Desktop and mobile website traffic data showed that Germany domain of Zalando had by far the highest number of visitors compared to all other European countries. Between July 2023 and December 2023, zalando.de recorded more nearly *** million visits. The Polish web domain followed in the ranking, as the total visits amounted to **** million.

The publication of tourism statistics often does not keep up with the highly dynamic tourism demand trends, especially critical during crises. Alternative data sources such as digital traces and web searches represent an important source to potentially fill this gap, since they are generally timely, and available at detailed spatial scale. In this study we explore the potential of human mobility data from the Google Community Mobility Reports to nowcast the number of monthly nights spent at sub-national scale across 11 European countries in 2020, 2021, and the first half of 2022. Using a machine learning implementation, we found that this novel data source is able to predict the tourism demand with high accuracy, and we compare its potential in the tourism domain to web search and mobile phone data. This result paves the way for a more frequent and timely production of tourism statistics by researchers and statistical entities, and their usage to support tourism monitoring and management, although privacy and surveillance concerns still hinder an actual data innovation transition.

Datos brings to market anonymized, at scale, consolidated privacy-secured datasets with a granularity rarely found in the market. Get access to the desktop and mobile browsing behavior for millions of users across the globe, packaged into clean, easy-to-understand data products and reports.

The Datos Domain Traffic Feed reports on panelist visitation to domains, benchmarking the popularity of internet properties worldwide by country. Additionally, we offer the ability to track the availability of domains with respect to whether traffic is being sent to sites which are currently unregistered. Customers can elect to focus on specific domains, countries, or domain registration status.

Now available with Datos Low-Latency Feed This add-on ensures delivery of approximately 99% of all devices before markets open in New York (the lowest latency product on the market). Our clickstream data is made up of an array of upstream sources. The DLLF makes the daily output of these sources available as they arrive and are processed, rather than a once-daily batch.

In November 2024, Google.com was the most popular website worldwide with approximately 6.25 billion unique monthly visitors. YouTube.com was ranked second with an estimated 3.64 billion unique monthly visitors. Both websites are among the most visited websites worldwide.

Explore the historical Whois records related to medical-tourism-statistics.com (Domain). Get insights into ownership history and changes over time.

Following are the correlation matrices for the original mobility indicators and the composite one (G) in each country. V1, V2, …, V6 represent the Google mobility indicators. (ZIP)

Tourism industries have the potential to contribute to the country's income, and as they should, we expect this industry to continue to grow each year. Indonesia is one of the well-known countries with incredible destinations to visit by domestic and international tourists that are continuously growing. There are many ways to determine a suitable strategy to understand tourist behavior, such as understanding tourist mobility, sentiment, and problems. Using tourist reviews or user-generated content (UGC) data on the Tripadvisor website, we employ social network analysis (SNA) to identify tourist mobility, favorite and in-between destination using network metrics and measurements. We use sentiment analysis to classify tourist sentiment. And multiclass text classification method to find out various problems in tourist reviews. We also construct a text corpus for the tourism domain to classify tourism problems. The results represent the complex tourist mobility to recognize the favorite destination, the tourist sentiment in each destination, and the problem in Bali tourism. The combined model benefits many stakeholders such as tourists, the government, and business organizations.

With the wide application of large models in various fields, the demand for high-quality data sets in the tourism industry is increasing to support the improvement of the model 's ability to understand and generate tourism information. This dataset focuses on textual data in the tourism domain and is designed to support fine-tuning tasks for tourism-oriented large models, aiming to enhance the model's ability to understand and generate tourism-related information. The diversity and quality of the dataset are critical to the model's performance. Therefore, this study combines web scraping and manual annotation techniques, along with data cleaning, denoising, and stopword removal, to ensure high data quality and accuracy. Additionally, automated annotation tools are used to generate instructions and perform consistency checks on the texts. The LLM-Tourism dataset primarily relies on data from Ctrip and Baidu Baike, covering five Northwestern Chinese provinces: Gansu, Ningxia, Qinghai, Shaanxi, and Xinjiang, containing 53,280 pairs of structured data in JSON format. The creation of this dataset will not only improve the generation accuracy of tourism large models but also contribute to the sharing and application of tourism-related datasets in the field of large models.

