Dataset's Source:

https://www.unwto.org/tourism-statistics/tourism-statistics-database

The most complete collection of statistical data on the tourist industry is provided by UN tourist, which methodically compiles tourism statistics from nations and territories worldwide.

Through a series of annual questionnaires, UN Tourism gathers data from nations in accordance with the United Nations-approved International Recommendations for Tourism Statistics (IRTS 2008) standard.

The provided UN Tourism dataset comprises multiple files, each focusing on a specific aspect of tourism data. Below is a detailed description of the columns found in each of these datasets. Please note that the "INDEX" column appears to be a sequential identifier, and years (e.g., 1995-2022) represent annual data for various indicators across the datasets.

Domestic Tourism - Trips

This dataset contains information related to domestic tourism trips.

C., S., C. & S.: These columns likely represent categorization or classification codes for the data entries. 'C.' could stand for Country Code, 'S.' for Series, and 'C. & S.' for a combined Country and Series identifier.

Basic data and indicators: This column describes the specific tourism indicator being measured (e.g., 'Total trips', 'Overnights visitors (tourists)', 'Same-day visitors (excursionists)').

Units: The unit of measurement for the data (e.g., 'Thousands').

Notes: Any specific notes or disclaimers related to the data for that row.

1995 - 2022: These columns represent the recorded values for the respective tourism indicators for each year.

Domestic Tourism - Accommodation

This dataset provides statistics on accommodation used for domestic tourism.

C., S., C. & S.: Similar to the "Trips" sheet, these are likely categorization or classification codes.

Basic data and indicators: This column specifies the type of accommodation data (e.g., 'Guests', 'Overnights' in total, or specifically for 'Hotels and similar establishments').

Units: The unit of measurement for the data (e.g., 'Thousands').

Notes: Any specific notes or disclaimers related to the data for that row.

1995 - 2022: These columns represent the recorded values for the accommodation indicators for each year.

Inbound Tourism - Arrivals

This dataset details the number of international tourist arrivals.

C., S., C. & S.: Categorization or classification codes.

Basic data and indicators: This column describes the type of arrival data (e.g., 'Total arrivals', 'Overnights visitors (tourists)', 'Same-day visitors (excursionists)', and 'of which, cruise passengers').

Units: The unit of measurement for the data (e.g., 'Thousands').

Notes: Any specific notes or disclaimers related to the data for that row.

Series: This column likely indicates the type of statistical series or methodology used for data collection (e.g., 'VF' for Visitor Flow, 'TF' for Tourist Flow).

1995 - 2022: These columns represent the recorded values for the arrival indicators for each year.

Inbound Tourism - Expenditure

This dataset focuses on the expenditure by inbound tourists within the country.

C., S., C. & S.: Categorization or classification codes.

Basic data and indicators: This column specifies the type of expenditure data (e.g., 'Tourism expenditure in the country', 'Travel', 'Passenger transport').

Units: The unit of measurement for the data (e.g., 'US$ Millions').

Notes: Any specific notes or disclaimers related to the data for that row.

Series: This column indicates the data source or methodology (e.g., 'IMF' for International Monetary Fund).

1995 - 2022: These columns represent the recorded values for the expenditure indicators for each year.

Inbound Tourism - Regions

This dataset breaks down inbound tourism arrivals by the region of origin.

C., S., C. & S.: Categorization or classification codes.

Basic data and indicators: This column describes the regional breakdown of arrivals (e.g., 'Total', 'Africa', 'Americas', 'East Asia and the Pacific', 'Europe', 'Middle East', 'South Asia', 'Other not classified').

Units: The unit of measurement for the data (e.g., 'Thousands').

Notes: Any specific notes or disclaimers related to the data for that row.

Series: This column likely indicates the type of statistical series or methodology used for data collection.

1995 - 2022: These columns represent the recorded values for arrivals from each region for each year.

Inbound Tourism - Purpose

This dataset categorizes inbound tourism arrivals by their main purpose of visit.

C., S., C. & S.: Categorization or classification codes.

Basic data and indicators: This column specifies the purpose of visit (e.g., 'Total', 'Personal', 'Business and professional'). 'Personal' can be further broken down into sub-categories such as 'Holiday, leisure and recreation', 'Visiting fr...

Tourism Revenues in the United States decreased to 20626 USD Million in July from 20913 USD Million in June of 2025. This dataset provides - United States Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Dataset Overview 📝

This dataset includes key tourism and economic indicators for over 200 countries, spanning the years from 1999 to 2023. It covers a wide range of data related to tourism arrivals, expenditures, receipts, GDP, unemployment, and inflation, helping to explore the relationship between tourism and economic growth globally.

