In this post, we are going deeper into the Dynamic Data project, looking at the Strava dynamic data (library available here on GitHub and at NPM). Opening the library, you’ll see there are twenty-five different types of data files: Activity, ActivityComments, ActivityKudoers, ActivityLaps, ActivityStream, ActivityZones, AthleteActivities, AthleteClubs, AthleteRoute, AthleteStats, AuthenticatedAthlete, Club, ClubActivities, ClubAdministrators, ClubMembers, Equipment, Route, RouteStream, SegmentEfforts, SegmentEffortsStream, Segments, SegmentsStreams, StarredSegments, Upload, and Zones. Learn more about dynamic data generators and the benefits of artificial data in software development. About the data sourceStrava is a fitness tracking application freely available on Android and iOS devices, and web browsers. Some of the many things you can do are:
Approach usedUsing the Strava API v3, a mockup file was created for each data object. A simple data model of these objects can be seen below. These mockup files make up the strava-data package. The strava-mockups package imports the aforementioned files and goes through them generating (artificial) new data using proprietary functions, such as those found in the utils package. For example, with the ActivityStream object:
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Ideas to combine with some other data sourcesCombing with data sources of other health devices (e.g., Oura, Garmin, Fitbit, or Whoop) could be useful to provide greater context for some activities. It is also possible to combine Strava data with some entirely different segment data, like air quality, weather, location or similar to find interesting new applications for the data. Open-source data libraryWe welcome contributions and forks to this data set, and look forward to seeing what developers build in our Liberty. Equality. Data. Slack channel. Considerations for next version/improvementsGeneral Improvements
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