This is the dataset of World's most visited Countries by international travellers. France has the most visitors in 2021 and dataset contains data of 50 countries. Spain is the second country for tourists.

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...

This table contains 45 series, with data for years 2014 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Country of origin (15 items: United States; United Kingdom; France; China; ...) Traveller characteristics (3 items: Trips; Nights; Spending in Canada).

This table contains 45 series, with data for years 2014 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Countries visited (15 items: United States; Mexico; United Kingdom; France; ...) Travel characteristics (3 items: Visits; Nights; Spending in country).

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.

This table contains 45 series, with data for years 2014 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) State visited (15 items: Florida; New York; Washington; California; ...) Travel characteristics (3 items: Visits; Nights; Spending in country).

Visit Britain publish data relating to international visitors to the UK. They produce the data in two formats - individual spreadsheets for each region that are updated annually, and a single spreadsheet for all regions, containing less detail but updated quarterly. Data shows London totals for nights, visits, and spend. Data broken down by age, purpose, duration, mode and country. This data is also available from Visit Britain website, including the latest quarterly data for other regions. All data taken from the International Passenger Survey (IPS). Some additional data on domestic tourism can be found on the Visit Britain website, and Visit England both overnight tourism and Day visits pages. Data on accomodation occupancy levels is also available from Visit England. An overview of all tourism data for London can be found in this GLAE report 'Tourism in London' Further information can be found on the London and Partners website. Comparisons of international tourist arrivals with other world cities are produced by Euromonitor and in Mastercard's Global Destination Cities Index of 2012, 2013, 2014, and 2015. This dataset is included in the Greater London Authority's Night Time Observatory. Click here to find out more.

The data presents year, nationality, and region wise distribution of people by purpose of visit. The purpose of visit includes business and professional, Indian diaspora, leisure holiday and recreation, medical, student, etc. It should be noted that the categorisation used in the report has undergone changes over the years. Data for the year 2009, 2014, and 2015 is unavailable. With respect to countries covered, the

Note: 1) key changes and exclusions over the years is as follows: 2) 2003-2006 & 2013-2015: Hungary was not included in the report. 3) 2003-2006: Kazakhstan, Russian Federation, and Ukraine were categorized under the Commonwealth of Independent States (CIS). 4) 2003-2005: Argentina was not part of the dataset. 5) 2003-2008: Sudan, Iraq, and Vietnam were omitted. 6) 2007 & 2008: The Czech Republic was missing from the dataset. 7) 2006-2018: The "Others" field for the North American region was not included in the dataset. 8) From 2021 onwards: India has not formally recognized the Republic of China Taiwan as a sovereign country, and it was excluded from the dataset. Since 2024, more countries and regions were added

US Travel Check-Ins - Analysis

In-Depth Study of Location, Date, Temperature, USIndex, and Crime Rates

By [source]

About this dataset

This comprehensive dataset offers an in-depth exploration into US travel check-ins from Instagram. It includes detailed data scraped from Instagram, such as the location of each check-in, the USIndex for each state, average temperature for each state per month, and crime rate per state. In addition to location and time information, this dataset also provides latitude and longitude coordinates for every entry. This extensive collection of data is invaluable for those interested in studying various aspects of movement within the United States. With detailed insights on factors like climate conditions and economic health of a region at a given point in time, this dataset can help uncover fascinating trends regarding how travelers choose their destinations and how they experience their journeys around the country

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How to use the dataset

This Kaggle dataset - US Travel Check-Ins Analysis - provides valuable insights for travel researchers, marketers and businesses in the travel industry. It contains check-in location, USIndex rating (economic health of each state), average temperature, and crime rate per state. Latitude and longitude of each check-ins are also provided with added geographic context to help you visualize the data.

This guide will show you how to use this dataset for your research or business venture.

Step 1: Prepare your data First and foremost, it is important to cleanse your data before you can analyze it. Depending on what sort of analysis needs to be conducted (e.g., time series analysis) you will need to select the applicable columns from the dataset that match your needs best and exclude any unnecessary columns such as dates or season related data points as they are not relevant here. Furthermore, variable formatting should be consistent across all instances in a variable/column category as well (elevation is a good example here). You can always double check that everything is formatted correctly by running a quick summary on selected columns using conditional queries like df['var'].describe() command in Python for descriptive results about an entire column’s statistical makeup including mean values, quartile ranges etc..

Step 2: Explore & Analyze Your Data Graphically Once the data has been prepped properly you can start visualizing it in order to gain better insights into any trends or patterns that may be present within it when compared with other datasets or information sources simultaneously such as weather forecasts or nationwide trend indicators etc.. Grafana dashboards are feasible solutions when multiple dataset need to be compared but depending on what type of graphs/charts being used Excel worksheet formats can offer great customization options flexiblity along with various export file types (.csv; .jpegs; .pdfs). Plotting markers onto map applications like Google Maps API offers more geographical awareness that could useful when analyzing location dependent variables too which means we have one advantage over manual inspection tasks just by leveraging existing software applications alongside publicly available APIs!

