In this post, I would like to present an opinion on how self-tracking could be useful for policymakers

Quantified self - what is it?

Quantified self, according to Wikipedia or, it’s a phenomenon of self-tracking. Its core philosophy could be explained as “self-knowledge through the numbers.” It all started with all those clever and smart gadgets, which are allowing us to log pretty much everything. From calories in/outtake, heart-rate, sleep time or a total distance while running, skiing or cycling.

Besides the disputes over the influence of self-tracking on our overall wellbeing, like does it lead to a happier and healthier life (more here), there is also another part of the debate challenging the issue of our privacy. Especially who else could see my data and for what purpose could be those data used?

Naturally, using any sort of self-tracking apps and storing our data about blood pressure, steps or routes we tend to take while going out for a run or commuting to work, could expose our privacy at risk. Even though, I now can’t imagine who and why should someone be interested in my heart-rate etc., the risk exists and someone else could “learn” from our dataset as simple as we could.

What hasn’t been strongly emphasized in such a debate is, that the same data represents a valuable source of knowledge. On the individual level could be such self-tracked data used well when shared with a doctor to prevent major illnesses (e.g. here and here). This is an undoubtedly great step towards greater awareness about our health condition. But that’s not all.

The potential

The same data, when used correctly (e.g. anonymized and with the user’s permission), could be beneficial not only at the individual level but also on the societal level. Mainly the policymakers and urban planners could use them as another additional source while creating a framework for effective urban strategies. The greatest advantage of those data is their coverage and actuality. Up to date data are must to when analyzing given processes and trends occurring in cities we live in.

For example, exploring everyday mobility patterns in certain areas, which is important for establishing an efficient urban system. In other words, the system, which would meet the actual demand of all participants (i.e. cars, pedestrians, cyclists etc.).

Commuting flows are for many cities crucial when challenging not only the topic of socio-economic development but, and in those days still more actual, the overall sustainability of such a development. That’s why there are now tendencies to have such an infrastructure, which would make life easier not only for car-users but also for pedestrians and cyclist. And this is just the spot, where many policymakers and urban planners are on thin ice due to insufficient sources on traffic flows intensities. And especially for non-motorized means of transport.

White spots

Nowadays options how to get a full picture of traffic flows is to some extent limited. Sure, cities could conduct many point-source type of research throughout the city at different daytime or days to collect data and have the idea. The problem is, that such research is due to its design very limited. For example, can’t cover all major infrastructure, explore different flows during a longer period or lacking the information about the origin/end of destination. Just imagine the number of people one would need for counting cars, cyclist and pedestrians not only at main routes and intersections but the whole city.

Sure, once could argue that you could install sensors for tracking at preferred points. This could solve part of the problem, especially with car traffic. But what about commuters on bikes, scooters or pedestrians? Is it still efficient to instal a network of trackers all around the city? Wouldn’t it be just better, if we combine point-source research with some other source of data?

First, and probably the best solution is at the moment represented by data from mobile operators. The only problem of those data is sometimes their availability (i.e. the willingness of telecommunications providers) or their price. A major disadvantage of those data is missing out the information regarding the means of transport, which could be crucial. Even though there is a solution to this problem, let’s now skip to the next option - the tracking apps.

Filling the gap

Tracking apps, as I wrote earlier, already contain pieces of information about our activities. It could be our morning jog to a bakery, Sunday ride on a scooter or our day to day commuting. Strava, (one of the biggest player in the field of sport tracking activities), after displaying global heatmap and establishing Strava Metro has decided to help cities to fill those white spots and help policymakers to reshape the transport system. Now, Strava’s users are helping their cities to know when, where and how people are moving to better understand their needs.

At this place, it is also important to mention, that users of Strava do not represent all the cyclist and pedestrians moving each day around the city, which is one of the limitations of data. Besides ongoing research which tends to explore the use of crowdsourced data (e.g. Roy et al. 2019; McArthur, Hong 2019 or Orellana, Guerrero 2019), there are cities, which are already working with data provided by Strava.

According to their experience, the app captures around 10 % of all bike movements. This is not much, but at the same time they have discovered, that those movements do not significantly differ from other bike movements, which are not recorded. Cyclist, no matter if tech-friendly folks, or just everyday commuters, are when moving around the city, using the same routes. It’s a question if the people are going to use tracking apps more in the future, but at the moment already available data represent great potential and addition for policymakers, which could be combined with other sources.

Additional reading

A Lane Over and a World Apart

Strava Metro - Global Heatmap Urban Planning

City Planners and Cycling Data