Details zur Publikation
Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.3897/BDJ.6.e20760
Lizenz creative commons licence
Titel (primär) spind: an R package to account for spatial autocorrelation in the analysis of lattice data
Autor Carl, G.; Levin, S.C.; Kühn, I. ORCID logo
Quelle Biodiversity Data Journal
Erscheinungsjahr 2018
Department BZF; iDiv
Band/Volume 6
Seite von e20760
Sprache englisch
Keywords Cohen's kappa coefficient; Generalised Estimating Equations; Goodness-of-fit; Multimodel Inferrence; Multiresolution Regression; Prediction accuracy; Spatial autocorrelation; Species distribution modelling; Wavelet Revised Models
UFZ Querschnittsthemen RU1;
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.
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=19986
Carl, G., Levin, S.C., Kühn, I. (2018):
spind: an R package to account for spatial autocorrelation in the analysis of lattice data
Biodiver. Data J. 6 , e20760 10.3897/BDJ.6.e20760