Success at High Peaks: A Multiscale Approach Combining Individual and Expedition-Wide Factors

This work presents a network-based data-driven study of the combination of factors that contribute to success in mountaineering. It simultaneously examines the effects of individual factors such as age, gender, experience etc., as well as expedition-wide factors such as number of camps, ratio of sherpas to paying climbers etc. Specifically, it combines the two perspectives through a multiscale network model, i.e., a network of network of climbers within each expedition at the finer scale, and an expedition similarity network at the coarser scale. The latter is represented as a multiplex network where layers encode different factors. The analysis reveals that chances of failure to summit due to fatigue, altitude or logistical problems, drastically reduce when climbing with people they have climbed with before, especially for experienced climbers. Additionally, centrality indicates that individual traits of youth and oxygen use while ascending are the strongest drivers of success. Further, the learning of network projections enables computation of correlations between intra-expedition networks and corresponding expedition success rates. Of expedition-wide factors, the expedition size and total time layers are found to be strongly correlated with success rate. Lastly, community detection on the expedition-similarity network reveals distinct communities where a difference in success rates naturally emerges amongst the communities.