Calculating the number of inbound visitors using air, sea and land transportationSource: Inbound Tourism Survey

This dataset is based on a sustainability perception survey conducted at Perla Ecological Park, located in the Northern Ecuadorian Amazon, during December 2024 and January 2025. A total of 383 visitors participated in the study, which aimed to: (1) quantify and compare key sustainability indicators across PERLA’s management zones, (2) identify the most influential predictors of overall sustainable performance, and (3) derive and prioritize a set of integrated strategies that balance ecological conservation with economic viability.

   The survey instrument included Likert-scale, dichotomous, and thematic categorical items designed to assess public perceptions on tourism sustainability, natural and cultural resource management, institutional support, and visitor satisfaction. The research was carried out through a collaborative effort among multiple public universities and independent researchers, including two international institutions—one of them based in Ecuador—as part of a broader scientific initiative to inform evidence-based sustainability planning in protected areas.

   This dataset provides valuable insight for researchers, practitioners, and policymakers interested in sustainable tourism, visitor management, and participatory planning in biodiversity-rich environments.

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

The data in this article was obtained from field surveys and through online questionnaires to obtain perception values and expectation values from visitors in tea plantations to assess the level of visitor satisfaction with the quality of tea plantation services. In this survey we researchers obtained 98 respondents. The category of respondents is people who have visited the tea plantation tourist attractions once. The data of this article can be presented to review management knowledge, problem solving and to reveal new insights from survey results.

Abstract

Tourism statistics traditionally represent an important field of official statistics, since they contribute significantly to the economic and market analysis of the tourism sector in Palestine.

Palestine attracts many tourists who come to tour its highly valued religious and historical sites. The household sample survey conducted from 24 March 2015 to 31 May 2015. Tourism is a key to many countries economy thanks to its significant contribution to GDP. For this reason, PCBS established a statistical program to monitor and produce reliable and timely statistics on the main indicators of tourism activity. This program began in 1996 with the implementation of the Hotel Survey, which provides periodic data on accommodation statistics.

Geographic coverage

Palestine

Analysis unit

Household the Palestine

Universe

It consists of all Palestinian households who are staying normally in Palestine during 2015.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame was based on master sample which was update in 2013-2014 for (Expenditure and Consumption Survey (PECS) and Multiple Indicator Cluster Survey (MICS)) surveys, and the frame consists from enumeration areas. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample size: The sample size is 7,690 households for Palestine level, 6,609 households responded.

Sampling Design: Two stage stratified cluster (PPS) sample as following:

First stage: selection of a PPS random sample of 370 enumeration areas.

Second stage: A random systematic sample of 20 households from each enumeration area selected in the first stage.

Sample strata: The population was divided by: 1- Governorate 2- locality type (urban, rural, camps)

Mode of data collection

Face-to-face [f2f]

Research instrument

The tourism questionnaire was design of the accordance with similar international experiences and with international standards and recommendations for the most important indicators, taking into account the special situation of Palestine.

Cleaning operations

The data processing stage consisted of the following operations: 1.Editing and coding prior to data entry: all questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

2.Data entry: The Domestic and Outbound Tourism Survey questionnaire was programmed and the data were entered into the computer in the offices in Nablus, Hebron, Ramallah and Gaza. At this stage, data were entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements: ·To prevent the duplication of questionnaires during data entry. ·To apply checks on the integrity and consistency of entered data. ·To handle errors in a user friendly manner. ·The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS.

Response rate

Response rate was 89.5%

Sampling error estimates

Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators and the variance table is attached with the final report. There is no problem with the dissemination of results on national.

