spind: an R Package to Account for Spatial Autocorrelation in the Analysis of Lattice Data

By

Carl, Gudrun
Levin, Samuel
Kühn, Ingolf

Type

Article

Date of Publication

2018-02-28

Original Publication

Biodiversity data journal

Volume

6

Pages

1--17

Subjects

Cohen's kappa coefficient , Generalised Estimating Equations , Goodness-of-fit , Multimodel Inferrence , Multiresolution Regression , Prediction accuracy , spatial autocorrelation , species distribution modelling , Wavelet Revised Models

Contributed by

Pensoft Publishers

Language

English

Published by

Sofia Pensoft Publishers [2013]-

Identifiers

DOI: https://doi.org/10.3897/BDJ.6.e20760
eLocator: e20760

 

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.