Earlier this year, Dr. Hoffman and Dr. Fafard published a book chapter on the efficacy and legality of border closures enacted by governments in response to changing COVID-19 conditions. The authors concluded border closures are at best, regarded as powerful symbolic acts taken by governments to show they are acting forcefully, even if the actions lack an epidemiological impact and breach international law. This COVID-19 travel restriction project was developed out of a necessity and desire to further examine the empirical implications of border closures. The current dataset contains bilateral travel restriction information on the status of 179 countries between 1 January 2020 and 8 June 2020. The data was extracted from the ‘international controls’ column from the Oxford COVID-19 Government Response Tracker (OxCGRT). The data in the ‘international controls’ column outlined a country’s change in border control status, as a response to COVID-19 conditions. Accompanying source links were further verified through random selection and comparison with external news sources. Greater weight is given to official national government sources, then to provincial and municipal news-affiliated agencies. The database is presented in matrix form for each country-pair and date. Subsequently, each cell is represented by datum Xdmn and indicates the border closure status on date d by country m on country n. The coding is as follows: no border closure (code = 0), targeted border closure (= 1), and a total border closure (= 99). The dataset provides further details in the ‘notes’ column if the type of closure is a modified form of a targeted closure, either as a land or port closure, flight or visa suspension, or a re-opening of borders to select countries. Visa suspensions and closure of land borders were coded separately as de facto border closures and analyzed as targeted border closures in quantitative analyses. The file titled ‘BTR Supplementary Information’ covers a multitude of supplemental details to the database. The various tabs cover the following: 1) Codebook: variable name, format, source links, and description; 2) Sources, Access dates: dates of access for the individual source links with additional notes; 3) Country groups: breakdown of EEA, EU, SADC, Schengen groups with source links; 4) Newly added sources: for missing countries with a population greater than 1 million (meeting the inclusion criteria), relevant news sources were added for analysis; 5) Corrections: external news sources correcting for errors in the coding of international controls retrieved from the OxCGRT dataset. At the time of our study inception, there was no existing dataset which recorded the bilateral decisions of travel restrictions between countries. We hope this dataset will be useful in the study of the impact of border closures in the COVID-19 pandemic and widen the capabilities of studying border closures on a global scale, due to its interconnected nature and impact, rather than being limited in analysis to a single country or region only. Statement of contributions: Data entry and verification was performed mainly by GL, with assistance from MJP and RN. MP and IW provided further data verification on the nine countries purposively selected for the exploratory analysis of political decision-making.

Comprehensive database of destinations, activities, and travel information

The Ministry of Transportation and Communications' Tourism Bureau collects spatial tourism information released by various government agencies, including data on tourist attractions, activities, dining and accommodation, tourist service locations, trails, bike paths, etc., to provide comprehensive tourism GIS basic data for operators to create added value applications. The XML field description for each dataset and Tourism Data Standard V1.0 can be found at https://media.taiwan.net.tw/Upload/

A manually curated database of protein-protein interactions (PPIs) for mammalian transient receptor potential (TRP) channels. The detailed summary of PPI data, fits into 4 categories: screening, validation, characterization, and functional consequence. These categorizations give answers for four basic questions about PPIs: how to identify PPIs (screening); how to confirm PPIs (validation); what are biochemical properties of PPIs (characterization); what are biological meaning of PPIs (functional consequence). Users can find in-depth information specified in the literature on relevant analytical methods, gene constructs, and cell/tissue types. The database has a user-friendly interface with several helpful features, including a search engine, an interaction map, and a function for cross-referencing useful external databases.

Travel Agents: Uttar Pradesh data was reported at 0.000 Unit in 2020. This stayed constant from the previous number of 0.000 Unit for 2019. Travel Agents: Uttar Pradesh data is updated yearly, averaging 7.000 Unit from Dec 2008 (Median) to 2020, with 13 observations. The data reached an all-time high of 15.000 Unit in 2011 and a record low of 0.000 Unit in 2020. Travel Agents: Uttar Pradesh data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under India Premium Database’s Tourism Sector – Table IN.QE005: Travel Agents.

The Ministry of Transportation and Communications Tourism Bureau collects spatial tourism information released by various government agencies, including information on tourist attractions, activities, dining and lodging, tourist service stations, hiking trails, bike paths, and other data, providing comprehensive tourism GIS basic data for operators to add value. The XML field descriptions for each dataset, tourism data standard V1.0 data, please refer to https://media.taiwan.net.tw/Upload/TourismInformationStandardFormatV1.0.pdf; tourism data standard V2.0 data, please refer to https://media.taiwan.net.tw/Upload/TourismDataStandardV2.0.pdf.

