moresamwilson

moresamwilson / running-heatmap

Public

Generate heatmaps from your Strava export - frequency, pace, heart rate and gradient.

80
9
100% credibility
Found May 10, 2026 at 80 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

This project converts a personal Strava data export into a single interactive HTML file displaying heatmaps of running frequency, pace, heart rate, and gradients.

How It Works

1
🔍 Discover Running Heatmap

You stumble upon this cool tool from a video that promises to turn your running history into a beautiful, interactive map of where you've been.

2
📥 Download Your Strava Data

Go to your Strava account settings and request a full download of your activities as a zip file.

3
📁 Unpack the Folder

Unzip the file and place the folder right where the tool expects it, ready to go.

4
📅 Choose Your Runs and Dates

Pick the types of activities like runs and the date range you want to map, making it personal to your story.

5
✨ Generate the Map

Let the tool process your data, automatically finding your home base and creating layers for frequency, speed, heart rate, and hills.

🗺️ Dive into Your Heatmap

Open the single ready-to-view file in any web browser to toggle colorful layers and relive your running adventures.

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Star Growth

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AI-Generated Review

What is running-heatmap?

This Jupyter Notebook turns your Strava data export ZIP into a single interactive HTML heatmap, plotting frequency, pace, heart rate, and gradient across your runs—no API keys or online services needed. Just unzip your export, tweak a few config lines for dates and activity types like "Run," and run the notebook to get six switchable layers: linear/log frequency, average pace, heart rate, absolute gradient, and directional slope. It's perfect for runners wanting to visualize path habits and performance without wrestling Excel to generate heatmaps.

Why is it gaining traction?

It skips Strava's API entirely, using only the free bulk export, and auto-detects your home base to focus the map—caching parsed GPS data for quick reruns. The output HTML works offline with precise layers like log-scale frequency to highlight rare routes, standing out from basic running heatmap apps or Garmin tools that demand subscriptions. Developers dig the zero-setup pip install and video tutorial tie-in for fast experimentation.

Who should use this?

Strava runners exporting data to analyze route frequency or pace trends over specific dates. Endurance athletes tracking heart rate patterns or gradient challenges around home. Data-curious folks building running heatmap Strava dashboards without coding from scratch.

Verdict

Grab it if you're a Strava exporter needing quick, layered heatmaps—80 stars and solid README docs make it usable now, despite the 1.0% credibility score signaling early maturity and no tests. Run it locally for free insights; fork if you want production polish.

(178 words)

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