By Clement Zhang, Founder, FlightBI

Understanding where airport passengers originate—and where they may choose competing airports—is essential for route planning and air service development. Because airport catchment areas shift over time, selecting the right data sources is key for meaningful analysis.

Common Data Sources for Catchment Area Analysis

1. Passenger Surveys
   Surveys collect information such as home ZIP codes through check-in, Wi-Fi login, or outreach. They’re easy and low-cost, and can provide demographic or motivational insights. However, surveys suffer from low participation, bias, and may quickly become outdated, capturing only a subset of travelers.
2. Parked Vehicle Analysis
   License plate data from parking facilities is matched to vehicle registrations, providing a look at where parkers are coming from. This method reveals drive-market behavior and can offer geographic detail. Its limitations include excluding those dropped off or using transit, out-of-date registrations, and no insight into passengers using competitor airports.
3. Billing Data from Ticket Purchases
   Airline ticket transaction data (from ARC, MIDT, or airlines) is used to map passenger origins via billing addresses. It offers a large sample size and is widely used for benchmarking. However, addresses for business travelers may reflect their company headquarters, not their home. Data may miss low-cost carriers or direct bookings.
4. IP Address Tracking in Travel Search Data
   Some analytics tools use IP addresses from online flight searches to infer traveler location. This can yield large, real-time data sets and support digital marketing. Still, IP addresses often reflect the ISP location rather than the traveler’s true home. VPNs and mobile browsing introduce further inaccuracy, making this data unreliable for market analysis.
5. User-Generated Content (UGC) & Social Media
   Social media geotags and travel posts can show trends or travel groups in real-time. However, there’s heavy participation bias, data tends to be sparse, and it lacks the robustness for quantitative planning.
6. Mobile Location (Air Mobility) Data
   The latest approach uses anonymized, permission-based mobile device data to track aggregated travel patterns to and from airports. Air mobility data offers direct measurement of traveler origins, covers all travel modes, and enables detailed leakage analysis. It provides near real-time insights and can segment between residents and visitors. However, data licensing is required, smaller airports may see limited samples, and expert analysis is needed.

Each data source brings value to airport catchment analysis, but all have limitations. Many airports use a combination of sources to cross-validate results. As travel patterns grow more complex, the need for integrated, detailed insights continues to increase.

Looking Ahead

Modern catchment and leakage studies increasingly rely on blending multiple data types for more comprehensive analysis. Solutions like FlightBI’s Fligence ZIP-OD exemplify this integrated approach, combining air mobility data, ticketing information, and onboard segment data to offer a complete, actionable view of passenger flows—from ZIP code of origin to true destination by airline. By leveraging these advanced tools, airports are better equipped to monitor trends, understand market share, and address leakage with confidence as the industry evolves.

 

Clement Zhang has 25 years of experience building IT solutions and consulting in the travel and transportation industry. He is the founder of FlightBI, served as Director of Business Intelligence at Cirium and Vice President at MergeGlobal, and holds an MBA from Georgetown and a Ph.D. from Xi’an JiaoTong University.

 

 

DISCLAIMER

This article was provided by a third party and, as such, the views expressed therein and/or presented are their own and may not represent or reflect the views of Airports Council International-North America (ACI-NA), its management, Board, or members. Readers should not act on the basis of any information contained in the blog without referring to applicable laws and regulations and/or without appropriate professional advice.