This week, I was scrolling through Facebook when I saw an article from The Salt Lake Tribune called “Utah is using your social-media data to make roads safer for bikers and pedestrians.” Essentially, the article is about how the Utah Department of Transportation (UDOT) is planning to use data from an app called Strava to improve transportation for cyclists and walkers.
Since I had never heard of Strava before, I looked into the app and its functions (you can find their website here). Strava is an app used by cyclists, hikers, mountain bikers, and runners to track their activity level. However, Strava also markets itself as a social media site because users can upload photos, share workouts, and see who is using the app around them. Strava made an agreement to give UDOT the data on its Utah users’ workouts. The data includes the type of workout (e.g. cycling or walking), the user’s route and the speed they traveled at, and how long someone had to wait at different intersections.
This data from Strava could help provide information that city planners don’t currently have—where, when, and how non-driving commuters are traveling. With this data, UDOT believes it can determine which paths should be prioritized for improvement and upkeep. UDOT also plans to use the data to determine if some places need separate walking and bike trails. However, the data is not representative of everyone in Salt Lake who walks and bikes. The data only represents those who use the Strava app—people who tend to be younger, wealthier, and into fitness. In many ways, this reminds me of the article that we read called “Big data: are we making a big mistake?” This article talks about Speed Bump, which had a lot of the same problems that UDOT’s use of Strava might have.
Based on some of the problems that this UDOT-Strava partnership could have, I started to wonder if this could be a weapon of math destruction. In order to determine this, I used Cathy O’Neil’s taxonomy, which looks at opacity, damage, and scale.
First, is the use of the data opaque? This one is a little hard to determine because UDOT hasn’t actually implemented anything yet. In fact, data on the Strava data is hard to come by. In both the Trib article, and statements put out by UDOT (like this one), there isn’t information on how many Utahns use Strava or how much data UDOT will have access to. For example, the Trib article references a study in Salt Lake that found only 2 percent of cyclists use Strava. However, there was no link or further information about this study, and I couldn’t find it anywhere. In this sense, there is some opacity. Additionally, the situation could become more opaque if UDOT isn’t clear about how much the Strava data influences their decisions. However, you could say that UDOT is being transparent because they have informed the public that they intend to use the data.
The next test is whether or not UDOT’s use of this data causes damage. In Cathy O’Neil’s words, “Does the model work against the subject’s interests?” (29). In response, I would argue that this situation works in the exact opposite direction. Because this data only provides UDOT with information about Strava users, UDOT might prioritize the improvement and creation of paths that would benefit the people who use Strava. Commuters from lower income areas who don’t use the Strava app might desperately need improvements to their routes, but the Strava data couldn’t show that. The damage, then, is done to those whose activity is not captured by the Strava data. I think this still counts as damage because a population is still facing negative impacts.
Finally, what is the scale of the damage? While several states have entered partnerships with Strava, this type of pairing is still not widespread. However, I think the use of this data in Utah could cause a lot of problems within communities. How widespread the damage is, I think, dependent on how much UDOT relies on this data. If the Strava data is more of a supplemental tool, then maybe it doesn’t have that much potential for wide-scale damage.
So is UDOT creating a WMD by using Strava data? Until UDOT actually starts implementing the use of Strava data, it will be hard to tell. Particularly, it is hard to make a prediction because I couldn’t find out any specific information about the data. But I do think there is the potential for the situation to go either way. If UDOT is fairly transparent with their data and how they use that data, I don’t think there is a weapon of math destruction. However, if UDOT doesn’t share any of the data they use, and they rely heavily on that data to make decisions, I think we would be looking at a WMD.
*The featured image for this post is a screengrab from the Strava Global Heatmap. The Heatmap shows all Strava users’ workouts (unless they have adjusted their privacy settings) onto a single map. On the map, you can easily pick out rural areas because they have much lower activity. However, lower-income areas like South L.A. and Chicago’s South Side also have significantly fewer data points. You can find the heatmap here.