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.

Purpose The dataset provides users with information about airport locations and attributes and can be used for national and regional analysis applications.

Source: http://www.bts.gov/programs/geographic_information_services/

Available only on the web, provides information for airport pair markets rather than city pair markets. This table only lists airport markets where the origin or destination airport is an airport that has other commercial airports in the same city. Midway Airport (MDW) and O'Hare (ORD) are examples of this. All records are aggregated as directionless markets. The combination of Airport_1 and Airport_2 define the airport pair market. All traffic traveling in both directions is added together.

https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports

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 dataset provides detailed information on airline flight routes, fares, and passenger volumes within the United States from 1993 to 2024. The data includes metrics such as the origin and destination cities, distances between airports, the number of passengers, and fare information segmented by different airline carriers. It serves as a comprehensive resource for analyzing trends in air travel, pricing, and carrier competition over a span of three decades.

Data Features:

• tbl: Table identifier
• Year: Year of the data record
• quarter: Quarter of the year (1-4)
• citymarketid_1: Origin city market ID
• citymarketid_2: Destination city market ID
• city1: Origin city name
• city2: Destination city name
• airportid_1: Origin airport ID
• airportid_2: Destination airport ID
• airport_1: Origin airport code
• airport_2: Destination airport code
• nsmiles: Distance between airports in miles
• passengers: Number of passengers
• fare: Average fare
• carrier_lg: Code for the largest carrier by passengers
• large_ms: Market share of the largest carrier
• fare_lg: Average fare of the largest carrier
• carrier_low: Code for the lowest fare carrier
• lf_ms: Market share of the lowest fare carrier
• fare_low: Lowest fare
• Geocoded_City1: Geocoded coordinates for the origin city
• Geocoded_City2: Geocoded coordinates for the destination city
• tbl1apk: Unique identifier for the route

Potential Uses:

• Market Analysis: Assess trends in air travel demand, fare changes, and market share of airlines over time.
• Price Optimization: Develop models to predict optimal pricing strategies for airlines.
• Route Planning: Identify profitable routes and underserved markets for new route planning.
• Economic Studies: Analyze the economic impact of air travel on different cities and regions.
• Travel Behavior Research: Study changes in passenger preferences and travel behavior over the years.
• Competitor Analysis: Evaluate the performance of different airlines on various routes.

Context

Have you taken a flight in the U.S. in the past 15 years? If so, then you are a part of monthly data that the U.S. Department of Transportation's TranStats service makes available on various metrics for 15 U.S. airlines and 30 major U.S airports. Their website unfortunately does not include a method for easily downloading and sharing files. Furthermore, the source is built in ASP.NET, so extracting the data is rather cumbersome. To allow easier community access to this rich source of information, I scraped the metrics for every airline / airport combination and stored them in separate CSV files.

Occasionally, an airline doesn't serve a certain airport, or it didn't serve it for the entire duration that the data collection period covers*. In those cases, the data either doesn't exist or is typically too sparse to be of much use. As such, I've only uploaded complete files for airports that an airline served for the entire uninterrupted duration of the collection period. For these files, there should be 174 time series points for one or more of the nine columns below. I recommend any of the files for American, Delta, or United Airlines for outstanding examples of complete and robust airline data.

* No data for Atlas Air exists, and Virgin America commenced service in 2007, so no folders for either airline are included.

Content

There are 13 airlines that have at least one complete dataset. Each airline's folder includes CSV file(s) for each airport that are complete as defined by the above criteria. I've double-checked the files, but if you find one that violates the criteria, please point it out. The file names have the format "AIRLINE-AIRPORT.csv", where both AIRLINE and AIRPORT are IATA codes. For a full listing of the airlines and airports that the codes correspond to, check out the airline_codes.csv or airport_codes.csv files that are included, or perform a lookup here. Note that the data in each airport file represents metrics for flights that originated at the airport.

