A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors

By Jin Li, Andrew D. Heap. Ecological Informatics, 2011

962 citations recorded by the source corpus.

Subfields: Environmental Engineering, Soil Science, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/a06f74510d67e51b2139dde5672aa4d7f813da42

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Citations in the corpus

Spatial interpolation methods applied in the environmental sciences: A review (2014, methodology): “Sampling design has significant influence on the prediction accuracy with irregular designs preferred to regular ones (Li and Heap, 2011).”
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (2018, methodology): “Apply a back-transformation if needed.166 The RK algorithm has been very successful over the past decades and is still the mainstream geo-167 statistical technique for generating spatial predictions (Li and Heap, 2011).”
The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data (2013, methodology): “…for categorical data, such as land-use type, only the nearest neighbor interpolation can be applied, whereas for continuous-numeric data types either a nearest neighbor, bilinear (in regular grids), or inverse weighted distance interpolation can be selected based on the resolution of data [45,46].”
A Comparison of Some Interpolation Techniques for Determining Spatial Distribution of Nitrogen Compounds in Groundwater (2019, methodology): “On the other hand, it is important to note that IDW method, which is the most frequently used deterministic method (also in the context of comparison with stochastic methods—kriging) (Li and Heap 2011), was characterised by the highest values of RMSE and MAE for NO− 2 and NO− 3 , and for NH+ 4 , onl”
Application of machine learning methods to spatial interpolation of environmental variables (2011, methodology): “No consistent findings have been acquired on how these factors affect the performance of spatial prediction methods, making it difficult to select an appropriate method for any given dataset (Li and Heap, 2011).”
A geographically weighted regression model augmented by Geodetector analysis and principal component analysis for the spatial distribution of PM2.5 (2020, background): “If the distribution of ground stations is sparse and non-uniform, the accuracy and predictive power of the interpolation will decrease significantly (Li and Heap, 2011).”
Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin (2017, neutral): “, We refer to (Heidinger et al., 2012;Li and Heap, 2011;Tobin and Bennett, 2010;Yin et al., 2008) who report that interpolation introduces unreliability and uncertainty to pixel basd rainfall estimates.”