Categorizing data, also known as data classification, is a fundamental practice in various fields such as data science, and knowledge management. Categorization offers several benefits and serves different purposes, depending on the context. Undoubtedly  categorizing data brings order and provides structure to information. It enhances data analysis, decision-making, and communication across various domains, making it an essential practice in today’s data-driven world. In this blog post we describe the most common data classifications in tourism

Tourism data classified by the data sources

We begin our deliberations with literature analysis. Various approaches and ways to categorize tourism-related data can be found in the scientific papers and textbooks.

One of the more widely used classifications is the one proposed by J. Li and co-authors [1].Researchers divide tourism data into three types based on the data sources, these are:

  1. user-generated data (UGC) provided by tourists themselves (e.g., in the form of photos or text)
  2. device data collected by devices (especially cell phones, GPS, RFID or WiFi) and sensors.
  3. transaction data derived from transactions carried out (among others booking or payment data).

A similar categorization of data was suggested by the European Commission. In addition to the three categories mentioned above, it also considers the fourth category, namely other data, such as statistics and business information [2].

Going beyond the approach presented above, Swiss researchers A. Liebrich and A. Stämpfli capture environment data and divide tourism data into the following thematic groups:

(1) tourism environment data,

(2) service provider-related data,

(3) personal data generated along the customer journey (a guest’s path from initial contact with a business, through purchase completion, evaluation activities, and loyalty programs),

(4) government and private statistics with a high degree of aggregation[3].

Tourism data categories used on our website

In our website we decided to include ten categories of data, to make navigation user-friendly and to include most of the categories mentioned above. We introduced following ten categories:

  1. Points of Interest (POIs) and routes
  2. Transaction data
  3. Tourism Environment data
  4. Tourism Offer data
  5. Traffic and mobility data
  6. Search and web traffic data
  7. User-generated content
  8. Tourist-related data
  9. Research and consulting
  10. Corporate data

Each category is explained in details and possible sources of data are described. Let’s take a look on the example of the first category: POIs and routes. POIs are specific point locations that tourist may find useful or interesting, including their geographical coordination, accessibility and other details such as opening hours. Several websites deliver information about POIs, those are: POI base, OpenStreetMap, Geoportal. Additionally information about routes is available on the federal geoportal and on SchweizMobil, Outdooractive and Guidle websites and apps.

Even more tourism data sources

We do our best to make the site clear and user-friendly. We are constantly trying to supplement it with new sources of data in tourism. If you know of data that are not listed on our site, please let us know. We look forward to hearing from you via email

[1] Li, Jingjing, Xu, Lizhi, Tang, Ling; Wang, Shouyang; Li, Ling (2018): Big data in tourism research: A literature review, Tourism Management 58, 301-323.

[2] European Commission (2022): EU guide on data for tourism destinations, Smart-Tourism-Destinations_EU-guide_v1_EN.pdf (,

[3] Liebrich, Andreas/ Stämpfli, Aline (2018): Daten und Statistiken im Tourismus, In: SECO (Hrsg): Digitalisierung in Schweizer Tourismus: Chancen, Herausforderungen, Implikationen, Schlussbericht, St. Gallen, 93-108.,%20SECO,,%20SECO,%20August%202021.pdf