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

Key characteristics

Work step
Data collection
Data analysis
Tool purpose
Numeric and Alphanumeric Data
Spatial Data
Properties
Professional
Experimental
Keywords
Pattern recognition
HUMANITA
Visitor monitoring
Mobile phone data
Spatial data

Tool description

Mobile phone network data can be used to understand how many people visit a protected area, when they visit, where they come from, and how long they stay. This method relies on anonymized and aggregated information generated when mobile phones interact with cellular networks (such as during calls, SMS messages, or mobile internet use). Mobile phone network data can provide robust, yearround information on visit intensity and visitor origin patterns at the scale of protected areas and surrounding landscapes, supporting planning, zoning, visitor management and communication. It enables comparison of weekday and weekend use, detection of seasonal patterns, and identification of the relative importance of local, regional, national and international visitors for different sites. Importantly, the data do not identify individual people. All information is processed in a way that ensures privacy and complies with data protection regulations.

Constraints

  • Partial population coverage – Only users of the specific mobile network and those carrying a mobile phone are included, extrapolation factors are used to estimate the total number of visitors.
  • Accuracy - Depends on network coverage and cell tower density.
  • Cost intensive – Mobile phone network providers charge a significant amount of money to aggregate and share their data.
  • Activity-dependent detection – Only devices generating network activity during the visit are recorded.
  • Threshold-based estimation – Visitor numbers depend on predefined rules (e.g. minimum stay duration, maximum gap between events).
  • Aggregated data only – Results are spatially and temporally aggregated (e.g. by area, month, weekday/weekend); no individual or high-frequency data.
  • Privacy masking – Small counts are suppressed (e.g. “<10”), reducing precision for small groups.
  • Not a standalone method – Requires calibration and interpretation alongside on-site counters and surveys.

Requirements

  • Contractual agreement and data‑sharing framework with the telecom operator that specifies polygons, time period, aggregation scheme and privacy safeguards
  • Technical setup for secure data transfer (SFTP) and handling of CSV files with UTF‑8 encoding
  • GIS capacity to define and manage polygons representing the areas of interest and surrounding “home” areas for local residents
  • Data analysis capacity to handle aggregated visit counts, apply quality checks, and interpret masked values (“<10”)
  • Awareness of methodological assumptions and communication of uncertainties and biases to stakeholders

Tool Impact

The use of mobile phone network data has no direct environmental impact, as it does not require field infrastructure, on-site equipment, or physical presence in protected areas. All data are generated passively through existing telecommunications networks, meaning the method does not disturb wildlife, habitats, or visitors. From a social perspective, the primary consideration relates to data privacy and public trust. Although the data are fully anonymized and aggregated, transparent communication is essential to ensure acceptance among stakeholders and visitors. No individual identities, trajectories, or personal behaviors are visible; only statistical patterns at aggregated spatial and temporal scales are analyzed.

Best Practices

  • Bükk National Park procured data from Magyar Telekom for the Interreg CE project HUMANITA. The company provided counts of visits and estimated places of residence for visitors for six polygons (Sár‑hegy, Totovics, Nagy‑Lápatető, Kékes Észak, Parádi legelő, Suba‑lyuk) over the period 1 January 2023 to 31 December 2024. Top originating visitor countries are Poland, Germany and Sweden.

Helpful hints to use the tool proficiently

  • Use polygons that closely match management relevant units (e.g. trail sections, zones, entire protected areas) and validate them with local experts.
  • Combine mobile network data with on‑site observations, automatic visitor counters and survey data to calibrate absolute visitor numbers and better understand user profiles.
  • Interpret small, masked values (“<10”) cautiously and avoid over‑interpretation of rare origin categories.
  • Consider excluding or specially treating public transport corridors and urban fringes where through-traffic may dominate.
  • Document all thresholds and processing steps (e.g. minimum stay duration, maximum gap between events, time windows for identifying home locations) for transparency and reproducibility.
  • Close collaboration with the telecom provider’s analytics team is very helpful for defining polygons, understanding network coverage, and interpreting specific artefacts (e.g. tower overlaps, seasonal changes in network usage).
  • Start with a pilot on a limited number of polygons and time periods to refine the setup before scaling to larger areas or longer time series.
  • In communications with park managers and the public, emphasize that the data are fully anonymized and aggregated, and that no individual movement paths are visible.

Specification

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

Linked tools

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Study object
Study focus
Work step
Tool purpose
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Camera traps for visitor monitoring

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GPS Loggers for visitor monitoring

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Strava Metro

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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