Tracking the slopes: a spatio-temporal prediction model for backcountry skiing activity in the Swiss Alps using user-generated content

Abstract. Backcountry skiing is a popular form of recreation in Switzerland and worldwide, yet little is known about where and when people venture outside and methods to monitor skiing behaviour are limited by the vast and remote nature of backcountry terrain. With avalanche fatalities documented each year, there is a need for spatially and temporally explicit information on the persons exposed to avalanche danger for effective risk estimations. To do so, we explored over 6800 user-generated GPS tracks and over 8 million clicks on a ski touring website to model backcountry skiing base rates on a daily scale in 126 regions in the Swiss Alps. We linked the data to weather, snow, temporal and environmental variables to train two different spatio-temporal prediction models based on the two data sources. We found that GPS and click data describe different types of behaviour (planning and real world behaviour), yet we could demonstrate that they correlate well with a 1 d time lag (ρ = 0.63), suggesting that online activity precedes actual skiing activity. Our results show that online and real-world behaviour are driven by similar underlying factors, with temporal aspects – such as weekends and the progression of the season – playing the most important role in both datasets. However, we found differences in how certain variables influenced behaviour: people tended to click on more routes in areas of high avalanche danger during more extreme weather conditions than they actually visited, and time spent on trip planning decreased as the season progressed. Our study demonstrates the potential of user-generated data sources to model skiing activity on regional and daily temporal scales, but also sheds light on specific limitations of the different data sources in approximating backcountry skiing activity.