Flights without a full package, Annual Average, Use electronic programs, Government Procedures and Bureaucracy, Workers, Total tourism establishments, Constraints facing setting up or practicing economic activities, Electricity Price, Access to Telecommunication (Phone & Internet), Road Passenger Transport, Number of international passengers, Water Price, Percentage distribution of tourism establishments which use electronic programs, Flights within a full package, Low demand, Percentage %, Labour Laws & Regulations, Electricity Supply (without interruption), Laptop, Percentage distribution of tourism establishments which use social media, Employees, Hotel rooms, Salaries and wages, Availability of Skilled Labour, Inbound international flights, Tourism Direct Gross Value Added, Water Passenger Transport, Number of local passengers, Average duration of residence in accommodation units, Other Activities, Professionals, Other Activities, Non-cloud Data, Railways Passenger Transport, Percentage distribution of devices used in tourism establishments, Operating surplus, Fuel Price, Managers, nights, Operating expenditure, Main Activity, Non-Saudi, Operating rate of international flights, Major performance indicators for passengers transport services, Licenses & Permits, Do not use social media, Cultural Activities, Outbound international flights, Land Passenger Transport, Workers problems, Furniture Apartments, Major challenges facing business environment development, There are constraints, Female, Transport Equipment Rental, Percentage distribution of tourism establishments that have cloud data, Number of available seats for international flights, Average daily price for accommodation units in Saudi Riyal, Number of available seats for local flights, Operating revenues distribution, Food and Beverage Serving Activities, Local Competition, Travel Agencies and Reservation Services, Cloud Data, Operating rate of local flights, Do not have Accounting Books, Security & Stability, Benefits and allowances, Water Supply (without interruption), Railway Passenger Transport, Percentage distribution of accounting books or budget usage, Percentage of sold flights for passengers by flight type, Operating revenues, Other Specific Tourism Characteristic Services, Government Inspection Procedures, Number, Fuel Supply (without interruption), Access to Finance, Thousands Riyals, Accommodation for Visitors, Total compensations, Gross value added of the tourism industries = operating revenues - operating expenses, Technicians, Local flights, Saudi Riyal per day, Total, Do not use electronic programs, Land / Rent of Space, Have Accounting Books, Retail trade of Country-Specific Tourism Characteristic Goods, No constraints, Air Passenger Transport, Employment percentage, Saudi, Occupancy rate for accommodation units, Handheld or tablet, Average daily income for accommodation units in Saudi Riyal, Desktop (PC), Specialists, -, Sports and Recreational Activities, Use social media, Number of employees, wages and Salaries, compensation, Flight, Tourism Establishments Survey, Economic Activity, Occupations

Saudi Arabia

Explore the Tourism Establishments Survey dataset in Saudi Arabia to uncover key insights on economic activities, workers, government procedures, and more. Access data on airline passengers, accommodation rates, transportation services, and challenges facing the tourism industry. Discover valuable information to enhance your understanding of the tourism sector in Saudi Arabia.Follow data.kapsarc.org for timely data to advance energy economics research..Preliminary estimated data based on supply and use tables.Gross value added of the tourism industries = operating revenues - operating expenses.Notes:Full package deals: packages that include the flight ticket as well as other services, such as the hotel booking, car rental... etc. Source: Administrative data from the Ministry of Human Resources and Social Development, Ministry of Tourism and the Saudi Railway Company

Abstract

Objectives of the Survey The survey provided data on The main purposes of the inbound visit. The length of stay of the visit. The amount and mode of expenditure during the visit

Report Structure This report comprises five chapters The first chapter presents the background of the Inbound Tourism Survey 2005, and the objectives of the survey The second chapter presents the main definitions used in the report The third chapter exhibits the main findings of Inbound Tourism Survey The fourth chapter discusses the methodology used in this survey The fifth chapter presents the quality of the data of the survey

Geographic coverage

Palestinian Terriotry

Analysis unit

Household in the palestinian territory

Universe

Palestinian Households

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame The sample is a two-stage stratified cluster random sample

Target Population All the Palestinian households living within the Palestinian Territory
Sampling Frame Sampling frame is a master sample from the Population, Housing and Establishment Census 1997. It consists of a list of enumeration areas, which were used as PSU's in the first stage of selection

Sampling Design The sample of this survey is a sub-sample of Labour Force Survey (LFS) sample, that is conducted every 13 weeks. The total sample of LFS is about 7,627 households distributed over 13 weeks. The sample of the inbound tourism survey occupies 13 weeks of the first quarter 2006 of LFS Stratification In designing the sample of LFS, four levels of stratification were made Stratification by governorate.
Stratification by place of residence which comprises: (a) Urban (b) Rural (c) Refugee camps
Stratification by locality size. Stratification by classifying localities, excluding governorate capitals, into three strata based on the ownership of households within these localities of durable goods

Sample Unit In the first stage, the sampling units are the enumerator areas (clusters) in the master sample. In the second stage, the sampling units are the households

Mode of data collection

Face-to-face [f2f]

Research instrument

Survey's Questionnaire The inbound tourism survey questionnaire was designed in accordance with similar country experience and with international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory

Cleaning operations

Data Processing The data processing stage consisted of the following operations Editing and coding before data entry All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field
Data entry At this stage, data was entered into the computer using a data entered template written in Access The data entry program was prepared to satisfy a number of requirements such as Duplication of the questionnaires on the computer screen Logical and consistency check of data entered Possibility for internal editing of question answers Maintaining a minimum of digital data entry and fieldwork errors User friendly handling Possibility of transferring data into another format to be used and analyzed using other statistical analytic systems such as SPSS

