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Recurrence network modeling and analysis of spatial data.

Cheng-Bang Chen1, Hui Yang1, Soundar Kumara1

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This study introduces a weighted recurrence network for spatial data analysis, overcoming limitations of traditional methods. The approach effectively visualizes and quantifies complex recurrence patterns in high-dimensional spatial systems.

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Area of Science:

  • Nonlinear Dynamics
  • Network Science
  • Spatial Data Analysis

Background:

  • Nonlinear dynamical systems display complex recurrence behaviors.
  • Recurrence plots and recurrence quantification analysis are standard tools for analyzing recurrence dynamics.
  • Traditional recurrence methods struggle with high-dimensional spatial data.

Purpose of the Study:

  • To propose a novel weighted recurrence network approach for spatial data analysis.
  • To address the limitations of existing methods in handling spatial data dimensionality and geometry.
  • To provide a comprehensive framework for visualizing and quantifying spatial recurrence patterns.

Main Methods:

  • Developed a weighted network model representing spatial data recurrence.
  • Nodes represent locations, while edges and weights capture pixel intensities and spatial distances.
  • Utilized network statistics for characterizing and quantifying recurrence properties.

Main Results:

  • The weighted recurrence network effectively visualizes spatial recurrence patterns.
  • The approach provides a complete representation of recurrence dynamics in spatial data.
  • Network statistics successfully extract salient features for characterizing spatial systems.

Conclusions:

  • The proposed weighted recurrence network approach offers superior performance for spatial data analysis.
  • This method enhances visualization and feature extraction for recurrence dynamics in spatial systems.
  • The generalized recurrence network is a powerful tool for understanding complex spatial phenomena.