Airport defines area on land or water intended to be used either wholly or in part for the arrival; departure and surface movement of aircraft/helicopters. This airport data is provided as a vector geospatial-enabled file format and depicted on Enroute charts.Airport information is published every eight weeks by the U.S. Department of Transportation, Federal Aviation Administration-Aeronautical Information Services.Current Effective Date: 0901Z 27 Nov 2025 to 0901Z 22 Jan 2026

The Aviation Facilities dataset is updated every 28 days from the Federal Aviation Administration (FAA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Aviation Facilities dataset is a geographic point database of all official and operational aerodromes in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the aerodrome, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. For more information about these data, please visit: https://www.faa.gov/air_traffic/flight_info/aeronav/Aero_Data/NASR_Subscription. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529011

Airports, Airlines, and Routes is a comprehensive dataset which includes air travel data including airports, airlines, routes, and airplanes. The database contains over 10,000 data points compiled by OpenFlights (https://openflights.org) collected from a variety of sources.

Additional information is available here - https://openflights.org

This dataset represents the location of public, private and military airports and heliports in Kentucky as derived from the Federal Aviation Administration Airport Data & Contact Information online database.Data Download: https://ky.box.com/s/c0k7h4jwz8u5d5e7fwmlbzq9qgqz11lb

FKB-Airport is part of the Common Map Database (FKB). FKB is a collection of datasets that form a central part of the basic map. See the metadata entry for Common Map Database for more info. FKB-Airport includes a limited range of object types for airports to be registered and managed in FKB.Avinor has a more detailed specification used for data capture and management of data for Avinor’s own airports. Data according to this specification may be derived from Avinor’s data. FKB data is non-sensistic and open data. The FKB data is financed through the Geovekst collaboration, or the municipalities alone for municipalities that are outside Geovekst. The FKB data is freely available to Norway digitally and can be downloaded through the download solution at Geonorge.Private operators must purchase access to the data through a retailer.See the metadata entry for Common Map Database for more info. FKB-Airport includes a limited range of object types for airports to be registered and managed in FKB. Avinor has a more detailed specification used for data capture and management of data for Avinor’s own airports. Data according to this specification may be derived from Avinor’s data. FKB data is non-sensistic and open data. The FKB data is financed through the Geovekst collaboration, or the municipalities alone for municipalities that are outside Geovekst. The FKB data is freely available to Norway digitally and can be downloaded through the download solution at Geonorge. Private operators must purchase access to the data through a retailer. FKB-Airport is part of the Common Map Database (FKB). FKB is a collection of datasets that form a central part of the basic map. See the metadata entry for Common Map Database for more info. FKB-Airport includes a limited range of object types for airports to be registered and managed in FKB. Avinor has a more detailed specification used for data capture and management of data for Avinor’s own airports. Data according to this specification may be derived from Avinor’s data. FKB data is non-sensistic and open data. The FKB data is financed through the Geovekst collaboration, or the municipalities alone for municipalities that are outside Geovekst. The FKB data is freely available to Norway digitally and can be downloaded through the download solution at Geonorge. Private operators must purchase access to the data through a retailer.

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight


    registration
    string
    Aircraft registration


    typecode
    string
    Aircraft ICAO typecode


    icaoaircrafttype
    string
    ICAO aircraft type


    wtc
    string
    ICAO wake turbulence category


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection

string

    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometre


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)


    operator_country
    string
    ISO Alpha-3 country code of the operator


    operator_region
    string
    Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)


    wind_speed_knts
    integer
    METAR, surface wind speed in knots


    wind_dir_deg
    integer
    METAR, surface wind direction in degrees


    wind_gust_knts
    integer
    METAR, surface wind gust speed in knots


    visibility_m
    float
    METAR, visibility in m


    temperature_deg
    integer
    METAR, temperature in degrees Celsius


    press_sea_level_p
    float
    METAR, sea level pressure in hPa


    press_p
    float
    METAR, QNH in hPA


    weather_intensity
    list
    METAR, list of present weather codes: qualifier - intensity


    weather_precipitation
    list
    METAR, list of present weather codes: weather phenomena - precipitation


    weather_desc
    list
    METAR, list of present weather codes: qualifier - descriptor


    weather_obscuration
    list
    METAR, list of present weather codes: weather phenomena - obscuration


    weather_other
    list
    METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

    Column name
    Type
    Description




    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    n_landings
    integer
    Total number of landings observed on this runway in 2019


    ga_rate
    float
    Go-around rate, per 1000 landings


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection
    string
    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometres


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic

load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc )

df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)")

iterate over flights and pull the data from OpenSky Network

flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time

# fetch the data from OpenSky Network
flights.append(
  opensky.history(
    start=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"),
    callsign=row["callsign"],
    return_flight=True,
  )
)

The flights can be converted into a Traffic object

Traffic.from_flights(flights)

Additional files

Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:

validation_table.xlsx: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.

validation_sample.zip: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.

