The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached **** million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to **** million in 2020. The flight volume increased again in the following years and was forecasted to reach ** million in 2025.

Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

Using a combination of OAG flight schedule and ch-aviation fleet data, Capacities - Scheduled provides an overview of future flights scheduled per calendar day with a breakdown of seat capacity for five cabin classes (Economy, Economy Plus/Comfort, Premium Economy, Business, First) by operator and route (Continent, Country, Subdivision, Metro Group, Airport).

The data set is updated weekly.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=capacities_scheduled/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/capacities-scheduled/

Our Flight Events data feed combines Spire Global satellite/terrestrial ADS-B flight event data with ch-aviation’s fleet, operator, and airport data providing an overview of all flights operated by airlines, business and general aviation players on a daily basis.

The value of our Flight Events data feed lies in its high-resolution integration of ADS-B flight tracking with ch-aviation’s comprehensive aircraft and operator data, delivering unmatched visibility into global aircraft movements. By identifying the aircraft type and registration for approximately 98% of all ADS-B-tracked flights, we offer an industry-leading solution for lessors, insurers, airports, OEMs, and analysts seeking precise, reliable, and actionable aviation intelligence.

• High-Resolution ADS-B Integration - Satellite and terrestrial ADS-B flight tracking combined with enriched aircraft and operator data for maximum accuracy and visibility • Comprehensive Aircraft Identification - Aircraft type and registration identified for approximately 98% of all ADS-B-tracked flights, using proprietary matching with ch-aviation data and supplementary publicly available authority data sources. • Global Flight Coverage - Tracks approximately 160,000–190,000 flights per day across commercial aviation, business jet, and general aviation sectors worldwide. • ACMI (Wet-Lease) and Cargo Customer Tracking - Detailed monitoring of ACMI operations, including identification of wet-lease activity between different operators as well as cargo customers identifying flights operated for integrators like DHL Express or FedEx as well as cargo customers such as Amazon. • Aircraft Utilisation Tracking - Tracking of flight hours and cycles at both the operator and individual tail number (aircraft) level • Matched Operator and Aircraft Data - Every flight is linked to comprehensive ch-aviation datasets, including aircraft ID, history, operator, variant, callsign, and airport details allowing customers to leverage the industry’s most comprehensive integration between ADS-B flight event and fleet/operator/airport data. • Fallback Data Enrichment - Where ch-aviation data is unavailable, civil aviation authority and ANSP sources are used to ensure continuity in aircraft identification and data accuracy. • Use Case-Driven Insights - Tailored for industry stakeholders like lessors, insurers, OEMs, airports, and analysts seeking operational, commercial, and technical flight data intelligence.

ch-aviation integrates its Commercial Aviation Aircraft Data and Business Jet Aircraft Data with Spire Global’s satellite-based ADS-B data that is fused by Spire with terrestrial feeds from AirNav and Wingbits.

This data is enriched with mapped callsigns, corrected hexcodes, regional partnership decoding, and identification of wet-leases and cargo customers, enabling detailed insight into each individual flight.

Where ch-aviation data is unavailable, public data from civil aviation authorities and ANSPs is used to ensure broad and reliable aircraft identification and coverage.

The data set is available historically going back to January 1, 2018.

The data set is updated daily.

The sample data shows flights on 2025-03-30, with Swiss, Alaska Airlines, Horizon Air, Jet Aviation Business Jets, and RVR Aviation as operators or wet lease customers.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=flights/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/flights-2/

India All Scheduled Airlines: International: Number of Flight data was reported at 18,502.000 Unit in Mar 2025. This records an increase from the previous number of 16,668.000 Unit for Feb 2025. India All Scheduled Airlines: International: Number of Flight data is updated monthly, averaging 7,797.000 Unit from Apr 2001 (Median) to Mar 2025, with 283 observations. The data reached an all-time high of 18,574.000 Unit in Jan 2025 and a record low of 273.000 Unit in May 2020. India All Scheduled Airlines: International: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

Daily aircraft utilisation is available for all commercial aviation and business jet aircraft showing the number of flight hours and cycles every day (in UTC) time based on a combination of Spire Global satellite/terrestrial ADS-B data and ch-aviation fleet data.

The data set includes hours, cycles, average stage length as well as data quality indicators for each record.

The data set is updated daily.

The sample data shows aircraft flown on 2025-03-30 by Swiss, Alaska Airlines, Horizon Air, Jet Aviation Business Jets, and RVR Aviation, with utilization metrics

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=aircraft_utilisation_daily/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/aircraft-utilisation-daily-ads-b-based-2/

Geospatial Dataset of GNSS Anomalies and Political Violence Events

Overview

The Geospatial Dataset of GNSS Anomalies and Political Violence Events is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database.