Response rate

Response rate 88.6%

Sampling error estimates

Sampling Errors These types of errors evolved as a result of studying a part of the society and not all of it. For this survey, variance calculations were made for type of expenditure during the inbound visit, be careful when you want to use these values

Non Sampling Errors These errors are due to non-response cases as well as the implementation of surveys In this survey, these errors emerged because of (a)the special situation of the questionnaire itself which depends on type of estimation (b)diversity of sources (e.g. the interviewers, respondent, editors, coders, data entry operator …etc)

The sources of these errors can be summarized in Some of the households were not in their houses and the interviewers couldn't meet them Some of the households didn't show attention toward the questionnaire.
Some errors occurred due to the way the questions were asked by interviewers Misunderstood of the questions by the respondents Answering the questions related to consumption by making estimations

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.

This dataset contains responses to a survey examining the effects of climate change on tourists' destination choices and preferences for sustainable tourism in Greece. The survey was conducted between August 1st and August 26th, 2024, with 580 participants recruited via snowball sampling from social media platforms targeting travel groups. Inclusion criteria required participants to be over 18 years of age and either have visited Greece in the past three years or plan to visit within the next three years. The survey explores various factors influencing tourists’ travel decisions, such as climate change awareness, perceptions of climate risks (e.g., temperature rise, wildfires), and preferences for sustainable practices (e.g., recycling, eco-friendly accommodations). The data also includes demographic information such as age, gender, education, employment status, and household income. Key findings from the data reveal that traditional factors like cost, safety, and beaches remain dominant in tourists' destination choices, while climate concerns such as temperature rise and wildfires significantly influence destination selection. Notably, there is a growing preference for destinations that prioritize sustainability, although respondents perceive the tourism industry’s response to climate change as insufficient. This dataset can be used to analyze how climate risk perceptions shape tourist behavior, the role of sustainability in travel decisions, and the potential for climate change to alter tourism patterns in Greece. It also provides insights into demographic variations in climate awareness and sustainable tourism preferences, offering a foundation for targeted communication and policy development to enhance resilience in Greece’s tourism sector.

The survey is a national consumer survey measuring the volume and value of overnight trips taken by the residents of Great Britain.

Abstract

Statistics South Africa collects data on foreign tourism from the South African Department of Home Affairs. Data on domestic tourism is also needed to measure its contribution to the national economy. The Domestic Tourism Survey (DTS) is aimed at addressing this need by collecting data on the travel behaviour and expenditure of South African residents travelling within and outside the borders of South Africa. This survey provides data on domestic tourism activity during the period January to December 2014.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The target population of the survey consists of all private households and residents in workers' hostels in the nine provinces of South Africa. The survey does not cover other collective living quarters such as students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data

Sampling procedure

For the Domestic Tourism Survey SSA used a household survey master sample of 3 080 primary sampling units from the 80 787 enumeration areas (EAs) created for the 2001 Population Census. The master sample used a two-stage, 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 and MS stratification was divided into two levels, primary and secondary stratification. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the 2001 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

Face-to-face

Research instrument

Household Questionnaire: This includes sections on: Household characteristcs, household listing, education, tourism employment, trips taken, day trips, overnight trips, barriers to taking trips, business and professional trips, recreation entertainment, sports trips, nature based trips, religious trips, medical trips, type of transport, expenditure on trips, social activites

Monthly microdata corresponding to the Tourism Survey of Residents since February 2015 The files are distributed in ASCII format and are accompanied by the registration design in excel or word format

BE: International Tourism: Receipts data was reported at 7.447 USD bn in 2020. This records a decrease from the previous number of 10.581 USD bn for 2019. BE: International Tourism: Receipts data is updated yearly, averaging 10.581 USD bn from Dec 2001 (Median) to 2020, with 19 observations. The data reached an all-time high of 15.249 USD bn in 2014 and a record low of 7.447 USD bn in 2020. BE: International Tourism: Receipts data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belgium – Table BE.World Bank.WDI: Tourism Statistics. 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.;World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.;Gap-filled total;

Abstract

Objectives of the Survey: The survey provided data on: The members of the household that took part in the trip. The main purposes of the visit. The main characteristics of the households that conducted/did not conduct trips. The length of stay of the trips. The amount and mode of expenditure during the trips.

