Dataset Overview: Flight Price Dataset of Bangladesh

Introduction

The "Bangladesh Flight Fare Dataset" is a synthetic dataset comprising 57,000 flight records tailored to represent air travel scenarios originating from Bangladesh. This dataset simulates realistic flight fare dynamics, capturing key factors such as airline operations, airport specifics, travel classes, booking behaviors, and seasonal variations specific to Bangladesh’s aviation market. It is designed for researchers, data scientists, and analysts interested in flight fare prediction, travel pattern analysis, or machine learning/deep learning applications. By combining real-world inspired statistical distributions and aviation industry standards, this dataset provides a robust foundation for exploring flight economics in a South Asian context.

Dataset Purpose

This dataset aims to: - Facilitate predictive modeling of flight fares, with "Total Fare (BDT)" as the primary target variable. - Enable analysis of travel trends, including the impact of cultural festivals (e.g., Eid, Hajj) and booking timings on pricing. - Serve as a training resource for machine learning (ML) and deep learning (DL) models, with sufficient sample size (50,000) and feature diversity for generalization. - Provide a realistic yet synthetic representation of Bangladesh’s air travel ecosystem, blending domestic and international flight scenarios.

Data Collection and Methodology

The dataset is synthetically generated using Python, with its methodology rooted in real-world aviation data and statistical principles. Below is a detailed breakdown of its construction:

1. Data Components

• Airlines:
  • Count: 25 airlines (21 international, 4 domestic).
  • Source: Compiled from Bangladesh Civil Aviation Authority and Airline History, including major carriers like Emirates, Qatar Airways, and Biman Bangladesh Airlines.
  • Selection: Random uniform choice per flight record to reflect operational diversity.
• Airports:
  • Source Airports: 8 domestic airports (e.g., DAC - Hazrat Shahjalal International Airport, Dhaka).
  • Destination Airports: 20 airports (8 domestic + 12 international, e.g., DXB - Dubai International Airport).
  • Coordinates: Sourced from World Airport Codes, used for distance calculations.
  • Full Names: Added for readability, mapped via a dictionary (e.g., "DAC" → "Hazrat Shahjalal International Airport, Dhaka").
• Travel Classes: Economy, Business, First Class, standard across the industry, randomly assigned with uniform distribution.
• Booking Sources: Direct Booking, Travel Agency, Online Website, reflecting common methods, per Statista, with uniform random selection.
• Aircraft Types: Boeing 777, Airbus A320, Boeing 737, Boeing 787, Airbus A350, assigned based on flight distance, sourced from Boeing and Airbus.

2. Key Calculations

• Distance:

  • Method: Haversine formula calculates great-circle distance: a = sin²(Δφ/2) + cos(φ₁) cos(φ₂) sin²(Δλ/2) c = 2 arctan2(√a, √(1-a)) d = R · c, R = 6371 km

• Purpose: Determines flight duration, aircraft type, and stopovers.

• Source: Wikipedia - Haversine Formula.

• Flight Duration:

• Formula: Duration = max(d/s · U(0.9, 1.1), 0.5), where s is speed (300 km/h for <500 km, 600 km/h for 500-2000 km, 900 km/h for >2000 km), and U is uniform random variation.

• Source: Speeds adjusted from World Atlas, ensuring realism (e.g., DAC to CGP ~45 minutes).

• Fares:

• Base Fares:

• Domestic: Economy (2000-5000 BDT), Business (5000-10000 BDT), First Class (10000-15000 BDT).

• International: Economy (5000-70000 BDT), Business (15000-150000 BDT), First Class (25000-300000 BDT).

• Source: Derived from Trip.com and Expedia, e.g., DAC to LHR ~$380-600 (~41800-66000 BDT at 1 USD = 110 BDT).

• Adjustments:

• Seasonal multipliers (Regular: 1.0, Eid: 1.3, Hajj: 1.5, Winter: 1.2), per demand trends from Timeanddate.com.

• Days Before Departure: 20% discount (60+ days), 10% discount (30-59 days), 20% surge (<5 days), per Skyscanner.

