Version 112 (modified by jbronn, 17 years ago) ( diff )

Updated.

GeoDjango

TOC(GeoDjangoBackground, GeoDjango, GeoDjangoInstall, GeoDjangoModelAPI, GeoDjangoDatabaseAPI, GEOSGeometry, GeoDjangoExtras) The ​GIS branch intends to be a world-class geographic web framework. Our goal is to make it as easy as possible to build GIS web applications and harness the power of spatially enabled data.

News:

Note: The documentation has been re-factored into several pages. See the table of contents on the right for a complete listing of topics. Other GeoDjango pages include:

This documentation is behind the current feature-set -- when in doubt use help on the feature in question, as most [browser:django/branches/gis/django/contrib/gis GeoDjango modules] and routines have docstrings.

Roadmap

Note: In order to minimize confusion, please don't modify the official roadmap below. If you have a suggestion, place it in the suggestion section or ask your question in the FAQ. If your request is urgent, submit a ticket requesting your feature or bugfix (make sure to specify the component as 'GIS'). Thanks for your cooperation!

Pending Features

The following is a discussion of features we hope to implement in GeoDjango. Unless stated otherwise, features in this list are not prioritized. If you have a pet feature you may accelerate its development by submitting code yourself.

  • Support for a mapping framework (e.g., OpenLayers, Google Maps/Earth, Yahoo Maps, MS Live, etc.)
    • Admin fields and forms (WKT field currently as of r4884, but we want widgets to view and manipulate geographic objects).
    • Progress has been made on this front by multiple parties; we are currently utilizing newforms-admin, but are waiting for it to merge before committing any code.
  • WMS views.
  • Additional features available in PostGIS API.
  • Support additional spatial databases:

Milestones

Significant GeoDjango changesets are mentioned below:

  • r7104: Added support for distance queries on models using geodetic coordinate systems.
  • r6886: Added support for distance queries.
  • r6527: MySQL backend added.
  • r6524: Oracle Spatial backend added.
  • r6467: The GeoMixin was completely deprecated.
  • r6108: Created the maps module, which includes rudimentary support for generating Google Maps.
  • r5657: "Lazy-Geometry" support was added (courtesy of Robert Coup's excellent patch in #4322) -- deprecating most of the extra instance methods.
  • r5529: Added utilities for importing vector data (e.g., SHP files) directly into GeoDjango models.
    • The [source:django/branches/gis/django/contrib/gis/utils/LayerMapping.py LayerMapping] class will import vector data from GDAL-supported data sources. This is still 'beta', and requires installation of the GDAL library.
  • r5397: Added a ctypes interface for ​GDAL/OGR in [source:django/branches/gis/django/contrib/gis/gdal django.contrib.gis.gdal].
    • OGRGeometry: Wraps ​OGR geometries, may be accessed with an extra instance method (e.g., z.get_poly_ogr()).
    • SpatialReference: Wraps ​OGR Spatial Reference objects, may be used to transform OGR geometries.
    • DataSource: Wraps ​OGR Data Source objects, may be used to explore GDAL-supported data sources
  • r5008: Added a ctypes interface for ​GEOS in [source:django/branches/gis/django/contrib/gis/gdal django.contrib.gis.geos].
    • GEOSGeometry: Wraps ​GEOS geometries; all accessed geometry fields are returned as GEOSGeometry instances.
  • r4851: Added PostGIS indexing capability.

