Ontario Airport Project 4

The client, an airport information technology company, requires a data analysis project in order to attract more airlines and increase flights. The project involves finding a relationship between economic and demographic indicators in the region and air travel demand at the airport. The student team is expected to determine if there is a relationship between economic growth and air travel demand in the So Cal region, and if so, quantify and model it. The team will also benchmark the results with other airports, such as BUR, ONT, LAX, LGB, SAN, and SNA. A script to automatically collect and process TSA data from its source is a bonus goal. The required skills for the student team members include Python/R or other computer language knowledge, API consumption, ETL, data analysis (especially related to economic modeling), communication, and presentation. The TSA data can be massive, so some cloud computing tools or processing in small batch samples may be needed. The client data includes TSA throughput data, Cirium/Diio/T-100 data sets, and 2010/2020 US Census data.

The overall goal is to establish a relationship between economic and demographic indicators in the region and air travel demand, so that the airport can demonstrate sufficient demand to make flights profitable and attract more airlines. The project involves data analysis, ETL, correlation analysis, and demand prediction. If successful, the project can help the airport make data-driven decisions and increase profitability. The TSA data, Cirium/Diio/T-100 data sets, and 2010/2020 US Census data will provide valuable information to measure overall economic growth by county and passenger demand by airline, airport, and specific origin and destination.