1,2,7,1,1,- 1Risklayer GmbH, Karlsruhe, Germany (james@risklayer.com)
- 2Center for Disaster Management and Risk Reduction Technology (CEDIM) & IPF, Karlsruhe Institute of Technology, Karlsruhe, Germany
- 3Center for Disaster Management and Risk Reduction Technology (CEDIM) & GPI, Karlsruhe Institute of Technology, Karlsruhe, Germany
- 4Independent Researcher, Barquisimeto, Venezuela.
- 5Vrije Universiteit Amsterdam , Institute for Environmental Studies, Water & Climate Risk, Amsterdam, Netherlands
- 6Independent Researcher, Adelaide, Australia.
- 7ISER, Adelaide University, Adelaide, Australia.
Tourism is one of the largest economic sectors globally contributing to 10% of the world’s GDP and also 1 in 10 jobs, however, there is comparatively little standardized data spatially for tourism globally. Around the world, there exist many approaches to collecting statistics for tourism across a country and many disparate sources: some countries have subnational data collection yearly, others collect certain parameters, others at a national level, but no standardized way globally to aggregate the statistics appropriately.
Tourist accommodation stats from open data sources include region/province and even district-level cuts in some countries, in others there are unique tourism regions different to administrative level boundaries within the country. A key part of this work is the collection of the GIS layer associated with these boundaries in order to use the collected statistics within each country.
Although there exist a lot of products using raster inputs like nighttime lights, population proxies, global vector inputs of hotel points (where available) and partial data such as OSM globally, as well as aggregated statistics at a national level via UNWTO, WTTC etc., this work is the first known global subnational level set of official country-by-country, region-by-region tourism statistics using tourism boundaries for use in risk modelling.
The analytics allow for checks of global datasets, as well as vice versa with the statistics coming from each country office given the spatial consistency.
Over 3,000 tourism regions are characterized as part of this work, with many more destinations globally saved. Millions of hotels, overnight stays and other statistics have been and continue to be added to the databank. This database forms the basis for risk modelling across regions and destinations speaking the same language as the tourism industry.
This work builds upon Daniell et al. (2025) with a Europe-wide tourism destination socioeconomic risk model for tourism and is a companion abstract to Schaefer et al. (2026) characterizing the development of a 1km global tourism hazard and risk screening classification.
How to cite: Daniell, J., Schaefer, A., Brand, J., Romero, R., Maier, A., Girard, T., Khazai, B., Michalke, S., Claassen, J., and Daniell, J.: The first Global Tourism Region statistics database for risk and exposure modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8853, https://doi.org/10.5194/egusphere-egu26-8853, 2026.
