This repository contains code and data used to track flights in Venezuela and visualize their patterns as part of my master's thesis in Data Visualization at Parsons School of Design. The goal of this project is to explore the relationship between flight patterns and the geopolitical landscape of Venezuela.
The flight data used in this project is obtained from public sources such as the OpenFlights and FlightAware. The data covers a period of several years and includes information such as flight paths, departure and arrival times.
The code used to process the flight data and generate the visualizations is written in Python and uses various libraries such as Pandas and BeautifulSoup. The code is designed to be scalable and can handle large amounts of flight data.
The visualizations included in this repository show the flight patterns of various airlines in Venezuela, as well as their frequency and duration. The visualizations are interactive and can be explored using the Plotly platform.
The repository contains the following files:
The results of this project provide insight into the geopolitical landscape of Venezuela and its impact on air travel. The visualizations reveal patterns in airline activity that correspond to political events and economic trends.
There are several areas of future work that could build upon this project, including expanding the dataset to include more countries and airlines, analyzing the impact of weather on flight patterns, and exploring the potential of machine learning techniques for predicting airline behavior.
Contributions to this repository are welcome! If you find an issue or have an improvement, please submit a pull request.
This repository is licensed under the MIT License.
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