1. STRAVA Spatial Data and Processes:
Utilizing GIS and spatial tools to generate new data products from raw GPS cycling tracks on Strava
Introduction and Thank you.

2. Strava’s Big Spatial Data
125+ million activities
300+ billion GPS points
60% international
Strava, Inc. 2014

3. Strava Labs Projects
Strava, Inc. 2014

4. How did the Geo Pod come to be?
Geo Reporting Bad GPS data Segment corrections Geo Team was created
With over 400 billion Gps points we have global data coverage.
Cutting edge routing
Initial work with ODOT, NYU
This data needs to be in the hands of planners. To many regions around the world have zero idea of where any one is riding in there region.
Strava, Inc. 2014

5. Strava Metro Quick Timeline
Data requests in early 2012
Planning, Research, Big Data
Raw data
No raw data sold or provided to groups
Strava Metro was born and first release to Oregon DOT on February 12, 2014
Strava, Inc. 2014

6. Huge Amounts of Data Needed: What can GEO/GIS fill in?
Restaurant Dev zones
Where do cyclist spend Money
Difference between men and women cyclists?
Bike peer-networks
Bike new or used
% restaurant sales by bike
How to gather feedback on design elements
Perceived dangerous or challenging areas
What is the mode share by street segments
Where do people ride vs where do the want to ride
bike ownership rates by location and demographics
Where are bikes stored
Bike purchasing after bike share opens
Heath insurance for those who work on a bike
Bike theft data
Crash data based on cause/mode
Bikes on transit
Perception of bikes and bicyclists
Locations of cycling habitat fragmentation
Perception of safety on various faculties
Bike tire and accessory sales data
What is the rate of adoption
Loner bike for visitors
Patterns of usage that suggest better, safer ways of designing streets
Bike share routes vs. bike network
Where are bikes parked
Health of riders over time
Locations of near-misses and close calls
Cycling demo- who is riding
Cycling lanes not used
Lidar of street surface
Traffic counters
% of journeys on bike lanes
Free indoor parking?
Structure, qualitive rating of route or route segment
What makes new bike commuters starts?
How many people starting or re-starting riding because of bike share
Meeting with planners, researchers and cyclists has generated a huge gap in data. Strava can fill a lot of this!
Strava, Inc. 2014

7. Who makes up San Francisco Strava?
Who is Strava?
Strava has grown into the leading online cycling and running community Our members cross all types of demographics Urban area uploads are around 40% commute
Who makes up San Francisco Strava?
Athlete ID Count: 23,714
Activity Count: 394,883
Average Distance: 28,904 m
Median Distance: 19,409 m
Average Time: 6,442 sec
Median Time: 4,188 sec
Commute Counts: 183,155
Male Count: 18,511
Male Count Under 25: 670
Male Count : 5,826
Male Count : 5,145
Male Count : 3,444
Male Count : 1,129
Male Count : 243
Male Count : 22
Male Count : 19
Male Count No Bday: 2,004
Female Count: 3,705
Female Count Under 25: 123
Female Count : 1,501
Female Count : 841
Female Count : 448
Female Count : 166
Female Count : 30
Female Count : 1
Female Count : 1
Female Count No Bday: 591
Blank Gender Count: 1,498
Strava is not just cat1 male racers.
FB like funnel growth
Cyclists like to upload. Strava has a great permanent social home feel. No ride is to small and no ride is to large. We view cyclists the same.
Strava, Inc. 2014

8. Locating Commutes
A key question is do we have commute data and if so how do we find it. We have spent a long time working through this. There are 3 key was we do this: Native Strava flag from the website (there was once a black jersey for leading commuters, carbon offset), Fuzzy name matching from cycling titles, GIS Point 2 point /w distance and time constants.
Strava, Inc. 2014

9. Strava Metro is a Custom Built Product
Vector GIS polyline Layers
Basemap is user dependent (OSM, TomTom, City Provided)
AM/PM commute times
On and Off Season
Polygon tables: Census Blocks, zip codes, wards or custom
City Bike & CycleTracks GPS Tracks Integration
Strava is dynamic and is built to have seamless integration into a planning department. User defined fields allow for a deep understanding of the data provided.
Strava, Inc. 2014

10. What does strava do. Blending GIS and Programming
What does strava do? Blending GIS and Programming. Taking noise and huge amounts of data and making them useable and ideal for deep analysis. Polylines do not show direction, time. Points are a cluster that can be challenging to work through. Strava’s custom format allows for quick data extraction and clean views of the data. Protecting the users privacy and not selling their raw data is very important to us.
Strava, Inc. 2014

11. Core Strava Metro Files
Strava Metro is really a suite of products with the Minute data being the key data to the group. These tables can be very large and often require a Postgres like database to use. We provide a set of rolled up data with the Minute data to allow for quick views od the data. These views are agreed upon between Strava and each group.
Strava, Inc. 2014

12. Minute View
Strava, Inc. 2014

13. Rolled Up View
The wiggle
Strava, Inc. 2014

14. Filtering by Commutes
Strava, Inc. 2014

15. Origin/Destination Matrix
The OD was the first logical new data feed for the Strava Metro suite. The data in this format shows great functionality of zonal transportation by bike. When paired with the minute routing data if then shows the key paths used between each zone.
Strava, Inc. 2014

16. Spatial Movement by Zones
Strava, Inc. 2014

17. Node Wait Times
To wait or not to wait. How long will a cyclist wait? What the busiest intersections in the city? Do cyclists stay away from them? These are all the questions that the Nodes product will answer.
Strava, Inc. 2014

18. Intersection Based Data
Strava, Inc. 2014

19. Strava Metro: The Big Picture
Strava, Inc. 2014

20. Strava Metro Infrastructure Change Adoption
Using Strava provides quick data back that can be used to evaluate the impacts and effectiveness of changes to cycling lanes or area.
Strava, Inc. 2014

21. Net Gain & Loss
Green bike trips and blue – 100 bike trips May to July.
Strava, Inc. 2014

22. Weekend vs Weekday Use
Strava Metro can be used to see quick trends or at the same time the data can be blended to show street by street differences.
Strava, Inc. 2014

23. Peak Riding Times and Days
Within 2 minutes of using the data you can pull out AM vs PM cycling peaks and seasonality trends. This can be then broken down even further to show trends at 8am for the year for one piece of road.
Strava, Inc. 2014

24. Strava Correlation: Oregon Sample
Correlation to cycling counters is key for many groups. This is because it let’s you start to visualize the data on every road from the % match at the counters. Strava market saturation varies by region but we have found that in mature areas its between 2 – 12 %.
Strava, Inc. 2014

25. Web Interface: OD and Streets Example
Many groups do not have the ability to use a GIS platform so we will start to offer a Strava Metro online experience that will provide insight for everyone into cycling. This is designed primarily for advocacy groups and MTB network managers.
Strava, Inc. 2014

26. Missing Cycling Corridors
Cyclists often take the less beaten path. This involves cut through, off road paths, walking with bike up stairs and so on . The missing corridors product will start to highlight these paths so that planners can start to understand why its happening and create a safer solution.
Strava, Inc. 2014

27. Final Metro Points
Strava is receiving over 3 million uploads a week and has doubled every year Strava is free to download/use, ad free and available on Iphone and Android phones Strava’s apps are translated in 13 languages and the website in 5 Strava GEO is pushing the edge of providing critical cycling data to planning groups
Strava, Inc. 2014

28. Animation Time
Strava, Inc. 2014

29. Strava, Inc. 2014