By Clement Zhang, Founder & Managing Director, FlightBI
Airport catchment analysis has evolved dramatically over the past decade. What was once driven primarily by passenger surveys and limited ticketing samples is now increasingly powered by digital datasets capable of estimating airport choice behavior at the ZIP-code level.
Today, the most common data sources used in airport catchment studies include:
- Mobile location (cell phone) data
- Flight search data
As airports compete more aggressively for passengers and airline service, data quality has become increasingly important. Poor-quality datasets can distort leakage estimates, misrepresent airport market share, and lead to flawed strategic decisions.
Why Mobile Location Data Has Become the Preferred Source
Among modern catchment datasets, mobile location data has emerged as the strongest primary source for airport catchment analysis.
Unlike search datasets, mobile location data measures actual movement behavior. By analyzing anonymized device movement patterns between residential areas and airports, analysts can estimate:
- Passenger origins by ZIP code
- Airport leakage to competing airports
- Cross-border airport usage
- Airport switching behavior
This is especially valuable for air service development because airport choice is heavily influenced by drive time, nonstop availability, fares, and airline preference.
Flight search data has major limitations for catchment analysis. Searches may indicate travel intent, but they do not necessarily translate into ticket purchases or airport usage. Travelers may compare multiple airports, search repeatedly without booking, or search on behalf of others. As a result, search data often reflects shopping activity rather than true passenger flows.
Mobile Data Alone Is Not Enough
Despite its strengths, mobile location data has one major weakness: mobile data panels are not stable over time.
The size and composition of mobile panels can change suddenly due to:
- Privacy policy updates
- User permission changes
- SDK removals
- App partnership changes
- Data provider methodology updates
These changes can create artificial traffic fluctuations unrelated to actual passenger demand. Without calibration, airports risk making decisions based on panel instability rather than true market behavior.
Why Aviation Data Is Required for Calibration
The most reliable airport catchment methodologies combine mobile location data with aviation passenger datasets.
Aviation data provides:
- Stable passenger benchmarks
- Market validation
- Historical consistency
- Carrier-level normalization
This hybrid approach preserves the geographic precision of mobile data while correcting for panel fluctuations and sampling distortion.
The Limitations of MIDT Data
MIDT data, sourced from Global Distribution Systems (GDSs), has historically been widely used for airline planning. However, it has important limitations for airport catchment studies.
Many low-cost carriers (LCCs), such as Allegiant and Avelo, do not participate in GDS systems. MIDT also excludes much of the growing direct airline booking market because it primarily reflects agency bookings. In addition, MIDT represents reservations rather than actual flown passengers, meaning no-shows and itinerary changes can overstate demand.
For airports with substantial low-cost carrier traffic, these limitations can significantly distort catchment analysis.
DDS datasets provide broader ticketing coverage in some markets, but many LCCs still do not participate because they prefer not to share proprietary sales data with competitors.
Why DOT Data Is the Strongest Calibration Source
For U.S. airport catchment analysis, DOT Origin and Destination data remains the strongest aviation calibration source, especially following the transition from DB1B to DB1C.
DB1C increases the ticket sample size from 10% to 40% and moves reporting from quarterly to monthly.
Most importantly, DOT data includes all major U.S. low-cost carriers. This is critical because LCCs play a major role in airport leakage behavior, with passengers often willing to drive farther for lower fares and nonstop service.
The strongest modern airport catchment methodology therefore combines:
- Mobile location data for geographic precision
- DOT aviation data for passenger calibration and market validation
As airports continue investing in data-driven decision-making, the industry is increasingly recognizing that data quality matters just as much as analytical sophistication.
Dr. Clement Zhang, C.M., has more than 25 years of experience developing IT solutions and delivering consulting services for the travel and transportation industry. He is the founder of FlightBI, previously served as Director of Business Intelligence at Cirium, VP of Product Development at Diio, and Vice President at MergeGlobal. Dr. Zhang holds an MBA from Georgetown University 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.