The data includes the location and related attributes for all public-use airports in the U.S. for 2007. More specifically, from the NTAD website: "Abstract: 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. There are 20491 airport landing facilities in this dataset. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product (Effective 18 Jaunary 2007)."

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. The Airports database is a geographic point database of aircraft landing facilities in the State of Maryland. 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 dataset only contains features located within the State of Maryland. Last Updated: 06/2013 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Transportation/MD_Transit/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This dataset shows airports in the United States, Puerto Rico and the U.S. Virgin Islands. The data were derived from an extract of The Public- Use Airports database of the National Transportation Atlas Databases-2001 (NTAD-2001), published by the Bureau of Transportation Statistics, Department of Transportation. This dataset was released in October 2001 and was found on-line at the National Atlas, www.nationalatlas.gov in Shape file format. This point data is intended for use within the United States, including Puerto Rico and the U.S. Virgin Islands. This data may be used for geographic display and analysis at the national level, and for large regional areas. Metadata: http://www.nationalatlas.gov/metadata/airprtx020.faq.html Online: www.nationalatlas.gov

Content

The Port Authority of New York and New Jersey quarterly produces a data file and provides information on count of the number of vehicles in the airport public parking lots. This dataset represents the number of cars parked in public parking lots at John F. Kennedy International Airport, LaGuardia Airport, Newark Liberty International Airport, and Stewart International Airport beginning in 2002.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by chuttersnap on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Data downloaded from https://geodata.bts.gov/datasets/usdot::aviation-facilities/about, selecting only the North Dakota records, then projecting to NAD83.For detailed information on each of the 89 public airports in North Dakota, please visit the North Dakota Aeronautics Commission website.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.

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. There are 20491 airport landing facilities in this dataset. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product (Effective 18 Jaunary 2007)

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

The Airports database is a geographic point database of aircraft landing facilities in the State of Maryland. 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 dataset only contains features located within the State of Maryland.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Transportation/MD_Transit/FeatureServer/18

This layer contains the boundary of the airports as well as the runways and taxiways on the airport facilities in and near Fairfax County. The original data in this layer was captured during the 1997 data conversion effort for Fairfax County. After that an update capture was completed in 2014 using stereo models from the 2009 Virginia State imagery. Subsequent to that an update capture was completed in 2022 using stereo models from the 2017 Virginia State imagery. The most recent airport footprints update was completed in 2024 using Orthophotos from the 2023 and 2022 Virginia State imagery.Contact: Fairfax County Department of Information Technology GIS DivisionData Accessibility: Publicly AvailableUpdate Frequency: As neededLast Revision Date: 3/1/2024Creation Date: 1/1/1997Feature Dataset Name: GISMGR.TRANSPORTATIONLayer Name: GISMGR.AIRPORTS

This dataset compiles data from all airports across the United States of America to analyze how their characteristics influence safety.

The analysis focuses on the number of incidents that occurred during 2023 and 2024. Each airport is represented as a row and identified by its FAA Location Identifier (Loc ID), a three- to five-character alphanumeric code assigned by the Federal Aviation Administration (FAA). The dataset includes the following variables as columns:

Intrinsic Characteristics of the Airports – This category includes variables such as the airport’s geographical coordinates (ARP latitude, ARP longitude, and elevation), the U.S. state, county, city where the airport is located, the biannual enplanements, and the airport size.

Biannual Weather Conditions – This category provides information on the average meteorological conditions in the area where the airport is situated. Variables include the minimum and maximum biannual average temperatures, average biannual precipitation (in inches), and biannual fog frequency.

Incident Variables (Recorded Incidents) – This category contains variables representing different types of incidents recorded at the airport. Each variable corresponds to a specific cause of an incident, including: Approach Incident, Maneuvering Incident, Emergency Descent Incident, Enroute Incident, Initial Climb Incident, Landing Incident, Standing Incident, Takeoff Incident, Taxi Incident, Uncontrolled Descent Incident, It also includes the highest level of injury for each incident being Fatal, Minor, None and Serious. Total number of incidents per U.S. airport and A final binary variable: "Has the airport experienced any incidents in 2023-2024?", which returns a TRUE/FALSE response.

This dataset aims to facilitate a comprehensive analysis of airport safety by examining how various characteristics correlate with incident occurrences.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

aeroway IS NOT NULL OR building = 'aerodrome' OR emergency:helipad IS NOT NULL OR emergency = 'landing_site'

Features may have these attributes:

• capacity:persons
• aeroway
• name
• emergency
• building
• operator:type
• source
• emergency:helipad
• addr:full
• addr:city

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

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. Data is downloaded from the National Transportation Atlas Database.

