Introduction
Every day, FAA’s Air Traffic Organization (ATO) oversees more than 45,000 flights and 2.9 million airline passengers across more than 29 million square miles of airspace according to the faa. The number of flights, passengers and the complexity of this system peaked my curiosity and lead me to attempt to illustrate the flights and the air space for the networks lab. There are plenty of examples of global network flights and acquiring the data to create the visual was not a difficult task. After searching around through github, I found a data set that contained the latitude and longitude of 5625 airports with 72406 routes from and to each airport. The purpose of this lab was to identify and locate airports that have the most connection to other airports and to find any other patterns in the network using Gephi to create meaningful network visuals.
Inspiration
Material
Software: Gephi, Microsoft Excel
Method
I was fortunate to find on github a network dataset that was organized in a csv format. The original dataset can be found on http://openflights.org/.
The dataset contained two separate files, one for the nodes with unique ids that belong to each airport along with the latitude, longitude and the name of the airport. The second file contained the airport departure and arrival labeled as source and target or “from and to”. The csv file also contained the proper labels on the header that was recognizable by Gephi. The labels made exporting the csv files to Gephi really easy with minimal adjustments. Because the node and edge tables were in a separate file each table had to be imported separately to their respective tables.
Nodes 
Links/Edges
After importing the tables to Gephi you can review your data on the data laboratory to ensure both Nodes and tables are properly labeled. Statistics were then run to calculate the average degree, diameter, density, and modularity. Gephi also had plug-ins that took in information on Latitude and longitude allowing the nodes and edges to be visually graphed like a traditional global map.
Result
In the images below we get a sense of how busy the sky is. Because the maps below have no actual geographic layer I purposely included all the nodes on the graph to bring out the shape and silhouette of the continents. Node and edge colors is based on the modularity grouping, with continents strongly connected to each other. To explore the airports that were the busiest, a filter was used to remove the edges with the least amount of degrees. In these images we are able to quickly identify the regions with the busiest air space and their routes.
N.America 
Europe 
Asia
Reflections
This network allowed me to learn the relationships between nodes and edges, the statistics used to create meaningful data and the basics of Gephi. After exploring online for datasets that made sense I chose this network because I understood the relationship between the airports and their flights. Many people would agree that the visual network of these flights look cool while some may find this graph to be inaccurate. For instance, the edge lines do not represent the actual flight path an airplane would take. Another inaccurate illustration is the flights from US to Asia are flown from west to east. One way to reduce the inaccuracy of these routes would be to rap the nodes on a sphere graph to better reflect actual directions a airplane might take.