wikipediaGATN

Overview

wikipediaGATN scrapes Wikipedia airport pages to assemble the Global Air Transportation Networks (GATN): two directed graphs in which each node is an airport (identified by its IATA code) and each directed edge represents a scheduled route between two airports for passengers (pax) or cargo.

Data

The collected data is available in the data/public/ directory. See data/public/README.md for a detailed description of the files.

Using Pre-built Data & Network Analysis

The global network data in data/public/ is automatically updated weekly via GitHub Actions. You do not need to run your own crawl to use this data. You can clone this repository (or download the data/ folder) and immediately load the provided sparse matrices to perform graph analysis.

For more details on available files and advanced analysis examples, see the Quickstart Tutorials.

Here is an end-to-end example showing how to load the passenger network from the pre-built .npz file and analyze it with networkx:

import numpy as np
import scipy.sparse
import networkx as nx

# 1. Load the node labels (IATA codes)
with open('data/public/nodes_pax.txt', 'r') as f:
    iata_codes = [line.strip() for line in f]

# 2. Load the sparse adjacency matrix
matrix = scipy.sparse.load_npz('data/public/adjacency_matrix_pax.npz')

# 3. Create a NetworkX directed graph
G = nx.from_scipy_sparse_array(matrix, create_using=nx.DiGraph)

# 4. Map node indices to their actual IATA codes
mapping = {i: code for i, code in enumerate(iata_codes)}
nx.relabel_nodes(G, mapping, copy=False)

print(f"Network has {G.number_of_nodes()} airports and {G.number_of_edges()} routes.")

# 5. Basic analysis: find the airport with the most incoming flights
in_degrees = dict(G.in_degree())
most_connected = max(in_degrees, key=in_degrees.get)
print(f"Most connected destination: {most_connected} ({in_degrees[most_connected]} incoming routes)")

Package pipeline

The package handles the full pipeline:

1. Crawling — breadth-first traversal from a seed airport, following destination links to neighbouring airport pages.
2. Parsing — extraction of IATA/ICAO codes, geographic coordinates, and route tables from Wikipedia infoboxes and HTML tables, supplemented by the authoritative OurAirports database for metadata.
3. IATA recovery — resolution of destination URLs that lack an obvious code, prioritizing offline lookups in the OurAirports database before falling back to Wikipedia scraping.
4. Export — sparse adjacency matrices (.npz), node lists, airport metadata CSVs ready for network analysis, and interactive Plotly visualisations (.html).
5. Updates — on demand maintenance of the network through incremental scraping and synchronization with upstream OurAirports metadata changes, keeping the graphs up to date. Updates are easy to perform as GitHub actions (see .github/workflows/refresh_data.yml).

The resulting networks can be used for empirical studies of air-travel connectivity, epidemic-spread modelling and transportation network analysis, among other things. They also provide great examples in courses about graphs/networks, data science and computational social science.

Installation

From PyPI (Recommended)

To install the latest stable version directly from PyPI:

pip install wikipediaGATN

From Source

For development or to access the latest changes, clone the repository and install it:

git clone https://github.com/julien-arino/wikipediaGATN.git
cd wikipediaGATN
pip install .

If you wish to contribute, install in editable mode with development dependencies:

pip install -e ".[dev]"

If running from the source repository before deploying the package, set:

export PYTHONPATH=src

and then call the code using, e.g.,

python -m scripts.grab_info_from_IATA

Note the nonstandard call: -m, . instead of / to indicate a subdirectory and no .py extension.

Required post-install step — spaCy language model

The NLP fallback for airline/destination extraction requires the en_core_web_sm model, which cannot be declared as a standard PyPI dependency:

python -m spacy download en_core_web_sm

Dependencies

Package	Purpose
requests, beautifulsoup4	Wikipedia HTTP requests and HTML parsing
mwparserfromhell	Wikitext infobox parsing
spacy	NLP fallback for unstructured route tables
geopy, pycountry	Coordinate and ISO 3166-2 parsing
numpy, scipy	Sparse adjacency matrix construction
pandas	CSV I/O and data manipulation
networkx	Graph construction and layout
plotly	Interactive HTML visualisation

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Example use

Some scripts are provided as examples in the scripts/ directory, which comprise instructions to carry out all steps of the pipeline..

For function example, the following builds a network for all airports reachable within two hops of Winnipeg (YWG) and exports it as a sparse adjacency matrix:

from wikipediaGATN.wikipedia_network_level import iterate_search_until_distance_N
from wikipediaGATN import (
    export_all_airport_data,
    create_outbound_connections_list,
    run_two_pass_iata_extraction,
    create_outbound_adjacency_matrix,
)

# 1. Crawl Wikipedia — save one JSON file per airport to data/tmp_results/
iterate_search_until_distance_N("YWG", dist=2, delay=0.5, verbose=True)

# 2. Process and export crawled airport data (creates public/airport_data/ and airports_information.csv)
export_all_airport_data(use_new_data=True, verbose=True)

# 3. Build connections CSV (maps destination URLs to IATA codes)
connections_csv, cargo_csv, unmapped_csv = create_outbound_connections_list(
    verbose=True, export_unmapped=True
)

# 4. Recover IATA codes for any destinations that could not be mapped automatically
#    (scrapes Wikipedia; allow ~15 minutes for a large unmapped set)
run_two_pass_iata_extraction(batch_size=50, delay=0.5, verbose=True)

# 5. Re-export airport data to apply the recovered manual mappings
export_all_airport_data(use_new_data=True, verbose=True)

# 6. Re-run connections with the enriched mapping
create_outbound_connections_list(verbose=True)

# 7. Export sparse adjacency matrices to data/public/
matrix_npz, nodes_txt = create_outbound_adjacency_matrix(symmetric=False, verbose=True)
matrix_sym_npz, nodes_sym_txt = create_outbound_adjacency_matrix(symmetric=True, verbose=True)

For a full global crawl (several hours) replace step 1 with:

from wikipediaGATN.wikipedia_network_level import iterate_search_until_empty
iterate_search_until_empty("YWG", delay=0.5, verbose=True)

To resume after an interruption:

from wikipediaGATN.wikipedia_network_level import continue_existing_search_until_empty
continue_existing_search_until_empty(delay=0.5, verbose=True)