Constraints:
Acknowledgment of the Federal Aviation Administration (FAA) and the Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS) National Transportation Atlas Databases (NTAD) 2007 would be appreciated in products derived from these data. Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

aeroway IS NOT NULL OR building = 'aerodrome' OR emergency:helipad IS NOT NULL OR emergency = 'landing_site'

Features may have these attributes:

• building
• operator:type
• addr:full
• aeroway
• emergency:helipad
• capacity:persons
• emergency
• name
• addr:city
• source

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This data shows where there are interconnections between public transportation modes at aiports, ferry, and intercity rail and bus stations in the United States. More specifically, according to the Bureau of Transportation Statistics: "The Intermodal Passenger Connectivity Database is a nationwide data table of passenger transportation terminals, with data on the availability of connections among the various scheduled public transportation modes at each facility. In addition to geographic data for each terminal, the data elements describe the availability of rail, air, bus, transit, and ferry services. This data has been collected from various public sources to provide the only nationwide measurement of the degree of connectivity available in the national passenger transportation system. At this point, data has been collected for intercity rail stations and airline airports only. Data on terminals of other modes is being collected and will be released when it is available. It is anticipated that the entire database will be complete by December 31, 2008."

The GeoJunxion Airports are part of the GeoJunxion Geo-Boundaries data suite. The GeoJunxion Airports are land use boundaries and are often used to define areas. Airport boundaries are shown in geographical data by making use of Polygons. Polygons form the geometry of boundary features. The GeoJunxion Airports Boundaries have attributes such as: Name, IATA name, type of airport, latitude/longitude and centre point.

With the GeoJunxion Airports, also named Area of Interest (AOI) and Points of Interest (POI), you can add relevant information to an area such as the name, latitude/longitude, road network and centre point. The GeoJunxion Airports database covers the geographic boundaries of Airports across the globe.

In select areas the detailed Runway and Taxiway boundaries are also available.

Additional attributes with more detailed information optionally available.

The prices vary depending on the application and individual requirements. Just talk to us, we'll be happy to make you an offer.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. The Airport Runways database is a geographic dataset of runways in the State of Maryland containing information on the physical characteristics of the runways. The 6716 runways in the dataset are runways associated with the 19721 airports in the companion airport data set. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product (Effective 17 March 2013). Last Updated: 06/2013 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Transportation/MD_Transit/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This dataset provides detailed information on flight arrivals and delays for U.S. airports, categorized by carriers. The data includes metrics such as the number of arriving flights, delays over 15 minutes, cancellation and diversion counts, and the breakdown of delays attributed to carriers, weather, NAS (National Airspace System), security, and late aircraft arrivals. Explore and analyze the performance of different carriers at various airports during this period. Use this dataset to gain insights into the factors contributing to delays in the aviation industry.

Purpose: The purpose of this dataset is to offer insights into the performance of U.S. carriers at various airports during August 2013 - August 2023, focusing on flight arrivals and delays. By providing detailed information on key metrics such as the number of arriving flights, delays over 15 minutes, cancellations, and diversions, the dataset aims to facilitate analyses of factors contributing to delays, including those attributed to carriers, weather, the National Airspace System (NAS), security, and late aircraft arrivals. Researchers, data scientists, and aviation enthusiasts can leverage this dataset to explore patterns, identify trends, and draw conclusions that contribute to a better understanding of the aviation industry's operational challenges.

Structure: The dataset is structured as a tabular format with rows representing unique combinations of year, month, carrier, and airport. Each row contains information on various metrics, including flight counts, delay counts, cancellation and diversion counts, and delay breakdowns by different factors. The columns provide specific details such as carrier codes and names, airport codes and names, and counts of delays attributed to carrier, weather, NAS, security, and late aircraft arrivals. The structured format ensures that users can easily query, analyze, and visualize the data to derive meaningful insights.

• year: The year of the data.
• month: The month of the data.
• carrier: Carrier code.
• carrier_name: Carrier name.
• airport: Airport code.
• airport_name: Airport name.
• arr_flights: Number of arriving flights.
• arr_del15: Number of flights delayed by 15 minutes or more.
• carrier_ct: Carrier count (delay due to the carrier).
• weather_ct: Weather count (delay due to weather).
• nas_ct: NAS (National Airspace System) count (delay due to the NAS).
• security_ct: Security count (delay due to security).
• late_aircraft_ct: Late aircraft count (delay due to late aircraft arrival).
• arr_cancelled: Number of flights canceled.
• arr_diverted: Number of flights diverted.
• arr_delay: Total arrival delay.
• carrier_delay: Delay attributed to the carrier.
• weather_delay: Delay attributed to weather.
• nas_delay: Delay attributed to the NAS.
• security_delay: Delay attributed to security.
• late_aircraft_delay: Delay attributed to late aircraft arrival.

Usage: Researchers, analysts, and data enthusiasts can utilize this dataset for a variety of purposes, including but not limited to:

Performance Analysis: Assess the on-time performance of different carriers at specific airports and identify potential areas for improvement.