The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product.

This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. Context on the original work is given in the related article (https://journals.open.tudelft.nl/joas/article/download/7201/5683) and on the associated GitHub page (https://github.com/AeroMAPS/AeroSCOPE/).A simple data exploration interface will be available at www.aeromaps.eu/aeroscope.The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data. 1- DISCLAIMER The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use. As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance. 2- DESCRIPTION Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple. Please refer to the support article for more details (see above). The dataset contains the following columns:

"First column" : index airline_iata : IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found. acft_icao : ICAO code of the aircraft type acft_class : Aircraft class identifier, own classification.

WB: Wide Body NB: Narrow Body RJ: Regional Jet PJ: Private Jet TP: Turbo Propeller PP: Piston Propeller HE: Helicopter OTHER seymour_proxy: Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model. source: Original data source for the record, before compilation and enrichment.

ANAC: Brasilian Civil Aviation Authorities AUS Stats: Australian Civil Aviation Authorities BTS: US Bureau of Transportation Statistics T100 Estimation: Own model, estimation on Wikipedia-parsed route database Eurocontrol: Aggregation and enrichment of R&D database OpenSky World Bank seats: Number of seats available for the data entry, AFTER airport residual scaling n_flights: Number of flights of the data entry, when available iata_departure, iata_arrival : IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal. departure_lon, departure_lat, arrival_lon, arrival_lat : Origin and destination coordinates, could be NaN if the IATA identifier is erroneous departure_country, arrival_country: Origin and destination country ISO2 code. WARNING: disable NA (Namibia) as default NaN at import departure_continent, arrival_continent: Origin and destination continent code. WARNING: disable NA (North America) as default NaN at import seats_no_est_scaling: Number of seats available for the data entry, BEFORE airport residual scaling distance_km: Flight distance (km) ask: Available Seat Kilometres rpk: Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor) fuel_burn_seymour: Fuel burn per flight (kg) when seymour proxy available fuel_burn: Total fuel burn of the data entry (kg) co2: Total CO2 emissions of the data entry (kg) domestic: Domestic/international boolean (Domestic=1, International=0)

3- Citation Please cite the support paper instead of the dataset itself.

Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2023). Compilation of an open-source traffic and CO2 emissions dataset for commercial aviation. Journal of Open Aviation Science. https://doi.org/10.59490/joas.2023.7201

This map provides the locations of airports, which the FAA defines as areas on land or water intended to be used either wholly or in part for the arrival, departure, and surface movement of aircraft/helicopters. Thus, places such as hospitals with helicopter pads are depicted as airports in this dataset. The data is provided as a vector geospatial-enabled file format.

This is a point layer of Public Use Airports currently permitted by the State of California, Department of Transportation (Caltrans), Division of Transportation Planning Aeronautics Program. Details for each site are updated regularly after airport permit safety inspections of each site.

The "flights.csv" dataset contains information about the flights of an airport. This dataset includes information such as departure and arrival time, delays, flight company, flight number, flight origin and destination, flight duration, distance, hour and minute of flight, and exact date and time of flight. This data can be used in management analysis and strategies and provide useful information about the performance of flights and placement companies. The analysis of the data in this dataset can be used as a basis for the following activities: - Analysis of time patterns and trends: by examining the departure and arrival time of the aircraft, changes and time changes, patterns and trends in flight behavior can be identified. - Analysis of American companies: By viewing information about airlines such as the number of flights, the impact and overall performance, you can compare and analyze the performance of each company. - Analysis of delays and service quality: By examining delays and arrival time, I can collect and analyze information about the quality of services provided by the airport and companies. - Analysis of flight routes: by checking the origin and destination of flights, distances and flight duration, popular routes and people's choices can be identified and analyzed. - Analysis of airport performance: by observing the characteristics of flights and airport performance, it is possible to identify and analyze the strengths and weaknesses of the airport and suggest improvements.

It provides various tools for data analysis and visualization and can be used as a basis for managerial decisions in the field of aviation industry.

Airline Company Codes (in order of frequency for this dataset)

WN -- Southwest Airlines Co.

DL -- Delta Air Lines Inc.

AA -- American Airlines Inc.

UA -- United Air Lines Inc.

B6 -- JetBlue Airways

AS -- Alaska Airlines Inc.