Dataset Files

The dataset consists of three CSV files:

1. Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv
• Description: Contains all grids and dates that had aircraft traffic during 2023.
• Number of Records: 6,777,228
• Purpose: Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.
2. Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv
• Description: A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.
• Number of Records: 718,237
• Purpose: Focuses on areas and times with GNSS anomalies for targeted analysis.
3. Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv
• Description: Contains aggregated monthly data for each grid cell, combining GNSS anomalies and ACLED political violence events. Summarizes aircraft traffic, anomaly counts, and conflict activity at a monthly resolution.
• Number of Records: 25,770
• Purpose: Enables temporal trend analysis and spatial correlation studies between GNSS interference and political violence, using reduced data volume suitable for modeling and visualization.

Data Fields: Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv and Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv

1. grid_id
• Description: Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.
• Format: String combining latitude and longitude (e.g., -10.0_-36.0).
2. day
• Description: Date of the recorded data.
• Format: YYYY-MM-DD (e.g., 2023-03-28).
3. geometry
• Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.
• Format: POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).
4. flights
• Description: Number of aircraft flights that passed through the grid on that day.
• Format: Integer (e.g., 28).
5. GPS_jumps
• Description: Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.
• Format: Integer (e.g., 1).
6. GPS_gaps
• Description: Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.
• Format: Integer (e.g., 0).
7. gaps_density
• Description: Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.
• Format: Decimal (e.g., 0).
8. jumps_density
• Description: Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.
• Format: Decimal (e.g., 0.035714286).
9. event_id_cnty
• Description: ACLED event ID corresponding to political violence events in the grid on that day.
• Format: String (e.g., BRA69267).
10. disorder_type
• Description: Type of disorder as classified by ACLED (e.g., "Political violence").
• Format: String.
11. event_type
• Description: General category of the event according to ACLED (e.g., "Violence against civilians").
• Format: String.
12. sub_event_type
• Description: Specific subtype of the event as per ACLED classification (e.g., "Attack").
• Format: String.
13. acled_count
• Description: Number of ACLED events in the grid on that day.
• Format: Integer (e.g., 1).
14. acled_flag
• Description: Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).
• Format: Integer (0 or 1).

Data Fields: Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv

The file contains monthly aggregated GNSS anomaly and ACLED event data per grid cell. The structure and meaning of each field are detailed below:

1. grid_id
• Description: Unique identifier for a grid cell on Earth measuring 0.5° latitude by 0.5° longitude.
• Format: String combining latitude and longitude (e.g., -0.5_-79.0).
2. year_month
• Description: Month and year of the aggregated data.
• Format: String in Mon-YY format (e.g., Jan-23).
3. geometry
• Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.
• Format: POLYGON((longitude latitude, ...))
  (e.g., POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))).
4. flights
• Description: Total number of aircraft flights that passed through the grid cell during the month.
• Format: Integer (e.g., 1230).
5. GPS_jumps
• Description: Total number of GNSS "jump" anomalies (possible spoofing events) in the grid cell during the month.
• Format: Integer (e.g., 13).
6. GPS_gaps
• Description: Total number of GNSS "gap" anomalies, indicating interruptions in aircraft routes, during the month.
• Format: Integer (e.g., 0).
7. event_id_cnty
• Description: Semicolon-separated list of ACLED event IDs associated with the grid cell during the month.
• Format: String (e.g., ECU3151;ECU3158;ECU3150).
8. disorder_type
• Description: Semicolon-separated list of disorder types (e.g., "Political violence", "Demonstrations") reported by ACLED in that grid cell during the month.
• Format: String.
9. event_type
• Description: Semicolon-separated list of high-level ACLED event types (e.g., "Riots", "Protests").
• Format: String.
10. sub_event_type

• Description: Semicolon-separated list of detailed subtypes of ACLED events (e.g., "Mob violence", "Armed clash").
• Format: String.

1. acled_count

• Description: Total number of ACLED conflict events in the grid cell during the month.
• Format: Integer (e.g., 2).

1. acled_flag

• Description: Conflict presence indicator: 1 if any ACLED event occurred in the grid cell during the month, otherwise 0.
• Format: Integer (0 or 1).

1. gaps_density

• Description: Monthly density of GNSS gaps, calculated as GPS_gaps / flights.
• Format: Decimal (e.g., 0.0).

1. jumps_density

• Description: Monthly density of GNSS jumps, calculated as GPS_jumps / flights.
• Format: Decimal (e.g., 0.0106).

Data Sources

• GNSS Anomalies Data:
• Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.
• GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).
• Political Violence Events Data:
• Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.

