Influence of Spatial Structure on Accuracy of Interpolation Methods

By A. Kravchenko. 2003

312 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/38bdcb7cfd6002d08b145c85ffcd517dae2ee9cc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Wenjiao Shi
2. Yingshuang Song
3. Chuanfa Chen
4. Z. Du
5. Olena Babak
6. T. Yue
7. Jiyuan Liu
8. Clayton V. Deutsch

In Environmental Engineering

1. Wenjiao Shi
2. Yingshuang Song
3. Chuanfa Chen
4. Z. Du
5. Olena Babak
6. T. Yue
7. Jiyuan Liu
8. Clayton V. Deutsch

Citations in the corpus

• Statistical approach to inverse distance interpolation (2009, background): “Moreover, a large number of comparative studies among different interpolators found that depending upon the situation at hand, IWD can be as good or better than geostatistical krigingbased techniques (Weber and Englund 1992; Gallichand and Marcotte 1993; Dingman 1994; Boman et al. 1995; Brus et al. ”
• Improving the Prediction Accuracy of Soil Mapping through Geostatistics (2012, unknown): “Although IDW is easier and quicker to implement than kriging, it does not have the statistical advantages of kriging [23].”
• Surface modelling of soil pH (2009, methodology): “Although IDW is easier and quicker to implement than kriging, it does not have the statistical advantages of kriging (Kravchenko, 2003).”