{"id":38319,"date":"2025-07-22T15:34:46","date_gmt":"2025-07-22T19:34:46","guid":{"rendered":"https:\/\/airportscouncil.org\/?p=38319"},"modified":"2025-07-22T15:34:46","modified_gmt":"2025-07-22T19:34:46","slug":"comparing-data-sources-for-airport-catchment-analysis","status":"publish","type":"post","link":"https:\/\/airportscouncil.org\/2025\/07\/22\/comparing-data-sources-for-airport-catchment-analysis\/","title":{"rendered":"Comparing Data Sources for Airport Catchment Analysis"},"content":{"rendered":"<p><em>By Clement Zhang, Founder, FlightBI<\/em><\/p>\n<p>Understanding where airport passengers originate\u2014and where they may choose competing airports\u2014is 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.<\/p>\n<h1>Common Data Sources for Catchment Area Analysis<\/h1>\n<ol>\n<li><strong> Passenger Surveys<\/strong><br \/>\nSurveys collect information such as home ZIP codes through check-in, Wi-Fi login, or outreach. They\u2019re 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 However, surveys suffer from low participation, bias, and may quickly become outdated, capturing only a subset of travelers.<\/li>\n<li><strong> Parked Vehicle Analysis<\/strong><br \/>\nLicense 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 Its limitations include excluding those dropped off or using transit, out-of-date registrations, and no insight into passengers using competitor airports.<\/li>\n<li><strong> Billing Data from Ticket Purchases<\/strong><br \/>\nAirline 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.<\/li>\n<li><strong> IP Address Tracking in Travel Search Data<\/strong><br \/>\nSome 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\u2019s true home. VPNs and mobile browsing introduce further inaccuracy, making this data unreliable for market analysis.<\/li>\n<li><strong> User-Generated Content (UGC) &amp; Social Media<\/strong><br \/>\nSocial media geotags and travel posts can show trends or travel groups in real-time. However, there\u2019s heavy participation bias, data tends to be sparse, and it lacks the robustness for quantitative planning.<\/li>\n<li><strong> Mobile Location (Air Mobility) Data<\/strong><br \/>\nThe 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.<\/li>\n<\/ol>\n<p>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.<\/p>\n<p>Looking Ahead<\/p>\n<p>Modern catchment and leakage studies increasingly rely on blending multiple data types for more comprehensive analysis. Solutions like FlightBI\u2019s 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\u2014from 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.<\/p>\n<p>&nbsp;<\/p>\n<p><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" class=\" wp-image-38320 alignleft\" src=\"https:\/\/i0.wp.com\/airportscouncil.org\/wp-content\/uploads\/2025\/07\/picture.png?resize=82%2C105&#038;ssl=1\" alt=\"\" width=\"82\" height=\"105\" \/><br \/>\nClement 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\u2019an JiaoTong University.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><strong>DISCLAIMER<\/strong><\/p>\n<p><strong>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.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>By Clement Zhang, Founder, FlightBI Understanding where airport passengers originate\u2014and where they may choose competing airports\u2014is essential for route planning and air service development. Because airport catchment areas shift over [&hellip;]<\/p>\n","protected":false},"author":13029,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-38319","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Comparing Data Sources for Airport Catchment Analysis - Airports Council International - North America<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/airportscouncil.org\/2025\/07\/22\/comparing-data-sources-for-airport-catchment-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comparing Data Sources for Airport Catchment Analysis - Airports Council International - North America\" \/>\n<meta property=\"og:description\" content=\"By Clement Zhang, Founder, FlightBI Understanding where airport passengers originate\u2014and where they may choose competing airports\u2014is essential for route planning and air service development. 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