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.

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/

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.

Comprehensive dataset containing 33,651 verified Travel agency businesses in United States with complete contact information, ratings, reviews, and location data.

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.

Metrics that can be unearthed will be ones contained in the email booking invoice such as Hotel name, type of room, dates, check in and check out times, price paid, duration of stay. We can go back to 5 years of history.

We also have cancellation emails.

Any hotel vendor can be requested too. We will conduct a search in our database to see if it justifies a parser build to extract the data.

The data included in the GIS Traffic Stations Version database have been collected by the FHWA from the State DOTs (NTAD). 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.

Comprehensive dataset containing 6 verified Travel businesses in Illinois, United States with complete contact information, ratings, reviews, and location data.

We present a new, global data base on tourist destinations. The data base differs from other data bases in that it includes both domestic and international tourists; and it contains data, for the most important destinations, data at national level as well as at lower administrative levels. Missing observations are interpolated using statistical models. The data are freely accessible on the internet.

Comprehensive dataset of 123 Travel agencies in Montana, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed. More information on the National Walkability index: https://www.epa.gov/smartgrowth/smart-location-mapping More information on the Smart Location Calculator: https://www.slc.gsa.gov/slc/

Comprehensive dataset of 20,115 Travel agencies in Turkey as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The description for this record is not currently available.

Database of air travel activity incurred in the execution of DFID business, including individual flights and CO2. 2010 onwards.

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, tourism service locations, trails, bike paths, etc., providing comprehensive tourism GIS basic data for industry practitioners to add value. The XML field descriptions for each dataset are provided in Tourism Data Standard V1.0, please refer to https://media.taiwan.net.tw/Upload/TourismInformationStandardFormatV1.0.pdf; Tourism Data Standard V2.0, please refer to https://media.taiwan.net.tw/Upload/TourismDataStandardV2.0.pdf.

Dataset

This dataset was created by Rupanshi_Rana

Released under Database: Open Database, Contents: Database Contents

Contents

The Travel Time to Work dataset was compiled using information from December 31, 2023 and updated December 12, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Travel Time to Work table from the 2023 American Community Survey (ACS) 5-year estimates was joined to 2023 tract-level geographies for all 50 States, District of Columbia and Puerto Rico provided by the Census Bureau. A new file was created that combines the demographic variables from the former with the cartographic boundaries of the latter. The national level census tract layer contains data on the number and percentage of commuters (workers 16 years and over who did not work from home) with a range of travel times to work. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529086

This is a comprehensive, synthetic dataset designed to emulate the booking and review data of a major hotel platform, perfect for aspiring data analysts and data scientists to build a strong portfolio. Going beyond simple transactional logs, this dataset focuses on the crucial relationship between hotels, user demographics, and the valuable feedback they provide through numerical scores. It offers a rich, interconnected environment to explore customer satisfaction, hotel performance, and market trends, making it a powerful resource for anyone looking to master data manipulation, visualization, and machine learning on realistic data.

The dataset is structured around three core tables that can be easily linked:

hotels.csv: This table serves as the hotel catalog, containing unique hotels with key attributes like hotel_id, name, city, and star_rating. This provides the foundational context for all other data, allowing you to analyze hotel performance based on location and quality.

users.csv: This file provides a list of unique customers, offering essential demographic insights with columns such as user_id, country, and age. This data is vital for segmenting customers and understanding how demographics influence review behavior.

reviews.csv: As the central transactional table, it is the heart of the dataset. It links users to the hotels they reviewed, capturing crucial details like review_id, hotel_id, user_id, and a numerical review_score. This table is particularly valuable for its focus on quantitative feedback.

This dataset is uniquely valuable due to its integrated design, allowing you to perform a wide array of analytical projects, from straightforward business intelligence to advanced predictive modeling. You can build visualizations to track the average review scores by city, analyze the distribution of star ratings, and understand which customer segments are leaving the most reviews. For a more advanced project, you can use the review scores to perform a variety of machine learning tasks. For example, you could build a model to predict a hotel's review score based on its star rating and location, or create a customer segmentation model to understand the profiles of users who leave very high or very low scores. This dataset provides a perfect, self-contained project to showcase your ability to work with a complete, structured dataset.

Comprehensive dataset of 9 Travel in Virginia, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.