GeoDjango and Taco Bell

I’ve been at it again with GeoDjango, this time I’ve pulled data on all Taco Bells locations from a popular social media site, took that data and added to a Django project, and finally plotted
them in a view using Google Maps:

Screen Shot 2015-11-22 at 8.13.51 AM









Wow, that is a lot of Taco Bells!

Since this is Django, we are also able to view and edit from the admin:

Screen Shot 2015-11-22 at 8.15.13 AM

As well as the shell:

Screen Shot 2015-11-22 at 8.15.47 AM
django-cities was used tie it all together, which allows me to do searches like how many Taco Bells do the cities with the highest population have:

In [1]: from cities.models import City

In [2]: from hub.models import Location

In [3]: for city in City.objects.order_by('-population')[:20]:
    locations = Location.objects.filter(cities_city=city)
    print '%s with population %s has %s Taco Bells' % (, city.population, len(locations))
New York City with population 8175133 has 0 Taco Bells
Los Angeles with population 3792621 has 34 Taco Bells
Chicago with population 2695598 has 15 Taco Bells
Brooklyn with population 2300664 has 5 Taco Bells
Borough of Queens with population 2272771 has 0 Taco Bells
Houston with population 2099451 has 54 Taco Bells
Philadelphia with population 1526006 has 13 Taco Bells
Manhattan with population 1487536 has 1 Taco Bells
Phoenix with population 1445632 has 34 Taco Bells
The Bronx with population 1385108 has 0 Taco Bells
San Antonio with population 1327407 has 22 Taco Bells
San Diego with population 1307402 has 22 Taco Bells
Dallas with population 1197816 has 27 Taco Bells
San Jose with population 945942 has 18 Taco Bells
Indianapolis with population 829718 has 30 Taco Bells
Jacksonville with population 821784 has 18 Taco Bells
San Francisco with population 805235 has 11 Taco Bells
Austin with population 790390 has 25 Taco Bells
Columbus with population 787033 has 23 Taco Bells
Fort Worth with population 741206 has 16 Taco Bells

Or how may Taco Bells each state in the United State has:

In [1]: from cities.models import Region

In [2]: from hub.models import Location

In [3]: for region in Region.objects.all()[:10]:
    locations = Location.objects.filter(cities_state=region)
    print '%s has %s Taco Bells' % (, len(locations))
Arkansas has 58 Taco Bells
Washington, D.C. has 5 Taco Bells
Delaware has 14 Taco Bells
Florida has 363 Taco Bells
Georgia has 193 Taco Bells
Kansas has 65 Taco Bells
Louisiana has 92 Taco Bells
Maryland has 91 Taco Bells
Missouri has 157 Taco Bells
Mississippi has 48 Taco Bells

Using GeoDjango to filter by Points

Just recently I found myself playing with GeoDjango, I’ve been using it on both a Ubuntu 14.04 cloud server and a Macbook Pro (OS X El Capitan).

GeoDjango allows us to query by geographic points directly on the data model.
We are then able to extend the model, and add a custom method to search by zipcode.

Using the Django shell we can easily check data in our favorite interpreter :

$ ./ shell

In [1]: from hub.models import Vendor

In [2]: Vendor.get_vendors(zipcode='78664', miles=5)
Out[2]: [<Vendor: Starbucks>]

In [3]: Vendor.get_vendors(zipcode='78664', miles=10)
Out[3]: [<Vendor: Starbucks>, <Vendor: Starbucks>,
<Vendor: Starbucks>, <Vendor: Starbucks>,
<Vendor: Starbucks>, <Vendor: Starbucks>, <Vendor: Starbucks>]

It’s then pretty easy to take that data and present it on a Google Map
(using the Django application’s views and templates):

Screen Shot 2015-11-17 at 12.39.06 PM

If you find any of this exciting; read on, I’m going to go over setting the
environment up from scratch (using a Macbook as the development environment).



It is a good idea to add‘s bin path to your $PATH.

