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...

Total travel and tourism expenditure by domestic visitors worldwide amounted to approximately *** trillion U.S. dollars in 2024, showing growth over the previous year. This figure also surpassed pre-pandemic levels.

In 2023, over 2.5 billion domestic tourist visits were made across India, an increase from the previous year. Although an exponential rise in the local tourist visits was seen from the year 2000 to the present across the country, the coronavirus pandemic halted the trend in 2020. Cheap and best with easy access Social media usage has played a significant role in giving domestic tourism a boost. With an increase in the number of social media users, travelers use online platforms for posting pictures and sharing information on the places they visited. Keeping up to date with the trending travel destinations, the cheapest travel and budget hotels are on the mind of every traveler. This is now possible with just the click of a button. The effects of political unrestWith this kind of dependency on the tourism sector on the internet, many incurred economic losses due to the internet shutdowns. Tourism markets in the Jammu and Kashmir regions, for example, have been directly affected by political unrest, in addition the latest India-Pakistan escalation. This situation has not only witnessed reduced tourist numbers but has affected the livelihoods of many Kashmiris who are solely dependent on employability within this industry. The region, which is a nature lover’s paradise, has been a major attraction for both domestic and foreign tourists for decades. Ancient shrines like Vaishno Devi and Jalandra Devi are present in the state that account for over half of the region’s domestic tourists.

Explore the dataset on expenditure on domestic tourism trips in Saudi Arabia by purpose of visit. Includes information on total expenditure, visits to relatives and friends, holidays and shopping, religious purposes, business and conferences, and more.

Total Expenditure, Visits To Relatives And Friends, Annually, Holidays and Shopping, Other Purposes, Religious Purposes, Business and Conferences, Expenditure, Tourism, Business, visitors, Tourism Statistics, SAMA Annual

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..

Thailand Domestic Tourism: Occupancy Rate: Bangkok: Ayutthaya data was reported at 64.950 % in May 2019. This records an increase from the previous number of 63.340 % for Apr 2019. Thailand Domestic Tourism: Occupancy Rate: Bangkok: Ayutthaya data is updated monthly, averaging 63.570 % from Jan 2017 (Median) to May 2019, with 29 observations. The data reached an all-time high of 73.360 % in Jan 2019 and a record low of 52.340 % in Sep 2017. Thailand Domestic Tourism: Occupancy Rate: Bangkok: Ayutthaya data remains active status in CEIC and is reported by Ministry of Tourism and Sport. The data is categorized under Global Database’s Thailand – Table TH.Q020: Tourism Statistics: Domestic Occupancy Rate: By Region and Province.

📢**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.

China Domestic Tourist data was reported at 5,615,000.000 Person-Time th in 2024. This records an increase from the previous number of 4,891,000.000 Person-Time th for 2023. China Domestic Tourist data is updated yearly, averaging 1,712,000.000 Person-Time th from Dec 1990 (Median) to 2024, with 33 observations. The data reached an all-time high of 6,006,000.000 Person-Time th in 2019 and a record low of 280,000.000 Person-Time th in 1990. China Domestic Tourist data remains active status in CEIC and is reported by Ministry of Culture and Tourism. The data is categorized under Global Database’s China – Table CN.QAA: Tourism Industry Overview.

The Bureau of Transportation Statistics releases non-seasonally adjusted air traffic data based on monthly reports from commercial U.S. air carriers.

Context

This dataset provides statistics on inbound(Foreign) and domestic tourism across all the provinces of Saudi Arabia. It includes information on tourist numbers, overnight stays, spending patterns, and growth trends over multiple years. It includes both a merged dataset(tourism_data.csv) containing all provinces in a single file, as well as individual files for each province for more granular analysis.

Content

Tourism_Data.csv

• YEARS – Year of observation
• Province – Province in Saudi Arabia
• Tourism_Type – Type of tourism (Inbound or Domestic)
• Tourists_Count – Number of tourists
• Overnight_Stays – Number of overnight stays
• Tourists_Spending – Total tourist spending (SAR)
• Avg_Spending_Per_Trip – Average spending per trip (SAR)

(1058 rows × 7 columns)

Acknowledgements

The dataset was compiled from official tourism statistics published by the Saudi Ministry of Tourism.

Potential Use Cases

• Analyzing tourism trends across Saudi Arabia
• Comparing regional tourist arrivals and growth
• Supporting data-driven insights for researchers, policymakers, and businesses
• Visualizing and forecasting tourism patterns

Monthly U.S. citizen departures are collected and reported in Tourism Industries U.S. International Air Travel Statistics (I-92 data) Program.

Domestic tourism statistics by region and year. All figures come from the Great Britain Tourism Survey (GBTS) and represent 3-year annual averages due to small sample sizes on regional level.

Abstract

The DTS is a large-scale household survey aimed at collecting accurate statistics on the travel behavior and expenditure of South African residents travelling within the borders of the country. Such information is crucial when determining the contribution of tourism to the South African economy, as well as helping with planning, marketing, policy formulation, and the regulation of tourism-related activities.

