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A range of early studies have been conducted to illustrate human mobilitypatterns using different tracking data, such as dollar notes, cell phones andtaxicabs. Here, we explore human mobility patterns based on massive trackingdata of US flights. Both topological and geometric properties are examined indetail. We found that topological properties, such as traffic volume (betweenairports) and degree of connectivity (of individual airports), including bothin- and outdegrees, follow a power law distribution but not a geometricproperty like travel lengths. The travel lengths exhibit an exponentialdistribution rather than a power law with an exponential cutoff as previousstudies illustrated. We further simulated human mobility on the establishedtopologies of airports with various moving behaviors and found that themobility patterns are mainly attributed to the underlying binary topology ofairports and have little to do with other factors, such as moving behaviors andgeometric distances. Apart from the above findings, this study adopts thehead/tail division rule, which is regularity behind any heavy-taileddistribution for extracting individual airports. The adoption of this rule fordata processing constitutes another major contribution of this paper.  Keywords: scaling of geographic space, head/tail division rule, power law,geographic information, agent-based simulations

Exploring Human Mobility Patterns Based on Location Information of US Flights