Step 3: Interpretation & Hypothesis Testing
After generating informative graphical interpretation from exploratory visualizations the next step would involve testing out various hypotheses based on established correlations between different variables derived from overall quantitative estimates vizualizations regarding distribution trends across different regions tends towards geographical areas where certain logistical processes could yeild higher success ratios giving potential customers greater satisfaction than

Research Ideas

• Travel trends analysis: Using this dataset, researchers could track which areas of the US are popular destinations based on travel check-ins and spot any interesting trends or correlations in terms of geography, seasonal changes, economic health or crime rates.
• Predictive Modeling: By using various features from this dataset such as average temperature, US Index and crime rate, predictors could be developed to suggest how safe an area would feel to a tourist based on their current location and other predetermined variables they choose to input into the model.
• Trip Planning Tool: The dataset can also be used to develop a tool that quickly allows travelers to plan trips according to their preferences in terms of duration and budget as well a...

Reports of rapid growth in nature-based tourism and recreation add significant weight to the economic case for biodiversity conservation but seem to contradict widely voiced concerns that people are becoming increasingly isolated from nature. This apparent paradox has been highlighted by a recent study showing that on a per capita basis, visits to natural areas in the United States and Japan have declined over the last two decades. These results have been cited as evidence of “a fundamental and pervasive shift away from nature-based recreation”—but how widespread is this phenomenon? We address this question by looking at temporal trends in visitor numbers at 280 protected areas (PAs) from 20 countries. This more geographically representative dataset shows that while PA visitation (whether measured as total or per capita visit numbers) is indeed declining in the United States and Japan, it is generally increasing elsewhere. Total visit numbers are growing in 15 of the 20 countries for which we could get data, with the median national rate of change unrelated to the national rate of population growth but negatively associated with wealth. Reasons for this reversal of growth in the richest countries are difficult to pin down with existing data, but the pattern is mirrored by trends in international tourist arrivals as a whole and so may not necessarily be caused by disaffection with nature. Irrespective of the explanation, it is clear that despite important downturns in some countries, nature-related tourism is far from declining everywhere, and may still have considerable potential both to generate funds for conservation and to shape people's attitudes to the environment.

How often do people visit the world’s protected areas (PAs)? Despite PAs covering one-eighth of the land and being a major focus of nature-based recreation and tourism, we don’t know. To address this, we compiled a globally-representative database of visits to PAs and built region-specific models predicting visit rates from PA size, local population size, remoteness, natural attractiveness, and national income. Applying these models to all but the very smallest of the world’s terrestrial PAs suggests that together they receive roughly 8 billion (8 x 109) visits/y—of which more than 80% are in Europe and North America. Linking our region-specific visit estimates to valuation studies indicates that these visits generate approximately US $600 billion/y in direct in-country expenditure and US $250 billion/y in consumer surplus. These figures dwarf current, typically inadequate spending on conserving PAs. Thus, even without considering the many other ecosystem services that PAs provide to people, our findings underscore calls for greatly increased investment in their conservation.

Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists). An increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.

Data Source - WorldBank

Data Description -

International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they usually reside, and outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on the number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead.

International outbound tourists are the number of departures that people make from their country of usual residence to any other country for any purpose other than a remunerated activity in the country visited. The data on outbound tourists refer to the number of departures, not to the number of people traveling. Thus a person who makes several trips from a country during a given period is counted each time as a new departure.

International tourism expenditures are expenditures of international outbound visitors in other countries, including payments to foreign carriers for international transport. These expenditures may include those by residents traveling abroad as same-day visitors, except in cases where these are important enough to justify separate classification. For some countries, they do not include expenditures for passenger transport items. Data are in current U.S. dollars.

This collection covers national tourism, in other words tourism by residents of a country to destinations in the country of residence (domestic tourism) or abroad (outbound tourism).

Alternatively, this part of tourism statistics is sometimes referred to as "the demand side".

Data are collected by the competent national authorities of the Member States (generally the national statistical institute) and are compiled according to harmonised concepts and definitions and recommended methodological guidelines, before transmission to Eurostat. Most of the time, data on domestic and outbound trips (where "outbound tourism" means residents of a country travelling to another country) is collected via sample surveys. However, in a few cases the data relating to outbound flows are compiled from border surveys. Surveys are generally conducted on a monthly or quarterly basis.

The concepts and definitions used in the collection of data are backed by the specifications described in the Methodological manual for tourism statistics.

The information on tourism demand concerns trips (for the population aged 15 years and over) of which the main purpose is holidays or business and which involve at least one or more consecutive nights spent away from the usual place of residence. Member States are transmitting microdata to Eurostat, which enables a more detailed analysis of the data, as well as better use of partner data.

Aggregated data on participation in tourim are also transmitted to Eurostat and cover the resident population aged 15 or over, participating in tourism for personal purpose during the reference year. Finally, the data also include aggregate data on same-day visits.

