Cycling Infrastructure Map — Exploring Toronto’s Elevation Challenges

Zarrin Tasneem
Nov 6, 2025
2 min read

Mapping the Ride

Toronto’s cycling network is expanding rapidly but not all routes are created equal. While some paths glide smoothly along the waterfront, others snake up steep ravines and hilly neighborhoods.

To visualize these differences, this map highlights bike lanes across Toronto, color-coded by slope gradient, which is a key factor impacting effort, accessibility, and ride enjoyment.

Using OpenStreetMap data for cycling infrastructure and elevation models from public terrain datasets, each segment was analyzed for its grade percentage, revealing where the ride gets tough.

Elevation Gradient Categories

🟩 Easy (0–2%) — Flat and fast; ideal for commuters and casual riders.
🟨 Moderate (2–5%) — Manageable climbs that may require a few gear shifts.
🟧 Hard (5–8%) — Noticeable hills common in Midtown and North York.
🟥 Very Hard (≥8%) — Steep segments, usually in ravine crossings or hilly districts.

The resulting Cycling Elevation Challenge Map provides a visual guide to how elevation interacts with Toronto’s cycling infrastructure from gentle downtown routes to steep climbs near the Don Valley and Humber River trails.

Insights from the Map

Downtown Core: Most routes are flat, making cycling accessible and efficient.
Midtown and North York: Gradual climbs appear frequently, reflecting the city’s glacial terrain.
Scarborough and Etobicoke: Steeper gradients emerge near creek valleys and park corridors.
Ravine System: Noticeable “red zones” indicate where topography significantly affects cycling comfort and accessibility.

This visualization encourages cyclists, planners, and policymakers to consider terrain equity ensuring that cycling infrastructure connects neighborhoods fairly, regardless of elevation challenges.

Tools and Data

Data Sources:
- OpenStreetMap — Bike lane geometry
- SRTM / Copernicus DEM — Elevation model
Software:
- Python (osmnx, geopandas, rasterio, numpy, folium)
- Basemap: CartoDB Dark Matter.
Computation: Each bike lane segment was paired with terrain slope derived from elevation profiles, then categorized by grade and rendered interactively using Leaflet/Folium.