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.

In 2023, the estimated number of scheduled passengers boarded by the global airline industry amounted to approximately *** billion people. This represents a significant increase compared to the previous year since the pandemic started and the positive trend was forecast to continue in 2024, with the scheduled passenger volume reaching just below **** billion travelers. Airline passenger traffic The number of scheduled passengers handled by the global airline industry has increased in all but one of the last decade. Scheduled passengers refer to the number of passengers who have booked a flight with a commercial airline. Excluded are passengers on charter flights, whereby an entire plane is booked by a private group. In 2023, the Asia Pacific region had the highest share of airline passenger traffic, accounting for ********* of the global total.

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.

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.

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.

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.

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 Rongowai Level 1 Science Data Record Version 1.0 dataset is generated by the University of Auckland (UoA) Rongowai Science Payloads Operations Centre in New Zealand. This initiative is supported by NASA and the New Zealand Space Agency. The data collection process is conducted using the Next-generation receiver (NgRx) mounted on the Air New Zealand domestic aircraft Q300.

This Level 1 (L1) dataset contains the Version 1.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument onboard an Air New Zealand domestic Q300 (tail number ZK-NFA). 20 DDMs are contained within a typical L1 netcdf corresponding to 10 Left-Hand-Circularly Polarized (LHCP) and 10 Right-Hand-Circularly Polarized (RHCP) channels. Other useful scientific and engineering measurement parameters include the co- and cross-polarized Normalized Bistatic Radar Cross Section (NBRCS) of the specular point, the Leading Edge Slope (LES) of the integrated delay waveform and the normalized waveforms. The L1 dataset contains a number of other engineering and science measurement parameters, including coherence detection and a coherence state metric, sets of quality flags/indicators, error estimates, Fresnel-zone geometry estimates (and thereby the estimated per-sample spatial resolution) as well as a variety of timekeeping, and geolocation parameters.

Each netCDF data files corresponds to a single flight between airports within New Zealand (flight durations typically range between 45 min and 1hr 30min with a median of 7 flights/day) and measurements are reported at 1 second increments. Latency is approximately 1 days (or better) from the last recorded measurement time.

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.

For summary please read the supplemental pdf-file.

Special remarks: 1) Every zipped file contains the NETcdf files of one single day. There could be up to three flights a day.
2) In timestep 20070619 no nitrogen measurment is recorded.
3) The radar-height of the aircraft ends at a height of 757m above the ground. All values above 757m are replaced with the _FillValue -9e+33f. The variable called event_counter exists as an integer value. Due to that the _FillValue is set to -900000000. For more informations about the flight patterns, the research aircraft, the measuring instruments and general informations about the measuring strategy you can also see the supplemental pdf-file.

(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.

Early spring sampling was performed in the eastern area of the Shelf-Basin Interactions Project using aircraft. Flights began on 1 April 2004 and finished on 16 April. During this time, we sampled 32 sites on a series of 5 transect lines (Fig. 1). Stations were about 10 km apart along each transect line. Transect lines B, C, and D were at the same spacing; while, lines A and E were 20 km from the nearest transect line. Typically, 4 stations were sampled on each flying day. At each site, a Seabird SBE-19 CTD and a water sampling bottle was deployed through an 25 cm hole augered through the pack-ice. Continuous profiles of pressure, temperature, and salinity were made from the ice hole to either the sediment surface or to about 500 m. CTD data were processed with Seabird software (Seasoft) and the data binned into 0.5 dbar layers. Data herein are the vertical profiles of temperature, salinity, and density. About 30 ml of seawater collected from the Niskin bottle were poured into a 50 ml clean dry polyethylene sample bottle. The nutrient sample bottles quickly froze and were kept that way until measurement of nitrate, nitrite, ammonium, phosphate and silicate by the Nutrient Chemistry Laboratory of the Scripps Institute of Oceanography.