From Podes to Antipodes: Positionalities and Global Airline Geographies

Abstract

Questions of cost and time distance have long been of interest to geographers and have become a more central concern as globalization advances. We analyze the global air travel system by examining the differences in the costs, distances, and times of one aspect of globalization. We review the extant literature on airline transportation by geographers and others, noting especially the near-century-long interest in unraveling cost, time, and distance issues and designing innovative ways to map these interrelated variables. We expand on this base to bring recent scholarship on power and positionality of cities to our understanding of air travel. Our analysis expands on previous work on airline transport geographies in four distinct ways. First, we developed an international database for a large number of cities worldwide that includes measures of distance, cost, frequency and flight duration of airline connections. Second, this database is examined statistically through ordinary least squares (OLS) regressions to measure variations in airline volume with selected socioeconomic variables. Third, these global airline data are mapped using conventional mapping techniques, and finally, we prepare a set of “position-grams” or intersecting spheres of regional variation that measure and map regional patterns and variations in airline connectedness.

Key Words:

• global cities
• innovative cartography
• power geometry
• transport geography
• wormholes

Notes

Note: The measures of population, government effectiveness, and bandwidth all refer to the destination city of the city-pair.

^aUNDP is the United Nations Development Programme.

Note: Values in parentheses are negative.

1. Worldspan provides the backend data for a number of other popular travel web sites including Expedia, Priceline, Orbitz, and Hotwire. Worldspan serves more than seventy countries worldwide and in 2003 processed 65 percent of all online airline transactions made in the United States (Worldspan 2005). Other major GDSs—Amadeus, Galileo, and Sabre—each provide a unique collection of airlines, although there is considerable overlap across systems (i.e., cross-listing) by major carriers. This differentiation in listings is a cause for some concern, but the overall marketplace for commercial flights from a city is reflected in the pricing and routing of airlines, which must compete with the lowest fares available, a particularly relevant issue in measuring the effect of low-cost airlines on pricing.

2. A number of U.S. airports that were identified by the ACI as among the busiest passenger airports were not included despite their high volume (e.g., Denver, Phoenix, and Las Vegas) because much of their traffic is domestic travel and this research is focused primarily on international airline patterns.

3. These decisions affected Brazil, China, Germany, India, Japan, and the United States.

4. The data used here build on a similar but smaller dataset collected in December 2003 for fourteen hub cities and 137 destinations. The patterns that emerge from analyses of the two datasets are extremely similar.

5. Time distance is based on the amount of travel time both outbound from and inbound to a hub city and a destination city in order to compensate for differences in westward versus eastward travel time due to prevailing wind patterns (see Warntz 1961). Although there is also directionality in cost distance (i.e., fares can be lower depending on the origin of the trip), we calculate all fares based on trips originating in hub cities. This was done both to standardize data and to facilitate its collection.

6. A number of other variables (GNP per capita, Internet domains or users, etc.) were also examined and showed a similar pattern as bandwidth which had the advantage of greater coverage.