Tourism & Economic Indicators:

• tourism_receipts 💰: The total income a country generates from international tourism, measured in current US dollars.
• tourism_arrivals 🌍: The total number of international tourists who arrive in a country, measured in count.
• tourism_expenditures 🛍️: The amount of money spent by international tourists within the country, measured in current US dollars.
• tourism_exports 📈: The percentage of a country’s total exports derived from international tourism receipts.
• tourism_departures ✈️: The number of citizens or residents of a country who travel abroad for tourism.
• tourism_expenditures 🛫: The percentage of a country’s total imports spent on international tourism.
• gdp 📊: The total value of all goods and services produced in a country, expressed in current US dollars.
• inflation 📉: The annual percentage change in the average price of goods and services in a country.
• unemployment 👷‍♂️: The percentage of people within the labor force who are unemployed but actively seeking work.

Data Source 🌐

• World Bank: The dataset is sourced from the World Bank’s economic and tourism databases, offering reliable and up-to-date statistics on global tourism and economic indicators.

Potential Use Cases 🔍

• Tourism & Economic Impact Analysis 🌏: Analyzing the contribution of tourism to a country’s GDP, as well as how tourism spending impacts national economies.
• Cross-Country Comparison 📊: Comparing tourism arrivals, expenditures, and receipts relative to GDP and unemployment across countries and regions.
• Predictive Modeling 🤖: Building models to predict the future impact of tourism on economic growth and identify emerging trends.
• Policy Evaluation 🏛️: Helping policymakers assess the role of tourism in economic planning, especially regarding inflation and unemployment.
• Economic Forecasting 📈: Using historical data to forecast how tourism will influence economic conditions, helping in the development of economic policies.

Key Questions You Can Explore 🤔

• How do tourism receipts correlate with GDP growth in different countries? 💵
• What is the relationship between tourism expenditure and inflation rates? 📉
• How do tourism arrivals and departures vary by region and what are the key drivers? 🌍
• What role does tourism play in shaping unemployment rates across countries? 👥
• Which countries have seen the largest increase in tourism receipts over the past two decades? 📊

Important Notes ⚠️

• Missing Data 🚨: Some values may be missing for certain years or countries, especially for specific tourism indicators. Techniques like forward filling, backward filling, or interpolation may help in handling missing values during time series analysis.
• Currency & Inflation Adjustments 💲: When comparing tourism receipts and expenditures across countries, consider adjusting for inflation and exchange rates to ensure accurate year-on-year comparisons.

Tourism Revenues in Egypt increased to 14.40 USD Billion in 2024 from 13.60 USD Billion in 2023. This dataset provides - Egypt Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

📝 About the Dataset

This dataset provides a comprehensive view of global tourism indicators, covering multiple economic and travel-related variables for countries across several years. It is built to support Exploratory Data Analysis (EDA), predictive modeling, economic research, time-series trend analysis, and machine learning preprocessing.

Each row represents a specific country-year record, containing tourism performance metrics, economic indicators, and travel expenditure information. The dataset is clean, structured, and ideal for producing insights about international tourism patterns and their economic impacts.

🎯 Purpose of the Dataset

This dataset is well-suited for:

• Tourism trend analysis
• Country-wise comparative analysis
• Exploratory Data Analysis (EDA) practice
• Outlier detection and handling
• Feature engineering & preprocessing techniques
• Correlation analysis between tourism and economy
• Visualization projects
• Machine learning model preparation

📂 Columns & Data Dictionary (Based on Your File)

1. country

• Type: String
• Description: Full name of the country.

2. country_code

• Type: String
• Description: 3-letter ISO country code (e.g., IND, USA, AFG).

3. year

• Type: Integer
• Description: The year for which tourism and economic indicators are recorded.

4. tourism_receipts

• Type: Float (USD)
• Description: Total international tourism receipts for the country in that year.
• Represents income generated from foreign visitors.

5. tourism_arrivals

• Type: Float
• Description: Total number of international tourist arrivals.
• Useful for understanding travel demand.

6. tourism_exports

• Type: Float
• Description: Tourism-related exports, including money spent by inbound tourists on goods/services.

7. tourism_departures

• Type: Float
• Description: Number of outbound tourists from the country in the recorded year.

8. tourism_expenditures

• Type: Float
• Description: Outbound tourism expenditures—money spent by citizens traveling abroad.

9. gdp

• Type: Float (USD)
• Description: Gross Domestic Product of the country.
• Helps relate tourism activity to economic performance.

