Spatial cross-validation is not the right way to evaluate map accuracy
For decades scientists have produced maps of biological, ecological and environmental
variables. These studies commonly evaluate the map accuracy through cross-validation with …
variables. These studies commonly evaluate the map accuracy through cross-validation with …
[HTML][HTML] Dealing with clustered samples for assessing map accuracy by cross-validation
Mapping of environmental variables often relies on map accuracy assessment through cross-
validation with the data used for calibrating the underlying mapping model. When the data …
validation with the data used for calibrating the underlying mapping model. When the data …
Nearest neighbour distance matching Leave‐One‐Out Cross‐Validation for map validation
Several spatial and non‐spatial Cross‐Validation (CV) methods have been used to perform
map validation when additional sampling for validation purposes is not possible, yet it is …
map validation when additional sampling for validation purposes is not possible, yet it is …
Less than eight (and a half) misconceptions of spatial analysis
I Kühn, CF Dormann - Journal of Biogeography, 2012 - Wiley Online Library
Spatial analyses are indispensable analytical tools in biogeography and macroecology. In a
recent Guest Editorial, Hawkins (Journal of Biogeography, 2012, 39, 1–9) raised several …
recent Guest Editorial, Hawkins (Journal of Biogeography, 2012, 39, 1–9) raised several …
Predictive performance of machine learning model with varying sampling designs, sample sizes, and spatial extents
Using machine learning and earth observation data to capture real-world variability in
spatial predictive mapping depends on sample size, design, and spatial extent …
spatial predictive mapping depends on sample size, design, and spatial extent …
Predicting into unknown space? Estimating the area of applicability of spatial prediction models
Abstract Machine learning algorithms have become very popular for spatial mapping of the
environment due to their ability to fit nonlinear and complex relationships. However, this …
environment due to their ability to fit nonlinear and complex relationships. However, this …
Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data
While the application of machine-learning algorithms has been highly simplified in the last
years due to their well-documented integration in commonly used statistical programming …
years due to their well-documented integration in commonly used statistical programming …
Importance of spatial predictor variable selection in machine learning applications–Moving from data reproduction to spatial prediction
Abstract Machine learning algorithms find frequent application in spatial prediction of biotic
and abiotic environmental variables. However, the characteristics of spatial data, especially …
and abiotic environmental variables. However, the characteristics of spatial data, especially …
Machine learning-based global maps of ecological variables and the challenge of assessing them
The recent wave of published global maps of ecological variables has caused as much
excitement as it has received criticism. Here we look into the data and methods mostly used …
excitement as it has received criticism. Here we look into the data and methods mostly used …
Geographical sampling bias in a large distributional database and its effects on species richness–environment models
Aim Recent advances in the availability of species distributional and high‐resolution
environmental data have facilitated the investigation of species richness–environment …
environmental data have facilitated the investigation of species richness–environment …