The Atlas of Economic Complexity Site

The Atlas is a site housing The Observatory, the master's thesis work of Alexander Simoes. This Observatory is a tool that allows users to quickly compose a visual narrative about countries and the products they exchange.

Data

The observatory provides access to bilateral trade data for roughly 200 countries, 50 years and 1000 different products of the SITC4 revision 2 classification. The source of the data we are using is:

1962 - 2000

The Center for International Data from Robert Feenstra

2001 - 2009

UN COMTRADE

Support

The Observatory will run in all modern browsers so long as they have Javascript turned on and have full support for SVG graphics. This includes the latest versions of Firefox, Chrome (Chromium), Safari (WebKit), Opera and IE.

Note: Internet Explorer versions 8 and below will not work as they do not have SVG support built in.

Adding the Observatory to computer via virtualenv

Getting The Observatory Running Locally via Virtualenv

1. Clone from github (this will create an atlas_economic_complexity folder in the current directory)

    git clone https://github.com/alexandersimoes/atlas_economic_complexity.git
   

2. Create the virtual environment

    mkvirtualenv atlas_economic_complexity
   

3. Activate this newly created environment

    workon atlas_economic_complexity
   

4. Install the required Python libraries

    pip install -r requirements.txt
   

5. Create a MySQL database on your local machine

6. Import the latest dump of the database from atlas.media.mit.edu/media/db/

    mysql -u username -p -h localhost DB_NAME < observatory_xxxx-xx-xx.sql
   

7. Be sure to create the following local environment variables

    export OEC_SECRET_KEY=some_s3cret_k3y
    export OEC_DB_USER=my_db_username
    export OEC_DB_PW=my_db_password
    export OEC_DB_HOST=localhost
    export OEC_DB_NAME=oec
    * export OEC_REDIS_HOST=localhost
    * export OEC_REDIS_PORT=6379
    * export OEC_REDIS_PW=redis_password
    
    * only necessary if using redis for caching
   

8. Updating translations (if something is changed)

    pybabel extract -F babel.cfg -o messages.pot oec
    pybabel update -i messages.pot -d oec/translations
    pybabel compile -d oec/translations
   

Getting The Observatory Running With Redis Caching enabled (Optional)

10. If you would like to run the Observatory with a cache (if, for instance, you wished to deploy it on a live server) All you will need to do is install the proper libraries and resources --

11. Download, extract and compile Redis itself with:

    $ wget http://redis.googlecode.com/files/redis-2.6.7.tar.gz
    $ tar xzf redis-2.6.7.tar.gz
    $ cd redis-2.6.7
    $ make  
    

12. Install the redis-py client with (from https://github.com/andymccurdy/redis-py)

    $ sudo easy_install redis
    $ sudo python setup.py install
    

13. Install the django-redis backend (from https://github.com/niwibe/django-redis)

    easy_install django_redis
    

14. You will also need the following serialization library: (from http://msgpack.org)

    easy_install msgpack-python
    

15. The constants defined in settings.py have REDIS turned on by default. The example constants in the comments can be used to turn it off.