the WorldBorder instance for San Marino instead\nof a queryset:

\n
Code
>>> from django.contrib.gis.geos import Point\n>>> pnt = Point(12.4604, 43.9420)\n>>> sm = WorldBorder.objects.get(mpoly__intersects=pnt)\n>>> sm\n<WorldBorder: San Marino>\n
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The contains and intersects lookups are just a subset of the\navailable queries -- the GeoDjango Database API documentation has more.

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\n
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Automatic Spatial TransformationsLink to this heading#

\n

When doing spatial queries, GeoDjango automatically transforms\ngeometries if they're in a different coordinate system. In the following\nexample, coordinates will be expressed in EPSG SRID 32140,\na coordinate system specific to south Texas only and in units of\nmeters, not degrees:

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

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

\n
Code
>>> pnt = GEOSGeometry('SRID=32140;POINT(954158.1 4215137.1)')\n
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GeoDjango's ORM will automatically wrap geometry values\nin transformation SQL, allowing the developer to work at a higher level\nof abstraction:

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

Lazy GeometriesLink to this heading#

\n

GeoDjango loads geometries in a standardized textual representation. When the\ngeometry field is first accessed, GeoDjango creates a GEOS geometry object\n<ref-geos>, exposing powerful functionality, such as serialization properties\nfor popular geospatial formats:

\n
Code
>>> sm = WorldBorder.objects.get(name='San Marino')\n>>> sm.mpoly\n<MultiPolygon object at 0x24c6798>\n>>> sm.mpoly.wkt # WKT\nMULTIPOLYGON (((12.4157980000000006 43.9579540000000009, 12.4505540000000003 43.9797209999999978, ...\n>>> sm.mpoly.wkb # WKB (as Python binary buffer)\n<read-only buffer for 0x1fe2c70, size -1, offset 0 at 0x2564c40>\n>>> sm.mpoly.geojson # GeoJSON (requires GDAL)\n'{ "type": "MultiPolygon", "coordinates": [ [ [ [ 12.415798, 43.957954 ], [ 12.450554, 43.979721 ], ...\n
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This includes access to all of the advanced geometric operations provided by\nthe GEOS library:

\n
Code
>>> pnt = Point(12.4604, 43.9420)\n>>> sm.mpoly.contains(pnt)\nTrue\n>>> pnt.contains(sm.mpoly)\nFalse\n
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\n
\n

Geographic annotationsLink to this heading#

\n

GeoDjango also offers a set of geographic annotations to compute distances and\nseveral other operations (intersection, difference, etc.). See the\nGeographic Database Functions documentation.

\n
\n
\n
\n

Putting your data on the mapLink to this heading#

\n
\n

Geographic AdminLink to this heading#

\n

GeoDjango extends Django's admin application\nwith support for editing geometry fields.

\n
\n

BasicsLink to this heading#

\n

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

\n

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

\n
Code
from django.contrib.gis import admin\nfrom .models import WorldBorder\n\nadmin.site.register(WorldBorder, admin.GeoModelAdmin)\n
\n

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

\n
Code
from django.conf.urls import url, include\nfrom django.contrib.gis import admin\n\nurlpatterns = [\n    url(r'^admin/', admin.site.urls),\n]\n
\n

Create an admin user:

\n
Shell
$ python manage.py createsuperuser\n
\n

Next, start up the Django development server:

\n
Shell
$ python manage.py runserver\n
\n

Finally, browse to http://localhost:8000/admin/, and log in with the user\nyou just created. Browse to any of the WorldBorder entries -- the borders\nmay be edited by clicking on a polygon and dragging the vertexes to the desired\nposition.

\n
\n
\n

OSMGeoAdminLink to this heading#

\n

With the OSMGeoAdmin, GeoDjango uses\na Open Street Map layer in the admin.\nThis provides more context (including street and thoroughfare details) than\navailable with the GeoModelAdmin\n(which uses the Vector Map Level 0 WMS dataset hosted at OSGeo).

\n

First, there are some important requirements:

\n\n

If you meet this requirement, then just substitute the OSMGeoAdmin\noption class in your admin.py file:

\n
Code
admin.site.register(WorldBorder, admin.OSMGeoAdmin)\n
\n

Footnotes

\n\n
\n
\n
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