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A Cyclist's Guide to Big Data

Blog Post created by onelson on Oct 11, 2016

I’m an avid cyclist. I use an app called Strava and Google Maps to plan and track my rides. How are these apps examples of big data?

 

screenshot of Strava app Strava tracks basic metrics: how long, how fast, and how far you ride each day. Using GPS, you can map your rides and share these routes with your friends. You can also compare ride statistics, such as your speed to other riders who took that route, or who are in the same age, gender, or weight group as you.

 

Strava has aggregated the route data of over 77 million bike rides into a heatmap. You can try it out here. The most commonly ridden segments light up brighter in the heatmap. In this screenshot below, the orange, yellow, and bright green routes show the most popular trails in San Francisco, including crossing the Golden Gate Bridge and riding along the coast. These routes are among the most scenic. The brighter areas are mostly paved bike trails, dedicated bike lanes, or bike-friendly roads. You’re also more likely to encounter other Strava-using cyclists on these roads, so if you break down you won’t have to wait very long before someone with a spare tube or allen wrench comes along.

 

screenshot of Strava heatmap showing San Francisco

 

Google Maps provides the option of choosing bicycle-friendly routes when planning directions. In the screenshot below, dark green lines indicate dedicated bike trails, while lighter green lines and dashed green lines indicate bike-friendly roads. Brown lines mark unpaved trails – suitable for mountain bikes, but not for road bikes.

 

screenshot of Google Maps bicycle directions

 

When you overlay the two maps, as seen below, you can see that a lot of the roads marked as bike-friendly by Google Maps are also the ones cyclists are really using, as seen in the Strava Heatmap. There are a few areas where Strava users don’t ride, like around Treasure Island, which Google Maps says is bike-friendly. On the other hand, there are many roads which aren’t labelled as bike-friendly by Google Maps, but which Strava users ride nonetheless.

 

 

Cyclists can also use Google Street View to see whether the road looks steep, heavily trafficked by cars, or if there are clearly marked bike lanes. In this Google Street View screenshot below, you can see that this segment of the San Francisco Bay Trail includes paved, dedicated bike lanes which are separated from car traffic by a raised curb. A quick check of Google Street View like this allows riders to verify their routes include wide, safe bike lanes and relatively little car traffic.

 

 

If you’ve taken ESS 100 – Introduction to Big Data (and you know you have!), you know that big data means having a huge amount of one or more of the following: volume, velocity, or variety. Over 20,000 terabytes of data provided by Google Maps and the millions of rides cataloged by Strava are a huge volume of data. The apps also provide data in real-time, meaning they need to deal with high-velocity data. Finally, GPS pings, images of maps, 360 panoramas taken by Google Street View cars, and millions of user profiles all combine into a huge variety of data types. These apps leverage all Three V’s of Big Data while still fitting in your pocket-sized smartphone!

 

Whether you’ve just moved to a new town or are planning a multi-day bikepacking tour, this information is invaluable. You can leverage the power of big data through these apps and plan a safe, scenic route.

 

How else has big data affected your everyday life? Share your stories in the comments below!

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