Among the 13 airlines in data.zip, there are a total of 161 individual datasets. There are also two special folders included - airlines_all_airports.csv and airports_all_airlines.csv. The first contains datasets for each airline aggregated over all airports, while the second contains datasets for each airport aggregated over all airlines. To preview a sample dataset, check out all_airlines_all_airports.csv, which contains industry-wide data.

Each file includes the following metrics for each month from October 2002 to March 2017:

1. Date (YYYY-MM-DD): All dates are set to the first of the month. The day value is just a placeholder and has no significance.
2. ASM_Domestic: Available Seat-Miles in thousands (000s). Number of domestic flights * Number of seats on each flight
3. ASM_International*: Available Seat-Miles in thousands (000s). Number of international flights * Number of seats on each flight
4. Flights_Domestic
5. Flights_International*
6. Passengers_Domestic
7. Passengers_International*
8. RPM_Domestic: Revenue Passenger-Miles in thousands (000s). Number of domestic flights * Number of paying passengers
9. RPM_International*: Revenue Passenger-Miles in thousands (000s). Number of international flights * Number of paying passengers

* Frequently contains missing values

Acknowledgements

Thanks to the U.S. Department of Transportation for collecting this data every month and making it publicly available to us all.

Source: https://www.transtats.bts.gov/Data_Elements.aspx

Inspiration

The airline / airport datasets are perfect for practicing and/or testing time series forecasting with classic statistical models such as autoregressive integrated moving average (ARIMA), or modern deep learning techniques such as long short-term memory (LSTM) networks. The datasets typically show evidence of trends, seasonality, and noise, so modeling and accurate forecasting can be challenging, but still more tractable than time series problems possessing more stochastic elements, e.g. stocks, currencies, commodities, etc. The source releases new data each month, so feel free to check your models' performances against new data as it comes out. I will update the files here every 3 to 6 months depending on how things go.

A future plan is to build a SQLite database so a vast array of queries can be run against the data. The data in it its current time series format is not conducive for this, so coming up with a workable structure for the tables is the first step towards this goal. If you have any suggestions for how I can improve the data presentation, or anything that you would like me to add, please let me know. Looking forward to seeing the questions that we can answer together!

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.

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.

About this dataset

Context

The dataset tabulates the Airport Drive population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Airport Drive. The dataset can be utilized to understand the population distribution of Airport Drive by age. For example, using this dataset, we can identify the largest age group in Airport Drive.

Key observations

The largest age group in Airport Drive, MO was for the group of age 20 to 24 years years with a population of 110 (14.01%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Airport Drive, MO was the 85 years and over years with a population of 9 (1.15%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Airport Drive is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Airport Drive total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Airport Drive Population by Age. You can refer the same here

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

United States No. of Flights: SEA Terminal: South Satellite data was reported at 38.000 Unit in 16 May 2025. This records a decrease from the previous number of 43.000 Unit for 15 May 2025. United States No. of Flights: SEA Terminal: South Satellite data is updated daily, averaging 31.000 Unit from May 2008 (Median) to 16 May 2025, with 6219 observations. The data reached an all-time high of 48.000 Unit in 19 Apr 2025 and a record low of 0.000 Unit in 08 May 2018. United States No. of Flights: SEA Terminal: South Satellite data remains active status in CEIC and is reported by U.S. Customs and Border Protection. The data is categorized under Global Database’s United States – Table US.TA: Airport Statistics: Number of Flights: by Airport. [COVID-19-IMPACT]

