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Strava Metro
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HUMANITA - Results of the analysis from Strava Metro
HUMANITA - Results of the analysis from Strava Metro
Image source: Greiler © 2025 All rights reserved

Strava Metro is a data service provided by Strava that uses aggregated and anonymized activity data (e.g. walking, running, cycling) from users of the Strava app to analyze movement patterns.

Key characteristics

Work step
Data collection
Data analysis
Tool purpose
Numeric and Alphanumeric Data
Spatial Data
Properties
Professional
Experimental
Keywords
HUMANITA
Visitor monitoring
Fitness apps
GPS Tracks
GNSS data

Tool description

Strava Metro is a data-service built on the GPS-tracked cycling, running, walking and hiking activities logged by users of the fitness app Strava. It aggregates and anonymizes the data so that no individual user can be identified. It delivers insights into where people move (routes and hotspots), how often routes are used, when activities take place (time of day, season), as well as the type of activity (e.g. cycling vs. running). In protected areas, Strava Metro is particularly useful for analyzing spatial patterns of recreational activities, especially cycling and hiking/running, on trails and paths. It can support the identification of high-use trails or routes, or unofficial activities, support infrastructure planning and maintenance as well as monitor changes in use over time.

Constraints

  • Non-representative user base – Strava users are predominantly younger, male, and sport-oriented; results are therefore not representative of the overall visitor population.
  • Activity bias – Cycling activities are typically overrepresented, while hiking and other leisure activities may be underrepresented.
  • Trail bias – Frequently used sport-oriented routes may be overrepresented, whereas touristic or short recreational trails (e.g. to viewpoints or waterfalls) may be underrepresented or absent.
  • Partial coverage – Only a small proportion of total trail use is captured, limited to visitors actively using the Strava app.
  • Limited resolution due to privacy rules – Data precision decreases at finer temporal scales (e.g. daily or hourly), and low counts (e.g. fewer than 5 users) are not reported.
  • Complementary method only – Strava Metro does not replace traditional monitoring tools such as automatic counters or visitor surveys and should be used in combination with them.
  • Access limitations – Although Strava Metro data may be provided free of charge, access is restricted and typically oriented toward active mobility or transport planning organisations; obtaining access can be challenging.

Requirements

  • Partnership with Strava Metro
  • GIS software and technical expertise for spatial data integration and analysis or use of build-in dashboard provided by Strava Metro
  • Calibration with on-site monitoring tools (counters, surveys)
  • Awareness of sampling bias and clear communication of uncertainty and limits
  • Privacy compliant data handling

Tool Impact

Strava Metro data collection itself has no direct environmental impact, as it is based entirely on passively recorded GPS activities from app users and does not require any field infrastructure or on-site equipment in protected areas. However, indirect impacts should be considered. Publicly visible activities on the Strava platform may unintentionally promote certain routes, including unofficial trails or environmentally sensitive areas. Popular segments and shared activities can reinforce existing hotspots, potentially increasing visitor pressure, trail erosion, and disturbance of wildlife in fragile habitats. From a social perspective, privacy protection is a central consideration. Strava Metro provides aggregated and anonymized data, ensuring that no individual users can be identified. Nevertheless, transparent communication about data use and limitations is important to maintain trust among stakeholders and the public.

Best Practices

  • Within the Interreg Central Europe project HUMANITA, data from the outdoor and fitness apps Bergfex, Komoot, Outdooractive, Trailforks, and Strava were analyzed to estimate spatial hotspots and low-use areas in pilot regions. The objective was to identify officially promoted and unofficial user-generated activities that may indicate recreational trends, development potential, or conflicts within protected areas. Tour data were systematically organized and integrated into a GIS project. This enabled protected area managers to detect clusters of digitally promoted routes, identify potentially problematic trails in sensitive zones, and proactively manage online content where necessary. The analysis provided strategic insights into how digital platforms shape visitor distribution and perception of landscapes.

Helpful hints to use the tool proficiently

  • Clearly define management-relevant spatial units (e.g. trail sections, zones, protected area boundaries) before analysis.
  • Interpret results as relative intensity patterns, not absolute visitor numbers.
  • Always combine Strava Metro data with on-site counters for calibration.
  • Pay particular attention to unofficial or newly emerging routes visible in the data.
  • Consider seasonal and activity-type filters (e.g. cycling vs. running) when analyzing patterns.
  • Be cautious when interpreting low-use or masked values due to privacy thresholds.
  • Monitor trends over multiple time periods to detect changes in activity distribution.
  • Communicate clearly that the dataset reflects a sport-oriented user group, not the entire visitor population.
  • Coordinate with park management when identifying sensitive areas that may require digital communication or mitigation measures.
  • Use the built-in Strava Metro dashboard for exploratory analysis before conducting detailed GIS-based assessments.

Specification

Category Software
Platform
Desktop Web
Operating system
OS-independent
ModeBoth online and offline

Linked tools

Category Tool title and description
Study object
Study focus
Work step
Tool purpose
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Automatic visitor counters

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Mobile phone network data for visitor monitoring

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Legend

Study objects

Microbiota
Humans
Animals
Vegetation, Funghi & Lichen
Habitats

Study focus

Anthropogenic factors and impacts
Biodiversity (Species populations and Community Composition)
Ecosystem State, Structure and Functioning

Work steps

Presentation
Data collection
Plot establishment
Data analysis
Data archiving

Tool categories

TBD
Device
Software
Manual / Guideline / Framework

Tool purposes

Spatial Data
Numeric and Alphanumeric Data
Audio Data
Genetic Data
Photo/Video Data
Non Data generative
Chemical Compound Data