About regression-kriging: From equations to case studies
Computers & Geosciences
Automatic Decision-Oriented Mapping of Pollution Data
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Application of machine learning methods to spatial interpolation of environmental variables
Environmental Modelling & Software
Surface interpolation by adaptive neuro-fuzzy inference system based local ordinary kriging
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Spatial sampling design based on spectral approximations to the random field
Environmental Modelling & Software
Geostatistical computing of acoustic maps in the presence of barriers
Mathematical and Computer Modelling: An International Journal
A Clustering-Assisted Regression (CAR) approach for developing spatial climate data sets in China
Environmental Modelling & Software
Position paper: Characterising performance of environmental models
Environmental Modelling & Software
New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package
Environmental Modelling & Software
Modeling airborne benzene in space and time with self-organizing maps and Bayesian techniques
Environmental Modelling & Software
Hybrid modeling of spatial continuity for application to numerical inverse problems
Environmental Modelling & Software
Spatial interpolation of McArthur's Forest Fire Danger Index across Australia: Observational study
Environmental Modelling & Software
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Spatially continuous data of environmental variables are often required for environmental sciences and management. However, information for environmental variables is usually collected by point sampling, particularly for the mountainous region and deep ocean area. Thus, methods generating such spatially continuous data by using point samples become essential tools. Spatial interpolation methods (SIMs) are, however, often data-specific or even variable-specific. Many factors affect the predictive performance of the methods and previous studies have shown that their effects are not consistent. Hence it is difficult to select an appropriate method for a given dataset. This review aims to provide guidelines and suggestions regarding application of SIMs to environmental data by comparing the features of the commonly applied methods which fall into three categories, namely: non-geostatistical interpolation methods, geostatistical interpolation methods and combined methods. Factors affecting the performance, including sampling design, sample spatial distribution, data quality, correlation between primary and secondary variables, and interaction among factors, are discussed. A total of 25 commonly applied methods are then classified based on their features to provide an overview of the relationships among them. These features are quantified and then clustered to show similarities among these 25 methods. An easy to use decision tree for selecting an appropriate method from these 25 methods is developed based on data availability, data nature, expected estimation, and features of the method. Finally, a list of software packages for spatial interpolation is provided.