NK -- Spirit Air Lines

G4 -- Allegiant Air

F9 -- Frontier Airlines Inc.

HA -- Hawaiian Airlines Inc.

SY -- Sun Country Airlines d/b/a MN Airlines

VX -- Virgin America

Context

Sans Francisco Airport Data Analysis This is dataset contain detail about the Sans Francisco Airport data set. I am sharing data to learn about the airport data.

Content

This data set contains Date, Geometry, Route, Route 1, Route 2, Event, Flight No, Gate.

Acknowledgements

Users can be download, share, copy this data set for their analysis. Please visit https://tinyurl.com/yd65vnf3.

Inspiration

Some Analysis worth exploring. - Identifying Where the various flights are going? - Which is the most lengthy route from the Airport? - What is the busiest route of the Airport?

The aerial photo flight index shows the aerial photo and flight information, including photo number, shooting position, photo coverage, date of flight, flying height etc., of the aerial photographs taken by Survey and Mapping Office (SMO). There are three file formats, XLS / XLSX, KML and KMZ for download. The file in KML and KMZ file format is geo-referenced to World Geodetic System (WGS84), containing both aerial photo and flight information, while the file in XLS / XLSX file format only contains the aerial photo information.

Graph and download economic data for Total Revenue for Airport Operations, All Establishments, Employer Firms (REVEF48811ALLEST) from 2009 to 2022 about operating, air travel, travel, employer firms, accounting, revenue, establishments, services, and USA.

The following ZIP files contain all of the Digital Enroute charts, except for Caribbean charts, for a given effective date range. Due to the large file sizes, it is best to download one zip file at a time using a broadband internet connection during off-peak internet hours. Each zip file contains multiple zip files each of which contain a mixture of PDF and HTML files.

Context

The European Flights Dataset from 2016 to 2022 provides an extensive record of air traffic activities across various European airports. The data includes essential metrics related to IFR (Instrument Flight Rules) movements, covering both departures and arrivals as reported by the Network Manager and Airport Operator. The dataset is comprehensive, with 688,099 entries and 14 columns, detailing flights over a span of seven years.

Content

Geography: Europe

Time period: Jan 2016- May 2022

Unit of analysis: European Flights Dataset

Variables

Acknowledgements

Datasource: Aviation Intelligence Unit Portal

Inspiration: Commercial air transport in August 2021: in recovery

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2Fb41aa2af36253223c44a36f11cea3d34%2FEU-NEWS-COMMERCIAL-FLIGHT-COMPARE.jpg?generation=1718278227722520&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2Fd36afbf88081d544dd855f6425816d0c%2FEU-NEWS-COMMERCIAL-FLIGHT.jpg?generation=1718278253126208&alt=media" alt="">

Current Effective Date: 0901Z 27 Nov 2025 to 0901Z 22 Jan 2026A runway is a defined rectangular area on a land airport prepared for the landing and takeoff of aircraft along its length. Runways are normally numbered in relation to their magnetic direction rounded off to the nearest 10 degrees. This geospatial vector file provides information on the airport runway and helicopter landing areas. This runway dataset is depicted on Enroute charts.Runway information is published every eight weeks by the U.S. Department of Transportation, Federal Aviation Administration-Aeronautical Information Services.

The T-100 Domestic Market and Segment Data dataset was downloaded on April 08, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). It shows 2024 statistics for all domestic airports operated by US carriers, and all information are totals for the year across all four (4) service classes (F - Scheduled Passenger/ Cargo Service, G - Scheduled All Cargo Service, L - Non-Scheduled Civilian Passenger/ Cargo Service, and P - Non-Scheduled Civilian All Cargo Service). This dataset is a combination of both T-100 Market and T-100 Segments datasets. The T-100 Market includes enplanement data, and T-100 Segment data includes passengers, arrivals, departures, freight, and mail. Data is by origin airport. Along with yearly aggregate totals for these variables, this dataset also provides more granular information for the passenger and freight variable by service class and by scheduled vs non-scheduled statistics where applicable. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529081

Airport Polygon

Snapshot img

The airport information systems market has the potential to grow by USD 1.66 bn during 2021-2025, and the market’s growth momentum will decelerate at a CAGR of 9.26%.

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The airport information systems market report provides comprehensive understanding of the subsets of our target market to earmark niche customer groups and simplify demographic requirements. In addition, the report provides insights on the impact of the unprecedented outbreak of COVID-19 on market segments. Through these insights, you can safely deduce transformation patterns in consumer behavior, which is crucial to gauge segment-wise revenue growth during 2021-2025 and embrace technologies to improve business efficiency. The airport in