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