Implementation

Design Issues

  • Client JS/Flash framework. The maps module will be added soon, support for the following APIs will be implemented, or is being considered
    • [browser:django/branches/gis/django/contrib/gis/maps/google django.contrib.gis.maps.google]: Contains the GoogleMap object, which generates basic Google Maps API JavaScript from customizable templates. Automatic zoom-level determination is included.
    • django.contrib.gis.maps.openlayers: Similar plans are in place for a ​OpenLayers module, which is the open source web mapping framework. Well-tested, BSD-licensed, and includes multitudes of features; for example, OpenLayers supports WMS/WFS/tiles as well as Google, MS Virtual Earth, and Yahoo layers. See also ticket #5472.
    • ​Yahoo! , ​MapQuest, and ​Mapstraction are all possibilities for API hooks.
  • Mapping Framework (generating custom tiles, layers, labels, etc.)
    • ​Mapnik is a modern C++ engine that was designed for use with Python and generates beautiful maps. However, documentation is lacking and installation is difficult. First to use AGG rendering.
    • ​Mapserver is the leading open source map renderer. MapServer is well documented and has strong community support. Uses text-based configuration files, which may be difficult to set up. AGG rendering and dynamic loading of configuration files (e.g., from a string) has been added to version 5.0.
  • WMS Server
    • I'm not satisfied with any of the current WMS/WFS implementations. One implemented in Django would be desirable, e.g., django.contrib.gis.wms. Thoughts anyone? (OWSLib looks good, see below)

Collaboration

  • PCL (Python Cartographic Library), now part of ​GIS Python, has done a lot of good work already. Let's apply the DRY principle. Strong opportunities for collaboration with regards to:
    • Mapping framework
    • WMS/WMF Framework -- ​OWSLib looks excellent for this (BSD licensed and has unit tests!)
    • Utilities
    • Database representation ideas
    • GEOS support, ​Sean Gillies (of PCL) was the maintainer of the old SWIG bindings, and is working on ​ShapeLy, a GeoDjango-inspired GEOS ctypes interface.
  • ​CoordinatesField.
    • Jannis Leidel has already come up with a way to manipulate points in the admin interface, BSD licensed.
  • ​geopy
    • Brian Beck has written a good foundation for geocoding and distance calculations, BSD licensed.

Suggestions

  • Decide what to do about "invalid" shape files, for example, float SHP field with ' ' value.

FAQ

  • Place your questions here.
  • Q: When dealing with points (say, degrees) from, do they need to be converted to be useful on the back-end data, assuming -that- data is in degrees? Is it enough to have the same datum and origin? (Reading the intro above is likely to answer the question.)
    • My (JDunck) reading indicates yes. Given the same coordinate system (i.e. datum, origin, and axes), degrees are useful without conversion.
  • Q: Can this implementation work with ​MySQL spatial-extensions. If not, it's planned?
    • No, unfortunately support is not planned. Support for MySQL was added as of r6527. The changes required for the Oracle backend made it relatively easy to implement one for MySQL. However, MySQL is limited to bounding-box queries only, and many advanced features (e.g., coordinate transformation, geometry_columns and spatial_ref_sys tables) are missing because of MySQL's implementation. Furthermore, MySQL's spatial indexes are limited to MyISAM tables, which are not transactional.
  • Q: Is this going to be a WMS Server/WMS Client/Both? OWSLib is just a WMS Client from what I have seen (from ruckc)
    • WMS Server first, client capability a possibility in certain situations (i.e. you want to cache data from another WMS server). Yes, OWSLib is a client, but it contains code for validating the correct parameters to send to a WMS server, thus it can be adapted into a Django view that validates whether the proper WMS parameters were given. Mapnik has an ogcserver module that can parse the correct parameters for WMS 1.1.1 and 1.3.0, however since it is licensed under the LGPL it cannot be easily incorporated into this branch (unlike OWSLib).
  • Q: Per ​this discussion in django-developers, "I can do spatial queries if they are directly between two models with polygon fields but I can't seem to get at the spatial queries through foreign keys."
    • Geographic queries require GeoManager, even if the model does not have a geographic field itself (in the case of a foreign key to a geo-field). The reply in the discussion gives detail into why.
  • Q: Why are the OGRGeometry methods transform and transform_to separate?
    • They are no longer separate, as transform now accepts CoordTransform and SpatialReference objects, as well as string WKT and integer SRID values (transform checks the input type and dispatches to the correct routine). The former reason for the separation was that each mapped to the separate OGR routines ​OGR_G_Transform and ​OGR_G_TransformTo. For better performance use CoordTransform objects when you will be performing the same transformation repeatedly.
  • Q: Why don't Q objects work?
    • Q object functionality is for use with models that use QuerySet. Because GeoDjango models use GeoQuerySet, use the GeoQ object instead: 
      >>> from django.contrib.gis.db.models import GeoQ
>>> from geoapp.models import City, Country, State # See django.contrib.gis.tests.geoapp
>>> tx = Country.objects.get(name='Texas')
>>> ks = State.objects.get(name='Kansas')
>>> City.objects.filter(GeoQ(point__within=tx.mpoly) | GeoQ(point__within=ks.poly))
[<City: Houston>, <City: Dallas>, <City: Lawrence>]

