Search

Airline mergers and acquisitions (MAs) are on the rise across the globe and have been a growing trend in the U.S. aviation industry in the last few years. MAs are taking several factors into consideration, such as cost efficiency, competition, and geographic coverage. For airlines, these transactions can eliminate overlapping routes and help reduce competition, leading airlines to achieve higher operating margins. For travelers, MAs often lead to lesser flight frequency, higher airfares or longer travel miles. To explore spatial-temporal variations from airline and passenger perspectives, this paper focuses on 55 major airports in the 50 largest cities between 2000 and 2010. The detailed results of passenger flow patterns suggest that some airports have more spatial imbalance than others in terms of passenger travel distances. Further, the findings indicate that the MAs have different effects on passenger flows and traveled distances, and the effect is complexly related to the airport’s spatial status.

With increasing availability of spatio-temporal data and the democratization of Geographical Information Systems (GIS), there has been a demand for novel statistical and visualization techniques which can explicitly integrate space and time. The paper discusses the nature of spatio-temporal data, the integration of time within GIS and the flourishing availability of spatial and temporal-explicit data over the Internet. The paper attempts to answer the fundamental question on how these large datasets can be analyzed in space and time to reveal critical patterns. The authors further elaborate on how spatial autocorrelation techniques are extended to deal with time, for point, linear, and areal features, and the impact of parameter selection, such as critical distance and time threshold to build adjacency matrices. The authors also discuss issues of space-time modeling for optimization problems.