You should run the following command (changing the version to match your install),
and add it to the bottom of your ~/.bash_profile:

export PATH=$PATH:/Applications/

Next lets create our PostgreSQL database, and enable the GIS extension.

Start the OSX application. Next click the elephant from your upper task bar, and select Open psql.

nessy=# create database geoapp;

nessy=# \c geoapp
You are now connected to database "geoapp" as user "nessy".

geoapp=# CREATE EXTENSION postgis;

You can now close the psql shell.

Next lets install Django into a virtualenv

# create and change to new app directory
mkdir ~/geoapp && cd ~/geoapp/

# create a fresh virtual environment
virtualenv env

# activate the virtual environment
source env/bin/activate

# install Django inside the virtual environment
pip install Django

To use PostgreSQL with Python we will need the adapter installed,
be sure you added’s bin path to your $PATH:

pip install psycopg2

GeoDjango requires the geos server to be available, we can install this with homebrew:

brew install geos

We are now ready to create the Django project and application.

# create a new project using Django admin tool
django-admin startproject geoproject

# change to the newly created project directory
cd geoproject/

# create a new application
./ startapp hub

Now you need to configure your Django application to use PostgreSQL and GIS,
open geoproject/ with your favorite text editor.

vim geoproject/

Append django.contrib.gis and hub to your INSTALLED_APPS:


Next find the DATABASES portion and set it to the postgis engine:

    'default': {
        'ENGINE': 'django.contrib.gis.db.backends.postgis',
        'NAME': 'geoapp',
        'PASSWORD': '',
        'HOST': 'localhost',
        'PORT': ''

The next step will be to create our model using GIS points,
add the following to hub/

from django.contrib.gis.db import models
from django.contrib.gis.geos import Point, fromstr
from django.contrib.gis.measure import D

class Vendor(models.Model):

    def __unicode__(self):
        return unicode(

    def save(self, *args, **kwargs):
        if self.latitude and self.longitude:
            self.location = Point(float(self.longitude), float(self.latitude))
        super(Vendor, self).save(*args, **kwargs)

    name = models.CharField(max_length=100)
    longitude = models.FloatField()
    latitude = models.FloatField()
    location = models.PointField(blank=True, null=True)

You will also want to add this model to the admin page, so update hub/

from django.contrib import admin

from hub.models import Vendor

class VendorAdmin(admin.ModelAdmin):
    list_display = ('name', 'longitude', 'latitude')
    exclude = ('location',), VendorAdmin)

At this point you are ready to create the database tables, use the provided script:

./ syncdb

I’m going to now jump into the Django shell to add data, but this can also be done using the admin (

./ shell

In [1]: from hub.models import Vendor

In [2]: Vendor.objects.create(longitude=-97.677580, latitude=30.483176,
   ...: name='Starbucks')
Out[2]: <Vendor: Starbucks>

In [3]: Vendor.objects.create(longitude=-97.709085, latitude=30.518423,
  ...: name='Starbucks')
Out[3]: <Vendor: Starbucks>

In [4]: Vendor.objects.create(longitude=-97.658976, latitude=30.481517,
   ...: name='Starbucks')
Out[4]: <Vendor: Starbucks>

In [5]: Vendor.objects.create(longitude=-97.654141, latitude=30.494810,
   ...: name='Starbucks')
Out[5]: <Vendor: Starbucks>

I can then define a point in the center of the city, and filter by locations within a 5 mile radius:

In [6]: from django.contrib.gis.geos import fromstr

In [7]: from django.contrib.gis.measure import D

In [8]: point = fromstr('POINT(-97.6786111 30.5080556)')

In [9]: Vendor.objects.filter(location__distance_lte=(point, D(mi=5)))
Out[9]: [<Vendor: Starbucks>, <Vendor: Starbucks>, <Vendor: Starbucks>,
<Vendor: Starbucks>]

Hope you found this article helpful; if you did, please share with friends and coworkers.