$ show moresamwilson/running-heatmap
This repository contains a Jupyter notebook that processes Strava data exports to generate interactive running heatmaps. The core functionality involves parsing GPS and sensor data from .fit.gz files, filtering activities by type and date, detecting a home location, rasterizing GPS tracks into multiple value grids (frequency, pace, heart rate, gradient, elevation change), applying Gaussian blurring and normalization, and rendering the results as overlay layers on a Leaflet basemap via Folium. The output is a single HTML file with six toggleable layers showing different aspects of running patterns. The code demonstrates solid data processing techniques including efficient caching, coordinate transformations, and custom visualization logic, though it remains tightly coupled as a monolithic notebook.
The LLM identified 5 files worth highlighting for reuse:
Haversine distance calculation for geographic coordinates. Self-contained function using only math module, demonstrating a fundamental geospatial pattern reusable in any GPS-related project.
Extracts starting GPS point and spatial spread from .fit.gz files. Handles binary FIT parsing with error resilience, returning None for missing data. Shows robust extraction of wearable sensor data with proper null handling.
Home location detection via activity start point clustering. Uses 0.01-degree binning (~1km grid) to find densest cluster of start points. Illustrates a lightweight, heuristic-based approach to location inference without complex clustering algorithms.
Presence-based alpha mask generation for smooth overlays. Converts sample counts to binary, applies Gaussian blur, and normalizes to a percentile threshold. Demonstrates a technique to avoid hard edges in data-driven visualizations through probabilistic alpha blending.
Custom colormap builder from RGBA node specifications. Creates LinearSegmentedColormaps with precise color stops for tailored visualizations. Shows a flexible, reusable pattern for defining smooth gradients beyond built-in matplotlib options.