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Joerg fritz demov2
- 1. RunFindr discover running routes like a local by Joerg Fritz
- 2. ?
- 3. Can we do better? http://www.runfindr.net
- 4. The Data Routes Points
- 5. Algorithm location Clusters closest clusters, distance Routes
- 6. Algorithm location closest clusters Routes Points select best routes Clusters
- 7. Algorithm place, distance closest clusters user refines display routes weights Routes Points select best routes Clusters
- 8. Are the features good? feature score first city (much more data to come)
- 9. Is this doing a good job? month of year
- 10. Is this doing a good job? old city new city month of year
- 11. Is this doing a good job? old city new city month of year RunFindr
- 12. Joerg Fritz
Editor's Notes
- Hi, I’m Joerg and I want to talk with you about runFindr. As you can probably tell from the title, I’m passionate about running and so I built this web app to address a problem I encountered recently. 10 s
- A few months ago, I moved from Boston to the DC area and I realized that my method of picking running routes markedly changed between the two cities. I had a very defined set of routes I enjoyed (you can see a heatmap of those below, and so I spent a lot of time actually running along the river you see in the photo above). In DC however, my running route selection looked a lot more like a random walk, with a little bit of bias from looking at google maps or using routeloops.com. And it took several months for me to aquire the necessary domain knowledge to have routes that I would subjectively have scored similarly than the ones in Boston. 20 s
- But spending several months every time you move (or visit a place) until you ran enough to aquire this domain knowledge seems silly. So, can we do better than this? And by better I mean precisely, can we leverage these datasets about running routes that people upload from their smartphones and smartwatches, and recommend running routes in a new city for me, based on the routes I’ve chosen in a place I know (like Boston in my case). Let’s have a look together. 20 s + 50s on website We initially just enter where we want to run and for how long we want to run. And right now, the website displays 3 routes for us, with predinfed weights for the features on these sliders to the right. We can adjust these, for example, I want a route with more hills, I don’t care as much about nature, but I enjoy going offroad a bit more. Then the website recalculates and proposed a new set of routes. For further exploration, we can also display heatmaps for certain features to make route discovery more intuitive. Once we’ve found a route we like, we can export the gps coordinates for that route, so we can use it in the tool of our choice.
- So how does this all work? The backbone of this data are two tables, that contain information about running routes and the points contained in those routes repectively. The routes table has about 100.000 tracks which I acquired through the mapmyfitness api, and based on that and information from a number of other APIs, we can calculate scores for all the features that you saw on the sliders in the app (and some extra ones not shown there). 20 s
- When you enter an address and distance in the web app, we want to do computation on those routes, and doing that on all 100.000 routes is too expensive. So we cluster the routes, then find the 3 clusters closest to your location, then only select routes from these clusters that have the right length. 10 s
- Calulate a total score for them, with means and standard deviation for the actual set of routes selected and with standard weights for now (that correspond to the average of all users), once we scored, we can rank and select the best ones and retrieve the points for those from the Points database. 10 s
- Then we can display those routes and let the user refine the weights to close a feeback loop that allows the user to customize the routes till he finds one he likes. 10 s (+ 20 s adv for data stories on website With this huge database of routes and their properties, we can tell cool data stories and there is a tab on my website where you can look up some of them, If you’re like me, excited about that kind of stuff. But I want to use my last minute trying to investigate if you should believe what this website tells you. And so in order for this to work, we need to satisfy two main conditions.)
- One, the features we selected for the sliders must be meaningful predictiors, something like fundamental dimensions of running route space. That is, if you liked routes with that heavily feature nature in one city, are you going to pick routes with lots of nature in the second city too. This plot shows exactly this data for people in my dataset that moved between several cities, and the color encodes which feature the points represent. If all features where perfect predictiors for every single user the correlation would be a straight line, and while there is some noise, the fit is really quite good. So the features encoded in the sliders are good. 30 s
- The second condition is that we actually cut the discovery process for routes short. So given the right features, can we select routes that improve his running routes selection comared to what he would have run without the app. The following plot shows data from one (very active) user, the y axis is his most consistent score, the thing he cares most about during run selection (this is a z score of 5, so he REALLY cares about this feature). And we are binning all runs during one month to have better statistics and use violinplots to summarize the data. At the end of the year shown here, this runner moved to a new city: 30 s
- And initially he struggled to find routes with the same subjective quality for him, and it takes almost a year to get to the same level (note that these are normalized locally, so the absolute quality might still be different, this also shows that normalizing locally works well). 30 s
- But if he had used RunFindr right away, this is where he would have started (all three top routes for the distances he typically runs are within the red dot). We can translate this to hours run on suboptimal routes, which in this case for one single user is about 150 hours. 10 s
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