Introduction to SKYNET

Filipe Teixeira


SKYNET is a flexible R package that allows generating bespoke air transport statistics for urban studies based on publicly available data from the Bureau of Transport Statistics (BTS) in the United States.

SKYNET’s segments

SKYNET is effectively divided into four segments:

  1. Import Data
  2. Generate Air Networks
  3. Plot Air Networks

Import Data

To import data, simply type import_db1b() or import_t100() including the path to your desired file. Note: we recommend naming the files with a similar structure as Ticket 2016Q1.csv or Coupon 2016Q1.csv respectively.

 import_db1b("folder/Coupon 2016Q1.csv", "folder/Ticket 2016Q1.csv")

The BTS DB1B data consists of 2 sets of files, Coupon and Ticket. They can be both downloaded at and respectively.

Despite being possible to download the complete zipped file, which includes all variables, due to its size, we recommend selecting the following set.

Coupon Ticket
Itinerary ID Itinerary ID
Market ID Roundtrip
Sequence Number Itinerary Yield
Origin City Market ID Passengers
Origin Itinerary Fare
Year Bulkfare Indicator
Quarter Distance Full
Destination City Market ID
Trip Break
Operating Carrier

Since version 1.0.2 that the import method changed being the netimport() function no longer available. When importing from the prezipped DB1B file, just add the argument zip = TRUE to the import_db1b() function. This does not apply to the T100 file which can be simply imported by typing import_t100(). In order to save space, it is possible as well to import the prezipped file, and convert it to a smaller file with only the necessary variables, with the function convert_raw().

When importing files from the T100 dataset, we recommend naming the file as T100 year mkt for the Market dataset and T100 year seg for the Segment dataset.

Create networks

SKYNET creates three types of networks and an extra option:

  1. Directed Network - make_net_dir()
  2. Undirected Network - make_net_und()
  3. Path Network - make_net_path()
  4. Metro Network - To be used as argument in make_net_dir() and make_net_und()
  5. International Option - make_net_int()

When generating a network, SKYNET, creates a list which includes:

  1. Dataframe from original data (example below)
  2. iGraph object
  3. Dataframe with nodes(airports) statistics
itin_id mkt_id seq_num origin_mkt_id origin dest_mkt_id dest trip_break op_carrier distance year quarter gateway roundtrip itin_yield passengers itin_fare bulk_fare distance_full
201112367302 2.011124e+13 1 30713 BOI 32457 SJC X WN 523 2011 1 0 1 0.2132 1 223 0 1046
201112373990 2.011124e+13 3 30325 DEN 30977 MDW WN 895 2011 1 0 1 0.1069 1 335 0 3135
201112643128 2.011126e+13 1 33667 ORF 31454 MCO WN 655 2011 1 0 1 0.0744 1 366 0 4919
201112707542 2.011127e+13 1 31714 RSW 30693 BNA WN 722 2011 1 0 0 0.0870 1 114 0 1310
201112477390 2.011125e+13 1 31453 HOU 32211 LAS WN 1235 2011 1 0 0 0.0810 1 128 0 1580

When generating a network with SKYNET, it is possible use the following arguments:
1. carriers - groups OD data per carrier when TRUE

To extract the backbone of the network:
1. cap (to be used with pct) - filters the network based on a given percentage (default percentage = 10%)
1. disp (to be used with alpha) - filters the network using the Serrano et all backbone extraction algorithm (default alpha = 0.003)

Create Maps

One of SKYNET’s advantages is the possibility of plotting maps without having to recur to external software.

Typing net_map(skynet_object) plots a ggplot2 based map with OD information. When specifying the group by carrier option when generating a network, net_map() distinguishes carriers with different colors. The pct argument allow to plot only a percentage of the available data. It is important to point the path to the dataframe created by SKYNET.

Extra Functions

SKYNET, allows as well to perform quick searches on both airports and carriers, by their IATA code. find_airport(), find_carrier().


With version 1.0.2, we included the option to bootstrap networks and retrieve certain network statistics.

test <- make_net_dir(OD_Sample)
boot_network(test$gDir, n = 10)
##                            0.5%       99.5% mean_random mean_empirical
## average.path.length   2.4518321   2.4809806   2.4675331      2.4479099
## transitivity          0.3373953   0.3452898   0.3413571      0.3302566
## betweenness         276.3929482 284.4480478 281.7438247    377.7058371