10. inflation

• Type: Float (percentage)
• Description: Inflation rate for the country during that year.

11. unemployment

• Type: Float (percentage)
• Description: Unemployment rate for the country.
• Useful for socio-economic correlation studies.

🔍 Key Insights the Dataset Can Produce

With these variables, you can analyze:

• Trends in global tourism
• Whether GDP affects tourism demand
• Relationship between inflation and travel spending
• Tourism arrivals vs receipts correlation
• Economic growth driven by tourism
• Country-level tourism performance comparison
• Travel behavior based on expenditures & departures

📁 File Format

• CSV format
• Clean column names
• Each row = country + year records

Hawaii Economic and Tourism Data

Hawaii Economic and Tourism Data Collection

By data.world's Admin [source]

About this dataset

The 2011 Data Book Sections and Tables dataset is a comprehensive collection of over 800 datasets sourced from various industries and sectors. It offers valuable insights into the economic development and tourism in Hawaii, making it a crucial resource for researchers, analysts, and policymakers. The dataset is organized by sections, with each section representing a specific category or theme such as education, employment, or healthcare. Within each section, there are multiple tables assigned with unique numbers that provide detailed information on specific topics within the category. The available data tables have descriptive titles or descriptions to give users an overview of the information they can expect to find. Additionally, the dataset provides hyperlinks to the exact sections in the Data Book where each table can be found for easy access and navigation. It is important to note that this dataset was last updated in 2014-11-06. With its extensive range of datasets and comprehensive coverage of various industries, this dataset serves as an invaluable tool for gaining insights into Hawaii's economy and tourism landscape in the year 2011

How to use the dataset

• Familiarize Yourself with the Sections:

  • Each dataset in the file is categorized into different sections based on their topic or industry.
  • The Section column provides the name of the section that contains the dataset.
  • Click on the Link to DataBook Section provided to directly access that section in the Data Book, where you can find more detailed information.

• Explore Tables within Sections:

  • Within each section, there are tables assigned with specific numbers denoted by the Table Number column.
  • These table numbers help identify individual datasets within their respective sections.

• Understand Available Data Table:

  • The Available Data Table column provides a description or title for each dataset.
  • It gives you an idea about what kind of data is available within that specific table.

• Utilize Hyperlinks for Quick Access:

  • To access a specific section or dataset quickly, click on its corresponding hyperlink provided under Link to Databook Section.

• Analyze Insights:

  • Once you have identified your desired section and table, dive into that particular dataset to explore economic development and tourism trends in Hawaii more deeply.
  • Use statistical analysis tools or visualization techniques (not included in this guide) to gain meaningful insights from these datasets.

Remember not to consider any dates mentioned as part of this guide since it explicitly states not including them.

This guide will help you navigate through this rich collection of economic and tourism data for Hawaii effectively. Make use of various analytical techniques available today, such as regression analysis, data visualization, and predictive modeling, to derive valuable insights from this dataset

Research Ideas

• Economic Analysis: This dataset can be used for conducting economic analysis by examining the various industries and sectors in Hawaii. By analyzing the data, researchers can gain insights into the economic development of Hawaii and identify trends and patterns across different sectors.
• Tourism Planning: The dataset provides valuable information on tourism in Hawaii, including visitor arrivals, spending patterns, and accommodation statistics. This data can be used by tourism planners to make informed decisions regarding tourism development, marketing strategies, and infrastructure planning.
• Comparative Studies: Researchers interested in comparative studies between Hawaii and other regions or states can use this dataset to compare economic indicators, industry growth rates, employment trends, or other relevant factors. This would help provide a comprehensive understanding of Hawaii's position relative to other regions and identify areas for improvement or potential opportunities for collaboration

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: ...

Tourism is important because it can contribute significantly to a country's economy by creating jobs, generating income, and promoting the development of infrastructure and services. It can also foster cultural exchange and understanding between people from different parts of the world, and help preserve natural and cultural heritage.

This dataset contains information about international tourism receipts for various countries. International tourism receipts refer to the expenditures made by international visitors on their trips to a country, including accommodation, food and beverage, transportation, and other tourism-related expenses. This data can help researchers, policymakers, and businesses gain insights into the tourism industry and its impact on economies around the world.

The dataset includes information on tourism receipts for over 100 countries from the year 1995 to 2020. Sourced from the World Bank,

The columns in this dataset:

• name: The name of the country
• code: The alpha_3 (iso3) code of the country
• year: The year the data was recorded
• value_$: The total amount of international tourism receipts in US dollars

The dataset can be used to analyze trends and patterns in international tourism receipts over time and across countries. It can also be used to compare the performance of different countries in the tourism industry, as well as to identify potential opportunities and challenges in the industry.