In May 2025, the United States accounted for over ** percent of traffic to the online search website search.yahoo.com. Taiwan and the United Kingdom ranked second and third, accounting for **** percent and **** percent of web visits to the platform each. Meanwhile, the domain Yahoo.com also received a similar distribution of its traffic from the United States and the countries composing the rest of its ranking.

The first column shows the available countries with ISO 3166–1 alpha-2 country codes (https://www.iso.org/iso-3166-country-codes.html. Last accessed the 2022/05/16).

We have collected the access logs for our university's web domain over a time span of 4.5 years. We now release the pre-processed web server log of a 3-month period for research into user navigation behavior. We preprocessed the data so that only successful GET requests of web pages by non-bot users are kept. The information that is included per entry is: unique user id, timestamp, GET request (URL), status code, the size of the object returned to the client, and the referrer URL. The resulting size of the 3-month collection is 9.6M page visits (190K unique URLs) by 744K unique visitors. The data collection allows for research on, among other things, user navigation, browsing and stopping behavior and web user clustering.

Analysis of ‘Access statistics by moers.de for 2017 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/032f7582-eb26-47b5-bf3c-aed43c3085bf on 16 January 2022.

--- Dataset description provided by original source is as follows ---

The dataset contains the access statistics for the year 2017. It is supplemented on a monthly basis.

The zip file contains the following CSV files:

• Visitors per year_1.csv
• Visitors per month — individual period _1.csv
• Visitors per month 1.csv
• Visitors per Stunde_1.csv
• Visitors per Stunde_1_1.csv
• Visitors per day 1.csv
• Visitors _1.csv
• Main browser versions by operating system_1.csv
• Main version browser _1
• browsers by operating system subversionen_1.csv
• Operating system_1 browsers
• Browser Unterversionen_1.csv
• Browser_1
• boarding sides_1.csv
• Event Tracker_1.csv
• Event Tracker_1_1.csv
• Event Tracker_1_2.csv
• Event Tracker_1_3.csv
• Origin overview _1.csv
• Klick-Tracker_1.csv
• Use per domain 1.csv
• Page Impressions per Page_1.csv
• Search word statistics Phrases by search engine 1.csv
• Search word statistics Phrasen_1.csv
• Search word statistics Wörter_1.csv

--- Original source retains full ownership of the source dataset ---

Snapshot img

Sports Tourism Market Size 2025-2029

The sports tourism market size is forecast to increase by USD 701.6 billion at a CAGR of 12.9% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing number of sporting events and the development of virtual reality technology. These factors are creating new opportunities for businesses to engage fans and tourists alike, offering immersive experiences that extend beyond the physical event. However, market expansion is not without challenges. Regulatory hurdles and financial constraints have led to the cancelation of several sports events, tempering growth potential. Despite these obstacles, companies can capitalize on the market's momentum by focusing on innovative solutions and strategic partnerships. The integration of technology, such as virtual reality, into sports tourism offerings, can help mitigate the impact of event cancellations and provide fans with unique experiences that transcend geographical boundaries.
Additionally, collaborations with sports organizations and travel agencies can expand reach and create new revenue streams. By staying attuned to market trends and addressing challenges proactively, businesses can effectively navigate the dynamic sports tourism landscape and seize opportunities for growth.

What will be the Size of the Sports Tourism Market during the forecast period?

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The market is experiencing significant growth and innovation, with digital marketing playing a pivotal role in reaching consumers. Mountain biking and ski resorts are popular attractions, driving demand for adventure tourism. Sporting events and festivals serve as key catalysts, boosting destination marketing and tourism branding. Tourism infrastructure development is crucial for accommodating the increasing number of visitors.
Sports tourism certification ensures adherence to industry standards, enhancing consumer trust. Sports governing bodies and associations collaborate to create a harmonious environment, fostering growth in the sports tourism sector. Ski resorts and mountain biking trails leverage digital marketing strategies, adhering to

How is this Sports Tourism Industry segmented?

The sports tourism industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Domestic sports tourism
  International sports tourism


Product

  Soccer tourism
  Cricket tourism
  Tennis tourism
  Others


Area

  Passive sports tourism
  Active sports tourism


Destination

  Urban Centers
  Coastal Regions
  Mountain Areas


Travel Services

  Tour Packages
  Accommodation


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The domestic sports tourism segment is estimated to witness significant growth during the forecast period.

