VivianGrom/ShannonEntropy: v1.0 - Initial Release

This repository contains code and examples demonstrating how to apply Shannon Entropy, Moran's I, and Geary's C to analyze Landscape Evolution Model (LEM) outputs. The repository accompanies the manuscript: Methods for Quantifying Spatial and Temporal Variation in Landscape Evolution Model Outputs, Geomorphica, 2026 (under review).

Files Overview

Code and notebook:

• 'Comparison.ipynb' – Jupyter notebook demonstrating the computation of Shannon Entropy, Moran’s I, and Geary’s C, and illustrating their application to LEM outputs. This notebook reproduces the analyses used to generate figures in the manuscript.
• 'ent_sautocor_class.py' – Standalone entropy calculation functions that were later integrated into ent_sautocor_class.py.
• 'shannon_entropy.py' – A collection of entropy calculation functions, later integrated into a class.

Case Studies (These scripts generate the processed outputs used for the paper's figures)

• 'exp1.py' – A case study analyzing steady uplift.
• 'exp2.py' – A case study exploring periodic alternating uplift.
• 'exp3.py' – A case study investigating spatially variable uplift.
• 'shannon_edem.py' – An example demonstrating entropy calculations using alternative data grids.

Raw data

• 'nc_files' - NetCDF (.nc) files containing time-resolved snapshots of Landscape Evolution Model outputs for each experiment. Each file stores gridded fields of topographic elevation (topographic__elevation) and soil depth (soil__depth) on a raster grid, with dimensions (time, y, x). Metadata include grid size, node spacing, spatial units (meters), and model configuration parameters. These files represent the raw model outputs from which processed datasets and figures are derived.

This repository provides a structured approach to quantifying spatial patterns and variability within LEM outputs, facilitating comparative analysis across different uplift scenarios.

Purpose and Benefits: This repository provides a structured approach for quantifying spatial patterns and variability within LEM outputs. By applying Shannon Entropy, Moran's I, and Geary's C, users can perform comparative analyses across different scenarios.

These tools can be used to:

• Explore how different uplift patterns influence landscape characteristics.
• Quantify spatial autocorrelation and distribution of attributes across landscapes.
• Facilitate comparisons across multiple LEM experiments.

License: MIT