📢**April 18, 2023 Update ( Please use Version 2):** - Renamed "profit" to "revenue" for better understanding. - Normalized the values in Thai Baht (previously MTHB).

This dataset contains statistics on domestic tourism in Thailand from Jan 2019 to Feb 2023, broken down by province. The dataset includes information on the number of tourists, the occupancy rate, and the profits generated by tourism in each province, as well as the nationality of the tourists (Thai vs. foreign).

Sourced from raw data provided by the Official Ministry of Tourism and Sports Statistics, which was manually entered into Excel files 🙃. So I pre-processed the data using Python with the intention of making it more accessible in the appropriate format which has the potential to provide valuable insights into the domestic tourism industry in Thailand, including trends and patterns across different provinces over time. Researchers, analysts, and policymakers with an interest in the domestic tourism sector in Thailand may find this dataset useful for their work.

Banner credit: Tourism Authority of Thailand.

Cleaned and ready-to-use 77 Provinces, 8 Variables, 3 Years and 2 Months with total 30,800 rows.

The dataset is part of the World Bank's extensive data on international tourism, specifically focused on capturing annual tourist arrivals across global destinations. This data helps in identifying tourism trends, economic impacts, and patterns in visitor growth by region and income level. By analyzing these trends, countries can gain insights into tourism’s role in their economy, the appeal of their destination on a global scale, and potential growth opportunities within the sector.

Sources The data is sourced from the World Bank's International Tourism dataset, which collects information from national tourism boards and government agencies worldwide. The World Bank collaborates with these sources to maintain consistent, reliable metrics on international tourism trends, which is essential for policy development and economic planning. The dataset is updated periodically to reflect new figures and adjusted estimates.

Inspiration This dataset serves as a foundation for understanding how tourism trends evolve over time and how global events can influence travel patterns. The inspiration for this analysis stems from the growing importance of tourism in the global economy and the need for destination countries to understand their positioning in the tourism market. Analyzing these data points offers a path to explore strategic initiatives, compare regional visitor trends, and identify emerging tourist hotspots.

While the tourism sector GDP share in Latvia was forecast to increase long-term between 2023 and 2028 by in total 1.7 percentage points, it is estimated to decrease in the years 2026, 2027 and 2028. The share is estimated to amount to 7.25 percent in 2028. While the share was forecast to increase significant in the next years, the increase will slow down in the future.Depited is the economic contribution of the tourism sector in relation to the gross domestic product of the country or region at hand.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 tourism sector GDP share in countries like Estonia and Lithuania.

The tourism sector GDP share in Saudi Arabia was forecast to continuously increase between 2023 and 2028 by in total 1.9 percentage points. The share is estimated to amount to 9.4 percent in 2028. While the share was forecast to increase significant in the next years, the increase will slow down in the future.Depited is the economic contribution of the tourism sector in relation to the gross domestic product of the country or region at hand.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 tourism sector GDP share in countries like Lebanon and Jordan.

The tourism sector GDP share in the United Kingdom was forecast to increase between 2023 and 2028 by in total *** percentage points. This overall increase does not happen continuously, notably not in 2027. The share is estimated to amount to **** percent in 2028. While the share was forecast to increase significant in the next years, the increase will slow down in the future.Depited is the economic contribution of the tourism sector in relation to the gross domestic product of the country or region at hand.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 *** 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).

Tourism Revenues in Turkey increased to 24300 USD Million in the third quarter of 2025 from 16280 USD Million in the second quarter of 2025. This dataset provides - Turkey Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The dataset is created to model the integration of Internet of Things (IoT) devices and edge computing technologies in tourism hotspots, aiming to enhance the efficiency and sustainability of tourism resource allocation.

The dataset consists of multiple variables representing key aspects of tourism resource management, including visitor flow, resource utilization, environmental factors, and resource allocation strategies. Real-time data, such as visitor count, temperature, air quality index, and noise levels, are included to simulate the ecological monitoring of tourism destinations. Additionally, the dataset includes predictive features, such as resource usage rates and visitor satisfaction, for optimizing resource management.

