us_airports — US airport networks (2002, 2010)
- Description
Two networks of flights among airports in the United States, distributed by T. Opsahl.
top500_2002: the 500 busiest commercial airports in the United States, connected if a flight was scheduled between them in 2002. This network was used by Colizza, Pastor-Satorras and Vespignani (2007), and was obtained by Opsahl from the website of the Complex Networks Collaboratory. Although flights are directed, the network is nearly symmetric, and is provided in undirected form. The airports are anonymized.bts_2010: all airports in the United States (and airports abroad with flights to or from them), connected by flights in 2010, according to the T-100 table of the Bureau of Transportation Statistics (BTS) Transtats website (with all geographies and months of 2010, and the columns passengers, origin and destination). Flights between the same airports were aggregated, links with zero passengers (cargo only) and self-loops were removed. A directed edge (i, j) indicates passengers flown from airport i to airport j.
The networks have the following properties:
top500_2002: Edges:weight, the number of seats available on the scheduled flights between the two airports in 2002.bts_2010: Nodes:name, the airport code (IATA/FAA). Edges:weight, the total number of passengers flown from airport i to airport j in 2010.
- Tags
- Transportation Airport Weighted
- Citation
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- V. Colizza, R. Pastor-Satorras, A. Vespignani, "Reaction-diffusion processes and metapopulation models in heterogeneous networks." Nature Physics 3, 276-282 (2007) 10.1038/nphys560 Sci-Hub
- T. Opsahl, "Why Anchorage is not (that) important: Binary ties and sample selection" (2011) toreopsahl.com
- Upstream URL OK
- https://toreopsahl.com/datasets/#usairports
- Networks
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Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv top500_2002 500 2,980 11.92 22.34 46.54 35.99 -0.27 0.35 7 1.00 Undirected Unipartite weight 12 KiB 20 KiB 19 KiB 18 KiB bts_2010 1,574 28,236 17.94 76.62 110.94 58.57 -0.12 0.41 8 1.00 Directed Unipartite name weight 84 KiB 157 KiB 159 KiB 138 KiB