Geographic coverage

The Palestinian Territory

Analysis unit

Household the Palestinian Territory

Universe

Household the Palestinian Territory

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame Sampling frame is a master sample from the Population, Housing and Establishment Census 1997. It consists of a list of enumeration areas, which were used as PSU's in the first stage of selection

Sampling Design The sample of this survey is a sub-sample of Labour Force Survey (LFS) sample, that is conducted every 13 weeks. The total sample of LFS is about 7,563 households distributed over 13 weeks The sample of the Outbound Tourism Survey occupies 11 weeks of the first quarter 2005 of LFS

Stratification: In designing the sample of LFS, four levels of stratification were made Stratification by governorate.
Stratification by place of residence which comprises (a) Urban (b) Rural (c) Refugee camps
Stratification by locality size . Stratification by classifying localities, excluding governorate capitals, into three strata based on the ownership of households within these localities of durable goods

Sample Unit: In the first stage, the sampling units are the enumerator areas (clusters) in the master sample. In the second stage, the sampling units are the households

Mode of data collection

Face-to-face [f2f]

Research instrument

Survey's Questionnaire The Outbound Tourism survey questionnaire was designed in accordance with similar country experience and with international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

Data Processing The data processing stage consisted of the following operations: Editing and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.
Data entry: At this stage, data was entered into the computer using a data entered template written in Access. The data entry program was prepared to satisfy a number of requirements such as: Duplication of the questionnaires on the computer screen. Logical and consistency check of data entered. Possibility for internal editing of question answers. Maintaining a minimum of digital data entry and fieldwork errors. User friendly handling. Possibility of transferring data into another format to be used and analyzed using other statistical analytic systems such as SPSS.

Response rate

response rate 85%

Sampling error estimates

Sampling Errors: These types of errors evolved as a result of studying a part of the society and not all of it. For this survey, variance calculations were made for the purpose of outbound trips specially in Gaza strip.

Non Sampling Errors: These errors are due to non-response cases as well as the implementation of surveys. In this survey, these errors emerged because of (a) the special situation of the questionnaire itself which depends on type of estimation (b) diversity of sources (e.g. the interviewers, respondent, editors, coders, data entry operator …etc).

BA: International Tourism: Receipts: for Travel Items data was reported at 426.000 USD mn in 2020. This records a decrease from the previous number of 1.173 USD bn for 2019. BA: International Tourism: Receipts: for Travel Items data is updated yearly, averaging 615.000 USD mn from Dec 1998 (Median) to 2020, with 23 observations. The data reached an all-time high of 1.173 USD bn in 2019 and a record low of 233.000 USD mn in 2000. BA: International Tourism: Receipts: for Travel Items data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bosnia and Herzegovina – Table BA.World Bank.WDI: Tourism Statistics. International tourism receipts for travel items are expenditures by international inbound visitors in the reporting economy. The goods and services are purchased by, or on behalf of, the traveler or provided, without a quid pro quo, for the traveler to use or give away. These receipts should include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except in cases where these are so important as to justify a separate classification. Excluded is the international carriage of travelers, which is covered in passenger travel items. Data are in current U.S. dollars.;World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.;Gap-filled total;

Economic exchanges generated by the income or expenditure of foreign currency from tourists entering or leaving the country.

Statistics South Africa provides data on international tourism based on secondary data obtained from the South African Department of Home Affairs. The information from this data used by stakeholders to measure and understand international tourism in South Africa. Detailed information about national domestic tourism is limited, however and there was a need to collect more detailed information on domestic tourism in order to better understand and measure the contribution of the tourism industry towards the national economy. The Domestic Tourism Survey (DTS) is aimed at addressing this need by collecting accurate statistics on the travel behaviour and expenditure of South African residents travelling within and outside the borders of South Africa. Such information is crucial in determining the contribution of tourism to the South African economy as well as helping with planning, marketing, policy formulation and regulation of tourism-related activities. This survey provides data on domestic tourism activity during the period December 2008 February 2009. For, this in addition to the basic demographic information collected in the majority of household surveys conducted by Statistics South Africa, the DTS covers five areas specifically related to travel and expenditure patterns. These include trips taken by the household, domestic day trips by the respondent and/or other household members, domestic day trips by other household members (without the respondent), domestic overnight trips by the respondent and/or other household members, domestic overnight trips by other household members (without the respondent).

Tourist Arrivals in the United States decreased to 5634382 in May from 5957985 in April of 2025. This dataset provides - United States Tourist Arrivals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2024, the number of international tourist arrivals worldwide recorded a **** percent annual increase. That year, global inbound tourist arrivals were just *** percent lower than in 2019, reaching **** billion.