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Edge directionality properties in complex spherical networks.

Frederik Wolf1,2, Catrin Kirsch1,3, Reik V Donner1,4

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Summary
This summary is machine-generated.

This study introduces geometric characteristics for spatial networks on a sphere, correcting for biases and revealing patterns in climate, transportation, and trade networks.

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Area of Science:

  • Network Science
  • Geomathematics
  • Spatial Analysis

Background:

  • Spatially embedded networks are increasingly studied, with a focus on incorporating spatial information.
  • Edge directionality properties are gaining interest for analyzing network structures.

Purpose of the Study:

  • To investigate geometric characteristics like mean edge direction, anisotropy, and local mean angle in complex spherical networks.
  • To identify and correct for systematic biases in spatial networks with varying node shares on a spherical surface.
  • To apply these properties to real-world spatial networks in spherical geometry.

Main Methods:

  • Analytical and numerical studies of geometric network characteristics.
  • Development of a strategy to correct for spatial bias in spherical networks.
  • Application and illustration on climate, air transportation, and world trade networks.

Main Results:

  • Demonstrated a systematic bias in spatial networks due to differing node shares on a sphere.
  • Developed a correction strategy for this bias.
  • Highlighted climate patterns (circulation cells, ENSO, Atlantic Niño), characterized air transportation zones, and identified global trade patterns linked to the EU.

Conclusions:

  • The proposed geometric characteristics and bias correction are effective for analyzing complex spatial networks in spherical geometry.
  • The approach provides novel insights into climate dynamics, global air travel, and economic interdependencies.