Could fitness app data help manage landscapes?

The use of fitness apps has become increasingly popular around the world, driven in part by the ever-increasing range and affordability of activity trackers, including smart watches.

These include Strava, a popular fitness app for cycling and running, MapMyRide, Runtastic and many more.

Late in November 2017, Strava released the second version of its Global Heat Map which displays all the publicly available information that users end up providing when they use its app.

The Global Heat Map made headlines early this year when a student worked out that it could be revealing potential locations of secret military bases in places like Afghanistan, Syria, Somalia and the Sahel where soldiers have been using fitness trackers.

Putting aside this controversy, the Global Heat Map is interesting to explore. For example, these kinds of apps might be used to reveal most of the prepared ski tracks around Norway.

Another look at Arendal, this time on the island of Tromøy, for water activities. You can clearly see a popular windsurfing spot (the track from Gjessøya running south-east) and also, in dark purple, the start and end points and swimming route of a popular triathlon, which takes place every June.

Fitness app providers are amassing huge amounts of data every year. How might it be used? What are the implications of collecting all this data?

Late in 2016, GRID-Arendal, UN Environment, the International Solid Waste Association and the International Environment Technology Centre published a report highlighting the problems with , and possible solutions to, waste management in mountain regions. Tourism in mountains was one of the focus of the report. Based on selected case studies, we know that tourism can be a big contributor to waste in mountains but we lacked a lot of data to make any broad conclusions.

It could be worth exploring the potential uses for the kind of data that underpins the Global Heat Map as well as similar products from other apps. For example, in mountainous areas where “traditional” data on tourism visits is lacking, such data could provide a proxy for which mountain regions, pathways and trekking routes are more popular. It could also tell us in which seasons these activities occur and thus provide an early indication of the number of tourists and their preferences. This in turn would help trail and waste management and other things. If the data is available over time, trends could be analyzed too.

A look at the ‘foot’ activity in Nepal reveals the most popular walking and trekking areas in the country. Kathmandu valley can be seen clearly, together with a trekking route up to the Langtang region. Other popular areas are trekking routes in and around Sagamartha National Park (where Everest is located) and the Annapurna circuit.

Of course, not everyone uses these apps. The population of apps users may not even be a fair representation of the population as a whole. Any statistical analysis would require accessing the original data. Strava data for example, appears to be proprietary and protected (although it can be accessed for a fee.

Despite these challenges, it could be an idea worth exploring.

Note to the reader: mention of a commercial company or product in this story does not imply endorsement by GRID-Arendal.