C-10 
Table C-1. Comparison of the capabilities of methods for interpreting data for Chesapeake 
Bay water quality criteria assessment. 
Attributes 
Sample-based 
IDW 
Kriging 
Provides Spatial Prediction 
Yes 
Yes 
Yes 
Provides Prediction 
Uncertainty 
No 
No 
Yes 
Uncertainty for CFD 
No 
No 
Yes 
Deal with Anisotropy 
No 
Possible, but 
not routine 
Yes 
Can include cruise track/ 
fly-over data 
No 
No 
Yes 
Feasibility of 3-dimensional 
interpolations 
No 
Yes 
Possible, but 
not routine 
Feasibility of mainstem- 
tributary interpolations 
No 
Yes 
Possible 
Inclusion of covariates to 
improve prediction 
No 
No 
Yes 
Predictions of non-linear 
functions of predicted 
attainment surfaces P(y>c) 
No 
No 
Yes 
Level of sophistication 
Lowest 
Low 
Very High 
Automation 
Yes 
Yes 
No 
Source: STAC 2006. 
LITERATURE CITED 
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Diggle, P.J., J.A. Tawn, and R.A. Moyeed. 1998. Model Based Geostatistics (with Discus¬ 
sion). Applied Statistics 47:299-350. 
Diggle, P.J. and P.J. Ribeiro. 2006. Model-based Geostatistics. Springer, New York, NY. 230 
pp. 
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Harding, L.W., Jr., J.G. Kramer, and J. Phinney. 2004. Estuarine and Watershed Monitoring 
Using Remote Sensing Technology Present Status and Future Trends: A Workshop Report, 
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Maryland Sea Grant College. Maryland Sea Grant Publication UM-6-SG-TS-2004-03. 
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Kitanidis, P.K. 1997. Introduction to Geostatistics: Applications in Hydrogeology. Cambridge 
University Press, New York, NY, 271 pp. 
Ouyang Y, J.E. Zhang, and L.T. Ou. 2006. Temporal and spatial distributions of sediment 
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appendix c 
Evaluation of Options for Spatial Interpolation 
