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A History in Big Data for Better Cities Brian Riordan - Customer Success Lead www.metro.strava.com. Strava?. What is Strava?. The social network for cyclists and runners. What is Strava Metro? ____________________________ Data-Driven Bike and Pedestrian Planning
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A History in Big Data for Better Cities Brian Riordan - Customer Success Lead www.metro.strava.com
What is Strava? The social network for cyclists and runners.
What is Strava Metro? ____________________________ Data-Driven Bike and Pedestrian Planning Aggregated, anonymized activity data from Strava’s millions of users Analyze popular or avoided routes, peak commute times, intersection wait times, and origin/destination zones Processed for compatibility with geographic information system (GIS) environments
Why Build Strava Metro? Data-Driven Bicycle and Pedestrian Planning __ • Global need for consistent cycling data • Continues to serve the Strava user • Further bonds the cycling and pedestrian community • Movement into Smart Cities • It’s the right thing to do
Strava By The Numbers • Over 8 million activities uploaded per week • Tens of Millions of Active Users • 1.5 Trillion + second-by-second GPS points globally • TfL 2016 dataset was over 400 billion records
Strava Metro Data Streets Origin / Destination Intersections Activity counts and wait times at every intersection Understand activity starting and ending points, by region Minute-by-minute counts across your entire network
Strava Metro - Government Challenges Minimal or no dedicated technical talent Red tape and process creates gaps between industry and government Budgets in constant flux Culture crushes the ability to take risk
Visualizing the Strava Community One hour of weekday morning activity in London
Strava Metro - Understanding culture & needs Industry in transition: Qualitative --> Data Cycled Coming to grips with data: Data is good but at what cost? Learning from successful groups: Taking cues from the Auto Industry Merging the cycling voice: synergy as opposed to inner turmoil
Strava Metro Bringing Data Layers Together As custom build product it’s designed to be merged with local datasets: traffic, crashes, proposed bike paths, etc.
Blending Data: Strava Metro and Counters Using counting programs with the Metro data allows the data to become even more useful. Strava correlation with counting programs is statistically amazing, with r-squared values typically around 0.8.
Blending Data: Strava Metro and Counters Cont’ 16,297 Strava Bike Trips X 27 Multiplier = 440,019 year bike trips (199,476 6- 9am) How far can we push this? ---> Total Miles Traveled in SDOT by Bike in 2014: 63,253,198
Detecting Behaviour Route Choice Metro provides key insight into how the cycling population is adapting to new cycleways, protected lanes and surging car populations. The left image shows the GPS points pre (pink), post (blue) after a new cycleway was opened. The Metro data on the right shows the actual change in percent with blue losing trips and red gaining trips.
Detecting Behaviour Route Choice Cont’ Impact can be felt 10 - 20 blocks away from the point where the infrastructure was enhanced. Blue areas on the right lost over 100 bike trips while the green gained 100+.
Being Present: Helping Metro Customers Succeed Metro Customers and Partners Cover the Spectrum for Bike/Ped Planning Across the World Louisiana DOT TfL Copenhagen Looking to employ ITS for cycling Justification that people ride bikes Bike Counters, Lanes and a vision
Strava Metro: Data Vis Big data <> Easy to Use: Therefore we need to create tools and view into the data that allow for quick interpretation. Deep analysis can always come after. TfL M25 Ring 2013 http://metro-static.strava.com/dataViewV1/tfl/london2013/london2013.html North America Week http://metro-static.strava.com/testing/NA/ride/NA.html London Week Animation https://www.youtube.com/watch?v=QZ5DuQTUPqk&list=UUdO8l6B6yeRDcsP1snssTPw