SCIENTIA GEOGRAPHICA SINICA ›› 2023, Vol. 43 ›› Issue (4): 606-616.doi: 10.13249/j.cnki.sgs.2023.04.004

Previous Articles     Next Articles

Concept and identification of multiple airport systems: A geographical empirical research

Xiao Fan1,2(), Mo Huihui3, Wang Jiaoe1,2(), Xiong Meicheng1,2   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. China Academy of Civil Aviation Science and Technology, Beijing 100028, China
  • Received:2022-06-16 Revised:2023-01-05 Accepted:2023-03-02 Online:2023-04-30 Published:2023-04-20
  • Contact: Wang Jiaoe E-mail:xiaof2.20b@igsnrr.ac.cn;wangje@igsnrr.ac.cn
  • Supported by:
    National Natural Science Foundation of China(42171187);National Social Science Foundation of China(20&ZD099)

Abstract:

Multiple/multi-airport system (MAS), that is, a set of significant airports that serve commercial transport in a metropolitan region regardless of ownership or political control of the individual airports, plays a key role in commercial aviation. According to this definition, there are 3 keywords to describe MAS: 1) a set of two or more significant airports, 2) that serve commercial traffic, and 3) within a metropolitan region. Therefore, MAS is a typical cross-border (or cross-regional) major infrastructure, which can serve more than one city, namely metropolitan area. However, recent studies' MAS identification method is mainly based on the administrative boundary or the spatial/temporal distance between airports. These methods fail to capture the cross-border characteristic, in addition to the typology of airport and city from a spatial perspective, resulting in the identification bias. After sorting out the concept of MAS, this study proposes a two-step search method to identify MAS. Specifically, the first step is to search for a neighboring primary city with the primary airport as the center, and the second step is to search for significant airports within a specific radius centered on the primary city (i.e., potential center of metropolitan area) obtained in the previous step. With the help of the Annual World Airport Traffic Dataset 2019 and World Cities Database, this study identified the MAS worldwide in 2018 based on the two-step search method. Then, it analyzed the geography of MAS worldwide and its relationship with the place's attributes through geo-visualizing MAS worldwide. The findings are as follows. First, the two-step search method works well, reflecting the relationship between the airport and the metropolitan area. Second, at the metropolitan scale, the maximum reasonable distance between the airport and the primary city can be 70 km; at the city scale, the ultimate reasonable distance between the airport and the city may be 30 km. Third, 59 MAS worldwide in 2018, including 142 civil transport airports, played an essential role in global civil aviation transportation. MAS carries 39.68% of the global throughput, including 3.379 billion passengers and 61 million tons of freight (or air cargo). The average number of airports in the MAS is 2.41. MAS typically contains a large airport with a smaller significant airport. The average airport-city journey time is 39.35 min. Forth, the geography of MAS shows a multicore structure, with most of MAS distributed in coastal areas and regional centers. The detailed characteristics of MAS, such as the transportation volume, the number of airports, and the ground distance between airports and metropolitan area, also shows prominent regional features. Fifth, serval factors, including air transport demand, socio-economic factors, and natural conditions, are jointly related to the geography of MAS. Our findings will pave the way for future research on MAS from a geographic perspective, such as the interaction between MAS and place.

Key words: multiple-airport system, two-step search method, space correlation, metropolitan

CLC Number: 

  • K902