Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1111/ecog.02593
Titel (primär) Spind: a package for computing spatially corrected accuracy measures
Autor Carl, G.; Kühn, I. ORCID logo
Journal / Serie Ecography
Erscheinungsjahr 2017
Department BZF; iDiv
Band/Volume 40
Heft 5
Seite von 675
Seite bis 682
Sprache englisch
Keywords Cohen's kappa coefficient; Confusion matrix; Evaluation method; Goodness-of-fit; Spatial autocorrelation; Species distribution modelling; Prediction accuracy
UFZ Querschnittsthemen RU1;
Abstract Using an appropriate accuracy measure is essential for assessing prediction accuracy in species distribution modelling. Therefore, model evaluation as an analytical uncertainty is a challenging problem. Although a variety of accuracy measures for the assessment of prediction errors in presence/absence models is available, there is a lack of spatial accuracy measures, i.e. measures that are sensitive to the spatial arrangement of the predictions. We present ‘spind’, a new software package (based on the R software program) that provides spatial performance measures for grid-based models. These accuracy measures are generalized, spatially corrected versions of the classical ones, thus enabling comparisons between them. Our method for evaluation consists of the following steps: (1) incorporate additional autocorrelation until spatial autocorrelation in predictions and actuals is balanced, (2) cross-classify predictions and adjusted actuals in a 4x4 contingency table, (3) use a refined weighting pattern for errors, and (4) calculate weighted Kappa, sensitivity, specificity and subsequently ROC, AUC, TSS to get spatially corrected indices. To illustrate the impact of our spatial method we present an example of simulated data as well as an example of presence/absence data of the plant species Dianthus carthusianorum across Germany. Our analysis includes a statistic for the comparison of spatial and classical (non-spatial) indices. We find that our spatial indices tend to result in higher values than classical ones. These differences are statistically significant at medium and high autocorrelation levels. We conclude that these spatial accuracy measures may contribute to evaluate prediction errors in presence/absence models, especially in case of medium or high degree of similarity of adjacent data, i.e. aggregated (clumped) or continuous species distributions.
dauerhafte UFZ-Verlinkung
Carl, G., Kühn, I. (2017):
Spind: a package for computing spatially corrected accuracy measures
Ecography 40 (5), 675 - 682