A short and snappy collection of Jupyter Notebooks to turn Strava activity data in geo-spatial data you can interrogate, and blend it with OpenStreetMap data for recommendations and plotting.
- Strava Export: OAuth flow and API export /activities
- GeoPandas: Converting Strava activity data to geo-spatial data that can be interrogated
- osmx: OpenStreetMap integration for blending other data with the Strava data
- Plotting: Leaflet mapping and other plotting features, seen above and deployed on the repo
- Deployment: Deployment of the maps to Github Pages
Replicating this Strava screen:
- Elevation / Distance Map
- KM / Pace / Elev
- Distance, Elevation, Grade, Pace view
- Avg strides
- Stopping / Walking view
- Forecasting for the route eg. based on last 10 runs expect performance increase / decrease
- Python 3.9+
- Conda (miniconda)
NB. Assuming you cloned the repo already, right?
STRAVA_C_ID=
STRAVA_C_SECRET=
STRAVA_REDIRECT=http://localhost:8080
STRAVA_C_AUTH_CODE=
STRAVA_C_ACCESS_CODE=
Open the ./strava_data.ipynb next for more instruction. *Some may require that you start running the Jupyter Notebook
conda env create --name strava_data_analysis --file=environment.yml
Start Conda
sh ./scripts/conda_init.sh
Start Jupyter Notebooks
jupyter notebook
Open strava_data.ipynb first and work through step until you downloaded activities.geojson
Follow notebook instructions, from there
./pyrosm.ipynb is WIP
More docs within the notebooks.
Key sections:
Contributions not accepted at this point.
Not implemented
Docs from:
Alan Ionita @2025
This project is licensed under the GNU Affero General Public License v3.0 - see the LICENSE file for details.
AGPL requires that:
- All modifications must be disclosed
- Network services using this code must release their source
- Derivative works must use same license