Microdata on trips of EU residents as well as participation data and data on same-day visits are transmitted to Eurostat one time per year. Data are disseminated when they respect agreed validation rules and other quality criteria.

This de-duped dataset is used by our customers for many purposes, primarily to understand which countries the people who visit specific locations (more accurately, the mobile devices carried by those people) - perhaps the locations that they own/operate, perhaps those owned/operated by their competitors, or visited by their customers - originated.

If, for instance, you operate a hotel brand and want to understand the top ten countries that visitors to your City came from; if/how that changes seasonally over time, and by type of location (perhaps higher end visitors are more likely to come from the UK or Germany versus France or Italy) - to help you build out your data models or marketing in those countries and/or to help tailor your product offers towards their needs.

This data can be useful as a way to understand, for instance, whether there are specific geographical areas you might consider putting a new location; where you might buy billboard ads, advertising the ‘local’ store; to build your own mobility data models to help better understand visitation into your own/your competitors premises, or test hypotheses around changes in visitation patterns over time.

The Intuizi Country Origin Dataset comprises fully-consented mobile device data, de-identified at source by the entity which has legal consent to own/process such data, and on who’s behalf we work to create a de-identified dataset of Encrypted ID visitation/mobility data.

The Top Websites in the World

How They Change Over Time

About this dataset

This dataset consists of the top 50 most visited websites in the world, as well as the category and principal country/territory for each site. The data provides insights into which sites are most popular globally, and what type of content is most popular in different parts of the world

How to use the dataset

This dataset can be used to track the most popular websites in the world over time. It can also be used to compare website popularity between different countries and categories

Research Ideas

• To track the most popular websites in the world over time
• To see how website popularity changes by region
• To find out which website categories are most popular

Acknowledgements

Dataset by Alexa Internet, Inc. (2019), released on Kaggle under the Open Data Commons Public Domain Dedication and License (ODC-PDDL)

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv | Column name | Description | |:--------------------------------|:---------------------------------------------------------------------| | Site | The name of the website. (String) | | Domain Name | The domain name of the website. (String) | | Category | The category of the website. (String) | | Principal country/territory | The principal country/territory where the website is based. (String) |

The international tourism receipts in Norway were forecast to continuously increase between 2024 and 2029 by in total 10 billion U.S. dollars (+26.47 percent). According to this forecast, in 2029, the tourism receipts will have increased for the ninth consecutive year to 47.9 billion U.S. dollars. Receipts denote expenditures by inbound tourists from other countries. Domestic tourism expenditures are not included. 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 international tourism receipts in countries like Finland and Denmark.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Census Bureau includes landmarks such as military installations in the MTDB for locating special features and to help enumerators during field operations. In 2012, the Census Bureau obtained the inventory and boundaries of most military installations from the U.S. Department of Defense (DOD) for Air Force, Army, Marine, and Navy installations and from the U.S. Department of Homeland Security (DHS) for Coast Guard installations. The military installation boundaries in this release represent the updates the Census Bureau made in 2012 in collaboration with DOD.

In December 1972 a study on holiday trips of the Austrian population is conducted in the course of the Mikrozensus. This survey is limited (as was the previous Mirkrozensus survey 3 years prior MZ6904) to the main holidays (with a minimum number of 4 overnight stays outside the residential area). In September 1971 there was a special survey on short vacations on certain official holidays. The massive increase in tourism is a characteristic feature of the economic and social development of the last decades. The ongoing statistic on tourism which registers arrival and overnight stays in lodging establishments and private homes provides information on the travel-flow but not on the composition of the groups of travellers. This additional information is provided through sampling in many European countries on recommendation of the Organization for Economic Cooperation and Development (OECD). With this Mikrozensus special survey Austria contributes to this household survey on holiday habits which is conducted simultaneously in most OECD-countries. The comparability of the results is warranted thanks to a survey catalogue fixed by the OECD some time ago. Therefore, the question program differs only slightly from the question program of the year 1969.

This dataset tells fascinating stories drawn from treasures of the National Archives collection - http://naa.gov.au/visit-us/exhibitions/memory-of-a-nation/interactive.aspx

The dataset includes a digitised version of every original item that has been put on display in the Archives’ permanent exhibition Memory of a Nation, from its launch in 2007 through to the present day. This includes an original musical score of Waltzing Matilda, a petition for Aboriginal land rights from the Larrakia people of Darwin, and Charles Kingsford Smith’s 1921 application for a pilot’s licence.

The dataset covers around 500 record items (eg. a file, object or photograph in the NAA collection).

Examples of content - http://naa.gov.au/visit-us/exhibitions/memory-of-a-nation/index.aspx

Record Item data fields include

• Theme • Subtheme • Title • Keyword tags (names, places, government activities), • Short description • Long description • More info • Year (of record) • Series number (note: a series is a group of records that has resulted from the same filing process), • Control symbol and barcode (record item reference numbers) • Collection (i.e. the government agency or person that created the series) • Format (for example, photograph, letter, bound volume, plan, film, etc.) • File name for scanned images • Number of images • Notes • Digitised and Folio / page number