I haven't followed the sport closely in a few years, but the economics of cycling mean most riders, men and women alike, are paid poorly relative to athletes in other sports; it's more comparable to sailing than football.
No one is paying to line up alongside the Tour de France route, and the teams are usually sponsored by corporate interests who don't see a huge return on their investment.
The article is interesting. I worry that by watching football I'm participating in something bad for the athletes (even though they want to do it of course, for money). I guess it's true for all pro athletes, it is bad for them physically in the end.
Football has evolved into something much more insidious. Physical breakdown is something to be expected at the highest level of sports, but concussions that end in dementia and suicide is something else altogether.
Using the API you can get both the Segment altitude data and the Segment Effort altitude data. The segment data is what's displayed on the website and can be bad. But the Segment Effort data would be a subset of the data from the activity. So you can get many many versions of the altitude for the segment and do any type of analysis you wish.
On this map I took out any point within 1km of the start or end of every ride. This is stronger than what users have set and eliminates 1000s of "hot spot".
I've looked into a couple of these cases and it's friends "coming by for a ride" or them stopping at their house mid ride.
I'm working on further eliminating these points by just not included "stopped" points. I've also tried doing some image processing to remove these hot spots but have had mixed results.
Found a lot of residential building footprints with activity inside just browsing around at z17. Maybe you could use slide to check local activity hotspot's and cross reference them against OSM building footprints or remove those gravity spots with no uniform direction or evenly weighted exit path slope altogether.
Another approach might be only displaying common paths of 2 or more unique client id's.
Would Strava be OK with people using this data to trace paths for Open Street Map?
I noticed there are quite a few paths through Golden Gate Park in SF (as just one of many examples) that are pretty clear running/biking paths that are not in any of the typical basemap providers' maps and not currently in OSM. If you reached out to the OSM community and gave the go ahead, this would be a great way to improve the trail data in OSM.
I'm amazed that there hasn't been more conversation about this - kudos to both you and Strava for this. These tools and this data set seem to have huge potential for crowd-sourced mapping.
Better still if it could be released an an official layer for Open Street Map. Any chance of that happening given the licensing conditions of the various data sets?
Unlike a traced layer, the heatmap is self-updating and objective, so I think has a value of its own.
This map is cool, but it doesnt help me use it for what I imagine is a pretty common use-case, finding a popular route from Place A to B. Would it be possible to drop placemarkers between two places and do routing based on Strava popularity? Right now Google bike directions are pretty bad where I live, and it would be nice to know what route the "insiders" are taking to do a popular route (MIT to Concord is one Im thinking of, but I can imagine other alternatives)
The overlay tiles zoom weird in leaflet. They get treated like a base layer so when you zoom, they get scaled 2x and the new tiles load on top. That doesn't look right when the tiles are transparent. There must be an option to fix this but couldn't find it.
I couldn't get the styles I wanted with mapbox so I just defaulted to google.
Per my other comment -- can you do a version which show avg speed of the route goers? It would be awesome to see the fastest path via bike or run between points... if you noted each stop the people made, it could be the diff between a smooth, fun ride vs a stop-heavy route.
Thank you for this map! Two questions:
- what implementation of quadtree are you using?
- will you ever consider opening the data for download as shapefiles/any other vector format?
Thanks again, great job!
Each pixel has a count. Each tile gets a 90 percentile value. Every corner gets the average of its 4 tiles. Every pixel in the currently requested tile gets a value [0, 1] based on the bi-linearly interpolated "max". It's then colored based on that final value.
It looks like it's losing a lot of dynamic range when you zoom in to a specific road and it's all completely pink (or whatever the hottest color is depending on the chosen theme).