Temporal and Spatial Coverage

• Temporal Coverage:
• From January 1, 2023, to December 31, 2023.
• Daily records provide temporal granularity for time-series analysis.
• Spatial Coverage:
• Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.
• Each grid cell represents an area on Earth's surface, facilitating spatial

The volume of air-freight transport in the United Arab Emirates was forecast to decrease between 2024 and 2029 by in total 0.02 billion ton-kilometers. This overall decrease does not happen continuously, notably not in 2026 and 2027. The volume of air-freight transport is estimated to amount to 14 billion ton-kilometers in 2029. As defined by Worldbank, air freight refers to the summated volume of freight, express and diplomatic bags carried across the various flight stages (from takeoff to the next landing). The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the volume of air-freight transport in countries like Oman and Israel.

Passengers enplaned and deplaned at Canadian airports, annual.

As a result of the continued annual growth in global air traffic passenger demand, the number of airplanes that were involved in accidents is on the increase. Although the United States is ranked among the 20 countries with the highest quality of air infrastructure, the U.S. reports the highest number of civil airliner accidents worldwide. 2020 was the year with more plane crashes victims, despite fewer flights The number of people killed in accidents involving large commercial aircraft has risen globally in 2020, even though the number of commercial flights performed last year dropped by 57 percent to 16.4 million. More than half of the total number of deaths were recorded in January 2020, when an Ukrainian plane was shot down in Iranian airspace, a tragedy that killed 176 people. The second fatal incident took place in May, when a Pakistani airliner crashed, killing 97 people. Changes in aviation safety In terms of fatal accidents, it seems that aviation safety experienced some decline on a couple of parameters. For example, there were 0.37 jet hull losses per one million flights in 2016. In 2017, passenger flights recorded the safest year in world history, with only 0.11 jet hull losses per one million flights. In 2020, the region with the highest hull loss rate was the Commonwealth of Independent States. These figures do not take into account accidents involving military, training, private, cargo and helicopter flights.

This dataset contains daily and monthly oceanic precipitation analyses on a 2.5-degree global grid. The data were constructed from the Microwave Sounding Units of seven TIROS-N series satellites, as described in Spencer (1993, J. Climate). Data are available for the period between January 1979 and May 1994.

The daily data are considered to be not as reliable as the monthly data. Before using the daily data, it is highly recommended that you read the documentation associated with it.

In the 2024/25 financial year, Qatar Airways revenues reached approximately 86 billion Qatari riyals. Launched in 1997, the state-owned airline, Qatar Airways, is based in Doha, Qatar, and headed by Mr. Badr Mohammed Al Meer. The airline’s hub is the Doha Hamad International Airport, one of the busiest airports, with circa 37 million passengers travelling through, as of 2016. Regional contextWith a revenue of 52 billion Qatar Riyals in the FY 2022, Qatar Airways became the second largest airline operating in the Middle East. In 2015, the number of airplane fleets leased in the Middle East increased to 43. The fleet size of the Qatar Airways Group grew to 207 airplanes by the year 2020. Qatar AirlineIn 2019, the airline transported a little over 29 million passengers to several of its 160 destinations. Besides handling passengers, The Qatar Airways Group also has a fast-growing cargo network based in Hamad International Airport, namely Qatar Airways Cargo. In 2014, the volume of air freight handled in the State of Qatar was just under 6 billion metric tons times kilometers travelled.

Realtime micro-scale weather simulations were performed to support UAV (Unmanned Aerial Vehicles) flights during ISARRA Lower Atmospheric Process Studies at Elevation – Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field deployment. These simulations were performed by driving a nested grid configuration of the Weather Research and Forecasting model with its innermost mesh being run at 111 m grid spacing. The innermost grid was nested within a grid with 1 km grid spacing. The outermost grid being driven using operational forecast models data as described below. While the MYNN2 PBL scheme is used to parameterize turbulence in the 1 km grid, the PBL scheme is turned off within the 111 m grid, thus, allowing large-scale turbulent eddies to be resolved by WRF primitive equations. Subgrid-scale turbulence is diagnosed and stored within the TKE variable using Lilly (1966, 1967).

LAPSE-RATE took place in the San Luis Valley of Colorado during July of 2018. Goals of LAPSE-RATE were to sample the finescale evolution of the boundary layer and associated sub-mesoscale flows across a sub-alpine desert valley using a combination of surface-based instrumentation and in situ data collected using numerous, low-flying small UAVs. The realtime simulations were produced twice per day in order to support mission planning and UAVs flight operations. A next-day simulation was run using forcing data from NCEP's Global Forecast System (GFS) while a day-of simulation was run using data from the High Resolution Rapid Refresh (HRRR). Both simulations were valid between 04:00 and 16:00 local time providing an opportunity to explore the impact of lateral boundary conditions on forecast skill. The dataset consists of a series of two sets of files: 3D grids and point profiles. The 3D grids consist of all relevant basic state parameters (p,T,U,RH) and diagnostics (e.g., sub-grid scale TKE, ceiling height, visibility) that have been interpolated to flight levels AGL using the Unified Post-Processor (UPP). The UPP was used to de-stagger the mass and wind fields, interpolate forecast data to flight levels AGL and to compute diagnostics such as visibility, ceiling height, and radar reflectivity. Profile data were stored for select grid points coincident with 3 fixed observation sites set up during LAPSE-RATE (i.e., Sagauche, Moffat and Leach Airfield). The 3D grid files are stored every 10 min, while point profile data have a time resolution of 0.666 seconds.