The market is characterized by the participation and attendance of domestic tourists at various sports events within their countries. Domestic sports tourism encompasses expenditures on hospitality, merchandise, and transportation related to these events. The convenience of intra-region transportation and the use of uniform regional currencies contribute to the popularity of domestic sports tourism. Professional sports leagues and teams facilitate fan travel between venues, further increasing domestic tourist attendance. Fitness events, water sports, mountain biking, golf courses, and adventure tourism are popular domains within sports tourism. Tourism stakeholders invest in tourism infrastructure, sports facilities, and digital marketing to attract and engage tourists.

Sustainability and environmental impact are becoming significant considerations in sports tourism development. Amateur sports, spectator events, and sports training also contribute to the market's growth. Social media marketing, customer relationship management, and online booking platforms streamline the tourist experience. Sports apparel, equipment, and mobile apps cater to the needs of active holidaymakers. Tourism policy and partnerships play a crucial role in sports tourism promotion and investment. Sports tourism development continues to evolve, integrating data analytics and event management to enhance the overall tourist experience.

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The Domestic sports tourism segment was valued at USD 546.90 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

Europe is estimated to contribute 35% to the growth of the global market during the forecast period.Technavio

Analysis of ‘Access statistics by moers.de for June 2015 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ff78d614-47cf-4cfc-868b-7c767fd1f415 on 13 January 2022.

--- Dataset description provided by original source is as follows ---

The zip file contains the following CSV files:

• Visitors per year_1.csv
• Visitors per month — individual period _1.csv
• Visitors per month 1.csv
• Visitors per Stunde_1.csv
• Visitors per day 1.csv
• Visitors _1.csv
• Main browser versions by operating system_1.csv
• browsers by operating system subversionen_1.csv
• Browser Unterversionen_1.csv
• boarding sides_1.csv
• Origin overview _1.csv
• Use per domain 1.csv
• Page Impressions per Page_1.csv
• Search word statistics Phrases by search engine 1.csv
• Search word statistics Phrasen_1.csv
• Search word statistics Wörter_1.csv

--- Original source retains full ownership of the source dataset ---

This dataset provides meteorological and snow indicators for Europe, characterizing operating conditions of winter ski resorts under past and future climate scenarios. The dataset consists of 39 indicators of atmospheric and snow conditions computed in a similar manner for all mountain regions in Europe at the scale of NUTS-3 regions (Nomenclature of Territorial Units for Statistics) and by steps of 100 m elevation. The snow indicators are generated using the Crocus snowpack model, a multi-layer snowpack model embedded in the land surface model, SURFEX (Surface Externalisée). In order to assess the impact of climate change, the model is run for four different climate scenarios: the present climate (labelled 'historical'), and three Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2020 (RCP2.6), a further optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century, often called the high emission scenario (RCP8.5). In order to simulate these climate scenarios the SURFEX model is forced with atmospheric fields provided by adjusted EURO-CORDEX ensemble climate projections (European branch of the Coordinated Downscaling Experiment). Regional climate models downscaled from global climate models are used to provide the high resolution, pan-European, indicators required to assess the snow reliability for all mountainous regions across Europe. In addition to the climate scenarios, a reanalysis dataset is computed using UERRA reanalysis. A total of 39 indicators are made available in this dataset, divided into seven distinct groups:

Start and end date of snow season Annual amount of machine made snow produced Precipitation Snow depth Snow water equivalent Air temperature Potential snow making hours

The Crocus model makes it possible to account for both snow grooming and mechanical snow-making based upon the physical representation of these snow management practices, adding further value to the end-user by providing indicated snow management requirements under future climate conditions. However, it is not designed to replace higher resolution products available in some European regions that provide a more detailed view of ski conditions; for example, accounting for slope, local meteorological phenomena and local snow management practices. Instead this dataset presents a homogenous product at a pan-European level and hence its main goal is to compare the main features of past and future snow conditions across Europe or to compare distant destinations; for example, Scandinavia and Eastern Europe (for a given elevation and time horizon). This dataset was produced on behalf of the Copernicus Climate Change Service.

Our Geospatial Dataset connects people's movements to over 200M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world.