• Taxes: Domestic: 200 BDT; International: 2000-6000 BDT + 15% base fare, per [Bangladesh Civil Aviation Authority](https://www.dgca.g...

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.

BACKGROUND The data contained in the compressed file has been extracted from the Marketing Carrier On-Time Performance (Beginning January 2018) data table of the "On-Time" database from the TranStats data library. The time period is indicated in the name of the compressed file; for example, XXX_XXXXX_2001_1 contains data of the first month of the year 2001.

RECORD LAYOUT Below are fields in the order that they appear on the records: Year Year Quarter Quarter (1-4) Month Month DayofMonth Day of Month DayOfWeek Day of Week FlightDate Flight Date (yyyymmdd) Marketing_Airline_Network Unique Marketing Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users, for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. Operated_or_Branded_Code_Share_Partners Reporting Carrier Operated or Branded Code Share Partners DOT_ID_Marketing_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Marketing_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Flight_Number_Marketing_Airline Flight Number Originally_Scheduled_Code_Share_Airline Unique Scheduled Operating Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users,for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. DOT_ID_Originally_Scheduled_Code_Share_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Originally_Scheduled_Code_Share_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Flight_Num_Originally_Scheduled_Code_Share_Airline Flight Number Operating_Airline Unique Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users, for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. DOT_ID_Operating_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Operating_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Tail_Number Tail Number Flight_Number_Operating_Airline Flight Number OriginAirportID Origin Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused. OriginAirportSeqID Origin Airport, Airport Sequence ID. An identification number assigned by US DOT to identify a unique airport at a given point of time. Airport attributes, such as airport name or coordinates, may change over time. OriginCityMarketID Origin Airport, City Market ID. City Market ID is an identification number assigned by US DOT to identify a city market. Use this field to consolidate airports serving the same city market. Origin Origin Airport OriginCityName Origin Airport, City Name OriginState Origin Airport, State Code OriginStateFips Origin Airport, State Fips OriginStateName Origin Airport, State Name OriginWac Origin Airport, World Area Code DestAirportID Destination Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused. DestAirportSeqID Destination Airport, Airport Sequence ID. An identification number assigned by US DOT to identify a unique airport at a given point of time. Airport attributes, such as airport name or coordinates, may change over time. DestCityMarketID Destination Airport, City Market ID. City Market ID is an identification number assigned by US DOT to identify a city market. Use this field to consolidate airports serving the same city market. Dest Destination Airport DestCityName Destination Airport, City Name DestState Destination Airport, State Code DestStateFips De...

General Aviation (GA) comprises all civil flights except scheduled passenger airline services. More than 90% of the roughly 220,000 civil aircraft registered in the United States (US) are GA aircraft. In contrast with airline service aircraft which operate with two pilots in a structured higher-altitude operational envelope, GA aircraft are often individually piloted in a more unstructured lower-altitude environment. This low altitude environment is also where a bulk of the next generation of Uncrewed Aerial Vehicles (UAVs) are expected to operate. These UAVs are expected to seamlessly interact with other UAVs and manned air traffic operating in this shared airspace. Nowhere is this manned-manned and potentially unmanned-manned interaction more pronounced than in low-altitude terminal airspace around airports. Low altitudes, multi-agent close-proximity interactions, dynamically changing conditions, and rapid decision making are hallmarks of this type of airspace as compared to en-route airspace where agents are typically well-separated.This dataset contains aircraft trajectories in an untowered terminal airspace collected over 8 months surrounding the Pittsburgh-Butler Regional Airport [ICAO:KBTP], a single runway GA airport, 10 miles North of the city of Pittsburgh, Pennsylvania. The trajectory data is recorded using an on-site setup that includes an ADS-B receiver. The trajectory data provided spans days from 18 Sept 2020 till 23 Apr 2021 and includes a total of 111 days of data discounting downtime, repairs, and bad weather days with no traffic. Data is collected starting at 1:00 AM local time to 11:00 PM local time. The dataset uses an Automatic Dependent Surveillance-Broadcast (ADS-B) receiver placed within the airport premises to capture the trajectory data. The receiver uses both the 1090 MHz and 978 MHz frequencies to listen to these broadcasts. The ADS-B uses satellite navigation to produce accurate location and timestamp for the targets which is recorded on-site using our custom setup. Weather data during the data collection time period is also included for environmental context. The weather data is obtained post-hoc using the METeorological Aerodrome Reports (METAR) strings generated by the Automated Weather Observing System (AWOS) system at KBTP. The raw METAR string is then appended to the raw trajectory data by matching the closest UTC timestamps.We also provide processed data that filters, interpolates and transforms data from a global frame to an airport-centred inertial frame. The inertial frame is centred at one end of the runway with the x-axis along the runway. Trajectories are filtered with aircrafts under 6000 ft MSL and around a 5km radius around the airport origin. We also remove duplicates and interpolate data every second. The proceed files also contain wind-data; a crucial factor in decision-making; separated in components along and perpendicular to the runway direction.More Information and Supplemental ToolsPlease visit http://theairlab.org/trajair/ for more information.

