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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Analysing human mobility patterns of hiking activities through complex network theory.

Isaac Lera1, Toni Pérez2, Carlos Guerrero1

  • 1Departamento de Matemáticas e Informática. Universitat de les Illes Balears, Palma de Mallorca, Spain.

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Analyzing hiker GPS data reveals seasonal mobility shifts. Human mobility patterns are influenced by weather, with summer showing significant coastal activity changes, informing natural resource planning.

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Area of Science:

  • Environmental Science
  • Human Mobility Studies
  • Network Theory

Background:

  • Geolocalized data from outdoor activity tracking apps offer insights into human mobility and resource management.
  • Hiker activity selection is influenced by personal, environmental, and trail factors.

Purpose of the Study:

  • To analyze human mobility patterns using hiker trajectories and complex network theory.
  • To understand the impact of seasonal weather variations on trail network topology and activity localization.

Main Methods:

  • Generated trail networks from overlapping hiker trajectories, identifying trail crossings and trailheads.
  • Applied complex weighted network theory to analyze the Balearic Islands' trail network across four seasons.
  • Compared the derived network model with existing studies for different purposes.

Main Results:

  • The number of visited places remained consistent across seasons despite varying sample sizes.
  • Significant shifts in activity frequency and location from inland to coastal areas were observed during summer.
  • Identified key regions within the trail network crucial for potential landscape interventions.

Conclusions:

  • Seasonal weather significantly impacts human mobility patterns and trail network usage.
  • The developed network model effectively identifies important areas for land management and conservation.
  • Geolocalized data analysis provides valuable tools for natural resource planning and understanding leisure behavior.