It includes information such as the name, address, coordinates, and category of these locations, which can range from restaurants and hotels to parks and tourist attractions

Location Intelligence Data Reach: Location Intelligence data brings the POI/Place/OOH level insights calculated on the basis of Factori’s Mobility & People Graph data aggregated from multiple data sources globally. In order to achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data. For instance, in order to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 56 data records possible for one POI based on the combination of these attributes.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly).

Use Cases: Credit Scoring: Financial services can use alternative data to score an underbanked or unbanked customer by validating locations and persona. Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas. Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape Urban Planning: Build cases for urban development, public infrastructure needs, and transit planning based on fresh population data. Marketing Campaign Strategy: Analyzing visitor demographics and behavior patterns around POIs, businesses can tailor their marketing strategies to effectively reach their target audience. OOH/DOOH Campaign Planning: Identify high-traffic locations and understand consumer behavior in specific areas, to execute targeted advertising strategies effectively. Geofencing: Geofencing involves creating virtual boundaries around physical locations, enabling businesses to trigger actions when users enter or exit these areas

Data Attributes Included: LocationID
name
website BrandID Phone streetAddress
city
state country_code zip lat lng poi_status
geoHash8 poi_id category category_id full_address address additional_categories url domain rating price_level rating_distribution is_claimed photo_url attributes brand_name brand_id status total_photos popular_times places_topics people_also_search work_hours local_business_links contact_info reviews_count naics_code naics_code_description sis_code sic_code_description shape_polygon building_id building_type building_name geometry_location_type geometry_viewport_northeast_lat geometry_viewport_northeast_lng geometry_viewport_southwest_lat geometry_viewport_southwest_lng geometry_location_lat geometry_location_lng calculated_geo_hash_8

Sentence aligned parallel corpus built by automatically crawling 25 websites from the tourism domain.

The United States remains by far the most important market for Amazon. In February 2023, its amazon.com domain registered nearly *** billion monthly visits. That is almost **** times more than the visits recorded to amazon.co.jp, Amazon's web domain for Japan.

The Visitor Area Feature Class sits within the National Parks and Wildlife Service (NPWS) Assets Geodatabase. The Visitor Area point layer includes Day Use Areas, Camping Areas and Cemeteries. The Assets Geodatabase is directly related to the Assets Maintenance System (AMS) which runs under SAP and contains similar fields, values and business rules. The Assets Geodatabase is the vehicle in which spatial assets are initially captured, edited and stored so that the features have coordinates and can be viewed spatially. The data is collected across the entire NSW National Parks Estate and includes some off-park features for fire management, access and mapping purposes. The spatial feature data is manually synchronised with the AMS. The two systems run side by side and are linked by an ID field. AMS is also set up to be used by other OEH Divisions eg. Botanic Gardens and Parklands and previously Marine Parks. The database includes the following asset Feature Class types - Barrier, Bridge or Elevated Walkway, Building, Communication Equipment, Crossing, Drainage Point, Environmental Monitoring Station, Extractive industry, Facility, Fence Handrail, Fire Management Zone, Gate, Hydraulic Point, Hydraulic Storage Point, Hydraulic Valve, Irrigation System, Landing, Landing Strip, Lookout, Natural Feature, Other Structure, Parking Area, Pipe Channel Section, Power or Communication line, Power or Communication point, Sign, Step point, Stormwater Drainage Line, Surface, Survey Mark, Tower, Track Section, Treatment Disposal System, Visitor Area, Visitor Monitoring Point. Detailed documentation is available including: - Data Dictionary (internal location - P:\Corporate\Tools\Information\Assets) - Data Model - Business Rules - Functional Location and Naming Convention Note that for external supply the dataset is simplified with certain attribute fields being removed. Those fields that have a name prefixed with "d_" contain descriptions extracted from the original geodatabase domains.

We have collected the access logs for our university's web domain over a time span of 4.5 years. We now release the pre-processed web server log of a 3-month period for research into user navigation behavior. We preprocessed the data so that only successful GET requests of web pages by non-bot users are kept. The information that is included per entry is: unique user id, timestamp, GET request (URL), status code, the size of the object returned to the client, and the referrer URL. The resulting size of the 3-month collection is 9.6M page visits (190K unique URLs) by 744K unique visitors. The data collection allows for research on, among other things, user navigation, browsing and stopping behavior and web user clustering. Date Submitted: 2016-04-28