The ​gis branch intends to add a contrib app implementing geographic support.

What's GIS?

• ​Series of blog posts giving intro to GIS; choice quote from ​an early post: "If you feel like ending a conversation with a developer then simply bring up the topic of character encodings ... [o]r ... coordinate systems. ... So in the spirit of Tim Bray's and Joel Spolsky's wonderful writeups of character encodings, I thought I'd put together a basic survival guide to coordinate systems over my next few posts and then tie it back to Google Maps."
• More on ​map projections, including why people can't agree on just one (utf-8).
• ​geodesy the field of science for this stuff.

Useful code

• ​PostGIS, the ​OpenGIS SQL Types (pdf) implementation for Postgresql
• ​GEOS, low-level C++ port of ​Jave Topology Suite, used by PostGIS
  • ​PyGEOS, now outdated due to PCL, below, and shedloads more stuff on "python, geospatial, and the web"
  • ​Python Cartographic Library - ​actively maintained, huge GIS package, GPL.
  • There are direct SWIG Python bindings in GEOS 3.0.0RC1 and above. In other words, the entire GEOS API may be called directly from Python.
• ​GeoTypes is a type (and conversion) library for PostGIS via psycopg.
• ​Geopy
  • Calculates distances using (very accurate) ​Vincenty, and uses the ​WGS 84 datum by default.
  • Has utility functions for unit of measurement (UOM) conversions (e.g. meters -> kilometers, kilometers -> miles, etc.)
  • Excellent GeoCoding capabilites. Has interfaces for Google, Yahoo, Microsoft Live, MediaWiki, and ​Geocoder.us.
• ​GDAL/OGR, a library for fiddling with raster geo images.
  • Has a Python interface. A SWIG interface is in development, but not yet stable (no access to full API).
• ​Geo::Coder::US An excellent Perl library for GeoCoding that powers ​Geocoder.us. Users can create their own Geographic databases using the Census Bureau's TIGER/Line data (see below).
• ​GeoRosetta, CC-BY-SA licensed, quality-controlled, collection of geocoding data. Not yet released to public(?).
• ​MapServer: University of Minnesota (UMN) "open source development environment for building spatially-enabled internet applications."
• ​MapNik: C++ and Python toolkit for developing mapping applications. Claimed benefits over MapServer: "It uses the AGG library and offers world class anti-aliasing rendering with subpixel accuracy for geographic data. It is written from scratch in modern C++ and doesn't suffer from design decisions made a decade ago." See ​MapNik FAQ.
• Ruby on Rails
  • ​IvyGIS: Google-maps type displays with RoR and UMN's MapServer
  • ​Spatial Adapter for Rails: A plugin for Rails which manages the MySql Spatial and PostGIS geometric columns in a transparent way (that is like the other base data type columns). This might have some useful techniques for when we try to support other spatial extensions other than PostGIS.
  • ​Cartographer GMaps plugin

Useful Data

• ​TIGER/Line: "The TIGER/Line files are extracts of selected geographic and cartographic information from the ​Census Bureau's TIGER® (Topologically Integrated Geographic Encoding and Referencing) database." This data is useful in creating your own geocoding database service. Currently 2006 First Edition is the latest, but second edition should be coming soon. Note: The Census Bureau will be ​providing SHP files in Fall, 2007.

Questions

• 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.

Implementation

Phase 1

• Create Geometry-enabled fields and manager. Status: complete (not yet in SVN).
• Allow for Geometry-enabled queries. Status: complete (not yet in SVN).
• Here is an example of how the model API currently works (assume this example is in geo_app/models.py):

  from django.contrib.gis.db import models
  
  class District(models.Model):
      name = models.CharField(maxlength=35)
      num  = models.IntegerField()
      poly = models.MultiPolygonField()
  
      objects = models.GeoManager()
  
  class School(models.Model):
      name  = models.CharField(maxlength=35)
      point = models.PointField()
  
      objects = models.GeoManager()
  

• Use the manage.py just like you normally would:

  $ python manage.py sqlall geo_app
  BEGIN;
  CREATE TABLE "geo_app_school" (
      "id" serial NOT NULL PRIMARY KEY,
      "name" varchar(35) NOT NULL
  );
  CREATE TABLE "geo_app_district" (
      "id" serial NOT NULL PRIMARY KEY,
      "name" varchar(35) NOT NULL,
      "num" integer NOT NULL
  );
  SELECT AddGeometryColumn('geo_app_school', 'point', 4326, 'POINT', 2);
  SELECT AddGeometryColumn('geo_app_district', 'poly', 4326, 'MULTIPOLYGON', 2);
  COMMIT;
  $ python manage.py syncdb geo_app
  
  

• PostGIS additions to the API may now be used. Geographic queries are done by calling geo_filter() and geo_exclude on geometry-enabled models. 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 for actual intersection of the two geometries, not just the bounding box.

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

• Both geo_filter() and 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').geo_filter(poly__contains='POINT(-95.362293 29.756539)')
  

Phase 2

• Add as much from the PostGIS API as possible.
• Add geometry-enabled routines to the fields that call directly on GEOS routines -- like area(), centroid(), etc.
• Contemplate a JS framework for mapping. I know Django community is against including any type of JS/AJAX framework, but having a way to generate maps would be a great addition. Also, any type of framework would be limited to the contrib package only.

Installation

Installation of the GeoDjango module will also require the installation of existing open source geographic libraries and a spatial database (currently only PostGIS). This section will describe the installation process for these libraries. Initially, these instructions will pertain only to a Linux platform (particularly Debian or Ubuntu). Mac & Windows support will be considered later; however, these instructions will most likely work through the Mac shell. Don't hold your breath for Windows support.

GEOS

• Latest GEOS version is 3.0.0RC4

  $ ./configure --enable-python
  $ make
  # make install
  

PROJ

• Latest PROJ version is 4.5.0

  $ ./configure
  $ make
  # make install 
  

PostGIS

• Latest PostGIS version is 1.2.1
• First build & install PostGIS. We are currently using v8.1 of PostgreSQL.

  $ ./configure --with-geos --with-proj
  $ make
  # make install
  

• Next, create a role and database for your application, and allow it to access PostGIS functionality:

  # su - postgres
  $ psql
  postgres=# CREATE ROLE <user> LOGIN;
  postgres=# \q
  $ createdb -O <user> <db_name>
  $ createlang plpgsql <db_name>
  $ psql -d <db_name> -f /usr/local/share/lwpostgis.sql
  $ psql -d <db_name> -f /usr/local/share/spatial_ref_sys.sql
  $ psql <db_name>
  <db_name>=# GRANT SELECT, UPDATE, INSERT, DELETE ON geometry_columns TO <user>;
  <db_name>=# GRANT SELECT ON spatial_ref_sys TO <user>;
  

• Finally, update your settings.py to reflect the name and user for the spatially enabled database. So far, we only plan to support the psycopg2 backend, thus: DATABASE_ENGINE='postgresql_psycopg2'.

GDAL

• Optional, but highly useful for coordinate transformations and reading/writing both vector (e.g. SHP) and raster (e.g. TIFF) geographic data.
• Latest GDAL version is 1.4.0. Configure with GEOS and Python support, then make and install:

  $ ./configure --with-geos --with-python
  $ make
  # make install
  

• Note: This is done without the 'next generation' SWIG Python bindings. I've had trouble getting them to work, and the rumor is this only works on Windows. The compilation flag to enable these is --with-ngpython, but our packages currently only use the old bindings.