Deadly Swiss Avalanches, in Maps

In my previous post, I explored a dataset on fatal avalanches in Switzerland from the Swiss Institute for Snow and Avalanche Research (SLF). The dataset also contains the location of each avalanche, and here I’ll explore a few ways to show the data geographically.

In the map above, the location and date of each avalanche is used to make a time lapse with CartoDB’s Torque function. Each flashing white marker is one fatal avalanche. Besides the general location of avalanche risk in Switzerland and the seasonal pulsation of events, this map does not convey all that much information. However, I think it is worthwhile because it drives home the sheer number of deadly avalanches – 361 – during this period. We have to keep in mind that each of these flashing markers is a separate tragedy that together represent the loss of  465 lives.

This map shows the geographical distribution of fatal avalanches by the activity or location involved in the accident. As I discussed in the last post, the great majority occurred in open country during recreational activities like backcountry touring or off-piste skiing. The map illustrates that backcountry touring accidents are distributed fairly evening across the high Alps, while off-piste skiing and snowboarding accidents tend to be clustered. Closer inspection reveals that these clusters occur around high mountain lifts, like this, the largest cluster, one on the north slope of Mt. Gele and Mt. Fort near the resort of Verbier:

Screen Shot 2016-04-01 at 9.43.47 PM

This map also lends itself well to exploration. The Open Street Map base has great detail upon zooming, and you can click on each point to get more information about each avalanche, such as elevation, aspect, date, and number of fatalities.

Finally, here’s a heatmap showing the density of fatal avalanches, with red areas having the highest densities. The cantons of Valais (in the southwest) and Grisons (in the east) have the highest concentrations of deadly avalanche accidents. I used a Landsat mosaic as a base map, which allows for comparison of the relationship between terrain and avalanche density.

All avalanche data from WSL Institute for Snow and Avalanche Research SLF, 25 March 2016. Data and code available here. Maps generated using CartoDB.

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Deadly Swiss Avalanches, in Charts

Snow-covered mountains are one of the most beautiful sights in nature, but in the wrong circumstances they can kill you. Skiers and other mountain enthusiasts sometimes refer to avalanches as the “white death”, and for good reason. Hundreds die in avalanches every year, and a great deal of effort is spent on trying to understand the factors that cause avalanches in the hope of decreasing this toll.

Located in the Alps and a mecca for winter sports, Switzerland takes avalanches seriously. The Swiss Institute for Snow and Avalanche Research  (SLF) monitors snow conditions, issues warnings, and collects data on avalanches. Their web site is very interesting for those interested in winter sports in the Alps. I find the snow maps particularly useful. But for this post I will use their data on fatal Swiss avalanches in the last 20 years to experiment with different ways to visualize some patterns and relationships.

The dataset includes information on the date, location, elevation, and number of fatalities, in addition to the slope aspect, type of activity involved (e.g. off-piste skiing), and danger level at the time of the avalanche. Over the last 20 years there have been 361 fatal avalanches in Switzerland, for a total of 465 deaths. Most avalanches killed only one victim.

Because I wanted to experiment with radial plots, I’ll focus on the variable of slope aspect in this post. Aspect is the compass direction that a slope faces. In this case we’re looking at the slope where the avalanche occurred. In Switzerland, the majority of avalanches occur slopes facing NW – NE, as you can see from this plot:

rose

The gaps at NNE and NNW are probably artifacts of how the aspect data was reported.

This pattern is common in the temperate latitudes of the northern hemisphere. Avalanches are more common on north-facing slopes because they are more shaded and therefore colder, which allows snowfall to remain unconsolidated for longer. When more snow falls, these unconsolidated layers can act as planes of weakness on which snow above can slide. It’s much more complicated that that, with factors like wind and frost layers coming into play. To learn more about how aspect and avalanches, see here. The pattern is unmistakable, but does it hold all year long? I separated the data by month to find out:

rosefacet

Fatal avalanches occurred in all months, but are much more common December – April

A few interesting insights emerge from this plot. First, February is clearly the most deadly month for avalanches.  In December there are actually quite a few avalanches on SE facing slopes, but by January the predominate direction is centered around NW. In February, and to some extent in March, it changes to N-NE. In April it’s NW again, but by then there are significantly few avalanches. So there are some monthly patterns, but I’m not exactly sure what the explanation is. Of course to really nail this down we’d want to do some statistics as well.

One pattern I expected, but did not see, was a decrease in the dominance of northern aspects later in the spring. I expected this because as the days get longer, the shading effect of north facing slopes decreases. It’s important to remember that these are fatal avalanches, and a dataset of all avalanches would look different. For example there are probably a lot of wet avalanches on southern slopes in the spring. But these are much less dangerous than the slab and dry powder avalanches, and therefore not reflected in the fatality data.

The rose style plots above are useful, but I wanted to try to illustrate more variables at once. So I tried a radial scatter plot:

Fatal Swiss avalanches 1995 - 2016: Slope aspect, elevation, and activity

Click on the image for the interactive Plot.ly version

This plot is similar to the previous ones in that the angular axis represent compass direction (e.g. 90 degrees means an east-facing slope). The radial axis (the distance form the center) represent the elevation where the avalanche occurred. And color represents the type of activity that resulted in the fatality or fatalities. Each point is one avalanche. The data are jittered (random variations in aspect) to minimize overplotting. This is necessary because the aspect data are recorded by compass direction (e.g. NE or ESE). The density of the points clearly illustrates the dominance of north-facing aspects. It’s also clear that most avalanches occur between 2000 and 3000 meters (in fact the mean is 2507 m). In terms of activity, backcountry touring and off-piste skiing and boarding dominate. And avalanches at very high altitudes are mostly associated with backcountry touring, which makes sense, as not many lifts go up above 3000m. Perhaps especially perceptive viewer can make out some other patterns in the relationships between variables, but I can’t. Any thoughts on the usefulness of this plot for the dataset?

Finally, I want to share a couple graphics from SLF (available here). Here is a timeline of avalanche fatalities in Switzerland since 1936:

The average number of deaths per year is 25, but this has decreased a bit in the 20 years. There were also more deaths in buildings and transportation routes prior to about 1985. Presumably improvements in avalanche control and warnings reduced fatalities in those areas. And what happened in the 1950/51 season. That was the infamous Winter of Terror. The next plot shows the distribution of fatalities by the warning level in place when the avalanche occurred:

Interestingly, the great majority of deaths happened when warning levels where moderate or considerable. There were significantly fewer deaths during high or very high warning periods. One reason must be that high/very high warnings don’t occur that frequently, but it’s also likely that skiers and mountaineers exercise greater caution or even stay off the mountain during these exceptionally dangerous times. There’s probably some risk compensation going on here. To really quantify risk, you have to know more than just the number of deaths at a given time or place. You also have to know how many people engaged in activities in avalanche country without dying. One clever approach is to use social media to estimate activity levels, as demonstrated in this paper.

Have fun in the mountains and stay safe!

Data and code from this post available here.

All data from WSL Institute for Snow and Avalanche Research SLF, 25 March 2016