This dataset was obtained from the National Household Travel Survey. Due the volume of the data, it was divided in two. This dataset shows the commuting time and transport mode to work all over the country.

The data included in the GIS Traffic Stations Version database have been collected by the FHWA from the State DOTs (NTAD 2015). Location referencing information was derived from State Offices of Transportation. The attributes on the point elements of the database are used by FHWA for its Travel Monitoring and Analysis System and by State DOTs. The attributes for these databases have been intentionally limited to location referencing attributes since the core station description attribute data are contained within the Station Description Tables (SDT). There is a separate Station Description Table (SDT) for each of the station types. The attributes in the Station Description Table correspond with the Station Description Record found in Chapter 6 of the 2001 Traffic Monitoring Guide. The SDT contains the most recent stations available for each state and station type. This table was derived from files provided UTCTR by FHWA. The Station Description Table can be linked to the station shapefile via the STNNKEY field.

The Travel Monitoring Analysis System (TMAS) dataset is as of June 6, 2017, and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The data included in the GIS Traffic Stations Version database have been collected by the FHWA from the State DOTs. Location referencing information was derived from State offices of Transportation The attributes on the point elements of the database are used by FHWA for its Travel Monitoring and Analysis System and by State DOTs. The attributes for these databases have been intentionally limited to location referencing attributes since the core station description attribute data are contained within the Station Description Tables (SDT). here is a separate Station Description Table (SDT) for each of the station types. The attributes in the Station Description Table correspond with the Station Description Record found in Chapter 6 of the latest Traffic Monitoring Guide. The SDT contains the most recent stations available for each state and station type. This table was derived from files provided UTCTR by FHWA. The Station Description Table can be linked to the station shapefile via the STNNKEY field. Some station where not located in the US, and were beyond available geographic extents causing display problems. These were moved to Lat and Long 0,0. This is in recognition that the locations of these stations where in error, but were moved to a less obtusive area.

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.

This dataset is one component of the National Transit Database (NTD). The NTD records the financial, operating, and asset condition of transit systems helping to keep track of the industry and provide public information and statistics. This dataset includes only monthly unlinked passenger trips for each urban transit system in the U.S. Transit systems are separated by type (mode) such as heavy rail, light rail, and bus. "Unlinked passenger trips" represents the number of passengers who board public transportation vehicles. Passengers are counted each time they board vehicles no matter how many vehicles they use to travel from their origin to their destination. More information about the NTD is available at https://www.transit.dot.gov/ntd.

Premium B2B leads from travel companies actively seeking business solutions. Access qualified prospects from travel agencies, online travel platforms, corporate travel management firms, and travel tech companies.

Context

This dataset is compiled from European data about tourism trips in Europe.

Content

The data contains information on tourism trips in Europe.

Geographies values are acronyms (2 characters) for EU countries and group of countries from Europe.

Source of metadata: https://ec.europa.eu/eurostat/ramon/cybernews/abbreviations.htm

Acknowledgements

The main data source is: https://ec.europa.eu/eurostat/data/database For the data dictionaries, the source is: https://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&dir=dic%2Fen as well: https://ec.europa.eu/eurostat/ramon/cybernews/abbreviations.htm

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

Archive information for the electronic travel system used by SSA employees. The production databases must stay around (by law) till 2017 for query use only.

.TRAVEL.PL Whois Database, discover comprehensive ownership details, registration dates, and more for .TRAVEL.PL TLD with Whois Data Center.

🇬🇧 영국

China Travel Agency: Number of Enterprise data was reported at 39,580.000 Unit in 2023. This records an increase from the previous number of 32,603.000 Unit for 2022. China Travel Agency: Number of Enterprise data is updated yearly, averaging 20,399.000 Unit from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 40,682.000 Unit in 2020 and a record low of 3,826.000 Unit in 1995. China Travel Agency: Number of Enterprise data remains active status in CEIC and is reported by Ministry of Culture and Tourism. The data is categorized under China Premium Database’s Tourism Sector – Table CN.QAA: Tourism Industry Overview.

All India Hotel Database – Verified & Updated Hospitality DirectoryThe All India Hotel Database is a comprehensive and regularly updated directory of hotels across India. This verified database is perfect for travel agencies, tour operators, hospitality service providers, corporate travel planne...

Personal Files - HMS Dauntless, Used to track travel warrants applied for and issued.