Key Features:

Timestamp: Represents the real-time timestamp of data collection, essential for time-based analysis and trend monitoring. Location ID: Denotes the tourism location (e.g., a specific tourist spot or attraction). Visitor Count: The number of visitors at a given time, reflecting the tourist flow. Resource Usage Rate: Measures the rate at which tourism resources (e.g., facilities, services) are being used. Environmental Factors: Includes temperature, air quality index, and noise level, which are critical for assessing the impact of tourism on the environment. Visitor Satisfaction: A score that reflects the overall satisfaction of visitors, which can guide optimization strategies. Resource Prediction: A derived feature based on visitor count and resource usage, predicting resource demand. Resource Allocation: A target column that classifies resource allocation into categories such as low, medium, or high, guiding optimal resource distribution.

Tourism Revenues in Greece decreased to 3421.30 EUR Million in September from 4523.70 EUR Million in August of 2025. This dataset provides - Greece Tourism Receipts- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This website provides statistics on the economic value of visitors to the state of Virginia. The analysis is commissioned by the Virginia Tourism Corporation, and is conducted by Tourism Economics, LLC. The analysis is based on multiple data sources, including the US census, STR, Longwoods International, lodging and sales tax receipts, and employment and wage data from the Bureau of Economic Analysis and Bureau of Labor Statistics. By combining these datasets, a comprehensive view of visitor activity is developed that is consistent with official economic and industry data for the state. The analysis measures visitor spending by category, tourism employment, personal income, and taxes generated by visitor activity. The data are available for several years of history and can be viewed and downloaded at the state level.

Justification:

Columns:

• Location: A ten- -character random string that represents the location's name.
• Nation: Selectively selected from a roster of nations.
• Type: Selectively selected from a range of travel-related categories.
• Visitor count: A random integer value that indicates how many people have visited.
• Rating: A random float number in the range of 1.0 and 5.0 is used to indicate the rating.
• Revenue: A float value chosen at random to indicate the revenue received.
• Accommodation_Available: Returns a value of "Yes" or "No" depending on the availability of accomodation.
• Target Size: During the loop, the file size is measured and rows are added until the file size reaches roughly 310.43 KB.

You will have a tourism_dataset.csv file, roughly 310.43 KB in size, after executing this code. Depending on the data distribution and file overhead, adjustments can be required.

This dataset provides a comprehensive view of tourism consumer behavior, combining demographic, behavioral, and booking-related information. It captures how individuals plan and engage in travel activities across different preferences and spending levels. The data includes features such as age, gender, income, travel type, and duration. It also incorporates feedback and booking methods to reflect real consumer interactions in the tourism sector. This dataset is useful for identifying distinct patterns among different types of travelers. It can support applications in service personalization and targeted marketing in the travel industry.

The number of international tourist arrivals in Asia was forecast to continuously increase between 2024 and 2029 by in total 174.7 million arrivals (+33.49 percent). After the ninth consecutive increasing year, the arrivals is estimated to reach 696.34 million arrivals and therefore a new peak in 2029. Depicted is the number of inbound international tourists. According to World Bank this refers to tourists travelling to a country which is not their usual residence, whereby the main purpose is not work related and the planned visitation period does not exceed 12 months. 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 number of international tourist arrivals in countries like North America and Caribbean.

The tourism industry is ever-evolving in nature, as it operates in a global marketplace that has become progressively global and offers great potential due to technological advances. The tourism industry faces challenges in accurately forecasting economic impacts and understanding visitor patterns that rapid global changes. Motivated by these needs, this research introduces the Tourism Variational Recurrent Neural Network (TourVaRNN), aiming to enhance the tourism industry by predicting economic impacts and visitor behaviors for effective marketing strategies through advanced Deep Learning (DL) techniques. The research applies a variational recurrent neural network for enhancing tourism demands and model the complex temporal dependencies within tourism data. The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. Marketing campaigns in the tourism sector can be fine-tuned through visitor segmentation, which seeks to comprehend and classify visitors according to their demographics, preferences, and behaviors. The model employs robust forecasting of economic impacts, visitor spending patterns, and behavior while accounting for uncertainty through variational inference. The implementation uses Python language on a tourism dataset comprising necessary attributes like visitor numbers, days, spending patterns, employment, international tourism samples over a specific region, and a diverse age group analyzed over a year. The proposed method is evaluated in terms of performance metrics such as economic impact assessment, visitor segmentation efficiency, inference time analysis, and budget allocation utilization for effective economic and marketing strategy analysis in the tourism industry. TourVaRNN’s improved segmentation efficiency of 15.7 percent allows for more targeted marketing, increasing engagement with visitors and income. Decisions may be made in real-time, improving operational efficiency in tourism management, thanks to a 17.5% reduction in inference time (to 40 ms). The most efficient use of funds is guaranteed by a 13.4% rise in budget allocation utilization, leading to maximum economic benefits.