Thailand tourist spending for 2020 was 15.36 billion US dollars, a 76.14% decline from 2019.

<ul style='margin-top:20px;'>

<li>Thailand tourist spending for 2019 was <strong>64.37 billion US dollars</strong>, a <strong>4.87% increase</strong> from 2018.</li>
<li>Thailand tourist spending for 2018 was <strong>61.38 billion US dollars</strong>, a <strong>7.58% increase</strong> from 2017.</li>
<li>Thailand tourist spending for 2017 was <strong>57.06 billion US dollars</strong>, a <strong>17.74% increase</strong> from 2016.</li>
</ul>International tourism receipts are expenditures by international inbound visitors, including payments to national carriers for international transport. These receipts include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Data are in current U.S. dollars.

Historical chart and dataset showing St. Martin (French part) tourist spending by year from N/A to N/A.

Historical chart and dataset showing Greenland tourist spending by year from N/A to N/A.

Quarterly report on DOC visitation metrics. New York City Local Law 85 of 2015 requires the New York City Department of Correction (DOC) to publicly report Jail Visitation Statistics within 30 days of the beginning of each quarter for the preceding quarter. Specifically, the DOC is required to report the following metrics on a quarterly basis: (1)The total number of visitors to city jails, the total number of visitors to borough jail facilities, and the total number of visitors to city jails on Rikers Island, (2) The total number of visitors that visited a person in custody at city jails, the total number of visitors that visited a person in custody at borough jail facilities, and the total number of visitors that visited a person in custody at city jails on Rikers Island, (3) The number of visitors unable to visit a person in custody at any city jail, in total and disaggregated by the reason such visit was not completed, (4) The person in custody visitation rate, which shall be calculated by dividing the average daily number of visitors who visited persons in custody at city jails during the reporting period by the average daily person in custody population of city jails during the reporting period, (5) The borough jail facility visitation rate, which shall be calculated by dividing the average daily number of visitors who visited persons in custody at borough jail facilities during the reporting period by the average daily person in custody population of borough jail facilities during the reporting period, and (6) The Rikers Island visitation rate, which shall be calculated by dividing the average daily number of visitors who visited persons in custody at city jails on Rikers Island during the reporting period by the average daily person in custody population of city jails on Rikers Island during the reporting period. The data within this report represents information that was available to the Department at the time of report production is subject to change based on updated information subsequently reported by staff members to the Department.

description: Visitor numbers are a key metric for a variety of conservation planning and management purposes. However, challenges remain in improving accuracy of visitor estimation at refuge field stations. This project provides focused resources to two Region 6 priority urban refuges to demonstrate that the information and methods of the Visitation Estimation Workbook and Technical Supplements are feasible, and that minor changes in methods can lead to significant improvement in visitor estimation accuracy. The objective of this project is to provide Rocky Mountain Arsenal National Wildlife Refuge and Bear River Migratory Bird Refuge with evaluations, recommendations, and data-collection tools to improve visitor estimation accuracy.; abstract: Visitor numbers are a key metric for a variety of conservation planning and management purposes. However, challenges remain in improving accuracy of visitor estimation at refuge field stations. This project provides focused resources to two Region 6 priority urban refuges to demonstrate that the information and methods of the Visitation Estimation Workbook and Technical Supplements are feasible, and that minor changes in methods can lead to significant improvement in visitor estimation accuracy. The objective of this project is to provide Rocky Mountain Arsenal National Wildlife Refuge and Bear River Migratory Bird Refuge with evaluations, recommendations, and data-collection tools to improve visitor estimation accuracy.

Historical chart and dataset showing Denmark tourist spending by year from 1995 to 2018.

The dataset provides metrics for the number of dataset views in catalog.data.gov broken down by month starting Jan 2014. There is a gap in the dataset for December 2016 due to a temporary halt in collection of metrics during infrastructure migration of the Data.gov site during that month.