(UNCLASSIFIED) - In general, transportation infrastructure in Liberia is sub-par by most standards. Likewise, air transportation and modern infrastructure lags behind due to both conflict and a lack of capital investment. That being said, several major airlines operate out of the two international airports in Liberia including Astraeus, Bellview and SN Brussels Airlines as well as Slok Air International and Weasua Air Transport. Roberts International Airport is actually located outside of the capital of Monrovia, but remains the nation’s busiest aviation facility. Spriggs Payne Airport is centrally located in Monrovia but is a smaller facility with only a few arrivals per day. The remaining aviation facilities in the nation consist of unpaved runways in various cities. Some are finished, maintained runways of packed dirt while others are simply grass.Further complicating the travel situation has been the recent outbreak of the Ebola virus. Several airlines have suspended all flights to the country and currently it is unknown when or whether regular service will resume. Many other international airlines have begun considering suspending flights to and from Liberia as well.Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name ADM1_NAME - Administration level one identification / name ADM2_NAME - Administration level two identification / name ADM3_NAME - Administration level three identification / name NAME - Name of airfield TYPE - Classification in the geodatabase (Civil, Military, Dual) ICAO - International Civil Aviation Organization four letter airport location indicator IATA - International Air Transport Association three letter airport location indicator RUNWAY - Paved or unpaved runway N_RUNWAYS - Number of runways R1_SURFACE - Runway surface type (Asphalt, Dirt, Grass, Concrete) R2_SURFACE - Second runway surface type (Asphalt, Dirt, Grass, Concrete) R_LENGTH - Length of runway (meters) R_WIDTH - Runway width (meters) USE - Use description (Regional, Local, International) CUSTOMS - Presence of customs (Yes or No) SPA_ACC Spatial accuracy of site location (1- high, 2 – medium, 3 – low) COMMENTS - Comments or notes regarding the airfield SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThe feature class was generated utilizing data from various air transportation websites as well as open source databases. DigitalGlobe imagery was used to assess and when necessary, improve the location of features. The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Aircraft Charter World, "Airports in Liberia." Last modified January 2009. Accessed September 29, 2014. http://www.aircraft-charter-world.com.DigitalGlobe, "DigitalGlobe Imagery Archive." Last updated September 2014. Accessed September 29, 2014. Falling Rain Global Gazetteer, "Directory of Airports in Liberia." Last modified 2010. Accessed September 29, 2014. http://www.fallingrain.com.Great Circle Mapper, "Liberia." Last modified January 2013. Accessed September 29, 2014. http://gc.kls2.com.GeoNames, "Liberia." September 23, 2014. Accessed September 23, 2014. http://www.geonames.org.Google, "Liberia." Last modified September 2014. Accessed September 29, 2014. http://www.google.com.World Airport Codes, "Directory of Airports in Liberia." Last modified 2010. Accessed September 29, 2014. http://www.fallingrain.com.Sources (Metadata)"Transport in Liberia." The Lonely Planet. September 29, 2014. Accessed October 2, 2014. http://www.lonelyplanet.com.Zennie, Michael. "U.S. Airlines in Contact with Government about Ebola Concerns." The Daily Mail, October 2, 2014. Accessed October 2, 2014. http://www.dailymail.co.uk.

India All Scheduled Airlines: Domestic: Number of Flight data was reported at 102,319.000 Unit in Mar 2025. This records an increase from the previous number of 92,291.000 Unit for Feb 2025. India All Scheduled Airlines: Domestic: Number of Flight data is updated monthly, averaging 48,100.000 Unit from Apr 2001 (Median) to Mar 2025, with 288 observations. The data reached an all-time high of 102,319.000 Unit in Mar 2025 and a record low of 188.000 Unit in Apr 2020. India All Scheduled Airlines: Domestic: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

China Air: Passenger Traffic: Domestic data was reported at 664.657 Person mn in 2024. This records an increase from the previous number of 590.516 Person mn for 2023. China Air: Passenger Traffic: Domestic data is updated yearly, averaging 95.618 Person mn from Dec 1970 (Median) to 2024, with 42 observations. The data reached an all-time high of 664.657 Person mn in 2024 and a record low of 0.210 Person mn in 1970. China Air: Passenger Traffic: Domestic data remains active status in CEIC and is reported by Civil Aviation Administration of China. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TI: Air: Passenger Traffic.