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Outdoor app data for visitor monitoring
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HUMANITA - Results of the analysis from Komoot tours
HUMANITA - Results of the analysis from Komoot tours
Image source: Greiler © 2025 All rights reserved

Analysis of outdoor app data (.gpx and metadata) to understand spatial distribution of digital trails and estimated usage of trails.

Key characteristics

Work step
Data collection
Data analysis
Tool purpose
Numeric and Alphanumeric Data
Spatial Data
Properties
Professional
Experimental
Keywords
HUMANITA
Visitor monitoring
Digital trail
Outdoor app
Navigation

Tool description

Data from outdoor apps can be used for visitor monitoring. Most outdoor apps provide .gpx-files showcasing tour suggestions that were uploaded either by official tourist operators, Alpine Associations (i.e. ÖAV), protected areas or regular “community” users. Other apps allow users to share POIs. Some apps suggest tours based on automatic algorithms. As any user can upload information, unofficial or even illegal activities in sensitive areas can unintentionally be promoted on such platforms. This is why it is important to monitor the promoted information on regular intervals and manage the contents on the platforms to avoid unwanted visitor movement. To understand spatial hotspots of digital trail information, tours can be downloaded from the platforms and integrated to a GIS system. A tour count along the paths network allows the calculation of the density of promoted tours along each dedicated path. With a higher number of promoted tours, a higher usage can be assumed. Additionally, most apps provide metadata about their tours, like page views, downloads, ratings or users. The metadata can be collected and weighted to calculate an estimated usage for each tour.

Constraints

  • Non-representative user base – Data reflect only users of specific apps and digitally active visitor groups.
  • Platform-specific bias – Different apps attract different user profiles (e.g. hikers, cyclists, mountain bikers).
  • Algorithm influence – Automatically suggested tours may amplify already popular routes.
  • Dynamic content – Tours and metadata change frequently, affecting reproducibility over time.
  • Incomplete or inaccurate information – User-uploaded routes may contain GPS errors or outdated information.
  • No direct visitor counts – Promoted tours do not equal actual on-site use.
  • Access limitations – Full metadata often require paid or pro accounts.
  • Legal and ethical considerations – Use of scraped or downloaded data must comply with platform terms of service.

Requirements

  • Pro or institutional accounts for selected outdoor apps
  • GIS software and technical expertise for spatial data integration and analysis
  • Standardized workflow for downloading, cleaning, and updating tour datasets
  • Capacity to manage and interpret metadata indicators (views, downloads, ratings)
  • Clear documentation of assumptions and weighting schemes
  • Calibration with on-site monitoring tools (counters, surveys)
  • Regular monitoring intervals to track content changes over time
  • Privacy compliant data handling

Tool Impact

The use of outdoor app data has no direct physical environmental impact, as it relies entirely on existing digital content and does not require field equipment or infrastructure. However, promoted tours and shared user content can indirectly influence visitor behavior. Trails leading through sensitive habitats, core zones, or restricted areas may increase visitor pressure and disturbance of vulnerable species. Proactive management of digital content can therefore contribute positively to conservation by reducing the promotion of ecologically sensitive routes and supporting responsible recreation.

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

  • Define clear spatial boundaries (polygons) that match management-relevant units before downloading data.
  • Regularly monitor and update datasets, as digital content changes frequently.
  • Use a standardized Tour Score approach to estimate relative promotion intensity across tours.
  • Clearly document and justify any weighting schemes applied to metadata indicators.
  • Compare multiple platforms to reduce single-platform bias.
  • Combine digital promotion data with field-based monitoring tools for calibration.
  • Identify and review tours crossing sensitive or restricted areas, and engage with platform providers if needed.
  • Communicate uncertainties and sampling bias transparently to stakeholders.
  • Consider assigning a staff member or “digital ranger” to oversee online content management.

Specification

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

Linked tools

Category Tool title and description
Study object
Study focus
Work step
Tool purpose
 Classic Professional Free to use Experimental
Automatic visitor counters

Automated sensor-based systems for quantifying visitor numbers and analyzing temporal and spatial visitation patterns in natural areas.
GPS Loggers for visitor monitoring

Portable devices used to record visitor movement patterns, routes, speed, and stay times, providing spatially explicit data to better understand visitor flows and site use.
Mobile phone network data for visitor monitoring

Use of anonymized and aggregated mobile network event data to estimate visitor numbers and origin areas for selected protected areas and surrounding regions.
Strava Metro

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.

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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