Geographic coverage

National coverage

Analysis unit

Households and individuals

Universe

The target population of the survey consists of all private households in all nine provinces of South Africa and residents in workers’ hostels. The survey does not cover other collective living quarters such as students’ hostels, old age homes, hospitals, prisons and military barracks, and is therefore only representative of non-institutionalized and non-military persons or households in South Africa.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the DTS 2019 was based on a Master Sample (MS) that has been designed for all household surveys conducted by Statistics South Africa.

The Master Sample used a two-staged, stratified design with probability-proportional-to-size (PPS) sampling of PSUs from within strata, and systematic sampling of dwelling units (DUs) from the sampled primary sampling units (PSUs). A self-weighting design at provincial level was used. Stratification was done in two stages: Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2011 data were summarized at PSU level. The following variables were used for secondary stratification: household size, education, occupancy status, gender, industry and income.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires were administered to collect the survey data:

1. Cover page: includes household information, response details, result codes, field staff information and demographic information (name, sex, age, population group and marital status).
2. Questionnaire: includes education, tourism employment, domestic day trips, domestic night trips and household living conditions.

Thailand Domestic Tourism: Occupancy Rate: Southern: Chiang Rai data was reported at 46.080 % in May 2019. This records an increase from the previous number of 45.630 % for Apr 2019. Thailand Domestic Tourism: Occupancy Rate: Southern: Chiang Rai data is updated monthly, averaging 48.600 % from Jan 2017 (Median) to May 2019, with 29 observations. The data reached an all-time high of 81.250 % in Jan 2019 and a record low of 37.490 % in Jul 2017. Thailand Domestic Tourism: Occupancy Rate: Southern: Chiang Rai data remains active status in CEIC and is reported by Ministry of Tourism and Sport. The data is categorized under Global Database’s Thailand – Table TH.Q020: Tourism Statistics: Domestic Occupancy Rate: By Region and Province.

Provide important statistical tables of domestic travel indicators for nationals over the years.

Thailand Domestic Tourism: Occupancy Rate: Western: Phuket data was reported at 68.650 % in May 2019. This records a decrease from the previous number of 87.530 % for Apr 2019. Thailand Domestic Tourism: Occupancy Rate: Western: Phuket data is updated monthly, averaging 79.530 % from Jan 2017 (Median) to May 2019, with 29 observations. The data reached an all-time high of 88.600 % in Feb 2019 and a record low of 60.800 % in Sep 2018. Thailand Domestic Tourism: Occupancy Rate: Western: Phuket data remains active status in CEIC and is reported by Ministry of Tourism and Sport. The data is categorized under Global Database’s Thailand – Table TH.Q020: Tourism Statistics: Domestic Occupancy Rate: By Region and Province.

Abstract

The DTS is a large-scale household survey aimed at collecting accurate statistics on the travel behaviour and expenditure of South African residents travelling within the borders of the country. Such information is crucial when determining the contribution of tourism to the South African economy, as well as helping with planning, marketing, policy formulation, and the regulation of tourism-related activities.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The target population of the survey consists of all private households in all nine provinces of South Africa and residents in workers’ hostels. The survey does not cover other collective living quarters such as students’ hostels, oldage homes, hospitals, prisons and military barracks, and is therefore only representative of non-institutionalised and non-military persons or households in South Africa.

Kind of data

Sample survey data

Sampling procedure

The sample design for the DTS 2020 was based on a Master Sample (MS) that has been designed for all household surveys conducted by Statistics South Africa.

The Master Sample used a two-staged, stratified design with probability-proportional-to-size (PPS) sampling of PSUs from within strata, and systematic sampling of dwelling units (DUs) from the sampled primary sampling units (PSUs). A self-weighting design at provincial level was used. Stratification was done in two stages: Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2011 data were summarised at PSU level. The following variables were used for secondary stratification: household size, education, occupancy status, gender, industry and income.

Mode of data collection

Computer Assisted Telephone Interview

Thailand Domestic Tourism: Occupancy Rate: Western: Songkhla data was reported at 76.740 % in May 2019. This records a decrease from the previous number of 86.450 % for Apr 2019. Thailand Domestic Tourism: Occupancy Rate: Western: Songkhla data is updated monthly, averaging 71.900 % from Jan 2017 (Median) to May 2019, with 29 observations. The data reached an all-time high of 86.450 % in Apr 2019 and a record low of 61.770 % in Jul 2017. Thailand Domestic Tourism: Occupancy Rate: Western: Songkhla data remains active status in CEIC and is reported by Ministry of Tourism and Sport. The data is categorized under Global Database’s Thailand – Table TH.Q020: Tourism Statistics: Domestic Occupancy Rate: By Region and Province.

In 2023, over 70 million Thanksgiving holiday travelers in the United States reached their destination by driving. Meanwhile, only 5.37 million travelers made it to their destination by flying. Both of these figures were forecast to increase over the following year.

The dataset consists of the statistics on domestic tourists with in India and foreign tourists from abroad. It should be noted that Foreign Tourist Visits (FTV) is the number of foreign tourists who visit a State or UT and the Domestic Tourist Visits (DTV) refers to the number of domestic tourists visiting a particular State or UT.

This dataset shows the Domestic Tourism Expenditure of Tourists by Products, 2000 - 2021