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openflights — Openflights airport network

Description

Network of airline routes among airports worldwide, from the OpenFlights route database (originally based on the Airline Route Mapper data). Nodes are airports and a directed edge $(i,j)$ indicates that an airline operates a regular route from airport $i$ to airport $j$. Each route is listed separately for each airline that operates it (including codeshares), so pairs of airports can be connected by multiple edges. The route database was last updated in June 2014 and is not a complete record of all flights; routes whose airports are not identified in the airport database were discarded, as well as airports without any routes.

The network has the following properties:

  • id: the OpenFlights ID of the airport.
  • name, city, country: the name of the airport, and the city and country where it is located.
  • IATA/FAA, ICAO: the three-letter IATA (or FAA) code and the four-letter ICAO code of the airport, if available.
  • latitude, longitude: the coordinates of the airport, in degrees.
  • altitude: the altitude of the airport, in feet.
  • timezone: the offset from UTC, in hours (99 if unknown).
  • DST: the daylight saving time convention, with E = Europe, A = US/Canada, S = South America, O = Australia, Z = New Zealand, N = none, U = unknown (\N if missing).
  • airline: the two-letter IATA or three-letter ICAO code of the airline.
  • airline_code: the OpenFlights ID of the airline.
  • codeshare: whether the route is a codeshare, i.e. operated by another carrier.
  • stops: the number of stops (0 for direct flights).
  • equipment: the three-letter codes of the aircraft types typically used on the route, separated by spaces.
  • distance: the great-circle distance between the two airports, in kilometers.
Tags
Transportation Airport Weighted Multigraph
Citation
Upstream URL OK
https://openflights.org/data.html
Networks
Tip: hover over a column header 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
openflights 3,214 66,771 20.78 109.43 68.15 51.53 0.00 0.34 12 0.99 Directed Unipartite id name city country IATA/FAA ICAO latitude longitude altitude timezone DST distance airline airline_code codeshare equipment stops 597 KiB 976 KiB 929 KiB 910 KiB
Ridiculograms*
openflights drawing
* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.