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.

Russia Number of Flights: Domestic data was reported at 67,658.000 Number in Feb 2022. This records a decrease from the previous number of 71,658.000 Number for Jan 2022. Russia Number of Flights: Domestic data is updated monthly, averaging 55,400.000 Number from Jan 2010 (Median) to Feb 2022, with 146 observations. The data reached an all-time high of 127,409.000 Number in Jul 2021 and a record low of 27,413.000 Number in Feb 2010. Russia Number of Flights: Domestic data remains active status in CEIC and is reported by Federal Agency for Air Transport. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TE003: Airlines Statistics: Number of Airlines, Aircrafts, Airports and Flights. [COVID-19-IMPACT]

Context

Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010–2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.

Content

Geography: Global

Time period: 2010 - 2019

Unit of analysis: Global Holidays and Travel Data

Variables

monthly_passengers.csv

global_holidays.csv

Acknowledgements

Thank you to Jon Harmon for curating this dataset.

Dataset Repository

The GRIP Campaign Reports dataset consists of various reports filed by scientists during the GRIP campaign which took place 8/15/2010 - 9/30/2010; however, several of the reports are from the planning and test flights. Reports included in this dataset contain information for the Tri Agency Mission Scientists; DC-8, Global Hawk, and WB-57 Platform Scientists; DC-8, Global Hawk, and WB-57 Flight Reports and WB-57 Flight Summary; GRIP Telecons; and TropicalGRIP Forecasts. The Tri Agency Mission Scientists reports, GRIP telecons and Forecast reports were primarily filed daily, while the Platform and Flight reports exist primarily for flight days.

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 two terrestrial ADS-B data providers.

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/

Geospatial Dataset of GNSS Anomalies and Political Violence Events (2023)

Overview

The Geospatial Dataset of GNSS Anomalies and Political Violence Events (2023) 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 two CSV files:

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.

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.

Data Fields

Both files share the same set of fields, which are detailed below:

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).

day

Description: Date of the recorded data.

Format: YYYY-MM-DD (e.g., 2023-03-28).

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))).

flights

Description: Number of aircraft flights that passed through the grid on that day.

Format: Integer (e.g., 28).

GPS_jumps

Description: Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.

Format: Integer (e.g., 1).

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).

gaps_density

Description: Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.

Format: Decimal (e.g., 0).

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).

event_id_cnty

Description: ACLED event ID corresponding to political violence events in the grid on that day.

Format: String (e.g., BRA69267).

disorder_type

Description: Type of disorder as classified by ACLED (e.g., "Political violence").

Format: String.

event_type

Description: General category of the event according to ACLED (e.g., "Violence against civilians").

Format: String.

sub_event_type

Description: Specific subtype of the event as per ACLED classification (e.g., "Attack").

Format: String.

acled_count

Description: Number of ACLED events in the grid on that day.

Format: Integer (e.g., 1).

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 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 analysis.

Usage and Applications

Security Analysis:

Assess potential correlations between GNSS anomalies and political violence events.