Example

Geographic Models

Here is an example of how the model API currently works (assume this example is in geoapp/models.py): 

from django.contrib.gis.db import models

class District(models.Model):
    name = models.CharField(maxlength=35)
    num  = models.IntegerField()
    poly = models.PolygonField()

    objects = models.GeoManager()

class School(models.Model):
    name  = models.CharField(maxlength=35)
    point = models.PointField()

    objects = models.GeoManager()

Notes: The GeoMixin syntax is no longer needed as of r6467. By default, a GiST index will be created for the School PointFields fields. This behavior can be turned off by using models.PointField(spatial_index=False).

Using syncdb

Both manage.py commands sqlall and syncdb work on geographic models: 

$ python manage.py sqlall geoapp

BEGIN;
CREATE TABLE "geoapp_school" (
    "id" serial NOT NULL PRIMARY KEY,
    "name" varchar(35) NOT NULL
)
;
CREATE TABLE "geoapp_district" (
    "id" serial NOT NULL PRIMARY KEY,
    "name" varchar(35) NOT NULL,
    "num" integer NOT NULL
)
;
SELECT AddGeometryColumn('geoapp_school', 'point', 4326, 'POINT', 2);
ALTER TABLE "geoapp_school" ALTER "point" SET NOT NULL;
CREATE INDEX "geoapp_school_point_id" ON "geoapp_school" USING GIST ( "point" GIST_GEOMETRY_OPS );
SELECT AddGeometryColumn('geoapp_district', 'poly', 4326, 'POLYGON', 2);
ALTER TABLE "geoapp_district" ALTER "poly" SET NOT NULL;
CREATE INDEX "geoapp_district_poly_id" ON "geoapp_district" USING GIST ( "poly" GIST_GEOMETRY_OPS );
COMMIT;

$ python manage.py syncdb

Note: The geometry columns are created outside of the CREATE TABLE statements by the AddGeometryColumn. This is done according to the OpenGIS specfication.

Spatial Queries

After a geographic model has been created, the PostGIS additions to the API may be used. Geographic queries are done normally by using filter() and exclude() on geometry-enabled models using geographic lookup types (see the Database API for lookup types). In the following example, the bbcontains lookup type is used which is the same as the PostGIS && operator. It looks to see if the bounding box of the polygon contains the specific point. The next example uses the PostGIS Contains() function, which calls GEOS library to test if the polygon actually contains the specific point, not just the bounding box. 

>>> from geoapp.models import District, School
>>> qs1 = District.objects.filter(poly__bbcontains='POINT(-95.362293 29.756539)') 
>>> qs2 = District.objects.filter(poly__contains='POINT(-95.362293 29.756539)')

Both spatial queries and normal queries using filter() may be used in the same query. For example, the following query set will only show school districts that have 'Houston' in their name and contain the given point within their polygon boundary: 

>>> qs = District.objects.filter(name__contains='Houston').filter(poly__contains='POINT(-95.362293 29.756539)')

In PostGIS versions 1.2.2 and above, Refractions introduced a feature called "inline index magic" that automatically combines the indexed bounding box queries with their more computationally expensive counterparts. Prior to this revision the following filter could be used to get the same performance benefits: 

>>> qs = District.objects.filter(poly__bbcontains='POINT(-95.362293 29.756539)').filter(poly__contains='POINT(-95.362293 29.756539)')

Lazy-Geometries

Geographic fields on models are proxies to GEOS Geometry objects, allowing for many interesting possibilities: 

>>> from django.contrib.gis.geos import fromstr
>>> pnt = fromstr('POINT(-95.362293 29.756539)', srid=4326)
>>> hisd = District.objects.get(name='Houston ISD')
>>> hisd.poly.contains(pnt)
True
>>> hisd.poly.within(pnt)
False
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