We extract public cycling routes, elevation profiles, segment leaderboards, and geospatial waypoints from MapMyRide. Delivered as clean GeoJSON, CSV, or Parquet to S3, BigQuery, or PostGIS on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Route Metadata objects from mapmyride.com. All fields typed and schema-versioned.
"route_id": "384910294", "name": "Richmond Park Outer Loop", "city": "London", "distance_km": 11.4, "elevation_gain_m": 124, "route_type": "Road Cycling", "privacy_status": "Public"
| # | route_id | name | city | country | distance_km | elevation_gain_m |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Geospatial Waypoints objects from mapmyride.com. All fields typed and schema-versioned.
"route_id": "384910294", "point_index": 45, "latitude": 51.4431, "longitude": -0.2734, "elevation_m": 45.2, "distance_from_start_m": 2400.5, "surface_type": "Asphalt"
| # | route_id | point_index | latitude | longitude | elevation_m | distance_from_start_m |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Segment Leaderboards objects from mapmyride.com. All fields typed and schema-versioned.
"segment_id": "94821", "segment_name": "Dark Hill Sprint", "distance_km": 0.8, "avg_grade_pct": 4.1, "rank": 3, "time_seconds": 112, "speed_kmh": 25.7
| # | segment_id | segment_name | distance_km | avg_grade_pct | athlete_id | athlete_name |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Public Profiles objects from mapmyride.com. All fields typed and schema-versioned.
"user_id": "8472910", "username": "cyclist_ldn", "location": "London, UK", "total_distance_km": 14204.5, "total_workouts": 412, "joined_date": "2019-04-12", "public_routes_count": 34
| # | user_id | username | location | total_distance_km | total_workouts | joined_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Events & Challenges objects from mapmyride.com. All fields typed and schema-versioned.
"event_id": "evt_9921", "title": "Spring Century Challenge", "start_date": "2026-03-01", "end_date": "2026-03-31", "participant_count": 14291, "goal_type": "Distance", "goal_value": "100 miles"
| # | event_id | title | location | start_date | end_date | participant_count |
|---|---|---|---|---|---|---|
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Our MapMyRide scraper bypasses the DOM and intercepts the underlying API payloads, extracting raw coordinate arrays, elevation profiles, and segment leaderboards with zero data loss.
Extract complete coordinate arrays (latitude, longitude, elevation) for any public route, translated into standard GeoJSON or WKT formats.
Capture the exact distance-to-elevation mapping for categorised climbs and full routes, essential for gradient analysis.
Scrape public segment efforts, tracking athlete ranks, completion times, and average speeds across specific road sections.
Input a spatial bounding box or city name; we recursively search and extract all public routes originating or passing through the area.
Monitor public fitness challenges, participant counts, and aggregate completion statistics sponsored by brands.
Extract aggregate statistics from public user profiles, including total distance ridden, preferred gear, and active regions.
Identify paved, gravel, and trail sections based on route metadata and underlying map layer tags.
We intercept XHR requests directly from the map renderer, capturing the raw JSON payloads before they are painted to the canvas.
Run continuous pipelines to monitor segment leaderboard changes or discover new routes published within a target geofence.
Brief in. Clean data out.
Provide bounding boxes, city names, route IDs, or segment URLs. We define the spatial extraction schema together.
We configure Playwright crawlers to intercept XHR map payloads, handle proxy rotation, and manage session tokens.
Geometry validation, coordinate standardisation, elevation outlier detection, and schema checks before full launch.
GeoJSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or PostGIS database on agreed cadence.
Scraping map applications requires intercepting network traffic and translating proprietary coordinate formats. Here is how we build reliable geospatial pipelines.
MapMyRide renders routes on an interactive map canvas. Scraping the DOM yields nothing. We run headless Playwright sessions to intercept the background XHR requests, capturing the raw JSON coordinate arrays directly from their backend APIs.
Raw route data often comes in compressed polyline strings or proprietary nested arrays. Our pipeline includes a transformation layer that decodes these formats into standard GeoJSON FeatureCollections or Well-Known Text (WKT) for immediate database insertion.
High-volume requests to map tile servers and routing APIs trigger rate limits. We distribute extraction across residential ISP proxies, rotating IPs per request and matching browser fingerprints to standard user agents.
To extract all routes in a city, we divide the requested bounding box into a grid of smaller tiles, recursively querying the search API to bypass pagination limits and ensure 100% spatial coverage.
When tracking segments, we maintain a hash index of the current leaderboard. Subsequent runs only emit records for new athletes or improved times, reducing downstream processing load.
City councils and transport planners analyse aggregated public route geometries to identify high-traffic cycling corridors and prioritise cycle lane investments.
Tourism boards extract popular local routes and elevation profiles to promote cycling holidays and build interactive regional guides.
Fitness applications monitor MapMyRide segment activity and event participation to benchmark user engagement against their own platforms.
Bicycle retailers and repair shops overlay route density maps with commercial real estate data to identify optimal locations for new stores.
Machine learning teams use large datasets of human-validated routes and elevation matrices to train proprietary route-recommendation algorithms.
Race organisers analyse historical route usage and segment times to design new event courses and estimate traffic impact.
"MapMyRide holds millions of human-validated cycling routes, but extracting geospatial polylines requires intercepting undocumented API payloads, not just parsing HTML."
Scraping map-based applications requires executing JavaScript renderers, intercepting XHR network requests, parsing custom coordinate arrays, and translating them into standard geospatial formats like GeoJSON. DataFlirt manages this pipeline end-to-end, delivering query-ready spatial data to your warehouse.
Everything supported by our mapmyride.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Playwright runs headless browser sessions, bypassing the DOM entirely to intercept background API requests. We capture the raw coordinate JSON before the map tile server renders it.
Raw polylines and nested coordinate arrays are passed through a Python-based spatial transformation layer, converting proprietary data into query-ready GeoJSON or WKT formats.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, geofence grid generation, and SLA alerting. Spatial state is managed in PostGIS.
Data delivered to where your team already works — no new tooling required.
About mapmyride.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available routes, segments, and public leaderboards is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract private workouts, circumvent authentication walls, or violate user privacy settings. Clients should review MapMyRide terms of service and consult legal counsel for specific use cases.
We distribute extraction requests across a large pool of residential ISP proxies, rotating IPs per request. We also randomise request timing and spoof browser fingerprints to match human interaction patterns with the map interface.
We natively support GeoJSON, Well-Known Text (WKT), and raw JSON coordinate arrays. If you require GPX or KML files for specific routing applications, we can configure the transformation layer to output those formats.
Yes. You can provide a city name, a central coordinate with a radius, or a specific bounding box. Our pipeline will recursively search the area and extract all public routes originating or passing through that geofence.
We extract the current public leaderboard for any specified segment. This includes the top times, athlete names, and dates recorded, as exposed by the public interface.
No. We strictly adhere to privacy boundaries and only extract profile statistics and routes that users have explicitly marked as public.
Our minimum engagement typically starts at 10,000 routes or a specific city bounding box with weekly delivery. For continuous segment monitoring or global extraction, we price based on compute volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of city cycling routes or a continuous feed of segment leaderboards, we build and operate the pipeline. Tell us your spatial requirements.
