Related Experiment Video
Updated: Feb 11, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
spind: an R Package to Account for Spatial Autocorrelation in the Analysis of Lattice Data
Gudrun Carl1, Sam C Levin2,3, Ingolf Kühn1,2,3
1Helmholtz Centre for Environmental Research - UFZ, Halle (Saale), Germany.
Abstract:
spind is an R package aiming to provide a useful toolkit to account for spatial dependence in the analysis of lattice data. Grid-based data sets in spatial modelling often exhibit spatial dependence, i.e. values sampled at nearby locations are more similar than those sampled further apart. spind methods, described here, take this kind of two-dimensional dependence into account and are sensitive to its variation across different spatial scales. Methods presented to account for spatial autocorrelation are based on the two fundamentally different approaches of generalised estimating equations as well as wavelet-revised methods. Both methods are extensions to generalised linear models. spind also provides functions for multi-model inference and scaling by wavelet multiresolution regression. Since model evaluation is essential for assessing prediction accuracy in species distribution modelling, spind additionally supplies users with spatial accuracy measures, i.e. measures that are sensitive to the spatial arrangement of the predictions.
Related Concept Videos
Lattice Centering and Coordination Number
Types of Unit Cells
Imagine taking a large number of identical...
DNA Packaging
Trends in Lattice Energy: Ion Size and Charge
Chromatin Packaging
Chromatin Packaging
The chromatin
In combination with specialized DNA binding protein called Histones, the DNA double helix forms a compact DNA: protein complex called chromatin. The chromatin itself is further compacted into higher-order...
Chromatin Packaging

