Modeling the Spatial Variation in U.S. Airfares Utilizing Geographically Weighted Regression

Modeling the Spatial Variation in U.S. Airfares Utilizing Geographically Weighted Regression

Hilton A. Cordoba (Department of History, Geography and Philosophy, University of Louisiana at Lafayette, Lafayette, LA, USA) and Russell L. Ivy (Department of Geosciences, Florida Atlantic University, Boca Raton, FL, USA)
Copyright: © 2014 |Pages: 18
DOI: 10.4018/ijagr.2014100104

Abstract

Modeling airline fares is quite challenging due to the constantly changing fare structure of the airlines in response to competitors, yield management principles, and a variety of political and economic changes, and has become more complex since deregulation. This paper attempts to add to the literature by providing a more in-depth look at fare structure using a multivariate approach. A total 6,200 routes between 80 primary U.S. airports are analyzed using linear and geographically weighted regression models. The results from the global models reinforce some of the expectations mentioned in the literature, while the local models provide an opportunity to analyze the spatial variation of influencing factors and predictability.
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1. Introduction

The deregulation of the airline industry created a myriad of changes in the U.S. system that has both defended and sparked debate on the wisdom of such policy change for over 3 decades (Bailey, et al, 1985; Borenstein, 1989; Brenner, 1988; Brown, 1987; Dobson, 1995; Goetz & Dempsey, 1989; Graham, et al, 1983; Meyer, et al, 1981; Rose, 1981; Shaw & Ivy, 1994; Toh & Higgins, 1985). Other parts of the world, particularly Europe, have been heavily influenced by the US experience in the deregulated era which has prompted waves of deregulation (liberalization), the build-up of large hubs to collect and redistribute passengers, mergers and acquisitions, marketing and operational alliances, as well as code-share partnerships to create larger systems that cut across political boundaries creating greater economies of scale and scope that has truly brought about the concept of a ‘global airline’ (Balfour, 1994; Barrett, 1993; Browne, 1993; De Wit, 1996; Dennis, 1994a; Doganis, 1994; Graham, 1990, 1993, 1995, 1998; Holloway, 2008; Ivy, 1995,1997, 2002; Leigh, 1990; Oster & Pickrell, 1988; Shaw & Ivy, 1994; Vasigh, et al., 2010; Vowles, 2000a).

Many geographers have been interested in the spatial change in network structure resulting from deregulation. These changes were required in order to maximize passenger loads and the usage of aircraft, which resulted in the hub-and-spoke system we see today. The hub-and-spoke system created a hierarchy in air transport where some airports, those selected as transfer and collection hubs in the networks of the major airlines (hubs), were elevated in connectivity importance, and those not selected as major transfer and collection points (spokes) often declined in relative connectivity importance within the system; and some airports were completely left behind in the hub-and-spoke structure losing service altogether (Chan, 1982; Ivy,1993a, 1993b; Jemiolo & Oster, 1987; Maraffa & Kiel, 1985; Warren, 1984). O’Kelly (1986a; 1986b, 1987) and others (Bauer, 1987; Chou, 1990; Fleming & Hayuth, 1994; Ivy, 1991; Lopuszynski, 1986; O’Kelly & Lao, 1991; O’Kelly & Miller, 1994; Shaw, 1993; Song, 2006) have made significant contributions to the understanding of the selection, positioning and interaction of hubs within the network design. The connectivity advantages of hub cities can often translate into economic advantages such as creating an attractive force to large firms when locating or relocating administrative and research based employees or helping to restructure an urban economy in general (Debbage, 2000; Debbage & Delk, 2001; Ivy, et al, 1995), as well as other positive and negative impacts for the airports, airlines, communities housing the hub airport and passengers living in the hub city (Kanafani & Ghobrial, 1985).

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