This dataset presents key performance metrics regarding tourism to specific Local Government Areas (LGA) around Australia. The LGAs covered in the data are a subset of the LGA boundaries classified in the 2016 Australian Statistical Geography Standard (ASGS). The data presents metrics for visits, spending and length of stay to the specified LGAs by their location of origin and visit duration. The data values are representative of a yearly average based on the four years of: 2013, 2014, 2015 and 2016. Tourism Research Australia (TRA) first developed Local Government Area tourism profiles in 2007 to assist industry and Government decision making and to identify and support investment opportunities, particularly in regional Australia. The latest profiles provide an update for over 200 Local Government Areas. Data are drawn from TRA's International Visitor Survey (IVS) and National Visitor Survey (NVS), along with demographic and business data from the Australian Bureau of Statistics (ABS). Profiles were only prepared for Local Government Areas with adequate International Visitor Survey (IVS) and National Visitor Survey (NVS) sample to present robust results. Further, data are averaged over four years, which minimises the impact of variability in estimates from year to year, and provides for more robust volume estimates. For more information please visit the Website of the TRA. Please note: AURIN has spatially enabled the original data.

This dataset presents key performance metrics regarding tourism to specific Local Government Areas (LGA) around Australia. The LGAs covered in the data are a subset of the LGA boundaries classified in the 2018 Australian Statistical Geography Standard (ASGS). The data presents metrics for visits, spending and length of stay to the specified LGAs by their location of origin and visit duration. The data values are representative of a yearly average based on the four years of: 2015, 2016, 2017 and 2018. Tourism Research Australia (TRA) first developed Local Government Area tourism profiles in 2007 to assist industry and Government decision making and to identify and support investment opportunities, particularly in regional Australia. The latest profiles provide an update for over 200 Local Government Areas. Data are drawn from TRA's International Visitor Survey (IVS) and National Visitor Survey (NVS), along with demographic and business data from the Australian Bureau of Statistics (ABS). Profiles were only prepared for Local Government Areas with adequate IVS and NVS sample to present robust results. Further, data are averaged over four years, which minimises the impact of variability in estimates from year to year, and provides for more robust volume estimates. For more information please visit TRA. Please note: AURIN has spatially enabled the original data.

Historical chart and dataset showing Bermuda tourist spending by year from 2011 to 2020.

This is a dataset of the datasets on the site. It is updated daily.

This dataset provides a comprehensive analysis of the tourism and hospitality industry, covering key metrics such as visitor trends, hotel occupancy rates, average spending per tourist, seasonal demand patterns, and revenue insights. It is useful for travel analysts, hospitality businesses, researchers, and policymakers to understand industry dynamics, predict trends, and optimize business strategies.

Potential Use Cases:

📊 Market Analysis: Identify travel trends and popular destinations.
📈 Revenue Forecasting: Predict hotel occupancy rates and revenue patterns.
🏨 Hospitality Business Insights: Analyze customer preferences and spending habits.
🌍 Tourism Policy Evaluation: Support government tourism strategies.
🧠 Machine Learning Applications: Build predictive models for visitor demand and pricing optimization.

Per the Federal Digital Government Strategy, the Department of Homeland Security Metrics Plan, and the Open FEMA Initiative, FEMA is providing the following web performance metrics with regards to FEMA.gov.rnrnInformation in this dataset includes total visits, avg visit duration, pageviews, unique visitors, avg pages/visit, avg time/page, bounce ratevisits by source, visits by Social Media Platform, and metrics on new vs returning visitors.rnrnExternal Affairs strives to make all communications accessible. If you have any challenges accessing this information, please contact FEMAWebTeam@fema.dhs.gov.

Historical chart and dataset showing Isle of Man tourist spending by year from N/A to N/A.

Historical chart and dataset showing Euro Area tourist spending by year from 1995 to 2019.

This dataset is related to an Augmented Reality application implemented in the Sierra Nevada National Park, specifically in the Barranco del Poqueira. This application has been designed to enhance the experience of visitors walking a circular path that connects the three villages of the ravine: Pampaneira, Bubión and Capileira, offering seven points of interest.

The dataset covers the period from June 2022 to October 2023 and includes information on the number of downloads, download dates, types of devices used to download the application, geographic origin of the devices, attractions visited and most demanded audiovisual resources.

These data provide an objective view of the effectiveness of the application in improving the visitor experience and its impact on the tourism promotion of Barranco del Poqueira. In addition, they provide relevant information about user preferences, which can guide future updates and improvements in the Augmented Reality application. The application contributes to enrich the visit to these villages and provides useful data for the sustainable management of tourism in this region of Sierra Nevada.