Identify regions with increased risk of GNSS spoofing or signal disruption.

Research and Development:

Develop models to predict socio-political events based on GNSS anomalies.

Study the impact of political instability on aviation safety.

Policy and Decision Making:

Inform aviation authorities and policymakers about regions requiring enhanced navigation security measures.

Support conflict analysis and monitoring efforts.

This dataset is sourced from the Airline Data Project established by the MIT Global Airline Industry Program. It describes financial metrics for Individual airlines, airline sectors and the industry as a whole for the American commercial airline industry. The Original data from the source is collected in the zip file "Original MIT data" and the data relating to Airline finances and the main industry metrics has been cleaned and written into csv files for ease of use.

MIT ADP Description The U.S. commercial airline industry is one of the most diverse, dynamic and perplexing in the world. It is fast-evolving, labor intensive, capital intensive, hyper-competitive and highly susceptible to the ebb and flow of business cycles as well as being among the most regulated of deregulated businesses.

The Airline Data Project (ADP) was established by the MIT Global Airline Industry Program to better understand the opportunities, risks and challenges facing this vital industry. The ADP presents the most important airline industry data in one location in an easy-to-understand, user-friendly format.

The data on this website is sourced from the U.S. Department of Transportation's Form 41 data product. It has been selected and analyzed to present a view of the industry and its important trends, as well as to identify fundamental drivers of success - and in some cases, the early signs of potential failure.

The ADP is designed to support the goals of the MIT Airline Industry Consortium. It is a unique repository of data and analysis that will allow individuals – from academia to the financial community to the news media – to monitor the evolution of the U.S. commercial airline industry.

The ADP is updated in June of each year pending the release of Form 41 data files by the U.S. Bureau of Transportation Statistics. The last update of the ADP was in June 2020 for calendar year 2019 data. If you have questions about what items are included in various Form 41 data categories, you can refer to the U.S. DOT's Form 41 Financial Reporting Categories Item List Guide.

You are invited to review the data on this site and share your feedback on the wealth of information that is available about this highly visible industry.

Glossary: Aircraft Utilization Measure of aircraft productivity, calculated by dividing aircraft block hours by the number of aircraft days assigned to service on air carrier routes. Typically presented in block hours per day.

Available Seat Miles (ASMs) A common industry measurement of airline output that refers to one aircraft seat flown one mile, whether occupied or not. An aircraft with 100 passenger seats, flown a distance of 100 miles, generates 10,000 available seat miles.

Average Aircraft Capacity Average seating configuration of an airline’s operating fleet. The measure is derived by dividing total available seat miles flown by the number of aircraft miles flown. It is important to understand the average aircraft size as it is an important determinant of employees needed to service the operation of a particular airline.

Block Hour Time from the moment the aircraft door closes at departure of a revenue flight until the moment the aircraft door opens at the arrival gate following its landing. Block hours are the industry standard measure of aircraft utilization (see above).

Cost per Available Seat Mile (CASM) Measure of unit cost in the airline industry. CASM is calculated by taking all of an airline’s operating expenses and dividing it by the total number of available seat miles produced. Sometimes, fuel or transport-related expenses are withheld from CASM calculations to better isolate and directly compare operating expenses.

Unit Cost per Unit of Output A measurement that gauges total operating costs in relation to output.

Form 41 Data Information derived from airline filings with the Bureau of Transportation Statistics. Airline financial data is filed with the BTS quarterly; traffic and employment numbers are filed monthly.

Load Factor The number of Revenue Passenger Miles (RPMs) expressed as a percentage of ASMs, either on a particular flight or for the entire system. Load factor represents the proportion of airline output that is actually consumed. To calculate this figure, divide RPMs by ASMs. Load factor for a single flight can also be calculated by dividing the number of passengers by the number of seats.

Operating Revenue ...