Airports Polygon is a 1:24,000-scale, feature-based layer that includes all airport features depicted on all of the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps that cover the State of Connecticut and are listed on the Federal Aviation Administration (FAA) "Airport Data (5010) & Contact Information" June 5, 2008 report. Airports in New York, Massachusetts and Rhode Island that are near the Connecticut state boundary are included. Airports that are listed by FAA and are visible on aerial photography (Connecticut 2004 Orthophotos and Connecticut 2006 NAIP Color Orthophotos from National Agriculture Imagery Program) are included. Airports that are listed by FAA but are not visible on aerial photography are not included. All airports listed by FAA are included in a separate point feature-based layer, Airport FAA CT. The airport point locations were generated from latitude and longitude coordinates contained in the FAA report and all the attribute information in the report was included. The airport layer is based partly on information from USGS topographic quadrangle maps published between 1969 and 1984 which does not represent airports in Connecticut at any one particular point in time. The layer does depict current conditions as to airports listed by FAA and having location identification codes and visible on aerial photography of 2004 and 2006. The layer delineates airports and heliports. It includes airport name, airport location code, type of facility, public or private use of facility and state the airport is located in. It does not include airport elevation, flight schedule, runway capacity, or ownership information. Features are polygonal and generally depict landing strips and perimeters for large and small airports and helicopter landing pads. Attribute information allows to cartographic representation (symbolize) and labeling of these features on a map. This layer was originally published in 1994 and slightly updated in 2005.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Airport Drive. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Airport Drive. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Airport Drive, householders within the 25 to 44 years age group have the highest median household income at $118,750, followed by those in the 65 years and over age group with an income of $77,750. Meanwhile householders within the 45 to 64 years age group report the second lowest median household income of $73,438. Notably, householders within the under 25 years age group, had the lowest median household income at $51,875.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Airport Drive median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Airport Drive by race. It includes the population of Airport Drive across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Airport Drive across relevant racial categories.

Key observations

The percent distribution of Airport Drive population by race (across all racial categories recognized by the U.S. Census Bureau): 94.81% are white, 0.30% are American Indian and Alaska Native, 0.74% are Asian, 0.44% are some other race and 3.70% are multiracial.

https://i.neilsberg.com/ch/airport-drive-mo-population-by-race.jpeg" alt="Airport Drive population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Airport Drive
• Population: The population of the racial category (excluding ethnicity) in the Airport Drive is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Airport Drive total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Airport Drive Population by Race & Ethnicity. You can refer the same here

This dataset contains scheduled and actual departure and arrival times reported by certified US air carriers that account for at least 1% of domestic scheduled passenger revenues. The data was collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS). The dataset contains date, time, origin, destination, airline, distance, and delay status of flights for flights between 2016 and 2018 The report, focusing on data from year 2016-2018, estimated that air transportation delays put a 4 billion dollar dent in the country's gross domestic product that year. Full report can be found here. In order to answer this question, we are going to analyze the provided dataset, containing up to 18 M different internal flights in the US for 2016-2018 and their causes for delay, diversion and cancellation; if any. The data comes from the U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics (BTS).

