Brazil air traffic network analysis

This work has as purpose to analyze under different metrics the air traffic in Brazil. Besides that, it makes it possible upload your own dataset (.graphml) and see all these analyses applied to him. Have fun!

Work of undergraduate course about Network Analysis (IMD1155) of Bachelor's degree in Information Technology from the Federal University of Rio Grande do Norte (UFRN), with Ivanovitch Medeiros Dantas da Silva as professor.

Group:

• Álvaro Ferreira Pires de Paiva
  • Enrolment: 2016039162
  • E-mail: alvarofepipa@gmail.com
• Marcos Vinícius Rêgo Freire
  • Enrolment: 20210053533
  • E-mail: mvinnicius22@hotmail.com

You can find the dataset used in this work in alvarofpp/dataset-flights-brazil.

You can find a detailed approach of these metrics above:

Metric	Description
Diameter	Diameter it is the shortest distance between the two most distant nodes in the network.
Radius	The Radius of a network is the minimum eccentricity.
Periphery	The Periphery of a network is a set of all nodes whose eccentriciy is equals the diameter.
Degree Centrality	Degree Centrality assigns an importance score based simply on the number of links held by each node.
Betweenness Centrality	Betweenness Centrality shows which nodes are "bridges" between nodes in a network.
Closeness Centrality	The Closeness Centrality scores each node based on their "closeness" to all other nodes in the network.
Eigenvector Centrality	The Eigenvector Centrality measures a node’s influence based on the number of links it has to other nodes in the network.
k-core	A k-core in a network is a subset of its nodes in which all nodes have at least k connections to each other.
k-shell	The k-shell of a graph G is the set of all nodes belonging to the k–core of G but not to the (k+1)–core.

Run the app

You can run the app in 3 different ways:

• Virtual environment;
• Docker container;
• Docker-compose.

Virtual Environment

In your environment:

# Install requirements for scripts
pip install -r requirements.txt

# (optional) If you want updates the data
python3 extract.py

# Run the app
streamlit run app.py

Docker

These instructions will cover usage information and for the docker container.

Prerequisites

In order to run this app you'll need docker installed.

• Get Started

Run

Step 1:

# Build the docker image
docker build -t streamlitapp:latest .
# Or specifying a file
docker build -t streamlitapp:latest .f Dockerfile

Step 2:

# Run a container
docker run -p 8501:8501 streamlitapp:latest

Docker-compose

If you prefer to use docker-compose to run the app with others services, this is the section.

Prerequisites

See the Docker prerequisites section before this section. After that, you need to install docker-compose:

• Install Docker Compose

After installed docker-compose, create a .env file (you can see .env.example as an example).

Environment Variables

• STREAMLIT_PORT - The port the app runs on. By default, is 8501.

Run

# Run the app
docker-compose up

Contributing

Contributions are more than welcome. Fork, improve and make a pull request. For bugs, ideas for improvement or other, please create an issue.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.