MaxMLang/hexsmoothR: hexsmoothR v0.1.0

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Authors: Max Melchior Lang
Cited by: 0

Abstract

hexsmoothR: Hexagonal Grid Smoothing for Satellite Data A comprehensive R package for creating hexagonal grids and applying spatial smoothing to satellite raster data. The package is specifically designed for hexagonal grid analysis, providing tools for extracting environmental variables from TIF files and applying Gaussian-weighted spatial smoothing using C++ optimization with chunking support for large datasets. Features Hexagonal Grid Creation: Create hexagonal grids from study areas (primary focus) Spatial Topology: Compute neighbor relationships and distances for hexagonal grids Raster Processing: Efficient extraction of values from TIF files C++ Optimization: High-performance spatial smoothing algorithms Custom Weights: Flexible Gaussian weight parameters Multiple Formats: Support for various raster formats and projections Flexible Grid Input: Accepts any sf POLYGON grid with proper structure Installation Prerequisites Windows users: You need Rtools to compile C++ code. Install from https://cran.r-project.org/bin/windows/Rtools/ macOS users: Install Xcode Command Line Tools: xcode-select --install Linux users: Install build-essential: sudo apt-get install build-essential (Ubuntu/Debian) or equivalent for your distribution. Install hexsmoothR # Install from GitHub devtools::install_github("MaxMLang/hexsmoothR") # Or install from local source install.packages("hexsmoothR_0.1.0.tar.gz", repos = NULL, type = "source") Troubleshooting If you get compilation errors: Windows: Make sure Rtools is installed and in your PATH macOS: Ensure Xcode Command Line Tools are installed Linux: Install build tools for your distribution All platforms: Make sure you have the latest version of Rcpp: install.packages("Rcpp") Quick Start library(hexsmoothR) library(sf) library(terra) # Create hexagonal grid hex_grid

Fields of Study & Subjects

Data Analysis with R Spatial and Panel Data Analysis Geography and Environmental Studies

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
16 March 2026
Language
EN