This dataset is composed by the following variables: Number Column Name Description 1 **Year **2016, 2017, 2018 2 **Month **1-12 3 **DayofMonth **1-31 4 **DayOfWeek **1 (Monday) - 7 (Sunday) 5 DepTime actual departure time (local, hhmm) 6 **CRSDepTime **scheduled departure time (local, hhmm) 7 **ArrTime **actual arrival time (local, hhmm) 8 **CRSArrTime **scheduled arrival time (local, hhmm) 9 **ActualElapsedTime **in minutes 10 **CRSElapsedTime **in minutes 11 **AirTime **in minutes 12 **ArrDelay **arrival delay, in minutes: A flight is counted as "on time" if it operated less than 15 minutes later the scheduled time shown in the carriers' Computerized Reservations Systems (CRS). 13 **DepDelay **departure delay, in minutes 14 **Origin **origin IATA airport code 15 **Dest **destination IATA airport code 16 **Distance **in miles 17 **TaxiIn **taxi in time, in minutes 18 **TaxiOut **taxi out time in minutes 19 **Cancelled ***was the flight cancelled 20 **CancellationCode **reason for cancellation (A = carrier, B = weather, C = NAS, D = security) 21 **Diverted **1 = yes, 0 = no 22 **CarrierDelay **in minutes: Carrier delay is within the control of the air carrier. Examples of occurrences that may determine carrier delay are: aircraft cleaning, aircraft damage, awaiting the arrival of connecting passengers or crew, baggage, bird strike, cargo loading, catering, computer, outage-carrier equipment, crew legality (pilot or attendant rest), damage by hazardous goods, engineering inspection, fuelling, handling disabled passengers, late crew, lavatory servicing, maintenance, oversales, potable water servicing, removal of unruly passenger, slow boarding or seating, stowing carry-on baggage, weight and balance delays. 23 **WeatherDelay **in minutes: Weather delay is caused by extreme or hazardous weather conditions that are forecasted or manifest themselves on point of departure, enrouted, or on point of arrival. 24 **NASDelay **in minutes: Delay that is within the control of the National Airspace System (NAS) may include: non-extreme weather conditions, airport operations, heavy traffic volume, air traffic control, etc. 25 **SecurityDelay **in minutes: Security delay is caused by evacuation of a terminal or concourse, re-boarding of aircraft because of security breach, inoperative screening equipment and/or long lines in excess of 29 minutes at screening areas. 26 **LateAircraftDelay **in minutes: Arrival delay at an airport due to the late arrival of the same aircraft at a previous airport. The ripple effect of an earlier delay at downstream airports is referred to as delay propagation.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Airport Drive, MO, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Airport Drive did not include 5, 6, or 7-person households. Across the different household sizes in Airport Drive the mean income is $87,478, and the standard deviation is $51,543. The coefficient of variation (CV) is 58.92%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $31,656. It then further increased to $128,756 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/airport-drive-mo-median-household-income-by-household-size.jpeg" alt="Airport Drive, MO median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Airport Drive median household income. You can refer the same here

Dataset Card for Airport Webcams

  Dataset Summary

This dataset contains information about 2,508 airport webcams extracted from airportwebcams.net. Each entry includes source URLs, embedded YouTube video links where available, and URLs to external webcam feeds.

  Dataset Structure





  Data Fields

This dataset includes the following fields:

source_url: URL of the airportwebcams.net page (string) youtube_embeds: List of embedded YouTube video URLs, if any… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/airportwebcams.

About this dataset

Context

The dataset tabulates the population of Airport Drive by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Airport Drive. The dataset can be utilized to understand the population distribution of Airport Drive by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Airport Drive. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Airport Drive.

Key observations

Largest age group (population): Male # 20-24 years (46) | Female # 60-64 years (65). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Airport Drive population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Airport Drive is shown in the following column.
• Population (Female): The female population in the Airport Drive is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Airport Drive for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Airport Drive Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Airport Drive, MO population pyramid, which represents the Airport Drive population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Airport Drive, MO, is 21.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Airport Drive, MO, is 29.2.
• Total dependency ratio for Airport Drive, MO is 50.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Airport Drive, MO is 3.4.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Airport Drive population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Airport Drive for the selected age group is shown in the following column.
• Population (Female): The female population in the Airport Drive for the selected age group is shown in the following column.
• Total Population: The total population of the Airport Drive for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Airport Drive Population by Age. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Airport Drive, MO, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 18,204, while the mean income for the highest quintile (20% of households with the highest income) is 174,958. This indicates that the top earners earn 10 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 229,451, which is 131.15% higher compared to the highest quintile, and 1260.44% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Airport Drive median household income. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Airport Drive. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Airport Drive, the median income for all workers aged 15 years and older, regardless of work hours, was $48,125 for males and $26,200 for females.

These income figures highlight a substantial gender-based income gap in Airport Drive. Women, regardless of work hours, earn 54 cents for each dollar earned by men. This significant gender pay gap, approximately 46%, underscores concerning gender-based income inequality in the village of Airport Drive.

- Full-time workers, aged 15 years and older: In Airport Drive, among full-time, year-round workers aged 15 years and older, males earned a median income of $54,375, while females earned $41,111, leading to a 24% gender pay gap among full-time workers. This illustrates that women earn 76 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Airport Drive.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Airport Drive median household income by race. You can refer the same here