Map Quality for Ordinary Kriging and Inverse Distance Weighted Interpolation

By T. Mueller, N. B. Pusuluri, K. K. Mathias, P. Cornelius, R. Barnhisel, Scott A. Shearer. 2004

277 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Chemistry, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/2607e578fce121e98dcedb571e958da81968d716

How researchers see this paper

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

In Economics and Econometrics

1. Jean-Baptiste Mouret
2. Alfred Stein

In Environmental Chemistry

1. Alfred Stein

In Environmental Engineering

1. Wenjiao Shi
2. Alfred Stein
3. Z. Du
4. Olena Babak
5. T. Yue
6. Jiyuan Liu
7. 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. ”
• Surface modelling of soil properties based on land use information (2011, background): “Kriging is a powerful interpolator which can provide the best linear unbiased estimates, a more accurate description of the spatial structure of the data and valuable information about estimation error distributions (Cressie, 1993; Kravchenko and Bullock, 1999; Mueller et al., 2004).”
• The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics (2013, result): “If not, Kriging methods lead to similar or even worse results than less computationally expensive approaches (it was compared to the IDW method in [37] and [44], for instance).”