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A supply-side categorization of airports across global multiple-airport cities and regions

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Abstract

Cities and regions with multiple airports providing commercial air service are common across the globe. Within these regions, both airlines and consumers have various opportunities to provide air transport services on the one hand, and consume them on the other. While a literature addressing features of consumer behavior related to multiple airports and their regions has developed over the past two decades, very little research has taken a comprehensive global view of the regions themselves, and more specifically, of the assortment of component airports. The results of the present paper provide structure to the activities at airports within multiple airport cities and regions as of 2015. Using hierarchical cluster analysis, 131 airports in 53 global multiple airport regions are analyzed utilizing supply-side variables such as aspects of competition, routes, and aircraft capacities among others. The results show that the airports, identified from the literature, can be classified into six primary groups. Some of the groups confirm prior expectation, for example based, upon size. Yet other groups are, at least on the surface, a bit more eclectic. Moreover, combinations of airport type in individual cities and regions vary widely across geographies, pointing to the complexity of places and the industry. As global commercial aviation continues to change quickly along multiple dimensions, understanding the roles airports play, and the ways airlines use them becomes enhanced. Multiple airport regions will provide both opportunities and difficulties in navigating these changes.

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Fig. 1

Source: O’Connor and Fuellhart (2016)

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Notes

  1. Some flight search engines do allow for the user to specify additional airports by manually entering the airport’s code. Others provide selection options to search a wider region to either the input of a city or airport code. Neither are usually default choices. For example, a Skyscanner flight search from WAS to Hong Kong shows only flights to HKG. Selecting the nearby airports option adds Macau (MFM), Zhuhai (ZUH), and Shenzhen (SZX). These search results link with the findings of Loo (2008).

  2. The formula for conversion to a standardized score (SC) of observation n on a 0–1 scale for a variable with maximum value Nmax and minimum value Nmin is simply: \(SC_{n} = \frac{{\left( {n - N_{min} } \right)}}{{\left( {N_{max} - N_{min} } \right)}}\).

  3. Where data were not available for 2009 (WMI—Warsaw Modlin; DWC Dubai—Al Mahktoum), data for the first year available were used in the calculation. This may inflate the values for these airports due to the law of small numbers (the values were 131 and 1339% respectively).

  4. We created the Middle America region by combining the Central America and Caribbean areas provided by Capstats.

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Authors and Affiliations

  1. Department of Geography and Earth Science, Shippensburg University, Shippensburg, PA, 17257, USA

    Kurt Fuellhart

  2. Faculty of Architecture, Building, and Planning, The University of Melbourne, Melbourne, VIC, Australia

    Kevin O’Connor

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  1. Kurt Fuellhart
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  2. Kevin O’Connor
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Correspondence to Kurt Fuellhart.

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Fuellhart, K., O’Connor, K. A supply-side categorization of airports across global multiple-airport cities and regions. GeoJournal 84, 15–30 (2019). https://doi.org/10.1007/s10708-018-9847-6

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