Trend Identification: Analyze temporal trends in delays, cancellations, and diversions to understand whether certain months or periods exhibit higher operational challenges.

Root Cause Analysis: Investigate the primary contributors to delays, such as carrier-related issues, weather conditions, NAS inefficiencies, security concerns, or late aircraft arrivals.

Benchmarking: Compare the performance of various carriers across different airports to identify industry leaders and areas requiring attention.

Predictive Modeling: Use historical data to develop predictive models for flight delays, aiding in the development of strategies to mitigate disruptions.

Industry Insights: Contribute to a broader understanding of the factors influencing operational efficiency within the U.S. aviation sector.

As users explore and analyze the dataset, they can gain valuable insights that may inform decision-making processes, improve operational strategies, and contribute to a more efficient and reliable air travel experience.

This is a point based representation of Airports. The dataset is comprised of 15044 features derived based on 1:3 000 000 data originally from RWDBII. The layer provides nominal at 1:3 000 000. Data processing complete globally. This data was collected from: http://www.fao.org/geonetwork/srv/en/metadata.show?id=29037&currTab=simple access date: October 15, 2007

The Air transport domain contains national and international intra and extra-EU data. This provides air transport data for passengers (in number of passengers) and for freight and mail (in 1 000 tonnes) as well as air traffic data by airports, airlines and aircraft. Data are transmitted to Eurostat by EU Member States, EFTA countries and some other reporting countries. Data are compiled following the provisions of the Regulation (EC) N°1358/2003, implementing Regulation N°437/2003 of the European Parliament and of the Council on statistical returns in respect of the carriage of passengers, freight and mail by air. The air transport data are collected at airport level. As from 2003 reference year the data are provided according to the legal act (some countries were given derogation until 2005). Until 2002 partial information (passenger transport only) are available for some countries and airports.

Airports handling less than 15 000 passenger units annually are excluded from the scope of the Regulation. Datasets A1 and B1 are provided on monthly basis, while dataset C1 can be provided either on monthly or annual basis. For some countries optional variable - total number of transfer passengers - is provided as well.

The data are disseminated by Eurostat in on-line database in four sub-domains:

• Air Transport measurement - Passengers
• Air Transport measurement - Freight and mail
• Air Transport measurement - Traffic data by airports, aircraft and airlines
• Air Transport measurement - Data aggregated at standard regional levels (NUTS).

The two first domains contain several data collections:

• Overview of the air transport by country and airport,
• National air transport by country and airport,
• International intra-EU air transport by country and airport,
• International extra-EU air transport by country and airport,
• Detailed air transport by reporting country and routes.

In the tables of the sub-domain "Transport measurement - Passengers", data are broken down by passengers on board (arrivals, departures and total), passengers carried (arrivals, departures and total) and passenger commercial air flights (arrival, departures and total). Additionally, the tables of collection "Detailed air transport by reporting country and routes" provide data on seats available (arrival, departures and total). The data is presented at monthly, quarterly and annual level.

In the tables of the sub-domain "Transport measurement - Freight and mail", data are broken down by freight and mail on board (arrival, departures and total), freight and mail loaded/unloaded (loaded, unloaded and total) and all-freight and mail commercial air flights (arrival, departures and total). The data is presented at monthly, quarterly and annual level.

In the tables of the sub-domain "Transport measurement - Traffic by airports, aircraft and airlines":

- Data by type of aircraft are broken down by total passengers on board, total freight and mail on board in tonnes, total passengers seats available, total commercial air flights (passengers + all-freight and mail), passenger commercial air flights, all-freight and mail commercial air flights. The data is presented at annual level since 2003.

- Data by type of airline are broken down by total passengers on board, total passengers carried, total freight and mail on board, total freight and mail loaded/unloaded, total passengers seats available, total commercial air flights (passengers + all-freight and mail), passenger commercial air flights, all-freight and mail commercial air flights. The data is presented at annual level since 2003.

- Data by airport are broken down by total passengers carried, total transit passengers, total transfer passengers, total freight and mail loaded/unloaded, total commercial aircraft movements, total aircrafts movements. The data is presented at monthly, quarterly and annual level.

The sub-domain "Transport measurement - Data aggregated at standard regional levels (NUTS)", contains two tables:

• Air transport of passengers at regional level
• Air transport of freight at regional level

The tables present the evolution of the number of passengers carried (if not available passengers on board) and the volume of freight and mail loaded or unloaded (if not available freight and mail on board) to/from the NUTS regions (level 2, 1 and 0) since 1999. The data is presented at annual level. The air transport regional data have been calculated using data collected at the airport level in the frame of the regulatory data collection on air transport.

For more details on datasets, data validation and dissemination refer also to Reference Manual on Air Transport Statistics available in the Annex part of the metadata.