The GRIP Campaign Reports dataset consists of various reports filed by scientists during the GRIP campaign which took place 8/15/2010 - 9/30/2010; however, several of the reports are from the planning and test flights. Reports included in this dataset contain information for the Tri Agency Mission Scientists; DC-8, Global Hawk, and WB-57 Platform Scientists; DC-8, Global Hawk, and WB-57 Flight Reports and WB-57 Flight Summary; GRIP Telecons; and TropicalGRIP Forecasts. The Tri Agency Mission Scientists reports, GRIP telecons and Forecast reports were primarily filed daily, while the Platform and Flight reports exist primarily for flight days.

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.

(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.

Please cite as "2020 COVID19 Global Daily Impact Dataset by criticalperegrine.tumblr" Please read the .ods file for sources

Purpose

A dataset containing statistics pertaining to : * Policy - what special mesures were applied during the year * Epidemic - how fast is COVID spreading, how deadly it is, how much has it spread & killed people * Population - How many people per country, how old they are, how urban and concentrated they are * Medical System - How many Physicians & Beds exist * Weather - Temperature, Humidity and Wind * Electrical Grid - How has the consumption of electricity changed * Aviation - How have the number of flights varied The reader can view the detailed sources for each statistic in "fullCOVIDsources.ods" with precise links / citations. The .csv dataset itself can be opened with Excel or any spreadsheet program. Wunderground.com was used for (almost) all Weather data. The Oxford Government response tracker was used for all Policy data.

Specific Rationales

Reff

The "Epidemic" Statistics contain the Reff, a measure of the propagation of the epidemic. This was computed through the "EpiEstim" package by Cori et al (https://pbil.univ-lyon1.fr/CRAN/web/packages/EpiEstim/index.html), through the used of the serial interval by Challen et al ( https://www.medrxiv.org/content/10.1101/2020.11.17.20231548v2 ). The choice behind this serial interval is due to the fact that it reportedly accounts for pre-symptomatic transmission, an important feature according to the literature, whilst showing similar Reff for most regions as a more cited distribution by Qun Li et al ( https://www.nejm.org/doi/full/10.1056/NEJMOa2001316 ). The reader can inspect the code that generates the Reff values by reading the file "Reff Computation.r". The choice behind the Reff itself is because it is a simple to interpret indicator : >1, we have an epidemic; <1, we do not.

Rationale for "Electrical Grid"

Electricity is used in most of the world, save for very very rural countries, for personal & industrial use. From cooking food, to transforming goods through the use of heavy machinery, to services (digital, or simply powering the light in venues providing services). It is essential for production, and a major decrease in consumption in electricity would imply a decrease in "daily" GDP (Gross Domestic Production) since : * Electricity is difficult to stock, so most electrical demand is related to needs for that day * There is no reported "major innovation" that decreases electricity consumption by more than 10% whilst maintaining a country's production * Electricity is used to transform most goods and produce most services in a country, as mentioned previously. So electricity is used to compare the shock done to the GDP due to different policies or infection rates.

Rationale for "Aviation"

Arrivals were used to make the effect of "Closed Borders" pop out. Aviation is used here as a non essential good, and also as a measure of international mobility throughout the year

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.

Microgravity as well as chronic muscle disuse are two causes of low back pain originated at least in part from paraspinal muscle deconditioning. At present no study investigated the complexity of the molecular changes in human or mouse paraspinal muscles exposed to microgravity. The aim of this study was to evaluate longissimus dorsi and tongue (as a new potential in-flight negative control) adaptation to microgravity at global gene expression level. C57BL/N6 male mice were flown aboard the BION-M1 biosatellite for 30 days (BF) or housed in a replicate flight habitat on ground (BG). Global gene expression analysis identified 89 transcripts differentially regulated in longissimus dorsi of BF vs. BG mice (False Discovery Rrate < 0,05 and fold change < -2 and > +2) while only a small number of genes were found differentially regulated in tongue muscle ( BF vs. BG = 27 genes). Overall Design: C57BL/N6 mice were randomly divided in 3 groups: Bion Flown (BF) mice flown aboard the Bion M1 biosatellite in microgravity environment for 30 days; Bion Ground (BG) mice housed in the same habitat of flown animals but exposed to earth gravity; and Flight Control (FC) mice housed in a standard animal facility.